posttraumatic stress and adaptation in patients following acute

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posttraumatic stress and adaptation in patients following acute
POSTTRAUMATIC STRESS
AND ADAPTATION
IN PATIENTS FOLLOWING
ACUTE CARDIAC EVENTS
Anna Wikman
Department of Epidemiology and Public Health
University College London
2009
Thesis submitted to the University of London for the degree of
Doctor of Philosophy
1
I, Anna Wikman, confirm that the work presented in this thesis is my own. Where
information has been derived from other sources, I confirm that this has been indicated
in the thesis.
Signature………………………………………
Date……………………
2
Abstract
The aim of this thesis is to investigate emotional recovery following an acute
coronary syndrome (ACS), and the factors that predict the development of
posttraumatic stress symptoms. The overall objective of this work is to increase
understanding of recovery and adaptation following ACS. This thesis will present data
from two prospective studies of psychological aspects of ACS; the ‗Acute Coronary
Syndrome Emotional Triggering‘ (ACCENT) study and the ‗Tracking Recovery after
Acute Cardiac Events‘ (TRACE) study. Although most commonly diagnosed in
individuals that have experienced traumatic events such as war, natural disasters or
assault, there is now increasing evidence of posttraumatic stress disorder (PTSD) in
individuals after onset, diagnosis, or treatment for physical illness. ACS, like other nonmedical trauma, is life-threatening, sudden and often unexpected. Many patients report
an intense fear of dying, and emotional distress such as anxiety and depression during
the acute phase and in the immediate aftermath is common. Although most patients
will fully recover from this emotional distress, some do not recover and distress can
persist for a significant period of time. The persistent and severe psychological distress
experienced by some patients may actually satisfy criteria for a diagnosis of PTSD.
PTSD risk factor research indicates that individuals‘ experiences during traumatic
events play a significant role in differentiating between those who subsequently go on
to develop PTSD and those who do not. Within this thesis, data will be presented on
the longer term prevalence and predictors of posttraumatic stress in patients following
ACS, as well as the underlying biological and cognitive correlates which may increase
vulnerability to emotional distress and risk of future cardiac events.
3
Table of contents
Abstract ................................................................................................................... 3
Table of contents .................................................................................................... 4
List of tables.......................................................................................................... 10
List of figures ........................................................................................................ 13
Publications .......................................................................................................... 14
Acknowledgments ................................................................................................ 15
List of abbreviations ............................................................................................. 16
CHAPTER 1. Literature Review: Psychosocial risk factors and Cardiovascular
Disease ...................................................................................................................... 18
1.1 Overview of Cardiovascular Disease ............................................................. 18
1.2 Psychological factors in the development of CHD ....................................... 19
1.2.1 Depression ................................................................................................. 20
1.2.2 Anxiety ....................................................................................................... 23
1.2.3 Anger and Hostility ..................................................................................... 26
1.3 Psychological consequences of CHD ........................................................... 29
1.3.1 Rates and prognostic implications of depression following CHD ................ 29
1.3.2 Anxiety ....................................................................................................... 31
1.3.3 Type D personality ..................................................................................... 33
1.4 Pathways between negative affect and CHD................................................. 35
1.5 Overlapping affective dispositions ................................................................ 38
1.6 Summary ......................................................................................................... 39
CHAPTER 2. Literature Review: Posttraumatic Stress Disorder ........................... 42
2.1 Introduction to Posttraumatic Stress Disorder ............................................. 42
2.2 Current understanding of PTSD..................................................................... 44
2.3 Models of PTSD ............................................................................................... 47
2.3.1 Emotional processing theory ...................................................................... 48
2.3.2 Dual representation model ......................................................................... 49
2.3.3 Ehlers and Clarke’s cognitive model........................................................... 50
2.3.4 Summary of current models of PTSD ......................................................... 52
2.4 PTSD following non-medical trauma ............................................................. 52
2.5 Medical events as traumatic stressors .......................................................... 55
2.6 PTSD following medical trauma ..................................................................... 57
2.7 PTSD as a consequence of Acute Coronary Syndrome ............................... 60
4
2.7.1 Prevalence of PTSD following ACS ............................................................ 60
2.7.2 Predictors of posttraumatic stress symptoms following ACS ...................... 63
2.7.3 Distinctive features of ACS-related PTSD .................................................. 70
2.7.4 Consequences of PTSD following ACS ...................................................... 71
2.7.5 The relationship between PTSD and depression ........................................ 72
2.8 The role of PTSD in the development of coronary heart disease ................ 77
2.9 Psychophysiology of PTSD ............................................................................ 79
2.9.1 Cortisol ....................................................................................................... 79
2.9.2 Heart rate ................................................................................................... 84
2.10 Chapter summary.......................................................................................... 85
CHAPTER 3. Methodology ACCENT study ............................................................. 87
3.1 Introduction and hypotheses ......................................................................... 87
3.2 Participants ..................................................................................................... 89
3.3 Study design and procedure .......................................................................... 90
3.3.1 My role in study design, data collection and analysis.................................. 90
3.4 Psychosocial measures ................................................................................. 91
3.4.1 Socio-demographic information .................................................................. 91
3.4.2 Clinical data ............................................................................................... 92
3.4.3 Psychological measures ............................................................................. 92
3.4.3.1 Beck Depression Inventory (BDI)......................................................... 93
3.4.3.2 Posttraumatic Stress Symptoms – Self Report Scale (PSS-SR) .......... 94
3.4.3.3 Hospital Anxiety Scale (HADS-A) ........................................................ 95
3.4.3.4 Medical Outcome Short Form 36 (SF36) ............................................. 95
3.4.3.5 Cook and Medley Hostility Scale (Ho) ................................................. 96
3.4.3.6 Type D (DS16) .................................................................................... 97
3.4.3.7 Fear, helplessness and horror – Acute stress. ..................................... 97
3.4.3.8 Acute stress disorder ........................................................................... 98
3.5 Data storage .................................................................................................... 98
3.6 Statistical analyses ......................................................................................... 98
CHAPTER 4. Results ACCENT Study .................................................................... 100
4.1 Results ........................................................................................................... 100
4.1.1 Data analyses .......................................................................................... 100
4.1.2 Patient characteristics .............................................................................. 101
4.1.3 Prevalence of posttraumatic stress symptoms at 12 and 36 months ........ 103
5
4.1.4 Comparisons of psychological variables between PTSD and non-PTSD
groups ............................................................................................................... 105
4.1.5 Predictors of posttraumatic stress symptom severity at 12 months........... 106
4.1.6 Predictors of posttraumatic stress symptoms at 36 months post ACS ...... 110
4.2 Discussion..................................................................................................... 112
4.2.1 Strengths and limitations .......................................................................... 116
4.2.2 Summary.................................................................................................. 117
CHAPTER 5. Methodology TRACE study .............................................................. 119
5.1 Introduction and hypotheses ....................................................................... 119
5.1.1 Acute post-ACS emotional responses and their relationship with short (2
week) and long term (six months) posttraumatic stress reactions...................... 119
5.1.1.1 Introduction to illness representations ............................................... 120
5.1.1.2 Causal attributions and CHD ............................................................. 121
5.1.1.3 Illness representations and post-MI recovery..................................... 123
5.1.1.4 The relationship between illness representations and post-MI
depression and quality of life ......................................................................... 125
5.1.1.5 Posttraumatic stress and illness representations ............................... 128
5.1.2 Biological determinants of early emotional responses to ACS .................. 129
5.1.2.1 Cortisol .............................................................................................. 129
5.1.2.2 Heart rate variability........................................................................... 134
5.1.3 The relationship between posttraumatic stress responses and post ACS
adaptation ......................................................................................................... 137
5.1.4 Influence of partner distress on patient posttraumatic stress reactions ..... 138
5.2 Study design ................................................................................................. 139
5.2.1 My role in study design, data collection and analysis................................ 140
5.3 Participants ................................................................................................... 140
5.4 Procedure ...................................................................................................... 142
5.4.1 Time 1 assessment .................................................................................. 142
5.4.2 Time 2 assessment .................................................................................. 143
5.4.3 Time 3 follow up assessment ................................................................... 144
5.5 Measures ....................................................................................................... 144
5.6 Measures – Time 1 ........................................................................................ 145
5.6.1 Socio-demographic information ................................................................ 145
5.6.2 Clinical data ............................................................................................. 145
5.6.3 Psychosocial measures............................................................................ 146
5.6.3.1 Profile of Mood States (POMS) ......................................................... 146
6
5.6.3.2 Medical Outcome Short Form 12 (SF-12) .......................................... 147
5.7 Measures – Time 2 ........................................................................................ 148
5.7.1 Psychosocial measures............................................................................ 148
5.7.1.1 DISH ................................................................................................. 148
5.7.1.2 Social Network .................................................................................. 149
5.7.1.3 Social Support ................................................................................... 149
5.7.1.4 Causal Beliefs ................................................................................... 150
5.7.1.5 Illness Perception Questionnaire – Revised ...................................... 151
5.7.2 Health behaviours .................................................................................... 152
5.7.2.1 Smoking ............................................................................................ 152
5.7.2.2 Alcohol consumption ......................................................................... 152
5.7.2.3 Diet.................................................................................................... 152
5.7.2.4 Physical activity ................................................................................. 153
5.7.2.5 Adherence to medications ................................................................. 153
5.7.3 Biological measures ................................................................................. 153
5.7.3.1 Salivary cortisol ................................................................................. 153
5.7.3.2 Heart rate variability........................................................................... 154
5.8 Measures – Time 3 ........................................................................................ 155
5.8.1 Psychosocial measures............................................................................ 155
5.8.2 Health behaviours .................................................................................... 156
5.10 Data storage ................................................................................................ 156
5.11 Statistical analyses ..................................................................................... 156
CHAPTER 6. Results TRACE Study I ..................................................................... 159
6.1 Data analysis ................................................................................................. 159
6.2 Patient characteristics .................................................................................. 160
6.3 Posttraumatic stress symptoms 3 – 4 weeks post ACS (time 2) ............... 162
6.3.1 Acute admission predictors of posttraumatic stress symptoms at time 2 .. 163
6.3.2 Multivariate predictors of posttraumatic stress symptoms at time 2 .......... 164
6.3.3 Psychosocial predictors of posttraumatic stress at time 2 ......................... 167
6.3.4 Illness representations and current mood state in relation to posttraumatic
stress reactions at time 2 .................................................................................. 169
6.4 Posttraumatic stress symptoms and salivary cortisol ............................... 173
6.5 Posttraumatic stress symptoms and heart rate variability at time 2 ......... 179
6.6 Discussion..................................................................................................... 181
6.6.1 Predicting short term posttraumatic stress symptoms from patients’ acute
post-ACS emotional responses ......................................................................... 181
7
6.6.2 Salivary cortisol and heart rate variability in the immediate aftermath of ACS
– predictors of acute emotional reactions .......................................................... 187
6.6.3 Summary.................................................................................................. 190
CHAPTER 7. Results TRACE Study II .................................................................... 191
7.1 Data analysis ................................................................................................. 191
7.2 Patient characteristics .................................................................................. 192
7.3 Posttraumatic stress symptoms six months (time 3) post ACS ................ 193
7.3.1. Multivariate predictors of posttraumatic stress symptoms at time 3 ......... 195
7.3.2 Cognitive predictors of posttraumatic stress symptoms at time 3 ............. 200
7.3.3 Multivariate predictors of posttraumatic stress symptoms at six months ... 212
7.4 Posttraumatic stress symptoms, health behaviour and psychosocial
adjustment at time 3 ........................................................................................... 214
7.5 Partner posttraumatic stress reactions and post ACS patient emotional
recovery............................................................................................................... 218
7.6 Posttraumatic stress at six months and salivary cortisol .......................... 222
7.7 Time 2 heart rate variability and six-month posttraumatic stress ............. 222
7.8 Discussion..................................................................................................... 224
7.8.1 Predicting six-month posttraumatic stress symptoms from patients’
emotional and cognitive post-ACS reactions ..................................................... 224
7.8.2 Adjustment ............................................................................................... 229
7.8.3 The influence of post ACS biological dysfunction on patients posttraumatic
stress responses at six months ......................................................................... 231
7.8.4 The association of partner distress with patients’ posttraumatic stress
reactions ........................................................................................................... 232
7.8.5 TRACE: Study strengths and limitations ................................................... 234
7.8.6 Summary.................................................................................................. 236
CHAPTER 8. General discussion of research carried out in this thesis ............. 238
8.1 ACCENT and TRACE studies – Aims........................................................... 238
8.1.1 Accent – the relationship between emotional reactions to ACS and long-term
posttraumatic stress .......................................................................................... 239
8.1.2 TRACE – the relationship between acute emotional reactions to ACS and
posttraumatic stress 3 – 4 weeks and six months post trauma .......................... 240
8.1.3 Comparability of ACCENT and TRACE findings ....................................... 243
8.1.3.1 Predictor variables – ACCENT and TRACE ...................................... 248
8.1.3.2 Cognitive factors – TRACE ................................................................ 250
8
8.1.4 Biological dysfunction post ACS and later posttraumatic stress ................ 253
8.1.4.1 Cortisol .............................................................................................. 253
8.1.4.2 Heart rate variability........................................................................... 255
8.2 General thesis limitations............................................................................. 257
8.2.1 Study design ............................................................................................ 257
8.2.2 Measurement issues ................................................................................ 258
8.2.2.1 PTSD assessment ............................................................................. 259
8.2.3 Cortisol assessment ................................................................................. 261
8.2.4 Response rate, sample size and power .................................................... 263
8.3 Clinical implications ..................................................................................... 264
8.4 Directions for future research ...................................................................... 266
8.5 Key message of thesis ................................................................................. 268
8.6 Conclusion .................................................................................................... 269
References .............................................................................................................. 271
Appendix I: ACCENT 12 and 36 month Interview…………………………………….308
Appendix II: ACCENT 12 and 36 month questionnaire..………………….……..….310
Appendix III: TRACE patient information sheet.………………………………….….318
Appendix IV: TRACE consent form…………………………………………………….320
Appendix V: TRACE time 1, time 2 and time 3 questionnaires..……………….….321
Appendix VI: TRACE time 3 telephone interview…………………………………….365
Appendix VII: TRACE cortisol diary…………………………………………………….367
9
List of tables
TABLE 2.1 DSM-IV DIAGNOSTIC CRITERIA FOR POSTTRAUMATIC STRESS DISORDER ......... 45
TABLE 2.2 PREVALENCE OF PTSD FOLLOWING ACS ...................................................... 62
TABLE 2.3 RISK FACTORS FOR PTSD FOLLOWING ACS .................................................. 66
TABLE 3.1 MEASURES OBTAINED AT EACH TIME POINT .................................................... 99
TABLE 4.1 PATIENT CHARACTERISTICS – 12 MONTH SAMPLE ......................................... 102
TABLE 4.2 PSS-SR SCORES....................................................................................... 103
TABLE 4.3 PSYCHOLOGICAL VARIABLES BY PTSD CASENESS ........................................ 105
TABLE 4.4 PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS .................................. 107
TABLE 4.5 CORRELATIONS BETWEEN PSYCHOLOGICAL PREDICTOR VARIABLES ............... 108
TABLE 4.6 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT 12
MONTHS ............................................................................................................. 109
TABLE 4.7 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT 36
MONTHS ............................................................................................................. 112
TABLE 5.1 REASONS FOR EXCLUSIONS AND REFUSALS ................................................. 142
TABLE 5.2 MEASURES OBTAINED AT EACH TIME POINT .................................................. 157
TABLE 5.3 CRONBACH‘S ALPHA FOR MEASURES ADMINISTERED IN TRACE .................... 158
TABLE 6.1 PATIENT CHARACTERISTICS ........................................................................ 161
TABLE 6.2 PSS-SR SCORES AT TIME 2 ........................................................................ 163
TABLE 6.3 CORRELATIONS BETWEEN BASLINE VARIABLES (TIME 1) AND TIME 2
POSTTRAUMATIC STRESS SYMPTOMS ................................................................... 164
TABLE 6.4 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME 2
......................................................................................................................... 166
TABLE 6.5 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME 2
......................................................................................................................... 166
TABLE 6.6 CORRELATIONS BETWEEN PSYCHOSOCIAL RISK FACTORS AND TIME 2
POSTTRAUMATIC STRESS SYMPTOMS ................................................................... 167
TABLE 6.7 PSYCHOSOCIAL PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME 2
......................................................................................................................... 168
TABLE 6.8 CORRELATIONS BETWEEN TIME 2 PSYCHOLOGICAL VARIABLES AND TIME 2
POSTTRAUMATIC STRESS SYMPTOMS ................................................................... 169
TABLE 6.9 ILLNESS REPRESENTATION CHARACTERISTICS AT TIME 2 ............................... 169
TABLE 6.10 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATION DIMENSIONS ... 170
TABLE 6.11 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 2
POSTTRAUMATIC STRESS SYMPTOMS ................................................................... 172
TABLE 6.12 COGNITIVE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME 2 173
10
TABLE 6.13 POSTTRAUMATIC STRESS SYMPTOMS AND SALIVARY CORTISOL AT TIME 2 .... 175
TABLE 6.14 POSTTRAUMATIC STRESS SYMPTOMS AND TOTAL CORTISOL OUTPUT AT TIME 2
......................................................................................................................... 176
TABLE 6.15 INDICES OF HEART RATE VARIABILITY AT TIME 2 .......................................... 179
TABLE 6.16 ASSOCIATIONS BETWEEN TIME 2 HEART RATE VARIABILITY AND TIME 2
POSTTRAUMATIC STRESS
.................................................................................... 180
TABLE 6.17 PTSD AND HEART RATE VARIABILITY AT TIME 2........................................... 181
TABLE 7.1 PATIENT CHARACTERISTICS AT TIME 3.......................................................... 192
TABLE 7.2 PSS-SR SCORES AT SIX MONTHS POST ACS ............................................... 194
TABLE 7.3 PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS .................................. 196
TABLE 7.4 CORRELATIONS BETWEEN PSYCHOLOGICAL PREDICTOR VARIABLES ............... 197
TABLE 7.5 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX
MONTHS ............................................................................................................. 199
TABLE 7.6 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX
MONTHS ............................................................................................................. 199
TABLE 7.7 TIME 3 ILLNESS REPRESENTATIONS ............................................................. 201
TABLE 7.8 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 3
ILLNESS REPRESENTATIONS
................................................................................ 201
TABLE 7.9 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 3
POSTTRAUMATIC STRESS SYMPTOMS ................................................................... 203
TABLE 7.10 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS
AT SIX MONTHS ................................................................................................... 204
TABLE 7.11 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC INTRUSION
SYMPTOMS AT SIX MONTHS .................................................................................. 205
TABLE 7.12 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC AVOIDANCE
SYMPTOMS AT SIX MONTHS .................................................................................. 205
TABLE 7.13 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC AROUSAL
SYMPTOMS AT SIX MONTHS .................................................................................. 206
TABLE 7.14 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX
MONTHS – CONCURRENT ILLNESS BELIEFS............................................................ 208
TABLE 7.15 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC INTRUSION SYMPTOMS AT SIX
MONTHS – CONCURRENT ILLNESS BELIEFS............................................................ 209
TABLE 7.16 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC AVOIDANCE SYMPTOMS AT SIX
MONTHS – CONCURRENT ILLNESS BELIEFS............................................................ 209
TABLE 7.17 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC AROUSAL SYMPTOMS AT SIX
MONTHS – CONCURRENT ILLNESS BELIEFS............................................................ 210
11
TABLE 7.18 COMBINED MULTIVARIATE PREDICTOR MODEL OF SIX MONTH POSTTRAUMATIC
STRESS SYMPTOMS ............................................................................................. 212
TABLE 7.19 UNADJUSTED COMBINED MULTIVARIATE PREDICTOR MODEL OF SIX MONTH
POSTTRAUMATIC STRESS SYMPTOMS ................................................................... 213
TABLE 7.20 HEALTH BEHAVIOUR CHANGE TIME 2 TO TIME 3 ........................................... 215
TABLE 7.21 PHYSICAL AND MENTAL HEALTH STATUS CHANGE TIME 2 TO TIME 3 .............. 216
TABLE 7.22 THE RELATIONSHIP BETWEEN TIME 2 POSTTRAUMATIC STRESS SYMPTOMS AND
PHYSICAL AND MENTAL HEALTH STATUS CHANGE .................................................. 217
TABLE 7.23 CORRELATIONS BETWEEN PATIENT AND PARTNER EMOTIONAL REACTIONS AT
TIME 2 ................................................................................................................ 219
TABLE 7.24 CORRELATIONS BETWEEN PATIENT AND PARTNER EMOTIONAL REACTIONS AT
TIME 3 ................................................................................................................ 219
TABLE 7.25 PARTNER PSS-SR SCORES AT TIME 2 AND TIME 3 ...................................... 220
TABLE 7.26 PARTNER POSTTRAUMATIC STRESS AS A PREDICTOR OF PATIENT
POSTTRAUMATIC STRESS AT SIX MONTHS ............................................................. 221
TABLE 7.27 POSTTRAUMATIC STRESS SYMPTOMS AT TIME 3 AND SALIVARY CORTISOL AT
TIME 2 ................................................................................................................ 222
TABLE 7.28 HEART RATE VARIABILITY (TIME 2) AND PTSD AT TIME 3 ............................. 223
TABLE 7.29 POSTTRAUMATIC STRESS SYMPTOMS (ORIGINAL CRITERIA – TIME 3) AND HEART
RATE VARIABILITY AT TIME 2 ................................................................................ 223
TABLE 8.1 OVERVIEW ACCENT AND TRACE STUDIES ................................................. 246
TABLE 8.2 SUMMARY OF PREDICTOR VARIABLES ACCENT AND TRACE........................ 249
12
List of figures
FIGURE 2.1 HYPOTHALAMIC-PITUITARY-ADRENAL AXIS .................................................. 80
FIGURE 3.1 FLOWCHART OF PATIENT RECRUITMENT ....................................................... 99
FIGURE 4.1 THE RELATIONSHIP BETWEEN DEPRESSION SCORES AT BASELINE AND 12 MONTH
POSTTRAUMATIC STRESS SYMPTOMS ................................................................... 110
FIGURE 5.1 TRACE STUDY DESIGN ............................................................................. 139
FIGURE 6.1 TYPE D PERSONALITY AND POSTTRAUMATIC STRESS SYMPTOMS ................. 168
FIGURE 6.2 PROFILE OF SALIVARY CORTISOL THROUGHOUT THE DAY AT TIME 2 ............. 174
FIGURE 6.3 THE RELATIONSHIP BETWEEN POSTTRAUMATIC STRESS SYMPTOMS AND TOTAL
CORTISOL OUTPUT CONTROLLING FOR DEPRESSION .............................................. 176
FIGURE 6.4 THE RELATIONSHIP BETWEEN ACUTE STRESS AND CORTISOL AT TIME 2....... 178
FIGURE 8.1 KEY FINDINGS OF ACCENT AND TRACE INVESTIGATIONS .......................... 268
13
Publications
Some of the research described in this thesis has been published, and other
sections have been submitted for publication. In addition, some of the research
described has been presented in conferences.
Publications:
Wikman, A., Bhattacharyya, M., Perkins-Porras, L., Steptoe, A. (2008) Posttraumatic
stress symptoms 12 and 36 months post acute coronary syndrome. Psychosomatic
Medicine; 70: 764-772.
Bhattacharyya, M., Perkins-Porras, L., Wikman, A., Steptoe, A. (2008) The long-term
effects of acute triggers of acute coronary syndromes on adaptation and quality of life.
International journal of cardiology, [In press, corrected proof, available online].
Conference presentations:
*American Psychosomatic Society Annual Conference, Chicago, USA, 2009.
‗Posttraumatic stress and anxiety symptoms in partners of patients following acute
coronary syndrome’.
*American Psychosomatic Society Annual Conference, Baltimore, USA, 2008.
‗Posttraumatic stress symptoms 12 and 36 months following ACS’.
*Erice International School of Ethology: The inevitable link between heart and
behaviour workshop, Erice, Sicily, 2007.
‘PTSD at 12 and 36 months post ACS’.
14
Acknowledgments
First and foremost, I wish to express my gratitude and thanks to my thesis
supervisor, Professor Andrew Steptoe, for his continued and invaluable support and
guidance throughout this process.
I am also grateful for the support offered by a number of my colleagues, who
have contributed to the collection and processing of data, as well as offered me advice
and support when needed, these include Dr Gerry Molly, Dr Nadine Messerli-Burgy, Dr
Linda Perkins-Porras, Dr Mimi Bhattacharyya and Dr Emily Williams. I would like to
extend my gratitude to Professor Chris Brewin for the much appreciated advice on
PTSD he has offered on many occasions. Thanks also to Dr Samantha Dockray for her
many helpful suggestions.
I would like to thank all the patients who agreed to participate in the research
projects. I am also grateful to the Medical Research Council for the funding I have
received to complete this thesis, and the British Heart Foundation for funding these
projects.
A special thank you goes to my very dear friends and colleagues Nina Grant
and Gemma Randall. Their presence have made every day in the office a joy. Without
their friendship, support and encouragement this thesis would never have been
completed, and I am forever grateful.
Finally I would like to thank my partner, Vicent Garcia, for being understanding
when I have been unreasonable, for being supportive when I have not deserved it, and
for always providing excellent IT support, which has been so desperately needed.
15
List of abbreviations
ACCENT
ACE
ACS
ACTH
ANS
ASD
AUC
BDI
BMI
BPM
CABG
CAD
CAPS
CAR
CHD
CI
CRP
CVD
DBP
DISH
DS14
DS16
DSM
DTS
ECG
ED
EE
GRACE
HADS
HF
HO
HPA
HR
HRV
IBI
IES
IL-10
IL-6
IPQ-R
LF
LVEF
MARS
MI
NA
NSTEMI
OR
PDS
POMS
PSS
PTSD
Acute coronary syndrome emotion and triggers study
Angiotensin converting enzyme
Acute coronary syndrome
Adreno corticotrophin hormone
Autonomic nervous system
Acute stress disorder
Area under the curve
Beck depression inventory
Body mass index
Beats per minute
Coronary artery bypass graft
Coronary artery disease
Clinician Administered PTSD scale
Cortisol awakening response
Coronary heart disease
Confidence interval
C-Reactive protein
Cardiovascular disease
Diastolic blood pressure
Depression Interview and Structured Hamilton
Type D personality scale -14
Type D personality scale -16
Diagnostics and statistical manual
The Davidson Trauma Scale
Electrocardiogram
Emergency department
Expressed emotion
Global registry of acute coronary events
Hospital anxiety and depression scale
High frequency
Cook-Medley hostility scale
Hypothalamic pituitary adrenal axis
Heart rate
Heart rate variability
Inter beat interval
Impact of events scale
Interleukin - 10
Interleukin - 6
Illness perception questionnaire - revised
Low frequency
Left ventricular ejection fraction
Medication Adherence Report Scale
Myocardial infarction
Negative affectivity
Non-ST elevation myocardial infarction
Odds ratio
Posttraumatic Stress Diagnostic Scale
Profile of mood states
Posttraumatic Stress Disorder Symptom Scale
Posttraumatic stress disorder
16
QOL
QRS
RMSSD
RR
R-R
SAM
SBP
SCID
SD
SDNN
SE
SES
SF12
SF36
SI
SRI
STAI
STEMI
TRACE
UA
UFC
VAM
VLF
Quality of life
Q, R, S waves
root of the mean square difference
Relative risk
Beat to beat
Semantically accessible memory
Systolic bloody pressure
Structured clinical interview
Standard deviation
standard deviation of normal mean
Standard error
Socioeconomic status
Medical Outcome Short Form 12
Medical Outcome Short Form 36
Social inhibition
Simple risk index
State trait anxiety inventory
ST elevation myocardial infarction
Tracking recovery after cardiac events study
Unstable angina
Urinary free cortisol
Verbally accessibly memory
Very low frequency
17
CHAPTER 1. Literature Review: Psychosocial risk factors and Cardiovascular
Disease
1.1 Overview of Cardiovascular Disease
Cardiovascular disease (CVD) is by far the most common cause of death, and
premature death, in the United Kingdom (UK). Approximately one in three deaths each
year are caused by CVD. Coronary heart disease (CHD) accounts for almost half
(48%) of deaths from CVD. CHD claims around 94,000 lives each year. CHD kills
approximately one in five men and one in six women each year. However these figures
have declined over the past 30 years (CVD down by 24% in past 10 years in patients
under 75 years, CHD down by 46% in past 10 year in patients under 65). These trends
are primarily due to reductions of risk factors, particularly smoking, though improved
clinical care has also increased survival following acute coronary syndrome (ACS)
(British Heart Foundation [BHF], 2008).
CHD occurs when the artery supplying blood to the heart becomes partially or
wholly blocked. This is caused by fatty deposits (cholesterol plaques) building up on
the inside lining of the arteries (atherosclerosis). When this narrowing of arteries
exceeds 50 – 70%, the blood supply beyond the plaque cannot meet the oxygen
demand of the heart on exertion. This causes symptoms of chest pain (angina), which
is temporary and treatable, though not everyone will experience pain. If arteries are
narrowed in excess of 90 – 99%, angina may occur even in a resting state (this is
referred to as Unstable Angina – UA). CHD can result in a heart attack (Myocardial
Infarction – MI) if the blood supply to the heart is stopped for long enough to cause
damage (death of heart muscle tissue). A MI will generally occur suddenly, due to a
rupture of an atherosclerotic plaque. Every six minutes someone dies from a heart
attack.
18
The term ‗acute coronary syndrome‘ encompasses a spectrum of unstable
coronary heart disease that includes UA and two forms of MI. The type of MI is
determined according to the appearance of the electrocardiogram (ECG/EKG) as ST
segment elevation myocardial infarction (STEMI) or non-ST segment elevation
myocardial infarction (NSTEMI). Combined data from prevalence studies of MI suggest
that 4% of men and 2% of women have had a MI (BHF, 2008). The improved survival
rates following ACS have led to an increase in the prevalence of patients with ACS, so
issues of emotional adjustment and quality of life are becoming increasingly important.
There are around 1.4 million people over the age of 35 in the UK who have survived a
heart attack (BHF, 2008).
Besides the traditional cardiovascular risk factors such as smoking,
hypertension and hypercholesterolaemia, four different types of psychosocial factors
have been found to be most consistently associated with an increased risk of CHD:
work stress, lack of social support, depression and personality (particularly hostility).
This chapter will focus on the role of psychosocial factors, in particular psychological
factors, in the development of CHD. Psychological consequences of CHD will be
discussed in detail and possible mechanisms underlying the association will be
evaluated.
1.2 Psychological factors in the development of CHD
Negative emotions have been claimed to be a cause of CHD as well as a
consequence of the disease. There is growing evidence that negative emotions have
cumulative pathophysiological effects that can ultimately lead to CHD events, via
accumulation of damage through a steady activation of neurohormonal systems and
other mechanisms (Everson-Rose & Lewis, 2005; Kubzansky et al., 2005; Rozanski et
al., 2005). Negative emotions may have direct physiologic effects on the development
of CHD through the repeated activation of the sympathetic nervous system and
19
hypothalamic-pituitary adrenocortical axis (HPA) activation, immune dysregulation, and
vascular inflammation. Alternatively negative emotions may affect the development of
CHD through indirect pathways such as their negative influence on health behaviours
such as smoking, exercise or adherence to medications (Steptoe, 2007). By identifying
such plausible causal pathways the argument that negative emotions, such as
depression, contribute to the development of CHD can be strengthened. As discussed
above a number of pathways have been proposed including behavioural and lifestyle
processes, psychosocial factors such as social support, and more direct biological
processes. These are discussed more fully in section 1.4 below in the context of linking
negative emotions with mortality and morbidity in patients with existing cardiac disease.
A body of research has to date shown strong positive associations between
three main negative emotions and increased risk of CHD in initially disease free
individuals: depression, anxiety and anger. The following sections will discuss the role
of depression, anxiety, anger/hostility and type D personality in the development of
CHD.
1.2.1 Depression
A number of prospective studies have noted an increased risk of MI and
cardiovascular mortality among depressed, but otherwise healthy individuals. However
it is important to note that the use of the term ‗depression‘ in the literature sometime
refers to depressive symptoms and sometimes to depressive illness. Most studies
investigating depression and the aetiology of CHD have used self-report measures to
assess ‗symptoms of depression‘. Other studies have used diagnostic interviews,
whereby a clinical diagnosis can be assigned. Only by adopting this method can one
refer to the depression observed as ‗depressive illness‘. In reviewing the literature there
appears to be a trend towards positive associations among studies that have involved
20
a clinical diagnosis of depression by interview, compared with studies utilizing selfreport questionnaire measures of depressed mood.
Several large representative epidemiological studies that have controlled for a
broad range of CHD risk factors have established a positive association between
depression and CHD. Anda and colleagues (1993) reported a relative risk (RR) of 1.5
for fatal CHD, adjusting for several other factors (e.g. age, gender, BMI, standard CHD
risk factors etc), in a sample of >2800 followed up for 12 years, where 11.1% of the
sample had depressed affect at baseline. A meta-analysis of 11 published studies
demonstrated a strong positive association between depression and incident CHD, with
a RR of 2.69 (95% Confidence Intervals [CI]:1.63 – 4.43) for individuals with clinically
relevant levels of depression, and a RR of 1.49 (95% CI: 1.16 – 1.02) for individuals
with depressed mood (Rugulies, 2002). A systematic review by Wulsin and Singal
(2003) reported a combined overall RR of depression of 1.64 (95% CI: 1.41–1.90) for
the onset CHD. This risk was greater than the risk conferred by passive smoking (RR=
1.25), however, it was less than the risk conferred by active smoking (RR= 2.5)
observed in this review. Findings from the INTERHEART study (Rosengren et al.,
2004; Yusuf et al., 2004) showed that psychosocial distress conferred a higher relative
risk for MI than did hypertension, abdominal adiposity, diabetes and several other
traditional risk factors. Compared with lower levels of depressive symptoms, a high
level was associated with a 2.5 – fold relative risk. This relationship remained even
when all other risk factors were included in the model simultaneously.
Some studies have demonstrated a dose-response relationship between
depression and future CHD suggesting that individuals with sub-clinical levels of
depression may still be at increased risk for CHD. Pratt et al (1996) found major
depression to be associated with a 4.5-fold increased risk of MI, whereas dysphoria
was associated with a 2.7-fold increased risk. A 17 – 27 year follow up of 730 health
men and women by Barefoot and Schroll (1996) demonstrated a relative risk of 1.71
and 1.59 for fatal/non-fatal MI and all cause mortality, respectively, for each 2-Standard
21
Deviation increase in depression score. These authors concluded, based on the
graded relationship observed between depression scores and CHD risk, as well as the
apparently long lasting effect, that depression should be considered as a continuous
variable, representing a psychological trait not as an episodic psychiatric condition or
threshold effect. This intensity effect has been replicated by Pennix et al (2001).
More recent research further reinforces the conclusions that higher depression
among healthy populations at baseline confers a heightened risk of CHD. A 2007 study
from Sweden (Janszky et al., 2007) prospectively followed patients who were
hospitalized for depression. The RR of developing an acute MI was 2.9, and this risk
persisted for decades after the initial hospitalization. A prospective UK cohort study of
initially disease free individuals revealed major depression to be associated with a
higher rate of death from CHD. Specifically, patients who had depression currently or in
the past 12 months had a 2.7-fold increased risk of dying than those who had never
had depression or who had had it more than 12 months previously (Surtees et al.,
2008).
Frasure-Smith and Lesperance (2005) reviewed 21 aetiological and 43
prognostic publications and concluded that despite the multiple methodological
differences between studies, data from prospective adequately powered aetiological
and prognostic studies with objective outcome measures (at least one outcome other
than angina or self-reported chest pain) and utilizing recognized indices of depression
were highly consistent in their support of depression as a risk factor for the
development of CHD (as well as the worsening of established CHD). Steptoe (2007)
reviewed 27 longitudinal observational studies published between 1964 and 2005, and
pointed overall towards a positive association between depression and CHD, although
inconsistencies were present.
A meta-analysis of cohort studies measuring depression with follow up for fatal
CHD/incident MI (aetiological) or all-cause mortality/fatal CHD (prognostic) by
Nicholson and colleagues (2006) found significant associations between depression
22
and CHD. The pooled RR across 21 aetiological studies was 1.81 (95% CI: 1.53 –
2.15) for future CHD. Adjusted results were included for 11 studies, with adjustments
reducing the crude effect marginally from 2.08 (95% CI: 1.69 – 2.55) to 1.90 (95% CI:
1.49 – 2.42). In the 34 prognostic studies included in this analysis, the pooled RR was
1.80 (95% CI: 1.50 – 2.15). However, these authors concluded that due to the biased
availability of adjustments, incomplete adjustments, and the possibility of reverse
causation, these findings cast doubt on the significant associations observed between
depression and future CHD, and whether depression can be considered an
independent risk factor is yet to be established. However, a more recent meta-analysis
by Van der Kooy and colleauges of 28 epidemiologic studies with nearly 80,000
patients showed depression to be an independent risk factor for the onset of a wide
range of CVD (Van der Kooy et al., 2007). The authors acknowledge that the evidence
is related to a high level of heterogeneity across studies, and only the overall combined
risk of depression for the onset of myocardial infarctions (n=8, RR=1.60, 95% CI: 1.341.92) was homogenous.
1.2.2 Anxiety
Anxiety has been defined as a state of emotional distress ‗resulting from
feelings of being unable to predict, control, or obtain desired outcomes‘ (Barlow, 2004).
Anxiety often involves feelings of apprehension and fear characterized by physical
symptoms such as palpitations, sweating, and feelings of stress. Anxiety disorders are
serious medical illnesses. Unlike the relatively mild, brief anxiety caused by a stressful
event such as a public speaking or a job interview, anxiety disorders are chronic,
relentless, and can grow progressively worse if not treated.
Though relatively fewer studies have investigated anxiety as a risk factor for
CHD compared with depression, there is emerging evidence of a prospective
relationship between anxiety and CHD in initially disease free individuals. Several large
23
studies have noted a relationship between phobic anxiety and sudden cardiac death.
An early report by Haines et al (1987) found that high levels of phobic anxiety as
measured by the Crown-Crisp index were associated with a RR for fatal CHD of 3.77
(95% CI: 1.64 – 8.64) in a sample of 1457 men. A later study found a dose-response
relationship between phobic anxiety and fatal CHD in men, with further analyses
revealing this association to be specific to the outcome of sudden cardiac death (RR=
2.5, 95% CI: 1.00 – 5.96). These findings were independent of smoking, alcohol use
and a broad range of cardiovascular risk factors. No association was observed
between phobic anxiety and either non-fatal MI or total CHD (Kawachi et al., 1994a).
Using data from a 32 – year follow up of the Normative Ageing Study of 2271 men,
aged 21 to 80 years in 1961, Kawachi et al (1994b) observed an age adjusted RR of
3.20 (95% CI: 1.27 – 8.09) for fatal CHD, and an RR for sudden cardiac death of 5.73
(95% CI: 1.26 – 26.1). As risk behaviours such as smoking and excessive alcohol
consumption have been related to anxiety, these analyses were adjusted for those
variables and other standard CHD risk factors. The authors found that effects became
non-significant after taking into account smoking, alcohol consumption and standard
CHD risk factors ( RR= 1.94, 95% CI: 0.70 – 5.41 for fatal CHD; RR= 4.46, 95% CI:
0.93 – 8.25 for sudden cardiac death). Albert et al (2005) reported findings from a
prospective study of 72 359 women with no history of cardiovascular disease or cancer.
At 12 – year follow up, women who had scored 4 or higher on the Crown-Crisp Index
were at a 1.59 – fold (95% CI: 0.97 – 2.60) marginally increased risk of sudden cardiac
death and a 1.31 – fold (95% CI: 0.97 – 1.75) marginally increased risk of fatal CHD
compared with those who scored 0 or 1. After control for possible confounding risk
factors (hypertension, diabetes, and elevated cholesterol), a trend toward an increased
risk persisted for sudden cardiac death (p= .06).
Further analyses in the Normative Ageing Study have examined an additional
dimension of anxiety – chronic worrying – as a risk factor for CHD (Kubzansky et al.,
1997). Compared with men reporting the lowest levels of anxiety, men with the highest
24
levels were at approximately 2.5 times the risk (95% CI: 1.49 – 4.31) for non-fatal MI,
but men with moderate anxiety were also at increased risk (RR=1.70, 95% CI: 1.01 –
2.86). However, it was not possible to determine in this study whether the risk stemmed
from the actual content of worry or the severity of worry. Similar effects have been
observed in a sample of women. Eaker et al (1992) reported findings from a 20 – year
follow up of 749 initially health women. A significant association of anxiety symptoms
with MI and fatal CHD among homemakers was found, however, this was not observed
among employed women. These findings showed that reporting any symptom of
anxiety (self-report) was associated with a 6 – fold increased risk compared with those
who reported no anxiety. This effect persisted after controlling for a wide range of other
CHD risk factors. Findings such as these are particularly striking, considering selfreport measures are likely to capture sub-clinical symptomatology as well as more
severe distress. This is of notable importance, considering the evidence showing
anxious or depressed individuals experience multiple difficulties, even when they may
not formally qualify for a clinical diagnosis (Kessler et al., 2003).
One explanation for the evidence linking [phobic] anxiety with risk of CHD might
be the influence of treatment drugs prescribed such as benzodiazepines, tricyclic
antidepressants, and barbiturates (Thorogood et al., 1992). However, it is difficult to
tease apart the risk conferred by drug treatments and the risk associated with
underlying anxiety. For example, Kawachi et al (1994a) found similar sizes of effect of
anxiety in their subgroup of drug free men compared with those who were on drug
treatment on CHD risk, making the explanation of effects of drugs less likely. Another
explanation for the association of anxiety and future CHD risk is that chronically
anxious patients have low heart rate variability (HRV), with decreased capacity for
heart rate change in response to stress (Miu et al., 2009). Diminished heart rate
variability has been identified as a potent risk factor for sudden cardiac death in
patients recovering from myocardial infarction (Bigger et al., 1992) and is a significant
independent predictor of mortality in high risk groups (Makikallio et al., 2001a, 2001b).
25
Overall the evidence has largely demonstrated positive associations, albeit
mainly in studies of male participants, and most effects have been independent of
standard cardiovascular risk factors and demographic indices. Inconsistencies do
however exist, probably due to varying sample sizes, differences in follow up periods,
type of measures used (clinical interview vs. self-report measures), and adequacy of
adjustment for confounders.
1.2.3 Anger and Hostility
Anger and hostile feelings are strongly associated with each other; generally,
anger is considered the emotional aspect of hostility. In turn, hostility is more
representative of a more enduring disposition or personality style. Hostility is defined as
a cynical, suspicious and resentful attitude towards others, often leading to negative
social exchanges and more opportunities to experience anger. In contrast, not all
individuals with high levels of anger can be characterized as hostile. Much of the early
work in this area focused on the type A behaviour pattern. Type A is defined as ‗an
action-emotion complex that can be observed in any person who is aggressively
involved in a chronic, incessant struggle to achieve more and more in less and less
time, and if required to do so, against the opposing efforts of other things or persons…‘
(Friedman & Rosenman, 1959). In other words, type A personality includes elements of
impatience, hard driving goal oriented behaviour, irritation and anger. Early studies
supported an association between type A personality and risk of CHD. In one major
longitudinal study (Rosenman et al., 1975) it was observed that individuals with type A
behaviour were more than twice as likely to suffer CHD than those without type A
characteristics. Another important study was the Framingham Heart Study (Haynes et
al., 1980), where type A personality was found to predict future CHD among men with
white-collar professions and in women working outside the home. Later work, however,
did not support the early evidence for a link between type A behaviour pattern and
26
future CHD (Matthews & Haynes, 1986). Type A is now considered a weak and
inconsistent predictor of CHD disease.
Hostility is a component of Type A personality, and is considered the ‗toxic‘
component of Type A, with regards to CHD risk (Rozanski et al., 1999). Similar to
findings with depression and anxiety, although there are fewer studies, both crosssectional and prospective studies reveal an association between anger/hostility and
clinical indices of CHD. In cross-sectional studies, various indices of anger have been
shown to correlate with CHD risk (e.g. Mittleman et al., 1995; Moller et al., 1999).
Prospective studies provide robust evidence of an association. For example, an early
study of initially disease free men, hostility predicted 10 – year risk of acute MI and
CHD mortality (Shekelle et al., 1983). Kawachi et al (1996) reported that compared with
men reporting the lowest levels of anger, the RR among men reporting the highest
levels of anger were 3.15 (95% CI: 0.94 – 10.5) for total CHD (nonfatal MI plus fatal
CHD) and 2.66 (95% CI: 1.26 – 5.61) for combined incident coronary events including
angina pectoris. This study demonstrated a dose-response relationship over a 7 year
follow up period. It is interesting to note that levels of risk increased significantly for
men who reported only two to four symptoms and dramatically for those reporting more
than five symptoms. Williams et al (2000) studied 12,986 individuals [men and women]
without known CHD at baseline and reported a strong graded relationship between
increasing ‗trait anger‘ and subsequent MI and CHD mortality.
A 1996 meta-analysis of 45 studies by Miller et al (1996) showed that chronic
hostility is an independent risk factor for CHD as well as all-cause mortality, with the
relationship being strongest among younger patients. A more recent meta-analysis also
showed that hostility yielded a significant association with CHD (Myrtek, 2001).
Although numerous studies have supported an association between hostility and CHD,
controversy persists due to the rarity of large-scale prospective cohort studies of
initially healthy populations. Surtees et al (2005) addressed this issue in a prospective
investigation of the association between hostility and cardiovascular (and all-cause)
27
mortality among 20,550 men and women, 41–80 years of age, participating in the
European Prospective Investigation into Cancer and Nutrition in Norfolk (EPIC-Norfolk),
United Kingdom study. These authors found that hostility was not associated with
cardiovascular mortality, after adjustment for age and prevalent disease, in either men
(RR= 1.09 for a 1 SD decrease in hostility score [representing increased hostility]; 95%
CI: 0.98 – 1.22) or in women (RR= 1.00; 95% CI: 0.86 – 1.26). Subgroup analysis
suggested hostility may be associated with cardiovascular mortality (independent of
age, prevalent disease and cigarette smoking) for participants reporting very high
hostility and for those aged less than 60 years.
Overall, evidence from methodologically sound population-based studies
suggests a role of anger and hostility in the increased risk of CHD in initially healthy
populations. However, in common with the research on anxiety and CHD, the majority
of studies have been of White males. Many reviews have been conducted of the
association between anger/hostility and CHD, but findings have been disparate.
Schulman and Stromberg (2007) compared seven meta-analyses, and showed that the
diverse conclusions about the role of anger and hostility in CHD, is largely due to the
varied study inclusion criteria. Chida and Steptoe (2009) conducted a review and metaanalysis of prospective cohort studies, addressing issues of methodological study
quality, follow-up periods, participant characteristics, and whether studies used initially
healthy participants or those with established CHD. These authors concluded that
anger and hostility are significantly associated with development of CHD, as well as
disease progression among those with existing CHD. In fact, they showed that the
effect was marginally greater in studies of CHD populations compared with initially
disease free populations. In addition, the association of anger and hostility with CHD
was stronger among men then women. However, studies included in these analyses
were observational in nature, and therefore causality cannot be established. When
controlling for a broad range of behavioural co-variates (possible mediating pathways),
28
the effect of hostility was no longer significant in either studies of disease free
individuals at baseline, or those with established CHD.
1.3 Psychological consequences of CHD
1.3.1 Rates and prognostic implications of depression following CHD
Having a heart attack is a distressing experience and the psychological
consequences of an ACS may be profound and persistent. Many patients report feeling
acutely upset and a proportion develops marked depressive symptoms. Major
depressive disorder (MDD) develops in approximately 15% of cardiac patients (post MI
and CABG), with a further 20% reporting either minor depression or elevated levels of
depressive symptoms (Davidson et al., 2004; King, 1997; Lett et al., 2004; Rozanski et
al., 1999). Depression is associated with significant impairment of functioning, which
can at times exceed that of an individual‘s physical illness (Wells et al., 1989). The
impact of depression on clinical recovery following MI has been extensively studied
since Frasure-Smith and colleagues reported its prospective association with cardiac
prognosis (Frasure-Smith et al., 1993). In this study 222 MI patients were assessed
between 5 and 15 days following admission and were followed up 6 months later. At
the 6 – month stage, approximately 5% of the sample had died from cardiac causes.
Depression measured at baseline was a significant predictor of mortality (Odds Ratio
[OR]: 5.74, 95% CI: 4.61 – 6.87). The impact of depression remained after control for
left ventricular dysfunction and previous MI (OR: 4.29, 95% CI: 3.14 – 5.86). FrasureSmith et al argued that the impact of depression on mortality following MI is at least
equivalent to that of left ventricular dysfunction and history of previous MI. The data
linking depression and adverse outcomes among patients with established cardiac
disease are particularly striking with a 1998 review noting that 11 of 11 studies reported
worsened outcome (Glassman & Shapiro, 1998).
29
Depression appears to be common and persistent in MI patients with
approximately one in three experiencing at least mild-to-moderate depressive
symptoms during hospitalization (Thombs et al., 2006). Depression has now gained
status as a risk factor alongside biomedical risk factors (Rumsfeld & Ho, 2005).
Findings reported by Kaptein et al (2006) suggested a potential ‗high-risk‘ group of
patients. This group of patients who had significant levels of depression during
hospitalization for MI, and whose symptoms increased in the subsequent year, were at
higher risk of recurrent cardiac events (Hazard Ratio [HR]: 2.5, 95% CI: 1.11 – 5.45)
compared with patients with no depressive symptoms. These patients were also more
likely to report a history of previous depression and experienced more severe initial
depressive symptoms at baseline. Lesperance and colleagues (2002) observed an
increased risk of cardiac death in patients with Beck Depression Inventory (BDI) scores
that started below the traditional cut-off point for identifying mild depression. Patients‘
baseline depression was associated with long term cardiac survival. Improvement of
depressed mood over time had little effect on prognosis in those patients with
moderate to severe depression (BDI > 19) whereas improvement of symptoms in
patients with mild to moderate depressed mood at baseline was associated with better
cardiovascular prognosis.
Although depression following an acute cardiac event is common it is important
to bear in mind that approximately 50% of these patients will have had previous
episodes of depressive symptoms or that the post-MI depressive symptoms are a
continuation of pre-MI depression (Freedland et al., 1992; Spijkerman et al., 2005).
One early report found that 40% of depressed MI patients with a history of depression
died by 18 months post the event in comparison with only 10% of patients with a first
time depression, while the group of patients with a history of depression but no
depression during hospitalization had the lowest mortality (2.2%) (Lesperance et al.,
1996). Other reports suggest that first time depression post MI increases the risk of
mortality. Grace et al (2005) reported that patients with self-reported depressed mood
30
following ACS but no history of depression had a 1.78 – fold increased risk of 5-year
mortality compared with depressed ACS patients with a history of depression. Similarly,
de Jonge and colleagues (2006) found that patients with first time depression following
MI had an increased risk of new cardiovascular events (HR: 1.76, 95% CI: 1.06 – 2.03)
compared with non-depressed patients. Patients with recurrent post-MI depression
were no more likely to experience recurrent cardiac events than were non-depressed
patients (HR: 1.39, 95% CI: 0.74 – 2.61). One recent study found no relationship
between depression and increased cardiac mortality at all. Dickens et al (2007)
measured depression before the MI and then 12 months later, and found that neither
increased risk of cardiac death following MI. These data therefore suggest that
depression in the weeks soon after MI onset may be particularly critical. This notion is
further reinforced by data collected as part of this thesis. The importance of depressive
symptoms observed in the early aftermath of ACS in relation to later adverse
psychosocial outcomes is demonstrated in chapter 4 in this thesis.
Although more recent systematic reviews have shown that depression
following an ACS is associated with a 2-fold increased risk of cardiac and all-cause
mortality in patients with established CHD (van Melle et al., 2004; Barth et al., 2004),
the findings are not universally consistent (Lane et al., 2001; Mayou et al., 2000).
However, the studies not to have found associations have typically been rather smaller
scale than others, and may have been underpowered.
1.3.2 Anxiety
Anxiety is often overlooked as a psychosocial risk factor in CHD. Much of the
focus remains on the role of depression. However, accumulating evidence indicates
that depression in CHD is often accompanied by symptoms of anxiety (Denollet et al.,
2006c), and that anxiety predicts cardiac events in post – MI patients over and above
the effect of depression (Grace et al., 2004; Strik et al., 2003). Considering the frequent
31
co-occurrence of depression and anxiety, greater focus should be directed to
investigating the influence of anxiety on prognosis in patients with recognized CHD.
Anxiety symptoms were assessed soon after admission in the two samples of ACS
patients I studied, and up to 3 years post the event. The importance of anxiety
symptoms, and posttraumatic stress (an anxiety disorder) in relation to post ACS
adjustment was assessed; these data are presented in chapters 4 and 6.
Frasure-Smith and colleagues reported an increased risk of cardiac events after
MI associated with anxiety (Frasure-Smith et al., 1995), in contrast to Jiang et al (2001)
who used the same measure and found no relationship with mortality. From
longitudinal investigations (3 – 5 year follow up), anxiety was associated with increased
occurrence of adverse events post MI (Strik et al., 2003). A review by Januzzi et al
(2000) highlighted the importance of studying anxiety in the context of CHD, as it was
found to increase risk of all-cause mortality three-fold following MI. This review also
found that anxiety almost doubled the risk of re-infarction at 5 years follow up, and
increased the risk of sudden cardiac death by a factor of 6. Studies of prognosis
following cardiac surgery have found anxiety to predict post-operative recovery after
CABG sugery (Rothenhausler et al., 2005). However, among these studies, symptoms
of anxiety assessed post-operatively have emerged as stronger predictors of adverse
outcomes following cardiac surgery then symptom levels recorded pre-operatively.
Oxlad et al (2006) reported that, following CABG, 6–month cardiac related hospital
readmissions were predicted by pre-operative depression levels and post-operative
anxiety, after controlling for a broad range of medical confounders. A more recent study
by Szekely et al (2007) demonstrated that trait anxiety, as measured by the Spielberger
State – Trait Anxiety Inventory, was associated with increased mortality and
cardiovascular morbidity following CABG and valve surgery. 180 patients who
underwent cardiac surgery were followed up at 6, 12, 24, 36 and 48 months post
discharge. 42% of the sample were classified as presenting clinically significant anxiety
symptoms. Trait anxiety emerged as an independent predictor of post-discharge
32
cardiovascular events and 4 year mortality. This study further supports the evidence of
stronger predictive value of post-operative anxiety scores than of pre-operative values.
At 6 month follow up, trait anxiety scores were found to be more strongly associated
with cardiovascular events than were values obtained pre-surgery. In this study,
anxiety and depression were strongly correlated, however, only anxiety was
significantly associated with increased mortality and morbidity.
1.3.3 Type D personality
Type D personality refers to the joint tendency to experience negative emotions
(negative affectivity) and to inhibit these emotions at the same time by avoiding
negative reactions from others (social inhibition). Type D personality has been
associated with a variety of adverse health outcomes in cardiac patients. These
adversities include poor prognosis, heightened emotional distress, poor selfmanagement, pro-inflammatory cytokine activation, and disturbances in cortisol
secretion in patients with CHD, heart failure, and heart transplantation (Denollet &
Brutsaert, 1998; Whitehead et al., 2007). Type D has also been associated with a wide
range of emotional distress, including anxiety, depression, and post-traumatic stress
(Denollet et al., 2000; Pedersen & Denollet, 2003; Pedersen & Denollet, 2004;
Pedersen & Denollet, 2006). Type D personality may be a pre-existing vulnerability
factor for development of posttraumatic stress in response to ACS. Chapters 4, 6 and 7
in this thesis address the role of type D personality in the prediction of post ACS
posttraumatic reactions.
Denollet et al (1995) showed in a prospective study of 268 men and 38 women
with established CHD, that Type D personality was associated with a six-fold increased
risk of death from cardiac events two to five years post MI (in men). In a sample of
patients undergoing cardiac rehabilitation, Type D personality emerged as an
independent risk factor associated with a four – fold increased risk of death from
33
cardiac causes (Denollet et al., 1996). A more recent study (Denollet et al., 2006b)
followed up 337 MI patient after 5 years. Multivariate analyses showed that left
ventricular ejection fraction <40%, not having coronary artery bypass surgery, and
Type D personality were independent predictors of major adverse events (OR= 2.90,
95% CI: 1.42 – 5.92), whereas psychological stress as measured by the General
Health Questionnaire was marginally significant (OR= 2.01, 95% CI: 0.99 – 4.11).
These authors concluded that Type D reflects more than temporary changes in stress
levels as it predicted cardiac events after controlling for concurrent symptoms of stress.
The very core of Type D research is the notion that the general tendency to
experience emotional and interpersonal difficulties may exacerbate progression of
CHD, irrespective of depression and anxiety. Although some overlap may exist
between depression and Type D personality in terms of negative affect, they clearly
differ in the inclusion of social inhibition and their conceptualization as either a disorder
(depression) or personality trait (Type D). Research has shown that the presence of
only one of the tendencies (negative affectivity and social inhibition) has no effect in
terms of cardiac prognosis, in fact it seems it is the interaction of the two that predict a
significantly increased risk of adverse clinical events in patients with existing CHD
(Denollet et al., 2006a).
Recent evidence supports the predictive value of Type D personality after
adjustment for depressive symptoms. Whitehead et al (2007) showed that Type D
predicted cortisol dysregulation after controlling for depression. Denollet and Pedersen
(2008) found Type D to be an independent [of depression] risk factor for major clinical
events in cardiac patients. Findings from the Myocardial Infarction and Depression
Intervention Trial (MIND-IT), demonstrated that depression was confounded by disease
severity, whereas Type D personality was not (de Jonge et al., 2007).
A number of pathways have been proposed to explain the association of Type
D with adverse outcomes among those with established CHD. Whilst numerous studies
have suggested a potential link, there is a lack of evidence of specific biological
34
pathways. And taken together, results from these studies suggest that Type D may be
explained by different underlying biological pathways compared with more established
psychological risk factors such as depression. An alternative explanation may be that
Type D personality is associated with negative health behaviours. The tendency of
social inhibition will lead to less social contact and availability of support, which may
have a detrimental effect on health. There is some evidence that social inhibition and
negative affectivity are in part due to genetic factors (Bouchard, Jr., 1994), and Type D
personality may therefore be the behavioural manifestation of underlying biology, which
would predispose an individual to cardiac outcomes associated with such underlying
genetic causes.
1.4 Pathways between negative affect and CHD
Anger, anxiety and depression are thought to play direct or indirect roles in the
disease process. One line of argument for the apparently worse prognosis among
cardiac patients that are depressed has been that these patients may just have
experienced a more severe clinical event. However, numerous studies show that there
is no reliable relationship between depression and any physiological index of disease
severity (Carney, 2002). There are several plausible mechanisms that may explain the
relationship between depression and prognosis in CHD patients. Some of these
suggest an indirect link where negative emotion is predictive of, but not causally related
to, morbidity and mortality in coronary heart disease (CHD). Other explanations imply a
direct influence of negative emotions on course and outcomes of CHD.
One general class of factors relates to adverse health behaviours. This is one
proposed pathway for the increased morbidity and mortality after acute cardiac events.
Individuals who are anxious, depressed or angry may engage in more adverse health
behaviours, and these behaviours may contribute and exacerbate underlying cardiac
disease. For example, smoking tends to be more common among angry, anxious or
35
depressed persons (Black et al., 1999). Depressed cardiac patients are also less
adherent to cardiac medication regimens (DiMatteo et al., 2000), lifestyle risk factor
interventions, cardiac rehabilitation programs (Ziegelstein et al., 2000) and are more
sedentary (Kritz-Silverstein et al., 2001). Cardiac medications significantly reduce
mortality in CHD patients and therefore non-adherence to prescribed medication can
have greatly detrimental effects on the course and outcomes of CHD. There is also
evidence that hostility and depression are associated with less social support and
greater social isolation, as a result these individuals may lack an important stress
buffer.
Some negative affects are also associated with elevated status on traditional
risk factors that result from adverse health behaviours and/or reflect environmental
factors or genetic predispositions. For example, some studies show that depression,
anxiety or anger increase risk of developing hypertension (Davidson et al., 2000; Jonas
& Lando, 2000). Further, diabetes, which confers a three – to four-fold increase in CHD
risk is twice as prevalent in depressed individuals (Anderson et al., 2001) and more
common in hostile persons (Niaura et al., 2002). In addition, individuals who score
highly on cynical hostility also tend to be obese and have elevated low-density
cholesterol (Weidner et al., 1987). Though many of the studies linking depression with
increased CHD risk have included these many of these standard risk factors as
covariates. However, a direct effect is unlikely, and multifactorial influences, mediating
pathways and unexplained variance in the relationship between depression and CHD
must be considered.
A more direct pathway between depression and poor cardiac prognosis
suggests that neurohormonal dysregulation is involved. Evidence of dysregulation of
the autonomic nervous system (ANS) and of the HPA axis in medically well patients
with major depressive disorder includes elevated plasma and urinary catecholamines
and cortisol. Elevated cortisol levels can promote the development of atherosclerosis
and accelerate injury of vascular endothelial cells. HPA dysregulation in depression
36
has been well documented (Broadley et al., 2006; Plotsky et al., 1998). Decreased
parasympathetic and increased sympathetic nervous system activity predisposes CHD
patients to myocardial ischemia, ventricular tachycardia, ventricular fibrillation, and
sudden cardiac death. The ANS abnormalities associated with depression could
therefore accelerate the progression of CHD and precipitate cardiac events by altering
cardiac autonomic tone and promoting procoagulant and proinflammatory processes.
Low heart rate variability (HRV) indicates excessive sympathetic and/or inadequate
parasympathetic tone, and it is a powerful, independent predictor of mortality in
patients with a recent MI or with stable CHD. There is growing evidence that HRV is
reduced in depressed compared with medically comparable non-depressed patients
following MI (Rottenberg, 2007). Lower HRV is a risk factor for cardiac arrhythmias and
cardiac arrest (Curtis & O'Keefe, 2002). Some of the strongest epidemiologic results
(Kawachi et al., 1994a) for anxiety are specifically with sudden cardiac death, which
tends to be associated with cardiac arrhythmias.
Depression is one way by which inflammatory processes can be affected and
may also be partly responsible for maintaining inflammatory responses once they have
been initiated. Depression may be involved in the maintenance of inflammatory
responses by inhibiting the immune system‘s sensitivity to glucocorticoid hormones,
which are responsible for terminating the inflammatory response (Carney, 2002).
Numerous studies report higher circulating levels of inflammatory risk markers (e.g. IL6, C-reactive protein [CRP], TNR-α) of cardiac morbidity and mortality amongst
medically health adults (Carney, 2002). A recent review by Howren and colleagues
(2009) assessed the magnitude and direction of associations of depression with CRP,
IL-1 and IL-6 in clinical and community samples. Articles published between 1967 and
2008 were systematically reviewed and results showed positive associations of CRP,
IL-1, IL-6 and depression. This pattern was observed in both clinical and community
settings, as well among studies using clinical interview or self-report measures. There
was evidence of a dose-response relationship of these inflammatory markers and
37
depression, supporting the notion that cardiac risk conferred by depression is not
limited to patient populations. However, the magnitude of effect was substantially
greater in studies of clinical patients utilising clinical interview to evaluate depression.
1.5 Overlapping affective dispositions
Anxiety, depression and anger correlate highly with each other in patients with
established CHD (Denollet & Brutsaert, 1998). This potential construct and
measurement overlap create not only ambiguity for theory, but also for interpretation of
evidence (Suls & Bunde, 2005). Another difficulty arises from an all too common singlefactor approach. The similarities of results from reports discussed in this chapter
suggests that negative emotions in general are related to CHD, however, there remains
a tendency to focus on only one of these emotions at a time in this context. Findings
from factor analytic studies suggest that anxiety, depression and anger are lower level
constructs that all load with a broad dimension of negative affectivity (NA) (rs = .71 –
85; Costa & McCrae, 1992). NA is a broadband personality dimension that is
conceptualized as a higher order construct that subsumes all of the negative emotions
(Watson & Clarke, 1984). To date, few studies have used broad measures of NA to
predict CHD, however, many studies report positive associations among anxiety,
depression, anger and CHD. However, it is important to note that evidence of overlap
does not imply that these emotions lack distinctive qualities.
The issue of conceptual distinctiveness of psychological constructs was
addressed in a sample of 822 healthy working men. Kudielka et al (2004)
demonstrated conceptual distinctiveness of depression, vital exhaustion, and negative
affectivity, with the factor structure of the original questionnaires maintained. A sample
of 565 cardiac patients (ischemic heart disease and chronic heart failure) completed
the Type D scale (DS14), HADS, BDI, and State Trait Anxiety Inventory (STAI). Pelle
and colleagues (2009) identified two higher-order constructs; negative affect and social
38
inhibition. Factor analysis was performed of all measures on item level, and
demonstrated
distinctiveness
of
the
constructs
measured
by
the
various
questionnaires. However, only the original structure of the DS14 was confirmed, items
on the HADS and BDI loaded more diffusely and did not tend to cluster together as
originally proposed. In this study, the higher order construct ‗negative affect‘ comprised
both state and trait facets, thereby highlighting the importance of assessing both
chronic (i.e. lasting >2 years) and episodic components (i.e. lasting several months and
up to 2 years), which might improve understanding of disease progression compared
when focusing on these components separately. Taken together, these preliminary
finding suggest that there is overlap between some, but not all psychological
constructs.
1.6 Summary
Overall there seems to be a growing body of research demonstrating that
depression, anxiety, anger/hostility and Type D adversely affect either onset or course
of CHD or both. Whereas depression seems to involve longer-term risk of adverse
outcomes (such as acute MI or death), anxiety seems particularly associated with
sudden cardiac death (and possibly acute MI). As discussed in the previous sections,
numerous studies have concluded that depression and anxiety predict CHD morbidity
and mortality, even after traditional CHD risk factors are controlled for. Studies of anger
and hostility have yielded more mixed results, but prospective associations with CHD
have been found in some. Posttraumatic stress disorder (PTSD) is linked closely with
both anxiety and depression and has been hypothesized to be associated both with the
development of CHD and as a consequence of acute cardiac events. Interestingly,
findings in select samples are beginning to emerge that are consistent with studies on
anxiety or depression and CHD (Kubzansky, 2009). This literature is reviewed in
Chapter 2.
39
Research has identified multiple behavioural and biological pathways by which
these psychological factors may contribute to the development and progression of
CHD. However, this area of inquiry has elicited controversy. Disagreement stems partly
from the inconsistencies in the literature, noted above. Findings from the ENRICHD
trial have intensified controversy. This trial, involving cognitive-behavioural treatment of
depression in post-MI patients, reduced depression but did not confer direct cardiac
benefits in patients with existing disease (ENRICHD, 2003), suggesting that a fresh
approach to understanding the pathways underlying associations with CHD is required.
Overall the evidence for a connection between depression and subsequent
CHD appear strong. Though the majority of studies support an association between
depression and CHD progression or mortality, there are also several negative findings.
One possible explanation for trends suggesting a declining effect of depression is that
publication biases initially favour positive studies. Later, when a research area begins
to mature,,failures to replicate earlier positive findings begin to appear. An alternative
explanation is that treatment of CHD has developed greatly in recent decades, and
post MI mortality has steadily declined. It is plausible that physiological or behavioural
pathways may be weakened by advances in treatment, which were not available in the
early days of this research. This explanation is strengthened by the findings that the
effect of depression has apparently declined but not disappeared. Further, criticism has
been directed at the inadequate adjustment for confounding risk factors in many
studies; the diagnosis of depression could be confounded by the patient‘s medical
condition; also, subclinical disease may have been present in some portion of
nominally healthy population. As is the case for depression, the evidence supporting a
role for anxiety in cardiac disease risk is more consistent in initially healthy samples
than in patient populations. This might indicate that negative emotions play less of a
role in CHD progression than in the development of CHD. However, a failure to
differentiate between state or trait anxiety may be responsible for some of the weak
and inconsistent effects observed in the literature. Findings from studies of anger and
40
hostility are mixed, and positive results have generally been found in samples of initially
healthy participants. The results from patient samples are weaker and more
inconsistent.
Negative emotions are prevalent among patients who have experienced cardiac
events. It is not only of importance to assess disease progression or fatal CHD as
outcomes in this population. With an increasing number of ACS survivors, assessment
of negative affect is of significant interest due to their association with poor adjustment
and quality of life. This thesis attempts to address these issues by assessing recovery
and emotional adjustment following ACS.
41
CHAPTER 2. Literature Review: Posttraumatic Stress Disorder
2.1 Introduction to Posttraumatic Stress Disorder
Although the concept of posttraumatic stress disorder (PTSD) dates back more
than a hundred years (recognised under terms such as shell shock, traumatic neurosis,
rape-trauma syndrome etc) it was first introduced, and formally recognised, in the third
edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) in 1980.
PTSD is an anxiety disorder that can develop following exposure to a traumatic event.
For current DSM-IV (APA, 1994) classification, trauma must involve actual or
threatened death or serious injury, or a threat to the physical integrity of self or others
(criterion A1). In addition, trauma must elicit intense fear, helplessness or horror in the
exposed individual during the event (criterion A2). There are three sets of symptom
clusters related to PTSD. The first symptom cluster, re-experiencing the event (criterion
B), may occur in several ways. The individual may have recurring nightmares about the
event, may experience intrusive and recurring thoughts about the event, may feel as
though the event is recurring (‗flashbacks‘), and/or may experience psychological
and/or physical reactions to stimuli associated with the event. The second symptom
cluster, avoidance of stimuli associated with the event and numbing of general
responsiveness (criterion C), occurs when the individual avoids thoughts and feelings
associated with the event as well as avoids other stimuli such as people, places, and
activities associated with the event. The avoidance symptom cluster is also
characterized by difficulty remembering important aspects of the event, diminished
enjoyment in pleasurable activities, feelings of detachment, a restricted range of affect,
and a sense of a foreshortened future. The last symptom cluster, increased arousal
(criterion D), is indicated by difficulty staying or falling asleep, anger and irritability,
hypervigilance, difficulty concentrating, and an exaggerated startle response. To qualify
for a diagnosis of PTSD, in addition to fulfilling criterion A, individuals must also report
42
at least one of the re-experiencing symptoms (criterion B), at least three of the
avoidance symptoms (criterion C) and at least two arousal symptoms (criterion D).
Symptoms should remain for at least one month (criterion E), and must result in
clinically significant distress or impairments in social, occupational or other important
areas of functioning (criterion F).
When a person experiences a traumatic event, it is common to experience
some of these symptoms in the immediate aftermath as part of a normal reaction to
severe stress. When these symptoms persist after two weeks, a diagnosis of Acute
Stress Disorder (ASD) may be appropriate; however, when the duration of the
symptoms persists for more than one month, a diagnosis of PTSD may be warranted
(APA, 1994). The diagnostic criteria for PTSD, as defined by the DSM-IV, are provided
in Table 2.1. However, the introduction of PTSD into the DSM has not gone
unchallenged and many of the issues concerning the concept are widely debated.
One of the major issues with the concept of PTSD concerns the expansion of
the stressor criterion (A). Since its introduction into the DSM the definitions for the
crucial factor, the traumatic event, have undergone significant change. In the early
days the traumatic stressor was considered an extreme event outside normal human
experience. In the latest version of the DSM the concept of the traumatic stressor has
broadened to such an extent that the vast majority of [American] adults have been
exposed to PTSD-qualifying events (McNally, 2004). Fulfilment of part one of criterion
A is sufficient if individuals have been ‗confronted with‘ a traumatic event rather than
directly experiencing it. This term is vague and merely ‗hearing about something
horrible‘ could cause PTSD, bearing with it the potential for over-inflating the rate of
PTSD and abuse of the syndrome for monetary reasons (malingering). McNally
(McNally, 2003) refers to this as ‗conceptual bracket creep‘. The second part of
criterion A, which requires a persons‘ response to include intense fear, helplessness, or
horror, is flawed by the retrospective nature of such reports and the potential for current
symptoms to influence memories of trauma reactions (Frueh et al., 2002).
43
A number of issues are also associated with the symptom criteria (B-D). In
particular, the limited support for the three-factor model of the condition. A number of
studies using factor analysis report best fit models ranging from 2 to 5 factors (Cordova
et al., 2000; Simms et al., 2002). Yet another issue is that many of the defining
symptoms of PTSD overlap with other well know disorders, in particular depression,
specific phobia and other anxiety disorders (Spitzer et al., 2007). It is important to bear
in mind that some of the symptoms of PTSD are also part of a non-pathological
response to intense stressful experiences. Some research suggests that PTSD may
not actually be a qualitatively distinct response to extreme stress but that it in fact
reflects the upper end of a stress-response continuum (Ruscio et al., 2002). The
implication of this argument for the purpose of my thesis is that it is important to
investigate levels of posttraumatic symptoms in cardiac patients as a continuum, rather
than simply classifying patients into those with and without PTSD.
2.2 Current understanding of PTSD
Much of the work on PTSD to date has focused on the role of memory in the
development and maintenance of posttraumatic stress symptoms during and after a
traumatic event. A number of changes in memory functioning have been identified
(Brewin & Holmes, 2003). PTSD is characterized both by enhanced recall of trauma
related materials as well as amnesia for details of the traumatic event (Buckley et al.,
2000). One distinctive memory feature of PTSD is ‗flashbacks‘ or the reliving of past
trauma as if it were happening in the present. These intrusive memories are not
44
TABLE 2.1 DSM-IV DIAGNOSTIC CRITERIA FOR POSTTRAUMATIC STRESS DISORDER
Criterion A (Stressor): The person has been exposed to a traumatic event in which both of the
following have been present:
(i) the person experienced, witnessed, or was confronted with an event or events that
involved actual or threatened death or serious injury, or a threat to the physical
integrity of self or others
(ii) the person's response involved intense fear, helplessness, or horror. Note: In
children, this may be expressed instead by disorganized or agitated behaviour
Criterion B (Re-experiencing): The traumatic event is persistently re-experienced in one (or more) of
the following ways:
(i) recurrent and intrusive distressing recollections of the event, including images,
thoughts, or perceptions. Note: In young children, repetitive play may occur in
which themes or aspects of the trauma are expressed
(ii) recurrent distressing dreams of the event. Note: In children, there may be
frightening dreams without recognizable content
(iii) acting or feeling as if the traumatic event were recurring (includes a sense of reliving
the experience, illusions, hallucinations, and dissociative flashback episodes,
including those that occur upon awakening or when intoxicated). Note: In young
children, trauma-specific re-enactment may occur
(iv) intense psychological distress at exposure to internal or external cues that
symbolize or resemble an aspect of the traumatic event
(v) physiological reactivity on exposure to internal or external cues that symbolize or
resemble an aspect of the traumatic event
Criterion C (Avoidance): Persistent avoidance of stimuli associated with the trauma and numbing of
general responsiveness (not present before the trauma), as indicated by three (or more) of the
following:
(i) efforts to avoid thoughts, feelings, or conversations associated with the trauma
(ii) efforts to avoid activities, places, or people that arouse recollections of the trauma
(iii) inability to recall an important aspect of the trauma
(iv) markedly diminished interest or participation in significant activities
(v) feeling of detachment or estrangement from others
(vi) restricted range of affect (e.g., unable to have loving feelings)
(vii) sense of a foreshortened future (e.g., does not expect to have a career, marriage,
children, or a normal life span)
Criterion D (Arousal): Persistent symptoms of increased arousal (not present before the trauma), as
indicated by two (or more) of the following:
(i) difficulty falling or staying asleep
(ii) irritability or outbursts of anger
(iii) difficulty concentrating
(iv) hypervigilance
(v) exaggerated startle response
Criterion E (Duration): Duration of the disturbance (symptoms in Criteria B, C, and D) is more than one
month.
Criterion F (Distress or Impairment): The disturbance causes clinically significant distress or
impairment in social, occupational, or other important areas of functioning.
45
triggered by a deliberate search but involuntarily by specific reminders that in some
way relate to the traumatic event (Brewin & Holmes, 2003). Another memory process
relevant to the disorder is working memory capacity. Individuals with greater working
memory capacity are better at suppressing unwanted thoughts (Brewin & Beaton,
2002; Brewin & Smart, 2005), and may be part of the explanation that low intelligence,
which is strongly associated with working memory, is a risk factor for PTSD (Brewin et
al., 2000).
There has been some research undertaken aiming to establish the role of
attention for risk of PTSD. Evidence shows an attentional bias early in processing of
traumatic material (Bryant & Harvey, 1997). However, studies are scarce and results
fairly inconsistent and do not show that the effects are unique to PTSD. Another
psychological process linked with the increased risk of PTSD following trauma is
‗dissociation‘. Dissociation has been defined as a kind of temporary interruption of, in
our otherwise continuous, interrelated processes of perceiving the world around us,
remembering the past, and our ability to link the past with our future (Spiegel &
Cardena, 1991). Dissociation most commonly encountered in response to traumatic
experience (peri-traumatic dissociation) includes states such as emotional numbing,
depersonalization and ‗out-of-body‘ experiences. These reactions appear to be related
to the severity of trauma, fear of death and feelings of helplessness (Holman & Silver,
1998; Reynolds & Brewin, 1998). A number of studies show dissociative responses to
trauma to be strong predictors of later PTSD (Ehlers et al., 1998; Engelhard et al.,
2003; Ursano et al., 1999). However, it is important to note that a number of studies
show this is only the case for peri-traumatic dissociation, not for dissociative
experiences that occur post trauma (Brewin et al., 1999; Harvey & Bryant, 1998; 1999).
Emotions experienced at the time of trauma or as a post trauma response, such as
intense fear, helplessness, horror, anger and ‗mental defeat‘ (defined as the perceived
loss of all autonomy, a state of ‗giving up‘), are strongly associated with increased risk
of PTSD, slowed recovery, and more persistent symptomatology (Andrews et al., 2000;
46
Ehlers et al., 2000). A central idea of PTSD is that traumatic events shatter people‘s
basic beliefs and assumptions (Bolton & Hill, 1996; Horowitz, 1986). Post trauma,
increased negative beliefs (about self, others, the world) are common among those
who later go on to develop PTSD (Dunmore et al., 1999; Foa et al., 1999).
Coping strategies adopted by trauma victims can influence recovery. Studies
show that avoidance and thought suppression are related to slower recovery and
greater symptom levels (Dunmore et al., 1999; Steil & Ehlers, 2000) and that attempts
to suppress unwanted thoughts may in fact lead to them returning even more strongly
(Wenzlaff & Wegner, 2000). A meta-analysis conducted by Brewin and colleagues
(2000) found social support, alongside gender and trauma severity, to be the strongest
risk factor for PTSD. Several studies show that a lack of positive social support is
associated with PTSD, but the existence of a negative social environment appears to
be an even greater indicator of PTSD symptomatology (Ullman & Filipas, 2001).
It is clear that PTSD is associated with disturbances in a wide range of
psychological processes (memory, beliefs, and emotions). The following section will
outline the frameworks that drive current thinking about PTSD. The three main
theoretical models of PTSD are described.
2.3 Models of PTSD
PTSD risk factor research indicates that individuals‘ experiences during
traumatic events play a significant role in differentiating between those who
subsequently go on to develop PTSD and those who do not (Brewin et al., 2000). The
way in which an individual processes these experiences is a part of this subjective
experience. Below, three current theoretical approaches to explaining PTSD are
described.
47
2.3.1 Emotional processing theory
One of the basic features of emotional processing theory (Foa et al., 1989; Foa
& Kozak, 1986) is that PTSD is a form of pathological fear. Specifically, PTSD
symptoms arise when information in the fear network is incompatible with pre-existing
memory structures. This model posits that pathological fear responses occur and are
maintained when there is an attempt to avoid or suppress the activation of the fear
network. Further, depending on the severity of the stressor, the cognitive processes of
attention and memory at the time of trauma may be disrupted, which may lead to
formation of a disjointed or fragmented fear network that is difficult to integrate with
existing memory structures. This model holds that resolution of the trauma occurs with
activation of the fear network, which will enable modification of the memory structure by
integrating information that is incompatible with the trauma so that new memories can
be formed.
More recent developments of the theory (Foa & Riggs, 1993; Foa & Rothbaum,
1998; Tolin & Foa, 2002) include elaboration on the relationship between PTSD and
knowledge available prior, during and after the trauma. It is hypothesized that
individuals who hold more rigid pre-trauma views will be more susceptible to PTSD.
These views may be positive or negative, for example, views about the self as being
extremely competent, and the world extremely safe, or the opposite, views about the
self as being extremely incompetent, and the world extremely dangerous. These recent
adaptations also place greater emphasis on negative appraisals of responses and
behaviours which can lead to exacerbations of negative self views. It is suggested that
these beliefs (present prior, during and post trauma) could interact and reinforce
negative views about the self and contribute to chronicity of PTSD.
This model fits well with the evidence showing successful reduction of PTSD
following exposure therapy (Foa et al., 1999; Foa et al., 1991). Repeated exposure
should lead to habituation of fear, allowing integration of new (safety) information
48
regarding the trauma into the trauma memory, and re-evaluation of negative beliefs or
appraisals. As proposed by this model, trauma can cause disruption of cognitive
processes such as memory and attention, leading to disjointed or fragmented fear
structures. By reliving the trauma through exposure therapy it is possible to generate a
more organized memory record that is easier to integrate into the rest of the memory
system.
Although this model has great explanatory power in terms of the effects of
exposure treatment, and the strongly supported inclusion of importance of appraisal
processes prior, during and post trauma, there are a number of issues with this
approach. Firstly, there is no consistent evidence that repeated exposure will lead to
changes in the structure of trauma memories, to the initial activation of fear, or
habituation. Secondly, the model is based on the assumption of a fear network, thus it
could be argued that reminders of the trauma would activate the entire fear memory
(stimulus information, response information, and meaning). This, however, does not fit
with the literature showing that many individuals with PTSD have memories of the
traumatic event that fragmented, where certain parts are clear and others less so
(Mechanic et al., 1998).
2.3.2 Dual representation model
In contrast to the emotional processing theory described above, dual
representation theory suggests that trauma memories are represented in fundamentally
distinctive ways from other types of memories. Brewin et al (1996) argue that there are
two separate memory systems, operating in parallel, where one may take precedence
over the other at different times. One of these systems, the ‗verbally accessible
memory‘ (VAM) system, records only information which has been consciously attended
to either prior to, during, or after the traumatic experience. This information receives
enough conscious processing to be integrated with other autobiographical memories,
49
and can thereby be retrieved deliberately. Emotions accompanying these memories
include both those that happened at the time of trauma (primary emotions) as well as
those generated by retrospective appraisals of the event (secondary emotions).
However, the information stored in this system is limited, as diversion of attention to the
immediate source of threat, and the accompanying high levels of arousal during trauma
greatly affect the volume of information that can be consciously registered. A second
system is proposed. The ‗situationally accessible memory‘ (SAM) system contains
information that has been obtained from lower level processing of the traumatic event,
such as sights, sounds, bodily responses etc. Memories (i.e. flashbacks) stored in this
system can be triggered by situational reminders of the trauma, these triggers can be
either external or internal. The emotions that accompany SAM memories are restricted
to primary emotions that were experienced during the trauma, and therefore flashbacks
tend to be more detailed and emotion-laden than ordinary memories.
This model of PTSD mainly focuses on the role of memory, emotions and
appraisals, however there is little discussion of other important features of PTSD such
as dissociation or emotional numbing. Further research is needed for the basic tenets
of this theory to be fully supported.
2.3.3 Ehlers and Clarke’s cognitive model
Two mechanisms for pathological responses to trauma are identified in Ehlers
and Clarke‘s cognitive model of PTSD (2000). These are the negative appraisals of the
trauma or its consequences and the trauma memory itself. Patients with PTSD feel
anxious about the future, even though the source of threat (the trauma) lies in the past.
This apparent paradox is addressed in this model of PTSD. These authors argue that
factors that increase the likelihood of negative appraisals are thought processes during
the trauma and prior beliefs and experiences. There is a particular focus on one state
of mind identified as ‗mental defeat‘. This reaction to trauma, emphasizing the inability
50
of the person to influence their fate, is a risk factor for such appraisals as being weak,
ineffective or unable to protect oneself.
This model also attempts to account for traumatic memories by suggesting a
number of peri-traumatic influences that affect the nature of trauma memories. Ehlers
and Clarke‘s approach to explaining research findings of traumatic memory was to
suggest that the memory of the traumatic event is poorly elaborated, not given a
complete context in the first place, and inadequately integrated into autobiographical
memory. Firstly, they distinguish between conceptual and data-driven processing of
information encountered during trauma. Conceptual processing (focused on the
meaning of the situation, organizing the information, and placing it in context),
facilitates integration of the trauma memory into general autobiographical memory,
whereas data-driven processing (focused on sensory impressions) leads to strong
perceptual priming (a reduced perceptual threshold for trauma-related stimuli),
accounting for re-experiencing in the present (absence of temporal context), and a
memory that is difficult to intentionally retrieve (indicating absence of clearly specified
retrieval routes). Secondly, inability to establish a self-referential perspective while
experiencing the trauma, dissociation, emotional numbing, and a lack of cognitive
capacity to evaluate aspects of the event clearly, are among the other peri-traumatic
influences proposed.
A number of maladaptive behavioural strategies post trauma are considered
risk factors for persisting PTSD, such as active attempts at thought suppression,
distraction, avoidance of trauma reminders, use of alcohol or medication to control
anxiety, abandonment of normal activities, and adoption of safety behaviours to
prevent or minimize trauma-related negative outcomes.
Although this model of PTSD is considered the most detailed account of PTSD
(Brewin & Holmes, 2003), there is a lack of consistent evidence in supporting the datadriven versus conceptual processing argument. However, the importance of ‗mental
51
defeat‘, negative appraisals of initial PTSD symptoms and safety behaviours and
avoidance have been strongly supported (Dunmore et al., 2001).
2.3.4 Summary of current models of PTSD
There is a great deal of overlap between these three theoretical accounts of
PTSD incorporating factors affecting encoding of trauma information, alterations in
memory functioning, appraisals of the trauma, coping strategies and cognitive styles
etc. The main difference between these models lies in their accounts of how trauma
impacts memory, the process whereby changes are brought about in memory, and how
changes are related to recovery. One major difference between the emotional
processing theory and the other two models are that in the latter two PTSD is
accounted for by two distinct memory systems whereas the emotional processing
theory relies on the idea of a single associative network in memory. Though, further
work is needed, as effectiveness of treatments for PTSD is likely to increase from
improved theoretical understanding of trauma, memory and PTSD.
2.4 PTSD following non-medical trauma
The majority of people will experience at least one traumatic event in their
lifetime.
Data from large, representative community samples in the US, estimate
lifetime prevalence of PTSD in the general population between approximately 7%
(Kessler et al., 2005) and 15% (Breslau et al., 2005). Certain types of traumatic events
are more likely to lead to PTSD. For example intentional acts of interpersonal violence,
in particular sexual assault and combat, are more likely to result in posttraumatic stress
than are accidents or natural disasters (Creamer et al., 2001; Kessler et al., 1995; Stein
et al., 1997). Further, men are more likely to experience more traumatic events than
women, but women tend to experience higher impact events and are also more likely to
52
develop PTSD in response to trauma (Stein et al., 1997). However, this gender
difference in risk is not explained by differences in the type of traumatic event (Kessler
et al., 1995).
Although many people experience posttraumatic symptoms in the immediate
aftermath of a traumatic event, prospective research suggests that rates decline rapidly
over time. One study reported that following the terrorist attacks on the 11 th of
September 2001 in New York, PTSD rates reduced from 7.5% one month post the
event to 1.6% at four months and 0.6% at six months (Galea et al., 2003). For some,
however, the disorder persists for a significant period of time (Kessler et al., 1995). It is
important to note that not everyone that encounters a traumatic experience will go on to
develop PTSD. Hence, exposure itself is not sufficient to explain the phenomenon and
it is being increasingly recognised that some pre or post trauma individual variability
factors have a role to play in understanding this condition.
A growing literature on PTSD shows that demographic characteristics such as
age, gender, ethnicity and socioeconomic status (SES) are associated with different
rates of PTSD, with younger persons, females, Latinos (in the US), and low SES
individuals being more likely to develop PTSD following a traumatic event (Norris et al.,
2002). Interpersonal and psychological factors, such as social support and negative
affect, have also been implicated in the onset and course of PTSD (Adams &
Boscarino, 2005; Breslau et al., 2004). A meta-analysis by Brewin et al (2000)
identified 14 risk factors for post-traumatic stress disorder including, trauma severity,
lack of education, younger age, female sex, minority status, psychiatric history, low
SES, other adverse childhood factors, other previous trauma, family psychiatric history,
lack of social support, childhood abuse, life stress and low intelligence. However,
closer inspection of the effect sizes reported in this study, suggest that the intensity of
trauma and factors that follow the traumatic event (such as social support and further
stressors) are the strongest predictors of PTSD, whereas pre-traumatic stressors have
smaller individual effects (Shalev, 2001). Thus, once a traumatic event has occurred,
53
the major risk factors for PTSD appears to lie ahead, suggesting ample opportunity for
secondary prevention of this disorder. However, it should be noted that there are
considerable differences observed in effect sizes of risk factors between studies. A
recent review by Wittchen and colleagues, highlight the considerable degree of
variability, and report that risk factors, their interaction, and effect sizes vary by type of
sample, cohort, and a number of other methodological factors (Wittchen et al., 2009).
The current conceptualization of PTSD implicitly assumes a dose-response
relationship (APA, 1994). That is, the more severe the event, or the greater the
proximity, the more intense the posttraumatic stress. Some studies support this
assumption. For example, studies of PTSD in combat exposed individuals (Goldberg et
al., 1990; Vuksic-Mihaljevic et al., 2000) as well as studies of civilians living under
combat conditions (Afana et al., 2002) support a dose-response theory of PTSD.
However, there is great variability in the results from such studies and many find no
evidence of greater PTSD with increased severity of trauma. For example a welldesigned Swiss study of severely injured motor vehicle accident victims found a onemonth PTSD rate of 4.7%, dropping to 1.9% at one year (Schnyder et al., 2001). One
study of Turkish torture victims found that the majority did not develop PTSD (Basoglu
& Paker, 1995).
A number of studies show that previous experience of traumatic events is one
of the strongest risk factors for PTSD in the community (Breslau et al., 1999), in war
veterans (Bremner et al., 1993), in female rape victims (Yehuda et al., 1998) and
individuals with PTSD following the events of September 11, 2001, in New York (Galea
et al., 2003). Although the multiple-trauma data suggest that some individuals show
sensitization, some studies also indicate that some people show adaptive responses to
events (Corneil et al., 1999; Falsetti & Resick, 1995) with some data suggesting that a
majority of trauma exposed adults respond to repeat trauma with greater confidence
and enhanced coping skills (Aldwin et al., 1996).
54
There are relatively few studies investigating the long-term natural course of
PTSD in the community. However, most of these suggest that in the majority of cases,
PTSD typically runs a chronic and persistent course (Perkonigg et al., 2005; Owashi &
Perkonigg, 2008). This is not to say that individuals continuously meet diagnostic
criteria for PTSD, as variations in severity around the diagnostic threshold appear quite
frequent. Community findings of predictors of chronicity suggest that occurrence of new
traumatic events, higher rates of avoidant symptoms at onset, and higher rates of other
anxiety or somatoform disorders are associated with persistence (Perkonigg et al.,
2005). Using 10-year longitudinal data, Perkonigg et al (2005) found few spontaneous
remissions, even after many years post trauma. Contrary to some other disaster
studies, where post-disaster PTSD typically decreases over time, a recent study of
survivors of the hurricane Katrina in the US (Kessler et al., 2008) observed increased
rates of PTSD from baseline (5 – 8 months after the traumatic event) to 1 year later
(14.9% to 20.9%).
More recently studies have begun investigating PTSD as a consequence of
medical illness. In the DSM-IV, ‘being diagnosed with a life threatening illness‘ was
added as an example of traumatic stress (APA, 1994). Although PTSD has been
recorded most commonly as a consequence of trauma in an external environment,
some physical illnesses and particularly those of an acute nature (such as acute
coronary syndrome) can occur just as suddenly and unexpectedly as other traumatic
stressors. This literature is reviewed in the following sections.
2.5 Medical events as traumatic stressors
Although most commonly diagnosed in victims of war, natural disasters or
assault, there is increasing evidence of PTSD in individuals after onset, diagnosis, or
treatment for physical illness.
After the inclusion of ‗being diagnosed with a life-
threatening illness‘ in DSM-IV the question has arisen whether the experience of
55
severe physical illness, essentially an event internal to the person, satisfies the
traumatic stress criterion (A) for PTSD. It seems, however, that medical stressors are
not dissimilar from other traumatic stressors, in that they are likely also to convey lifethreat. Medical diagnoses, like other traumatic events, are also precipitants of extreme
fear, helplessness or horror. PTSD may develop as a result of direct threat, such as
diagnosis of, or treatment for serious illness, or more indirectly as a function of
witnessing or care giving for individuals with serious illness (Tedstone & Tarrier, 2003).
So what distinguishes medical stressors from other more traditionally viewed traumatic
stressors? One of the major differences is the relative prevalence of PTSD. In general,
the likelihood of PTSD is lower among medical patients, than other trauma victims. For
example, the rate of PTSD as a consequence of myocardial infarction ranges between
0% and 32% (see table 2.2), PTSD as a result of cancer ranges between 0% and 32%
(Kangas et al., 2002), compared with 35 – 47% in studies of rape or battery (Rothbaum
et al., 1992; Riggs et al., 1992; Resnick et al., 1992). These lower observed rates have
a number of plausible explanations.
Lower rates of PTSD among medically ill patients reflect general findings that
development of psychopathology in medical populations is also relatively low (Mundy &
Baum, 2004). Differences in methodology may also account for these lower rates.
Limits on the severity of disease and stages of disease, or at which time point
assessments are made, may have an impact on rates. For example, many studies of
PTSD following cancer have used early stage breast cancer patients, who have a
relatively good prognosis, which may in fact artificially limit the likelihood of
experiencing
psychological
trauma.
There
are
also
distinct
differences
in
symptomatology. Patterns of falling and rising posttraumatic symptom experience are
not uncommon following medical trauma, whereas in studies of non-medical stressors,
distress tends to be greatest in the immediate aftermath of the event and decrease
(often rapidly) over time (Rothbaum et al., 1992; Kessler et al., 1995).
56
Possibly the most significant difference between many medical stressors and
what is considered more ‗traditional‘ traumas is the focus of threat in time. Whereas
traditional trauma can be considered a discrete or acute event, occurring in the past,
medical stressors not only share this characteristic but there is an added dimension
containing future-oriented aspects of the threat, such as fears about treatment,
survival, recurrence and persistence of life-threat (Compas et al., 2002; Kangas et al.,
2002). One implication of this is the effect it may have on PTSD symptomatology, in
particular regarding intrusive thoughts. For medical patients, intrusions may not only
consist of past events, but may also include future-oriented aspects of the trauma. This
possibility must be taken into account when assessing intrusions following medical
trauma. For this study population, it may be more appropriate to ask about the specific
nature or content of intrusions, rather than simply assessing existence of intrusive
thoughts. The appropriateness of adopting PTSD as a model explaining distress
related to medical trauma must therefore be questioned.
2.6 PTSD following medical trauma
Tedstone and Tarrier (2003) reviewed the prevalence and predictors associated
with posttraumatic symptoms in a number of medical conditions (myocardial infarction,
cardiac surgery, haemorrhage and stroke, childbirth, miscarriage, abortion and
gynaecological procedures, intensive care treatment and human immunodeficiency
virus (HIV) infection) and found that the highest prevalence rates were identified in
patients treated, in intensive care units, for life-threatening events (14 – 59%) and
those with HIV infection (30 – 35%). The lowest rates where found in studies of PTSD
post-childbirth (1.7 – 5.6%). A number of factors were identified across studies which
may predispose individuals to the development of PTSD. Patient characteristics,
including personality factors, previous life adversity, and previous mental health
problems were associated with increased risk of PTSD. Age as a risk factor was
57
supported in some studies but not in others. Aspects of the trauma itself, such as poor
partner/confidant support during or after the traumatic event and negative perceptions
of medical staff were also implicated as risk factors for later PTSD. Interestingly, the
majority of studies report no relationship between objective severity of illness and later
PTSD. However, one study found more severe pre-operative cardiac conditions to be a
risk factor (Stoll et al., 2000). Also, some studies have found that the severity of
medical intervention is associated with increased risk of PTSD (Creedy et al., 2000;
Menage, 1993). The studies included in Tedstone and Tarrier‘s review (2003), which
measured PTSD at two time points, all found a reduction in the number of cases with
PTSD over time. This has also been found in epidemiological studies (Kessler et al.,
1995), where PTSD cases have significantly been reduced over time, in particular by
12 months post trauma. Thus the timing of symptom assessment is of crucial
importance to understand the nature of PTSD.
There has also been a greater focus in recent years on the issue of PTSD
following cancer. Kangas et al (2002) reviewed the literature on PTSD as a
consequence of cancer and found prevalence rates (13 studies, 10 of which studied
breast cancer, all but one were cross-sectional) between 0% and 32%. A further 21
studies were included in this review, these focused only on the prevalence of intrusion
and avoidance symptoms. Across studies patients reported high rates of both intrusion
(16-43%) and avoidance (15-80%) symptoms at least within the first month following
cancer diagnosis. Numerous studies have investigated the predictors of PTSD
following cancer. Many predictors of PTSD from the general trauma literature have also
been found to predict PTSD in cancer patients, including female gender (Hampton &
Frombach, 2000; Nordin & Glimelius, 1998), younger age at diagnosis (Andrykowski et
al., 2000; Cordova et al., 2000; Green et al., 2000), lower SES (Cordova et al., 1995),
and lower education (Cordova et al., 1995; Jacobsen et al., 1998). Other risk factors
associated with PTSD responses following cancer diagnosis are prior negative life
stressors (Andrykowski & Cordova, 1998; Mundy et al., 2000), a history of
58
psychological disturbance (Green et al., 2000; Mundy et al., 2000), elevated
psychological distress subsequent to the diagnosis (Epping-Jordan et al., 1999;
Jacobsen et al., 1998; Smith et al., 1999), avoidant coping style (Nordin & Glimelius,
1998), poor social support (Andrykowski & Cordova, 1998; Butler et al., 1999; Green et
al., 2000), poor social functioning (Kelly et al., 1995; Smith et al., 1999), and reduced
physical functioning (Jacobsen et al., 1998; Smith et al., 1999).
There is mixed evidence on the predictive value of medical variables such as
type, severity, stage and prognosis of cancer. Whereas some studies have found no
relationship between such variables (Alter et al., 1996; Cordova et al., 1995; Green et
al., 1998), other have found more advanced stages (Andrykowski & Cordova, 1998;
Epping-Jordan et al., 1999; Jacobsen et al., 1998), the recency of treatment for cancer
at PTSD assessment (Andrykowski & Cordova, 1998; Kornblith et al., 1992), and
patients experiencing at least one cancer recurrence (Butler et al., 1999; Cella et al.,
1990) to be associated with increased severity of PTSD symptoms post cancer
diagnosis.
There are however a number of major methodological issues that must be
taken into account when interpreting findings from these studies. Firstly, the majority of
studies have been cross-sectional in nature. Retrospective accounts of symptoms may
be strongly influenced by current symptomatology, and cross-sectional designs does
not allow researchers to delineate the relative contribution of diagnosis, treatment, side
effects or changing prognosis of cancer on PTSD symptomatology. Another major
issue is the reliance on self-report measures, and the wide variety of measures used
across studies makes results difficult to generalise. There is also a marked variability in
the samples used across studies in terms of type and stage of cancer. The overemphasis on using female patients is also problematic considering the strong evidence
that females are more likely to develop PTSD then males.
59
2.7 PTSD as a consequence of Acute Coronary Syndrome
ACS, like other non-medical trauma, is life-threatening, sudden and often
unexpected. Many patients report an intense fear of dying (Whitehead et al., 2005),
and emotional distress such as anxiety and depression during the acute phase and in
the immediate aftermath is common. Although most patients will fully recover from this
emotional distress, many patients do not recover and distress can persist for a
significant period of time. The persistent and severe psychological distress experienced
by some patients may actually satisfy criteria for a diagnosis of PTSD.
2.7.1 Prevalence of PTSD following ACS
Table 2.2 summarizes the prevalence levels of PTSD that have been observed
in patients following ACS (mostly acute Myocardial Infarction - MI). As can be seen
from the table, the prevalence of PTSD ranges from 0 and 32% in these studies. In
their review, Spindler and Pedersen (2005) put the average rate at 15%. There may be
several reasons for this variability, one is the measurement tool. As can be seen in
Table 2.2, a wide variety of questionnaires have been used, including the Impact of
Events scale (IES), the Posttraumatic Diagnostic Scale (PDS) and the Posttraumatic
Stress Disorder Symptom Scale (PSS). The criteria derived from these different
instruments vary, so prevalence may not be uniform. The IES in particular is likely to
elevate estimated prevalence rates as it only assesses avoidance and intrusion
symptoms. Second, some studies have used a prospective design while others have
been cross-sectional. Subject selection and loss to follow up is a major cause of
potential bias in prospective research. Cross-sectional studies have the disadvantage
of selection processes operating, and it may also be that patients retrospectively
attribute posttraumatic symptoms to the cardiac event that have other origins. Although
cross-sectional methods are the most cost-effective and efficient way of assessing
60
prevalence, in such selected samples, no one individual may actually have the disorder
[under investigation], thus results may not reflect ‗true‘ prevalence rates. Third, the
interval between ACS and measurement of PTSD has varied widely, this impairs direct
comparison of results. Most prospective studies have ranged from 3 to 9 months, with
only one (van Driel & Op den Velde, 1995) lasting more than 12 months. There is no
clear relationship between the duration of studies and the prevalence of PTSD. On the
other hand, studies that have taken measurements at more than one time point have
typically shown some diminution in the prevalence of PTSD (Pedersen et al., 2003;
Pedersen et al., 2004; Sheldrick et al., 2006). It is therefore not very clear whether
PTSD persists in the long run, or diminishes over time. It is also very striking from table
2.2 that sample sizes have been relatively small. The mean sample size is 79, and only
7 studies have involved more than 100 patients. These limitations in the literature
stimulated the work undertaken in this thesis, and data on the longer-term prevalence
in presented in chapter 4.
61
TABLE 2.2 PREVALENCE OF PTSD FOLLOWING ACS
Reference
Sample size
Design
PTSD
measure*
Time since ACS
Prevalence
Kutz et al. (1994)
100 (88 male, 12 female)
Retrospective
PTSD
Inventory
6 to 18 months
16%
chronic, 9%
acute
Doerfler et al. (1994)
27 male
Cross-sectional
IES and
others
6 to 18 months
8%
Van Driel et al. (1995)
23 (14 male, 9 female)
Prospective
SCID
22 to 26 months
0%
Bennett et al. (1999)
44 (30 male, 14 female)
MI patients
Cross-sectional
PDS
6 to 12 months
10.75%
Bennett et al. (2001)
70 (52 male, 18 female)
MI patients
Prospective
PDS
3 months
3%
Shemesh et al. (2001)
102 (81 male, 21 female)
MI patients
Prospective
IES
6 months
9.8%
Prospective
PTSD
Inventory
<1 week; 7
months
16% at 7
months
Prospective
PDS
4 to 6 weeks; 9
months
T 1 – 24 %;
T2 – 14%
Case-control
PDS,
SCID,
IES
3 to 18 months
7% (SCID)
Ginzburg et al. (2002)
Pedersen et al. (2003;
2004)
O‘Reilly et al. (2004)
116 (94 male, 22 female)
MI patients; 72 (51 male,
21 female) healthy
matched controls
112 (79 male, 33 female)
MI patients; 115 (72 male,
43 female) matched
controls; 9 months 102 MI
patients
27 (24 male, 3 female); 27
Sudden cardiac arrest
patients
Shemesh et al. (2004)
65 MI patients
Prospective
IES
6 months
20%
Doerfler et al. (2005)
52 (36 male, 16 female)
MI patients
Cross-sectional
PSS
3 to 6 months
7.7%
Sheldrick et al. (2006)
17 MI patients; 27
Subarachnoid
Haemorrhage patients
Prospective
DTS
<2 weeks; 5 to 7
weeks; 11 to 14
weeks
T1 – 13.3%;
T2 – 23.8%;
T3 – 11.8%
Whitehead et al.
(2006)
135 (99 male, 36 female)
Prospective
PSS
3 months
14.8%
Jones et al. (2007)
111 ( 81 male, 23 female)
MI patients
Cross-sectional
PDS
<5 years to >10
years
32%
Chung et al (2008)
120 MI patients (94 male,
26 female)
Cross-sectional
PDS
Average time
since first MI:
9.92 years
31%
Rocha et al. (2008)
31 MI patients
Prospective
IES, SCID
1 to 2 months
4% (SCID),
16% above
threshold on
IES
Wiedemar et al.
(2008)
190 MI patients
Prospective
CAPS
Average 95 days
post MI
9.4%
Guler et al. (2009)
394 MI patients
Cross-sectional
CAPS
Screening Q
sent on average
98 days post MI
10.2%
*CAPS – Clinician Administered PTSD scale; IES – Impact of Events Scale; SCID – The Structural Clinical Interview;
PDS – Posttraumatic Stress Diagnostic Scale; DTS – The Davidson Trauma Scale; PSS – Posttraumatic Stress
Disorder Symptom Scale.
62
Studies investigating symptom levels within the subscales of PTSD have found
more prevalent or higher scores for avoidance symptoms than for intrusions (Bennett et
al., 2001; Bennett & Brooke, 1999; Shemesh et al., 2001). One explanation for higher
avoidance symptom scores may be that post-ACS patients avoid activities such as
physical exertion or avoid exposure to stress for fear of provoking recurrent cardiac
symptoms. Bennett et al (2001) also found that women were more likely than men to
avoid reminders of their MI. Prevalence rates of PTSD following cardiac surgery
(Coronary Artery Bypass Grafting – CABG) are similar to those observed following MI,
and range between 10.8% and 18% (Tedstone & Tarrier, 2003), however,
investigations of the PTSD subscales in particular show that intrusive symptoms are
more prevalent than are avoidance symptoms (e.g. Doerfler et al., 1994). This is an
interesting finding, considering that the research with MI patients suggests that
avoidance symptoms may be more common in cardiac population.
2.7.2 Predictors of posttraumatic stress symptoms following ACS
As discussed in previous sections, several factors predictive of PTSD have
been identified from studies of natural disasters, victims of war and interpersonal
violence. There are an increasing number of studies aiming to identify the factors that
are predictive of posttraumatic stress symptoms following an acute cardiac event.
Although many risk factors have been associated with the development of PTSD
following trauma, several of these appear not to be associated with the disorder in
cardiac populations, suggesting that PTSD may have distinct correlates in this group of
patients. The findings from these studies are discussed below and a summary of
studies is provided in table 2.3.
In contrast to findings suggesting a dose-response relationship between trauma
and PTSD, emotional distress following MI appears not to be related to the severity of
the infarction. However, there are some data suggesting that patients who experience
63
multiple cardiac related events, such as multiple hospitalizations, cardiac arrest, or
repeat MI, are at increased risk of developing posttraumatic stress (Doerfler et al.,
2005). Accumulating research now suggests that the subjective perception of the MI
and its perceived impact on future activities and longevity are important factors
determining distress (van Driel & Op den Velde, 1995). In addition, emotional reactions
to acute cardiac events appear to be important in the development of posttraumatic
stress symptoms. Whitehead et al (2006) found that the most robust predictor of
posttraumatic symptoms 3 months post ACS was emotional state at the time of
admission. In this study, 135 ACS patients completed psychological measures 7-10
days post admission and again at 3 months. 14.8% of patients could be identified with
PTSD at 3 months. No demographic variables were predictive of PTSD, and whereas
the subjective rating of severity as indexed by degree of chest pain was predictive of
posttraumatic symptoms severity, objective clinical severity was unrelated to
posttraumatic symptoms. Early (in-hospital) emotional responses were strongly
predictive of posttraumatic stress symptoms. Acute stress symptoms, depressed mood,
negative affect and hostility were independent predictors of symptom severity at 3
months.
A recent pilot study by Rocha and colleagues (Rocha et al., 2008) showed that
higher scores of posttraumatic symptoms, 2 months following MI, were associated with
depressive symptoms and self-reported anxiety at baseline. In this study, prevalence of
PTSD was low, 4%. However, when including subsyndromal cases this figure rose to
16%. Further support for the importance of subjective perceptions of MI in the
prediction of later PTSD, was demonstrated recently by Wiedemar et al (2008). In this
study, 190 MI patients were assessed for PTSD approximately 3 months following the
index event. There was no association between demographic variables or left
ventricular ejection fraction (LVEF – a proxy measure of objective MI severity) and later
posttraumatic stress. Helplessness and greater subjective pain intensity emerged as
independent predictors of posttraumatic stress symptoms in multivariate analyses.
64
However, this study did not control for depression. This study also assessed the value
of fulfilling the A2 criterion for a DSM-IV diagnosis of PTSD. Most research to date has
not assessed subsyndromal symptoms of PTSD. This is now recognised as a specific
nosologic subcategory of PTSD (Mylle & Maes, 2004). Including only patients who
reach a certain threshold of symptoms for a diagnosis of PTSD can be misleading, as
studies show that subsyndromal PTSD also endorse marked levels of psychological
distress and low functional status (Grubaugh et al., 2005). This study found no
difference between those who fulfilled the A2 criterion (i.e. negative emotional
response to MI) and those who did not, on the prevalence of full PTSD. There was,
however, a small decrease in prevalence of subsyndromal symptoms when criterion A2
was applied. These results suggest that those who did not respond to the MI with
intense fear, helplessness or horror, were less likely to develop PTSD (subsyndromal
level). These findings are in line with previous results from a large community-based
study showing that traumatic events not involving symptoms of the A2 criterion rarely
result in PTSD (Breslau & Kessler, 2001).
Guler et al (2009) extended the study reported by Wiedermar et al (2008).
PTSD was assessed in 394 patients, the prevalence remained largely the same, 10%,
using a structured clinical interview. Patients with clinical PTSD were younger and
reported greater fear of dying and more intense helplessness during the MI, supporting
the notion that subjective experience of MI is a stronger predictor of PTSD than is
objective measures of MI severity. These authors suggest that there is little evidence
for the assumption that certain risk factors identified in the literature (demographic,
clinical, psychological) can differentiate clinical PTSD status from elevated PTSD
symptom level (i.e. subsyndromal levels). However, this might be expected since
posttraumatic stress occurs on a continuum of severity (Whitehead et al., 2006).
65
TABLE 2.3 RISK FACTORS FOR PTSD FOLLOWING ACS
Risk factor
Positive Association
MI severity (objective)
No Association
Negative Association
Whitehead et al (2006);
Ginzburg et al (2002)
Ginzburg (2006a);
Pedersen et al (2003); Kutz
et al (1994); Doerfler et al
(2005)
MI severity (i.e. subjective
pain)/ Subjective perception
of threat/ Importance
attached to the event
Whitehead et al (2006);
Ginzburg et al(2002);
Bennett et al (2001);
Wiedemar et al (2008);
Guler et al (2009) – fear of
dying/helplessness
Duration of MI/ Pain
duration
Doerfler et al (2005)
Prior MI/cardiac
hospitalization
Kutz et al (1994)
Times re-hospitalized (any
reason)
Doerfler et al (2005)
Awareness of event as MI
Van Driel & Op den Welde
(1995); Bennett & Brooke
(1999)
Dissociation during
event/Acute stress disorder
Bennett et al (2002);
Ginzburg (2006a);
Whitehead et al (2006)
Fear at time of MI
Bennett & Brooke (1999);
Bennett et al (2001); van
Driel & Op den Welde
(1995)
Subjective expectation of
incapacitation
Kutz et al (1994)
Previous PTSD
Kutz et al (1994); van Driel
& Op den Welde (1995)
Pre-MI stressful life events
Ginzburg (2006a)
Post-MI stressful life events
Ginzburg (2006a)
Age
Doerfler et al (2005), Guler
et al (2009) – subjective
pain
Doerfler et al (2005)
Bennett et al (2001);
Whitehead et al (2006)
Ethnic origin
Kutz et al (1994) – Asian
or Mediterranean; Rocha et
al (2008) – AfricanAmerican
History of depression
Whitehead et al (2006)
Negative affect in
hospital/Depressed mood
Bennett et al (2001);
Pedersen et al (2003);
Whitehead et al (2006);
Rocha et al (2008)
Hostility
Whitehead et al (2006)
Bennett & Brooke (1999);
Rocha et al (2008)
Bennett et al (1999)
Optimism
Whitehead et al (2006)
66
Risk factor
Positive Association
Neuroticism
Pedersen et al (2003)
Anxiety
Pedersen et al (2003);
Rocha et al (2008)
Whitehead et al (2006)
Alexithymia
Bennett & Brooke (1999)
Bennett et al (2002)
Type D
personality/Neuroticism
Bennett & Brooke (1999);
Bennett et al (2001);
Whitehead et al (2006);
Pedersen & Denollet
(2004)
Social support/ social
network
Whitehead et al (2006)
Perceived control
Non-repressive coping
No Association
Negative Association
Bennett et al (2002);
Pedersen et al (2004)
Doerfler et al (2005)
Ginzburg et al (2002)
There are some inconsistencies in the literature on risk factors for PTSD in MI
patients, in particular regarding the relation between subjective perceptions and PTSD.
For example, Doerfler and colleagues (2005) studied the psychological variables
related to PTSD in MI patients 3 to 6 months post the event. The sample included 52
MI patients (36 male, 16 female). PTSD was measured with the Impact of Events Scale
and the PTSD symptom scale. Measures of quality of life, perceived control, stressful
events at the time of the MI, cardiac history, subjective pain, degree of danger, fear of
dying and the degree of predictability of the MI were obtained 3 to 6 months post
admission. In this study between 4% and 8% of patients qualified for a diagnosis of
PTSD depending on cut-off. Elevated scores on the PTSD measures were associated
with poorer quality of life. Factors associated with PTSD at 3 to 6 months post the
event included readmissions to hospital, longer lasting MI and ratings of pain duration.
Patients reporting lower ability to control their emotions during the acute event and any
future MI, as well as perceived lower controllability over aversive events more generally
were associated with higher PTSD scores. However, cross-sectional studies such as
this have limited ability to determine the direction of the relationships. It is unclear
whether perceptions of control lead to the development of PTSD or whether these
67
perceptions are produced by psychological distress. In contrast to other research, this
study found that subjective pain was not related to PTSD nor were ratings of perceived
danger, predictability and fear of dying. A recent study by Guler et al (2009) also report
no association between subjective pain scores and later posttraumatic stress
symptoms. A study by Bennett and Brooke (1999) found that awareness of having an
MI during the acute phase was strongly predictive of posttraumatic stress 6 – 12
months post admission and in particular the level of intrusive symptoms. In a second,
longitudinal study, Bennett et al (2001) found that low mood while in hospital and the
fright experienced at the time of the MI were predictive of the frequency of the
symptoms of PTSD three months following the event.
Some research suggests that personality variables may be important in the
development of posttraumatic stress following MI. There is relatively little research in
this area and so far the main focus has been on two factors in the context of MI
patients. The first, alexithymia, is a stable personality trait implicated as a risk factor for
the development of PTSD in general (Krystal et al., 1986) and PTSD in MI patients in
particular (Bennett & Brooke, 1999). Alexithymia is characterized by an inability to
process emotions, and it has been postulated that this may result in failure to integrate
memories and their emotional associations into more general memory systems (Brewin
et al., 1996). Although some cross-sectional data suggest an association between
alexithymia and PTSD (Bennett & Brooke, 1999), longitudinal studies have not
supported this relationship (Bennett et al., 2002). The second personality factor
associated with PTSD post MI is negative affectivity which reflects an individuals‘
propensity to experience negative moods and interpret events in a negative way, even
without the presence of an obvious stressor (Watson & Clarke, 1984).
A number of studies support the role of negative affect in the development of
PTSD. Low positive and high negative affect is predictive of PTSD (Bennett & Brooke,
1999; Bennett et al., 2001). Type D personality trait (i.e. negative affectivity and social
inhibition) is another factor which has been found to relate to posttraumatic symptoms.
68
The negative affectivity component of Type D, conceptualized as neuroticism, has
been shown to be associated with post-MI PTSD (Pedersen & Denollet, 2004;
Whitehead et al., 2006). A recent study showed higher neuroticism among patients with
full-PTSD compared with patients with no or partial PTSD (Chung et al., 2007). These
findings support the proposed link between Type D and post-MI PTSD. This is because
the negative affectivity dimension of Type D constitutes partly the characteristics of
neuroticism. Some argue that the trait of high negative emotionality or neuroticism is
the primary personality risk factor for developing full PTSD (Miller, 2003).
One line of argument suggests that a particular type of coping style may have a
protective function on PTSD development following MI due to its effect on patients‘
appraisals of the stressful event, their own self-esteem and belief in ability to cope.
Ginzburg et al (2002) investigated the relationship between repressive coping and
PTSD. This study showed that only 7.1% of repressors 7 months post MI were
identified as having PTSD compared with between 17.2% and 20% of non-repressors.
These authors argue that repressive coping may serve as a stress buffer. Repressive
coping is defined as the cognitive and emotional effort to ignore or divert attention from
threatening stimuli, whether internal or external. There has been little research on the
role of repression on PTSD in MI patients. One study of the related concept of
avoidance shows that such strategies are effective in the short term following an MI
(Esteve et al., 1992). The efficacy of avoidance strategies in the longer run is less
clear. Some studies show that avoidance is related to increased posttraumatic stress
symptoms after various traumatic events (Amir, 1997; Reynolds & Brewin, 1998).
However, due to the cross-sectional nature of these studies it is not clear whether
avoidant coping is a risk factor for PTSD or a manifestation of the disorder.
It is clear from the literature reviewed above that there is great variability in
studies of risk factors for PTSD following MI and that the risk factors identified do not
seem to overlap greatly with those identified in the general population. Although age,
gender, and socioeconomic status have generally been identified as risk factors for
69
PTSD following trauma, the majority of the post-MI PTSD literature reports no such
associations. However, lack of social support and negative affect in the wake of trauma
do seem to be important factors for developing posttraumatic stress overall. One of the
core assumptions of the PTSD construct is the idea of a dose-response relationship
between trauma and subsequent symptoms. Although there has been some support for
this argument in the literature on PTSD from various causes, there is an overwhelming
lack of support for such a relationship among post-MI PTSD patients. Due to the lack of
agreement of risk factors between studies of PTSD in MI patients and of individuals
exposed to other types of trauma, it is difficult to make inferences from the broad PTSD
literature, thus, it is important to further explore the potential risk factors for PTSD in
cardiac populations. Data on predictors of posttraumatic stress symptoms are
presented in chapters 4, 6 and 7 in this thesis. The salience of emotional and
psychological predictor variables for posttraumatic stress in response to acute cardiac
events is highlighted.
2.7.3 Distinctive features of ACS-related PTSD
The core constructs of PTSD include the traumatic stressor, re-experiencing
symptoms, avoidance symptoms and arousal symptoms. These may be potentially
distinctive among ACS patients. Firstly, the experience of an acute coronary syndrome
represents a distinctive stressor within the PTSD framework because it involves a
potentially chronic and debilitating illness that may be accompanied by a range of
aversive associated events (e.g. angina). The stressor causing PTSD is distinctive also
because it is triggered by an internally induced event rather than an external source of
threat. The threat associated with the stressor is not only immediate but the outcome is
also future oriented. This is problematic in terms of the DSM-IV‘s description of reexperiencing symptoms, which implies that the intrusive thoughts and associated affect
pertain to an event that has occurred in the past (APA, 1994). Many intrusive thoughts
70
reported by ACS patients appear to be future oriented fears about one‘s health. A
number of the avoidance symptoms in the DSM-IV definition of PTSD may also pose a
problem. Avoiding reminders of the stressor may be difficult because many of the cues
are either internally (e.g. symptoms such as shortened breath, fatigue, and angina) or
externally imposed (e.g. medical visits, rehabilitation attendance, and taking prescribed
medication). Arousal symptoms may also be problematic because they can index
somatic responses that can overlap with effects of ACS and its treatment. It is
erroneous to attribute somatic problems to PTSD if they are in fact secondary to the
effects of ACS and its treatment. These issues highlight the difficulties associated with
applying the current PTSD framework to distress reported in response to medical
illness, and how cautiously results must be interpreted.
2.7.4 Consequences of PTSD following ACS
Posttraumatic stress is associated with a number of adverse consequences
following ACS. PTSD commonly co-occurs with depression in cardiac patients and
depressive symptoms are themselves associated with a 2-fold increased risk of
mortality (van Melle et al., 2004). Posttraumatic stress symptoms are associated with
outcomes such as impaired quality of life (Doerfler et al., 2005), inadequate coping
(Alonzo, 1999), increased smoking and alcohol intake (Op den Velde et al., 2002),
which are, in themselves, independent risk factors for cardiovascular complications
after MI (Shemesh et al., 2003), and overall worse general health (Jones et al., 2007).
Stukas et al (1999) report that PTSD is also a strong predictor of mortality after a heart
transplant. In this study, those patients who survived beyond a year post transplant and
who met criteria for PTSD during that year had over a 13 times greater risk of mortality
by 3 years post-transplant. This effect was independent of other known transplant
related predictors of mortality. Alonzo (1999) highlighted the variation in patients‘
reactions and suggested that as distress increases, their ability to seek help when
71
experiencing early signs of a further MI is reduced. The impact of posttraumatic stress
symptoms on patients health behaviours and adjustment is presented in chapter 7 in
this thesis, further demonstrating the significant negative influence of posttraumatic
stress symptoms on patients post ACS recovery.
Levels of social support may also be affected in MI patients who develop PTSD.
Lack of social support is a known risk factor for coronary heart disease (CHD) and has
also been related to adverse prognosis (Everson-Rose & Lewis, 2005). Kutz et al
(1994) reported that patients who developed PTSD following MI were more likely not to
return to work, to decrease their social activity level, and to avoid social situations, all of
which result in less social contacts and less potential support. Since lack of support is
an independent risk factor for CHD it is possible that MI patients who suffer
posttraumatic stress symptoms have a higher risk of developing recurrent cardiac
events. PTSD is also associated with nonadherence to cardiac medications in survivors
of MI, which in turn is related to poor medical outcome (Shemesh et al., 2001). It is
speculated that taking prescribed medication may serve as a reminder of the traumatic
event, something which those with PTSD would rather avoid. A study by Shemesh and
colleagues (2004) found that PTSD was associated with almost a 3-fold increased risk
of readmission for cardiovascular events 1.5 years post admission for MI. It can be
speculated that these findings relate to the increased nonadherence observed in the
sample, or they may be related to biological correlates of PTSD (see section 2.9),
which may put an additional strain on an already ailing heart.
2.7.5 The relationship between PTSD and depression
The comorbidity between PTSD and anxiety, depression, substance abuse,
somatoform disorders, and personality disorders is well documented (McFarlane &
Papay, 1992) In a large scale epidemiological study in the general American population
[National Comorbidity Study], lifetime prevalence of PTSD was estimated at 7.8%,
72
based on interviews with over 5000 people (Kessler et al., 1995). Approximately 48%
(47.9% of males and 48.5% of females) of those who met criteria for lifetime PTSD also
met criteria for major depression, secondary to the trauma. These comorbidity patterns
can also arise in the context of acute coronary syndromes. Rates of depression among
cardiac patients are relatively high, with approximately 15% of patients developing
major depression and a further 20% minor depression (Davidson et al., 2004; Lett et
al., 2004; Rozanski et al., 1999). Previous research show that a substantial proportion
of PTSD sufferers also meet diagnostic criteria for depression, with prevalence ranging
from about 21% to 94% (e.g. Mollica et al., 1999; Salcioglu et al., 2003). As discussed
in the previous section, PTSD is associated with a number of adverse outcomes
following ACS. Similarly, high levels of depression are associated with psychosocial
maladjustment (Drory et al., 1999), increased hospitalization and mortality among
cardiac patients (Burg et al., 2003). The clinical implications of comorbidity of PTSD
and depression are unclear, though a number of studies suggest that it may complicate
adjustment. For example, studies have found more severe PTSD, greater depressive
symptoms and greater difficulties in psychosocial adjustment among those with
comorbid PTSD and depression compared with those with PTSD alone (e.g. KozaricKovacic & Kocijan-Hercigonja, 2001; Maes et al., 2000; Momartin et al., 2004). These
findings are replicated in those with comorbid PTSD and depression compared with
those who suffer from depression without PTSD (e.g Constans et al., 1997; Frayne et
al., 2004; Holtzheimer, III et al., 2005).
There, is however, a lack of empirical research on the comorbidity of PTSD and
depression following MI. One study (Ginzburg, 2006a) assessed the issue of comorbid
PTSD and depression in a prospective study of 116 MI patients at one week and seven
months post the index event.
73
At one week following the acute MI, 6% of patients fulfilled criteria for ASD 1,
13% were identified as suffering from depression, and 12% had comorbid ASD and
depression. At follow up 8% met diagnostic criteria for full PTSD, 14% had depression
alone, and 8% were classified as having comorbid PTSD and depression. Predictors of
comorbidity were investigated and no association was found with baseline objective MI
severity, or with subjective appraisals (threat of death, perceived severity). However,
immediate levels of dissociation, intrusion, arousal and symptoms of depression were
significantly associated with comorbidity at time 2. Dissociative responses to the MI
and arousal symptoms were higher in the depression and comorbidity groups. The
depression group reported the highest levels of intrusion, and the comorbid group
reported the highest levels of initial depression. Comorbidity was significantly
associated with PTSD symptomatology, depression, psychosocial functioning and
somatic complaints. However, the PTSD group reported the highest levels of somatic
complaints, whereas the comorbid group reported the lowest level of psychosocial
functioning.
It is clear from these findings that comorbidity is an important issue following
acute cardiac events. Causal pathways for explaining the association between PTSD
and major depression post trauma include; (1) Pre-existing depression may render
individuals more vulnerable to PTSD in the aftermath of trauma; (2) The presence of
PTSD may increase the risk of first onset of depression. These pathways suggest the
possibility of a shared vulnerability for both disorders. However, not all support the view
that comorbidity is genuine clinical phenomenon, some argue that what is observed is
an artifact of symptom overlap. In fact, when examining the DSM-IV criteria for these
disorders,
one
finds
that
three
of
the
17
symptoms
of
PTSD
(sleep
disturbance/insomnia, difficulty with or impaired concentration, and loss of interest in
1
ASD – Occurs within 1 month following trauma. Similar etiology, symptoms and course as PTSD, but of limited
duration - 2 days to 4 weeks. If the symptoms and behavioral disturbances of the acute stress disorder persist for more
than a month, and if these features are associated with functional impairment or significant distress to the sufferer, the
diagnosis is changed to posttraumatic stress disorder (APA, 1994). The usefulness of ASD in the prediction of later
PTSD has been questioned (Marshall et al., 1999).
74
previously enjoyed activities – anhedonia) are also three of the nine symptoms needed
for a diagnosis of major depression. Two important research questions must be
addressed; (1) Are depression and PTSD independent consequences of trauma, each
having its own course and prognosis? (2) Which symptoms are shared, and which
separate the two disorders?
Constans et al (1997) investigated the phenomenological features of
depression occurring in PTSD patients, and the relationship between depressive
features and PTSD symptoms in a sample of 217 veterans of war. 84% of the sample
met criteria for PTSD. These were subsequently re-categorised into three groups:
comorbid depression/melancholic features; comorbid depression/non-melancholic
features; no comorbid depression. Results showed that those with melancholic and
non-melancholic features of comorbid depression did not differ on measures of
depression, PTSD, and anxiety. However, the melancholic sub-group reported
excessive guilt compared with the other groups. Further, the presence of melancholic
features was related to severity of emotional-numbing experienced by the PTSD
sufferers. These authors concluded that a subset of PTSD patients experience a
depression subtype of PTSD, distinguished by higher frequency and severity of
emotional-numbing symptoms. Franklin and Zimmerman (2001) investigated the role of
overlapping symptoms in diagnostic comorbidity. These authors argued that if
contaminated symptoms (i.e. the three symptoms common to both PTSD and major
depression) are responsible for the observed comorbidity, they would be more
frequently endorsed among PTSD patients with major depression compared with those
with PTSD only, and that these symptoms would show less specificity, that is, they
would correlate less highly with the total PTSD symptom score than would the other 14
PTSD unique symptoms. These authors found no evidence to support the notion that
overlapping symptoms contribute to the comorbidity observed between PTSD and
depression. Results showed that the contaminated symptoms did not correlate less
75
strongly with the total PTSD scores, nor were they more frequently endorsed by the
comorbidity group.
A prospective study of posttraumatic stress disorder and depression in 211
trauma survivors (a variety of traumatic stressors) showed that the intensity of
depressive symptoms in PTSD resembles that of major depression. Results showed
that the comorbidity group had higher scores on a range of symptoms typically thought
to reflect depression than did the depression only group (i.e. diminished interest,
detachment/estrangement, restricted range of affect) (Shalev et al., 1998a). However,
29% of the trauma survivors with major depression did not have comorbid PTSD.
These authors concluded that depression and PTSD may be independent sequelae of
trauma. Contrasting findings indicate that risk of depression only increases among
trauma victims who develop PTSD, suggesting that PTSD and major depression are
not influenced by separate vulnerabilities (Breslau et al., 1997; Breslau et al., 2000).
A more recent study by O‘Donnell et al (2004) assessed predictors of PTSD,
depression and comorbid PTSD/depression in a sample of 363 injury survivors at
hospital discharge, and at 3 and 12 months. They argued that if indeed PTSD and
depression are separate constructs, then symptom severity and diagnostic group
should be a function of differential groups of predictors. Full diagnostic criteria were
met in almost equal numbers for each of the three groups at 3 months (4% depressed,
6% PTSD and 5% comorbid) and at 12 months (4%, 4% and 6% respectively). A
surprising degree of movement between diagnostic categories was observed, with
approximately half of those with a diagnosis at both time points changing diagnostic
category by 12 months follow up. The pattern of change was similar for the PTSD and
comorbid groups with comparable proportions recovering, maintaining their diagnosis,
or changing their diagnostic group. The depression group in contrast showed a
strikingly different pattern. The majority of those with depression at 3 month had
recovered by 12 months. Further, results indicated that the PTSD and comorbid
PTSD/depression were predicted by the same range of variables at both time points.
76
Depression at 3 months was predicted by different variables. However, at 12 months,
depression was no longer found to be an independent construct suggesting that in the
immediate aftermath of a trauma, depression may exist as a separate and independent
construct, with its own unique set of predictors. However, by 12 months, as the
psychopathology becomes more chronic, this construct may become less well
differentiated and no longer possible to identify. At this point, it may be most
appropriate to consider the psychopathology observed as a more general traumatic
stress factor that is characterized by a combination of PTSD and depressive
symptoms. These results suggest that PTSD alone and comorbid PTSD/depression
may be the one and same construct. The findings presented in this section highlight
the complex interaction and overlap between these diagnoses.
2.8 The role of PTSD in the development of coronary heart disease
Not only is PTSD an outcome of cardiac disease, in fact a number of studies
have found a relationship between a diagnosis of PTSD and increased risk of
developing CHD in initially disease free individuals. Boscarino (1997) found lifetime
PTSD to be associated with a higher prevalence of circulatory disorders (OR= 1.62),
independent of demographic factors, smoking and substance abuse. A later study
found an association between PTSD and increased risk of MI (OR= 4.44) (Boscarino &
Chang, 1999). This effect was independent of a number of traditional risk factors such
as smoking, body mass index and alcohol use. Consistent with work suggesting that
PTSD rather than trauma exposure alone may mediate between trauma and risk of
adverse health outcomes, a study by Dong and colleagues (Dong et al., 2004) found
that men and women who had experienced numerous adverse childhood events were
at increased risk of CHD (OR= 3.6, C.I: 2.4 – 5.3), this effect was explained more
completely by psychological distress than by traditional risk factors.
77
The association between PTSD and increased risk of CHD may be explained
by pathophysiological changes that occur during a stress response. A persistent state
of arousal may contribute to the progression of CHD mediated through changes in
haemostatic parameters (von Känel et al., 2001). A recent review suggests that PTSD
confers a heightened pro-inflammatory state (Gander & von Känel., 2006). Although
there are some inconsistencies in the evidence for a pro-inflammatory state in PTSD,
most consistency emerged in studies showing higher Interleukin (IL) -1β in individuals
with PTSD compared with controls (Sondergaard et al., 2004; Tucker et al., 2004). It
has also been found that patients with PTSD have lower peripheral cortisol levels both
at rest and in response to trauma related stimuli (McFall et al., 1990; Yehuda et al.,
1992) as well as reduced heart rate variability (Cohen et al., 1997). The
pathophysiology of PTSD will be discussed in the following section. If PTSD indeed has
pathophysiological effects, it seems they would be likely to be most evident when
symptoms of PTSD follow a pattern of persisting or recurring over time. PTSD may also
motivate health-related behaviours that can influence risk of developing CHD. For
example, PTSD has been associated with greater likelihood of smoking and excess
alcohol consumption, behaviours that increase risk of CHD (Breslau et al., 2003).
A large scale prospective study of PTSD and CHD (data from the Normative
Ageing Study) assessed the relationship directly and independently of depression
(Kubzansky et al., 2007). It is important to establish a link between PTSD and CHD that
is independent of depression due to concerns that self-report measures of PTSD may
over-emphasize the depression component of PTSD and because depression has
been identified as a risk factor for CHD (see chapter 1). Some investigators have
suggested that any apparent PTSD-CHD association is largely due to depression.
Kubzansky et al (2007) report that PTSD symptoms are associated with an increased
risk of incident CHD. Although modest, the effects were maintained after controlling for
depressive symptoms, and most clearly apparent in relation to the hard outcomes of
nonfatal MI and fatal CHD. These findings were also suggestive of a dose-response
78
relationship, as for each standard deviation increase in posttraumatic stress symptoms
there was a significant increase in the risk of developing CHD (OR= 1.21). A more
recent study investigated PTSD and increased risk of CHD in a sample of community
dwelling women [non-military], and tested whether the relationship was independent of
other forms of distress (Kubzansky et al., 2009). These authors found that women
reporting five or more posttraumatic symptoms were at over three times greater risk of
developing CHD compared with those with no symptoms (OR= 3.21, C.I: 1.29 – 7.98).
Findings were maintained after controlling for standard risk factors as well as
depression and trait anxiety.
The findings discussed in this section are provocative, and suggest that being
exposed to a traumatic event and experiencing a prolonged stress reaction may not
only lead to psychological disability but may also have cardiotoxic effects. More
research is clearly called for to further the understanding of a possible relationship
between posttraumatic stress and cardiovascular endpoints.
2.9 Psychophysiology of PTSD
2.9.1 Cortisol
Although a great many individuals are exposed to one or more traumatic events
in their lifetime (Kessler et al., 1995), and many develop symptoms in the early
aftermath of trauma, the intensity of the initial response and the number of individuals
who manifest these responses substantially decreases as time goes on. Posttraumatic
symptoms become chronic only in a subgroup of trauma survivors. Thus, PTSD can be
best considered a possible, not inevitable, outcome following trauma exposure. As
discussed in previous sections, there are a number of psychological variables that
influence the development of PTSD in the days following the initial trauma. There also
79
appear to be some salient predictors of PTSD that manifest within hours after the
traumatic event. These are not psychological variables, but rather biological ones.
Evidence is emerging of a distinct pathophysiology for posttraumatic stress. The
hypothalamic-pituitary-adrenal (HPA) axis is activated in response to stress. HPA axis
activity is governed by the secretion of corticotropin-releasing hormone (CRH) from the
hypothalamus (figure 2.1). CRH activates the secretion of adrenocorticotropic hormone
(ACTH) from the pituitary. ACTH, in turn, stimulates the secretion of glucocorticoids
(cortisol in humans) from the adrenal glands. Cortisol interacts with their receptors - the
corticosteroid receptors - in almost every tissue in the body, and the best known effect
is the regulation of energy metabolism. By binding to corticosteroid receptors in the
brain, cortisol also inhibits the further secretion of CRH from the hypothalamus and
ACTH from the pituitary (negative feedback). The major function of cortisol is to contain
these stress-activated reactions (Munck et al., 1984).
FIGURE 2.1 HYPOTHALAMIC-PITUITARY-ADRENAL AXIS
80
The neuroendocrine profile observed in those with chronic PTSD is somewhat
paradoxical as the alterations in patterns make the patterns almost exactly the opposite
of the patterns observed in chronic stress and major depression (Chrousos & Gold,
1992). Like depression, PTSD is associated with increased secretion of corticotropin
releasing factor. Unlike depression, however, the increased secretion is associated
with hypocortisolaemia (Baker et al., 1999), suggesting a grossly exaggerated negative
feedback inhibition of the HPA axis that is possibly secondary to up-regulation of
glucocorticoid receptors (Liberzon et al., 1999). That is, chronic PTSD is characterized
by a decrease in levels of circulating cortisol and a concomittant increase in
responsiveness of glucocorticoid receptors, an increased sensitivity of the HPA
negative feedback inhibition, and a progressive sensitization of the entire HPA axis
(Yehuda et al., 1998). These findings raise two questions; (1) are these alterations
observed in the HPA axis characteristic of chronic PTSD, and; (2) are there
fundamental differences in the way the HPA axis functions normally that will influence
the way in which an individual will respond to traumatic stress?
There are inconsistencies in the literature on the relationship of cortisol and
PTSD, with some studies reporting increased levels of cortisol and others reporting
decreased cortisol. Whereas a number of studies of women with PTSD have found
evidence of HPA hyperactivity, particularly following childhood abuse (Heim et al.,
2000; Lemieux & Coe, 1995; Maes et al., 1998; Rasmusson et al., 2001), studies of
male combat veterans and elderly Holocaust survivors have found evidence of a low
cortisol profile, persisting even in the presence of major depression (e.g. Boscarino,
1996; Yehuda, 2002a; Yehuda et al., 2002). Some prospective research has shown
that low cortisol levels at the time of exposure to psychological trauma predict the
development of PTSD (e.g. Resnick et al., 1995; Yehuda et al., 1998), suggesting
hypocortisolism might be a pre-existing risk factor that is associated with maladaptive
stress responses such as PTSD. Consequently, administration of hydrocortisone
directly after exposure to psychological trauma has been shown to effectively reduce
81
risk of developing PTSD (de Quervain, 2008; Schelling et al., 2004). Resnick et al
(1995) found significantly lower cortisol levels in a sample of female rape victims
(assessed during emergency room visit within hours of the trauma) with prior history of
sexual assault. This group of women was three times more likely to develop PTSD at
4-month follow up than were women without a history of prior sexual assault. These
authors argued that prior traumatization may have been the cause of altered HPA axis
function in response to subsequent trauma, and that the attenuated cortisol response
consequently increased the risk of PTSD from this ‗new‘ trauma. This study was
however limited by the fact no other psychiatric or psychological disturbances were
assessed or controlled for. In a study of motor vehicle accident victims, McFarlane et al
(1997) found lower levels of cortisol in the immediate aftermath of the trauma among
those who at 6 months were classified as having PTSD, compared with no diagnosis or
depression (higher levels of cortisol observed). These findings were independent of
accident severity, time of day and minutes post-trauma.
The studies by Resnick et al (1995) and McFarlane et al (1997) suggest a
paradoxically lower cortisol response is present in trauma victims who later go on to
develop PTSD compared with those who either develop depression or those who
subsequently do not develop any psychiatric disorder. However, in both these studies
cortisol was assessed only post trauma, there was no examination of cortisol levels
prior to trauma, therefore no statement can be made about the cortisol response
relative to these individuals‘ baseline levels.
Not all studies have demonstrated lower cortisol levels among groups of PTSD
patients. Indeed some have reported the opposite pattern. Liberzon et al (1999) found
significantly higher baseline cortisol among PTSD diagnosed patients than among nonPTSD controls. Pitman and Orr (1990) reported significantly higher 24-hour cortisol
values in those with PTSD compared with controls. A study by Young and colleagues
(2004a) found normal levels of cortisol in a low SES sample of women with high
exposure rates to trauma, with either current or lifetime PTSD. However, a non-
82
significant trend of higher cortisol in women with comorbid lifetime PTSD and past-year
major depression was observed.
Young and Breslau (2004a) assessed 24-hour urinary free cortisol (UFC) at a
sleep research centre, in a community sample and demonstrated no effect of exposure
to trauma, or lifetime PTSD, on UFC. They did however find a significant increase in
UFC among women with comorbid major depression and PTSD. Using a larger subset
of this community sample, Young and Breslau (2004b), using morning and early
evening salivary cortisol from 516 participants (265, exposed to trauma, 183 not
exposed to trauma, 68 current or past PTSD), found significantly higher evening
cortisol in participants with PTSD compared with those who had been exposed to
trauma but did not develop PTSD. Further, analyses comparing PTSD only with major
depression only, comorbid depression/PTSD and no disorder did not show higher
levels of cortisol for either the PTSD or depression only groups. However, the comorbid
group demonstrated elevated evening levels of cortisol in comparison with the no
disorder group. Whereas this effect was observed among women only in the earlier
study (Young & Breslau, 2004a), in this study the effect was observed in participants of
both genders.
Though findings are inconsistent, there is strong evidence for persistent cortisol
abnormalities. These abnormalities may be a trait rather than state phenomenon,
possibly reflecting pre-existing abnormalities rather than a consequence of prior
disorder. The findings of elevated cortisol in those with comorbid psychiatric disorders
further highlight the importance of assessing comorbidity among those with PTSD.
More research needs to be undertaken to further the understanding of the role of
cortisol in PTSD. The cortisol profiles in a sample of ACS patients are investigated in
relation to posttraumatic reactions in chapters 6 and 7. These data show some
interesting findings in relation to co-morbid depression.
83
2.9.2 Heart rate
One of the criteria that must be fulfilled for a diagnosis of PTSD is that of
psychophysiological arousal such as sleep disturbances, hypervigilance and an
exaggerated startle. A number of studies now also suggest an important role of heart
rate (in the immediate aftermath of trauma) for subsequent development of PTSD. In a
prospective study of 91 injured trauma survivors (not requiring admission), heart rate
was assessed at the emergency department, then at 1 week, 1 month and 4 months
post trauma (Shalev et al., 1998b). The CAPS was also administered at each
assessment point to assess posttraumatic stress. The results showed higher heart
rates but not blood pressure, among those who later went on to develop PTSD, at the
emergency department and at 1 week post trauma heart rate assessment. At 1 and 4
months follow up, there was no longer a difference in heart rate. These results were
independent of age, trauma severity, response intensity and peri-traumatic
dissociation. It is important to note, however, that those who did not develop PTSD by
4 month follow up, also had elevated heart rate at the emergency room, which would
be expected from a stress response. Consistent with these findings are those of Bryant
et al (2000). In this study, discharge heart rate of motor vehicle accident survivors was
significantly predictive of 6-month PTSD. Unexpectedly, higher heart rates were
demonstrated among those with sub-clinical ASD. This might be due to the lack of
dissociation in this group. The presence of dissociative symptoms distinguishes ASD
from sub-clinical ASD, and it has been proposed that dissociation may reduce
overwhelming distress and arousal in the acute phase thereby providing one
explanation for the higher heart rates observed in the sub-clinical ASD group. A more
recent study by Zatzick et al (2005) further support these results. Emergency heart rate
of 95 beats per minute was a significant independent predictor of posttraumatic stress
symptoms in severely injured surgical inpatients, over the course of one year
(assessment points in-hospital, 1 month, 4-6 months, 12 months). These authors found
84
no significant association of heart rate and subsequent depression, even though major
depression has been associated with elevated heart rate in a number of studies (e.g.
Carney et al., 1999; Moser et al., 1998). Shalev et al (1998b) report that although
emergency department heart rate significantly predicted PTSD at 4 months, it showed
no relationship with depression. These findings suggest that autonomic nervous
system disruptions may develop as a result of depression, whereas for PTSD these
alterations may contribute to the pathogenesis of the disorder. Although these three
prospective studies have differed in terms of heart rate measurement, type of trauma
and trauma severity, inclusion and exclusion criteria, and the timing and duration of
follow up, taken together they provide support for an association between elevated
sympathetic arousal and the development of enduring posttraumatic stress symptoms.
However, contrasting findings have been demonstrated by Blanchard et al (2002). In
this study of 76 motor vehicle accident survivors, heart rate assessed in the emergency
department was significantly negatively associated with increasing posttraumatic
symptom levels (CAPS interview) at 13 months follow up.
The use of heart rate as a predictor of later PTSD among cardiac patients is
problematic due to the use of beta-blockers as part of treatment for myocardial
infarction. However, I did investigate both heart rate and heart rate variability in relation
to PTSD in the second of the studies I conducted (see chapters 6 and 7).
2.10 Chapter summary
Although in recent years more attention has been directed to posttraumatic
stress reactions in patients following an ACS, it is becoming clear that the risk factors
for posttraumatic stress in cardiac patients may not be the same as the risk factors for
PTSD following other trauma. Risk factors for developing posttraumatic symptoms
following MI may include pre-event vulnerabilities such as prior trauma or personality,
event related variables such as the subjective experience of pain and emotional
85
experiences soon after hospitalization, and post-event experiences such as social
support and coping. Together these findings clearly show that a significant minority of
cardiac patients will go on to develop posttraumatic stress symptoms, and that this may
have a significant impact on morbidity and mortality. Considering the well established
link between depression and CHD it is becoming clear that PTSD may have similarly
detrimental effects on outcomes. Many studies in this area have used relatively small
samples and follow up periods have generally been shorter than 9 months. In order to
fill this gap, I carried out a study to assess the longer term prevalence and predictors of
posttraumatic stress symptom in patients 12 and 36 months post admission. These
results are presented in chapter 4. The second study I carried out assessed
posttraumatic stress in a sample of ACS patients at 2 weeks, 6 months and 12 months
post the acute event investigating the change in symptoms during the first year post
trauma. The results of this study are presented in chapters 6 and 7.
86
CHAPTER 3. Methodology ACCENT study
3.1 Introduction and hypotheses
There is growing evidence that PTSD may develop as a consequence of an
acute cardiac event. The average prevalence rate of PTSD across studies of post-MI
patients is approximately 15% (Gander & von Känel, 2006). Most prospective research
on PTSD has measured posttraumatic stress symptoms 3 – 9 months following cardiac
events, and only one study (van Driel & Op den Velde, 1995) has involved longer
periods. Since longer term prevalence has not been studied in detail, it is not known
whether symptoms persist or there is recovery over time. Cross-sectional studies
suggest that posttraumatic symptoms may last for many years (Jones et al., 2007;
Bennett & Brooke, 1999), but dysphoric reporting biases may be present (Chung et al.,
2007). The first aim of this study was therefore to establish the intensity of
posttraumatic symptoms and incidence of PTSD at 12 and 36 months following
admission to hospital with an acute coronary syndrome (ACS) in a prospective design.
The second aim of the study was to identify early predictors of later PTSD. For
most traumatic events, posttraumatic symptoms increase with the severity of the
stressor (Brewin et al., 2000). However, studies of cardiac patients have not typically
found a relationship between severity of cardiac damage or occurrence of other cardiac
symptoms and the development of posttraumatic stress symptoms (O'Reilly et al.,
2004; Pedersen et al., 2003; Ginzburg et al., 2003). But although objective clinical
severity appears not to be predictive, several studies have found that subjective
intensity of the event (Ginzburg et al., 2002; Ginzburg et al., 2003; Whitehead et al.,
2006) and the anticipation of incapacitation (Kutz et al., 1994) are predictive of later
PTSD.
Acute cardiac events can be very distressing and expectations of death or
serious disability can be highly influential. Fear during and immediately after the acute
87
event is associated with greater posttraumatic stress symptoms (Bennett & Brooke,
1999; Bennett et al., 2001). It is also possible that recurrence of cardiac symptoms in
the period following hospital discharge will increase posttraumatic symptoms by
providing vivid reminders of the acute event. A growing number of studies suggest that
emotional responses and negative affective states soon after admission are strongly
predictive of posttraumatic symptoms following ACS (Pedersen et al., 2003; Bennett et
al., 2001; Whitehead et al., 2006). However, the significance of early emotional
reactions for persistent long-term posttraumatic symptoms has not been established.
In this study, my focus was on how acute emotional reactions to ACS and
patients‘ general psychological dispositions would influence the development and
course of PTSD 12 and 36 months after the acute event. My predictions were that
depressed mood following hospital admission would be predictive of posttraumatic
stress
symptoms
12
and
36
months
later,
independently
of
clinical
and
sociodemographic factors. In addition I hypothesised that patients‘ psychological
disposition, in particular type D personality and hostility, would also be predictive of
posttraumatic stress symptoms 12 and 36 months following hospital admission.
Previous research suggest that posttraumatic stress symptoms may become
chronic, if left untreated, beyond as early as nine months post trauma (Gander & von
Känel, 2006; Freedman et al., 1999). Therefore, I hypothesised that patients‘
posttraumatic stress symptoms would show chronicity, remaining stable between 12
and 36 month follow up. The analyses presented in this chapter were undertaken as
part of the ACCENT study (see section 3.2). Posttraumatic stress symptoms were
previously assessed at 3 months, and results from this subgroup of ACCENT patients
have been presented elsewhere (Whitehead et al., 2006).
88
3.2 Participants
Participants were 284 patients admitted with ACS to one of three London
hospitals. ACS was diagnosed based on the presence of chest pain and the following
criteria: verification by electrocardiographic changes (ECG; new ST elevation > 0.2mV
in two contiguous leads [V1, V2, or V3] and > 0.1mV in two contiguous other leads, ST
depression > 0.1mV in two contiguous leads in the absence of QRS confounders, new
left branch bundle block, or dynamic T wave inversion in more than one lead), and/or
elevated cardiac enzymes (troponin T measurement > 0.01µg/l or a creatine kinase
measurement more than twice the upper range of normal for the measuring laboratory).
As the original focus of this study was to investigate acute triggering of ACS,
patients were eligible to participate if they could recall the specific time of symptom
onset. Patients with co-morbid conditions which could influence either symptom
presentation or mood state (such as severe psychiatric illness, unexplained anaemia,
ongoing infection or inflammatory conditions, neoplasia and renal failure), and
conditions that might cause false troponin positivity, were excluded. Eligible patients
were aged between 18 and 80 years old and were able to complete the in-hospital
interview and questionnaire measures in English.
Data for this study were collected between 2001 and 2004. 360 potentially
eligible patients were admitted on the days recruitment was conducted during this
period. Of these, 46 patients (12.8%) had been discharged or transferred on to a
different hospital before the researchers could conduct the in-hospital interview. A
further 30 patients (8.3%) declined to participate in the study (fig. 3.1). These data were
collected as part of a larger study of emotional experiences related to ACS (The
ACCENT study), and other results from this study have been published previously,
including data about the triggering of cardiac events (Strike et al., 2006), the delays
between symptom onset and admission to hospital (Perkins-Porras et al., 2009), the
role of ongoing stress and social support in determining adherence to advice following
89
discharge (Molloy et al., 2008) and factors predicting return to work (Bhattacharyya et
al., 2007).
3.3 Study design and procedure
Potential participants were approached as soon as possible after their
admission for ACS, at which point the study was explained and informed consent was
obtained. The in-hospital interview took place on average 2.56 ± 1.5 days following
admission, with the majority (95%) taking place within five days of admission. These
interviews primarily focused on the circumstances surrounding symptom onset and
hospital admission. After the interview, within 7 to 10 days of admission, participants
completed a battery of self-report questionnaires (detailed in the section below).
Patients were re-contacted at 3 (reported elsewhere, Whitehead et al., 2006), 12 and
36 months post admission for ACS.
Only data collected at the baseline hospital assessment and at 12 and 36
month follow up have been used for the purpose of this thesis. The follow-up interviews
included a semi-structured telephone interview assessing recurrence of symptoms,
other health problems, adherence to medication, and health behaviours (Appendix I)
(details on health behaviours from this data set not shown as I did not incorporate
these in any of my analyses or hypotheses). The participants were also mailed a
battery of standardized questionnaires, at both follow up points, to complete and return
by post.
3.3.1 My role in study design, data collection and analysis
As the ACCENT study was already underway at the time of my joining, my
responsibilities at that stage included data collection, data entry and data analysis. I
carried out the majority of the 36 month interviews, though a proportion was conducted
90
by a colleague, Dr Mimi Bhattacharyya. The statistical analyses included in this thesis
were undertaken by myself, with additional guidance from my thesis supervisor.
3.4 Psychosocial measures
3.4.1 Socio-demographic information
Patients‘ age, marital status and ethnicity were obtained and data on patients‘
socio-economic status (SES) were collected during the in-hospital interview. A social
deprivation index was created using an adaptation of the Townsend Material
Deprivation Index (1988). This index has been shown to be related to increased
cardiovascular risk factors (Sunquist et al., 1999) and is a broad measure of social
deprivation and access to resources. Social deprivation was computed based on the
following four criteria: renting one‘s home (as opposed to owning a home), not having
access to a car or van, living in a crowded household (defined as more than one
person per room) and being in receipt of state benefits. Scores on these items ranged
from 0 to four, with four being the highest level of deprivation. Participants were
classified as low deprivation (negative on all items), medium deprivation (one positive
score) and high deprivation (two to four positive items). In addition, patients‘ level of
educational attainment was also assessed. The level of reported education was
categorised into seven groups: no educational qualifications, up to school certificate,
CSE‘s, GCSE‘s, A level, Degree and Other. For the purposes of statistical analyses
educational attainment was reclassified into a categorical variable with three levels;
‗none‘, ‗up to O-level‘ and ‗A-level or above‘.
91
3.4.2 Clinical data
Information
on
clinical
factors
during
admission,
management
and
cardiovascular history was collected from patients‘ hospital admission notes. Admission
ECGs and troponin T or creatine kinase data were reviewed by a cardiologist and
patients were classified for presentation with ST- elevation myocardial infarction
(STEMI), non ST- elevation myocardial infarction (NSTEMI) or unstable angina (UA).
For purposes of analyses these were categorised into a binary variable ‗STEMI‘ vs
‗NSTEMI/UA‘. Composite clinical risk scores were also computed based on the
algorithm developed in the Global Registry of Acute Coronary Events (GRACE) study
(Eagle et al., 2004). This algorithm uses the following nine criteria to define risk of six
month post discharge death applicable to all types of ACS: age, history of congestive
heart failure, history of MI, systolic blood pressure and heart rate on admission, ST
segment depression, initial serum creatine, raised cardiac enzymes and no in-hospital
percutaneous coronary intervention. Patients‘ subjective pain ratings were also
recorded. Chest pain during ACS was rated on a 10-point scale from 1= very low to
10= excruciating; however, this measure was not introduced until midway through the
study, so was available only for a subset of patients.
3.4.3 Psychological measures
A range of well established standardized questionnaires were used in this study
(Appendix II). These were used to assess emotional states and well-being as well as
psychological traits. The questionnaires described in this section have been widely
used with cardiac populations in previous research. A range of emotional factors was
measured, including depression, anxiety and posttraumatic stress symptoms. Other
measures included negative affectivity and hostility. The full range of questionnaires
92
employed is described in detail below. Details on which measures were obtained at
each time point are presented in table 3.1.
3.4.3.1 Beck Depression Inventory (BDI)
Patients‘ level of depression was assessed using the Beck Depression
Inventory (BDI), a standard measure of depressive symptoms (Beck et al., 1988). This
measure has been widely used in cardiac populations and is considered a valid
measure of depression (Buchanan et al., 1993; Crowe et al., 1996; Frasure-Smith et
al., 1997). Findings from a meta-analysis of studies of non-psychiatric participants
show a mean coefficient alpha of .81 (Beck et al., 1988). The BDI is a 21-item selfreport measure that assesses the severity of depressive symptoms over the past week.
Patients rate symptoms from none (0) to severe (3). The scores can range from 0 to 63
with higher scores indicating more severe depressive symptoms. The standard cut-off
points are as follows; 0-9 indicates that the person is not depressed, 10-18 suggests
mild-to-moderate depression, 19-29 indicates moderate to severe symptoms and a
score between 30-63 would suggest presence of severe depression. The Cronbach‘s
alpha for this scale was .88.
The BDI suffers the same problems as other self-report measures; for example,
the way the measure is administered can influence the responses. For instance, if a
patient is asked to fill the form out in front of other people in a clinical environment,
social expectations might elicit a different response compared to administration via a
postal survey (Bowling, 2005). More closely relevant to this study of ACS patients is the
issue of the BDI‘s inclusion of physical symptoms such as fatigue, which might
artificially inflate depression scores in a medically ill population, due to symptoms
related to their physical health status rather than depression.
93
3.4.3.2 Posttraumatic Stress Symptoms – Self Report Scale (PSS-SR)
The presence and severity of posttraumatic stress symptoms was assessed
using the PTSD Symptom Scale - Self Report version (PSS-SR) (Foa et al., 1993), the
precursor of the Posttraumatic Diagnostic Scale (Foa et al., 1997). The PSS-SR is a
17-item scale with items corresponding to the DSM-IV criteria for diagnosis of PTSD for
the three dimensions of intrusions/re-experiencing, avoidance and arousal. Each item
is rated on a 4-point scale, ranging from 0 (not at all) to 3 (5 or more times per week) to
indicate frequency of patients‘ experiencing symptoms during the past 2 weeks. The
PSS-SR was primarily analyzed as a continuous variable to identify predictors of
posttraumatic stress symptoms. However, the scale can also be used to identify
individuals who meet criteria for a probable diagnosis of PTSD, and has been endorsed
by the UK National Institute for Health and Clinical Excellence (National Institute for
Health and Clinical Excellence, 2005) as a suitable measure of PTSD. Sensitivity of the
scale is 62% and specificity 100% based on DSM criteria (Foa et al., 1993). The
presence of PTSD is defined by a score of 1 or more on at least one re-experiencing,
three avoidance and two arousal symptoms. For the purpose of analysis in this study, I
used an adaptation of the original scoring system, such that the presence of PTSD was
defined by a score of ≥ 2 (two to four times a week) on at least one intrusion, three
avoidance and two arousal items. These scoring criteria are more conservative than
the original guidelines but are more closely related to original DSM-IV criteria (APA,
1994). Brewin and colleagues (1999) found that when using original scoring criteria,
some patients were identified with PTSD even though no symptom was rated more
than 1 and the total symptom scores were as low as 9 (possible range 0-51). Thus in
order to eliminate low scores and to more closely match DSM criteria for persistence of
symptoms these authors argue that the adapted scoring system will yield a more
accurate estimate of posttraumatic symptoms. This modified version of the scale
showed good internal reliability with a Cronbach‘s alpha of .92.
94
3.4.3.3 Hospital Anxiety Scale (HADS-A)
Anxiety was measured using the anxiety sub-scale from the Hospital Anxiety
and Depression scale (HADS). This is a well-established measure of psychological
distress in medical patients, and was originally developed to assess anxiety and
depression in a clinical population of medical outpatients suffering from a wide variety
of illnesses (Zigmond & Snaith, 1983). The anxiety sub-scale of the HADS has seven
items (five which are reverse scored) which are scored from 0 (not at all anxious) to 3
(very often anxious). Total scores range from 0 to 21, with higher scores reflecting
greater anxiety. The recognised cut-off for moderate anxiety is scores ≥ 8. Cronbach‘s
alpha for this scale was .85. The HADS has shown good psychometric properties with
Cronbach‘s alpha over .60 in populations of both medical and psychiatric settings, and
in the general population (Bjalland et al., 2002).
3.4.3.4 Medical Outcome Short Form 36 (SF36)
The SF36 is a general outcome measure which assesses health status and
health-related quality of life (Ware & Sherbourne, 1992), and has been frequently used
to assess quality of life among cardiac patients (Brown et al., 1999; Fogel et al., 2004;
Rumsfeld et al., 1999). Previous research has shown good psychometric properties of
this scale, with internal reliability in excess of .70 (Ware & Gandek, 1998). The SF36
uses eight sub-scales to measure three aspects of health – functional status, wellbeing, and overall evaluation of health. The Cronbach‘s alphas ranged between .77
and .94 for these scales. This measure has 36 items, which are grouped into eight
multi-item sub-scales representing the three domains described above. These are as
follows: (1) functional status – physical functioning (limitations in physical activity due to
physical problems), social functioning (interference with social activities due to physical
and emotional health problems), role limitations due to physical problems (problems
95
with work and daily activities due to physical health), role limitations due to emotional
problems (problems with work and daily activities due to emotional problems); (2) wellbeing – mental health (anxiety and depression), vitality (energy and fatigue), bodily
pain (severity); (3) overall evaluation of health – general health perception (evaluation
of physical health and likelihood of improvement). Each sub-scale is scored from 0
(worst health status) to 100 (best health status) to indicate level of function. The SF36
can also provide scores on two summary components, by averaging scores on the subscales relevant to these; physical health status (physical functioning, role limitations
due to physical problems, bodily pain and general health perception) and mental health
status (social functioning, limitations due to emotional problems, vitality and general
mental health).
3.4.3.5 Cook and Medley Hostility Scale (Ho)
Hostility is a key component of Type A personality (Williams, Jr. et al., 1980)
and has been linked to CHD rates. Hostility is also associated with poor health
behaviours such as smoking and alcohol use. The link between hostility and CHD may
be mediated by poor health behaviours. Hostility was assessed using the Cook-Medley
Hostility Scale (Ho) (Cook & Medley, 1954). This 39-item version contains items from
the four subscales identified by Barefoot and colleagues; cynicism, hostile attribution,
hostile affect and aggressive responding (Barefoot et al., 1989). Responses to these
items were rated 0 (false) or 1 (true), with total scores ranging from 0 (lowest hostility)
to 39 (highest hostility). The Ho scale has been reported to have good psychometric
properties, including adequate internal validity, good test-retest reliability and construct
validity (Barefoot & Lipkus, 1994). In this study the Cronbach‘s alpha was .87.
96
3.4.3.6 Type D (DS16)
Type D personality is associated with worse prognosis following myocardial
infarction and is defined as the joint tendency towards negative affectivity and social
inhibition. Type D was assessed using the 16-item DS16 (Denollet, 1998). Each item is
rated according to a 5-point Likert scale with scores from 0 (false) to 4 (true). Patients
who score high on both the social inhibition and negative affectivity scales, as
determined by median split, are classified as having Type D. This scale shows
satisfactory psychometric qualities and prognostic power, with Cronbach‘s alpha of .89
and .82 for the negative affectivity and social inhibition scales respectively (Denollet,
1998; Denollet et al., 2000). The Cronbach‘s alphas for the negative affectivity and
social inhibition scales in this study was .85 and .73, respectively.
3.4.3.7 Fear, helplessness and horror – Acute stress.
During the in-hospital interview, patients were asked to rate their experience of
acute distress and fear at the time of their ACS. This was measured using three items;
‗I was frightened when the symptoms came on‘, ‗I thought I might be dying when the
symptoms came on‘, and ‗I found my cardiac event stressful‘. Each item was rated on a
five point scale; not at all true, slightly true, somewhat true, very true and extremely
true. A combined score was created by averaging these ratings. Participants were
subsequently categorised into one of three groups; no distress and fear (average
ratings of ‗not at all true‘), moderate distress and fear (average ratings of ‗slightly true‘
and ‗somewhat true‘) and high distress and fear (average ratings of ‗very true‘ and
‗extremely true‘). This measure was used as a proxy measure for fulfilment of criterion
A of the DSM-IV classification for a diagnosis of PTSD.
97
3.4.3.8 Acute stress disorder
Patients acute stress reactions (acute stress disorder - ASD) to the ACS was
assessed using a composite scale derived from the Peritraumatic Dissociative
Experiences Questionnaire (Marmar et al., 1997) and the Acute Stress Disorder Scale
(Bryant et al., 2000). This scale consisted of 14-items rated on a five point scale (‗not at
all true‘ to ‗extremely true‘). Higher scores indicate greater dissociation, flashbacks,
intrusions and fear duing the event. Cronbach‘s alpha showed good reliability of the full
scale (α = .87). As for the subjective pain measure, the acute stress measure was also
not introduced until midway through the study and therefore data were only available
for a subset of patients for this scale.
3.5 Data storage
All data
collected
were
treated as
confidential.
Interview data
and
questionnaires from all data collection points were kept separate from consent forms,
and all were kept in locked filing cabinets with restricted access. Data were
anonymised and entered into a database which was password protected.
3.6 Statistical analyses
All statistical analyses were performed using the statistical programme SPSS
17.0 (SPSS Inc). The significance level was set at p < .05 for all analyses. Specific
details on the analyses conducted are presented in the results sections of chapter 4.
98
TABLE 3.1 MEASURES OBTAINED AT EACH TIME POINT
Time point
Measure
BDI
In-Hospital
12 m follow up
36 m follow up
X
X
X
PTSS-SR
X
X
HADS-A
X
X
X
X
X
SF-36
X
Ho-Scale
X
Type D
X
Acute stress
X
ASD
X
X: Measure was administered at this time point.
Hospital
Recruitment
N=360
Discharged or
Transferred
N=46
Declined to
Participate
N=30
In-Hospital
Interview
N=284
Untraceable
N= 54
Excluded from
analyses at 12m
N=13
Deceased
N=4
12 month
Follow up
N=213
Untraceable
N=34
36 month
Follow up
N=179
FIGURE 3.1 FLOWCHART OF PATIENT RECRUITMENT
99
CHAPTER 4. Results ACCENT Study
4.1 Results
4.1.1 Data analyses
226 of the 284 patients recruited at baseline were assessed at 12 months. Four
patients were deceased and 54 could not be traced. 13 of the patients responding at 12
months had to be excluded from the analyses because their questionnaires provided
insufficient data. At 36 months 179 patients were re-assessed. The principal analyses
were therefore carried out on 213 patients at 12 month follow-up, and 179 at 36 months
(for flowchart of recruitment see chapter 3, fig. 3.1). Patients who did not complete the
12 month follow up were similar to those who completed the study on most baseline
clinical, demographic and psychological variables.
However, non-completers were
more likely to be in the high deprivation category (χ² = 19.26, p < .001), scored
significantly lower on the GRACE index (t = -2.19, p < .05) and were more likely to be
unmarried (χ² = 6.63, p < .05) than completers. At 36 months, a significantly greater
proportion of non-completers scored highly on the index of social deprivation (χ² = 6.84,
p < .05).
There were no differences on any of the clinical, demographic or
psychological characteristics collected during admission between those patients who
completed 12 month follow-up only, 36 months follow-up only, or those who completed
follow-up at both time points.
The prevalence of PTSD and severity of posttraumatic symptoms at each time
point were examined. Repeated measures analysis of variance was employed to test
whether symptom levels changed between 12 and 36 months. Associations between
posttraumatic stress symptom severity at 12 and 36 months and demographic, clinical
and psychological factors were analyzed using product-moment correlations for
continuous variables and analysis of variance for categorical variables. Multiple
100
regressions on posttraumatic symptoms at 12 and 36 months were conducted in order
to identify independent predictors of symptom levels. I selected variables into these
models based on previous literature and the results from the univariate analyses. Two
models were tested for each follow up point. Model 1 included demographic and clinical
factors. In Model 2, demographic and clinical factors were entered on step 1, and
psychological predictors on step 2. In the regression on 36 month posttraumatic
symptoms, 12 month symptom levels were included at step 1. Standardized regression
coefficients (β) are presented.
4.1.2 Patient characteristics
The characteristics of the patients participating at 12 month follow up are
presented in Table 4.1. Patients were aged 61 years on average, and the majority were
men of white European descent. They were poorly educated, with only 30% having
completed high school or college. The majority had experienced an STEMI rather than
an NSTEMI/UA. ACS severity as defined by the GRACE score was moderate, and only
9.9% had experienced a previous MI. Depression scores on the BDI 7-10 days
following admission were elevated, with 33.1% scoring ≥ 10.
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TABLE 4.1 PATIENT CHARACTERISTICS – 12 MONTH SAMPLE
Mean (SD)
Demographic factors
Age
Gender
Educational attainment
60.95 (11.22)
Men
164 (77.0)
Women
49 (23.0)
None
65 (42.5)
Up to O-level
42 (27.5)
A-level +
46 (30.1)
Ethnicity (white)
Social deprivation
182 (85.4)
Low
Medium
High
101 (47.4)
57 (26.8)
55 (25.8)
Marital status (married)
Clinical factors
ACS type
GRACE score
N (%)
140 (65.7)
STEMI
153 (71.8)
NSTEMI/UA
60 (28.2)
96.02 (26.63)
Previous MI (yes)
21 (9.9)
Recurrence of cardiac symptoms (yes)
43 (21.5)
Psychosocial factors
BDI (following admission)
8.13 (7.51)
Anxiety
5.70 (3.92)
Acute stress disorder symptoms (n = 154)
27.42 (9.62)
Subjective pain (n = 120)
7.49 (2.22)
Hostility
15.54 (7.96)
Type D (positive)
60 (33.1)
History of depression (yes)
38 (17.8)
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4.1.3 Prevalence of posttraumatic stress symptoms at 12 and 36 months
At 12 months post-ACS, 26 patients (12.2%) met the diagnostic criteria for
PTSD, and the posttraumatic symptom severity score was 12.65 (SD 10.40). At 36
months 23 (12.8%) patients were identified as having PTSD, and the severity score for
the sample averaged 11.78 (SD 10.27). Table 4.2 shows the scores on the PSS-SR for
the sample at 12 and 36 months on all sub-scales. It can be seen that scores did not
differ markedly between 12 and 36 months, indicating no reduction in posttraumatic
stress symptoms. This was confirmed by analysis of those patients who provided PSSSR ratings at both time points (F (1, 162) = 2.27, p = .134, η2 = .014).
TABLE 4.2 PSS-SR SCORES
12 months
36 months
N
Means (SD)
Range
N
Means (SD)
Range
PSS-SR Total score
213
12.65 (10.40)
0-47
179
11.78 (10.27)
0-43
PSS-SR Avoidance
213
5.95 (5.95)
0-21
177
5.72 (5.04)
0-21
PSS-SR Arousal
213
4.35 (3.83)
0-18
177
4.03 (3.75)
0-18
PSS-SR Re-experiencing
213
2.35 (2.66)
0-12
179
1.98 (2.48)
0-12
PTSD Diagnosis (positive) – modified
213
12.2%
179
12.8%
PTSD Diagnosis (positive) – original
211
51.7%
179
41.9%
As discussed in chapter 3, section 3.4.3.2, the scoring criteria used for a
probable diagnosis of PTSD in this sample is more conservative than the original
guidelines for this scale. When adopting the original diagnostic scoring of the PSS-SR,
this measure yielded a prevalence of 51.7% of PTSD among patients at 12 months
(n=109). At 36 month follow up, 75 patients (41.9%) met the diagnostic criteria for
PTSD. These rates appear highly inflated in comparison with previous research on
posttraumatic stress symptoms within this population, and the use of the modified
criteria for scoring is supported. When the analysis was limited to patients with a
103
classic STEMI, the prevalence was 9.8% and mean severity score 12.30 (SD 10.26) at
12 months, and 12.2% and 11.26 (SD 9.98) respectively, at 36 months.
At 36 months there were 8 new cases of PTSD that had not scored above the
diagnostic threshold at 12 months, while 6 cases showed improvements from 12 month
follow up and were no longer classified as having PTSD at 36 months. A symptom
severity change score was calculated by subtracting the 36 month total symptom score
from the 12 month total symptom score. 163 patients had completed the PSS-SR scale
at both time points. The mean symptom change score was .77 (SD 6.56), range -18 to
36, with negative scores indicating a worsening of symptoms. None of the
psychological, sociodemographic or clinical variables were significantly associated with
change in symptom severity, with the exception of a history of previous Myocardial
Infarction (r = -.171, p = .029). Patients who had experienced a previous MI had a
significantly worse change score (mean -2.61, SD 6.52) than did those who had not
had a previous MI (mean 1.14, SD 6.48) (F (1, 161) = 4.837, p = .029, η2 = .029),
suggesting these patients‘ emotional status declined with time. The correlation between
posttraumatic symptom severity at 12 and 36 months was .79 (p < .001), suggesting
that generally symptom intensity remained stable over time. As seen above, in
repeated measures analysis of variance, there was no significant change in
posttraumatic symptom intensity between 12 and 36 months (p = .134).
Patients with posttraumatic symptoms above threshold at 12 months had
markedly impaired physical and mental health status at 12 months as measured by the
SF-36, averaging 29.7 (SD 15.1) and 32.3 (SD 15.5) for the two scales, compared with
68.2 (SD 23.9) and 73.1 (SD 20.8) for physical and mental health status respectively in
the non-PTSD patients (both p < .001). At 36 months, patients meeting criteria for
PTSD continued to have impaired physical and mental health status (means 30.9, SD
11.3 and 35.7, SD 17.7 respectively) compared with the remainder (means 50.9, SD
19.5 and 72.4, SD 21.5, both p < .001), indicating that PTSD was associated with
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impairment in other important areas of functioning, fulfilling criteria F of the DSM-IV
classification for a PTSD diagnosis.
4.1.4 Comparisons of psychological variables between PTSD and non-PTSD groups
Patients who went on to develop PTSD at 12 months reported significantly
greater depression in hospital, higher hostility scores, and were more likely to have a
history of depression (Table 4.3). None of the other psychological variables assessed
at baseline differed between the diagnostic groups (significance level adjusted to p <
.007, by means of Bonferroni correction). Patients who scored above threshold at 36
months had higher depression, hostility and acute stress disorder symptoms at
baseline (all at p < .007) (data not shown).
TABLE 4.3 PSYCHOLOGICAL VARIABLES BY PTSD CASENESS
12 months
Non-PTSD
PTSD
p-value
Posttraumatic stress symptoms
9.75 (6.90)
33.51 (6.95)
<.001*
BDI
7.07 (6.10)
17.97 (11.57)
=.001*
Anxiety
5.66 (3.86)
6.06 (4.58)
=.691
Acute stress disorder symptoms
26.73 (8.91)
33.41 (13.25)
=.066
Hostility
14.79 (7.51)
22.63 (8.83)
<.001*
Subjective pain
7.35 (8.71)
2.23 (1.74)
=.044
Type D (positive)
61.1%
30.1%
=.008
History of depression (yes)
15%
38.5%
=.003*
* Sig. at p < .007 by means of Bonferroni correction.
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4.1.5 Predictors of posttraumatic stress symptom severity at 12 months
Associations between demographic, clinical, and psychological variables and
posttraumatic symptom intensity at 12 months are summarized in Table 4.4.
Posttraumatic symptom intensity was greater in younger patients (r = -.138, p = .045),
those from ethnic minorities (F (1,211) = 4.51, p = .035, η2 = .021) and more socially
deprived patients (F (2,210) = 5.31, p = .006). None of the clinical measures obtained
at the time of hospital admission predicted later posttraumatic symptoms, but recurrent
cardiac symptoms were strongly associated with 12 month posttraumatic stress
symptoms (F (1,198) = 25.7, p <.001, η2 = .115).
All psychological variables (BDI measured in hospital, anxiety, acute stress
disorder symptoms, hostility, Type D personality, subjective pain ratings and a history
of clinical depression) were associated with higher posttraumatic stress symptom
scores at 12 months (all p < .001, pain ratings p < .05). Although subjective pain and
acute stress symptoms were predictive of posttraumatic stress symptoms in this
sample at 3 months (Whitehead et al., 2006), it was not possible to include patients‘
subjective pain ratings and acute stress disorder symptoms in multivariate analyses in
the present study due to the large amount of missing data on these variables. These
measures were not introduced until midway through the study and therefore the
inclusion of these variables for multivariate analyses would reduce the overall number
of cases included in the models, and could thereby cause failure to detect other
significant relationships.
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TABLE 4.4 PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS
Demographic factors
Age
12 month posttraumatic
symptoms
Means (SD) or
r
P
36 month posttraumatic
symptoms
Means (SD) or
r
P
-.138
.045
-.129
.085
Gender
Men
Women
12.19 (10.59)
14.19 (9.69)
.239
12.19 (10.83)
10.50 (8.23)
.347
Education
None
Up to O-level
A-level +
13.93 (11.00)
11.80 (10.89)
11.50 (8.91)
.267
13.15 (10.94)
9.28 (9.18)
11.85 (9.94)
.142
Ethnicity
White
Other
12.03 (9.85)
16.29 (12.79)
.035
10.80 (9.56)
16.87 (12.33)
.003
Social deprivation
Low
Medium
High
10.62 (8.75)
12.87 (11.42)
16.18 (11.29)
.006
10.70 (9.04)
11.86 (10.64)
13.90 (11.96)
.246
Marital status
Married
Not married
12.01 (10.02)
13.89 (11.06)
.212
11.83 (9.57)
11.70 (11.53)
.933
STEMI
NSTEMI/UN
12.31 (10.26)
13.54 (10.80)
.438
11.26 (10.87)
12.26 (9.98)
.308
-.119
.084
-.106
.158
Clinical factors
ACS type
GRACE score
Previous MI
Yes
No
13.96 (11.65)
12.51 (10.31)
.545
16.59 (11.76)
11.18 (9.94)
.026
Recurrence of cardiac
symptoms
Yes
No
19.33 (12.37)
10.82 (8.94)
.001
16.99 (11.60)
9.65 (8.89)
<.001
Psychosocial factors
BDI (following
admission)
.674
<.001
.677
<.001
Anxiety
.294
<.001
.196
.017
Acute stress disorder
symptoms (n =154/130)
.442
<.001
.400
<.001
Subjective pain
(n =120/101)
.231
.011
.144
<.001
Hostility
.354
<.001
.281
.001
Type D
Yes
No
16.15 (11.56)
9.91 (8.21)
<.001
15.29 (10.58)
9.72 (9.66)
.002
History of depression
Yes
No
18.21 (12.32)
11.45 (9.56)
<.001
17.02 (11.38)
10.56 (9.62)
.001
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Examination of the intercorrelations (table 4.5) between the psychological
variables measured show that symptoms of depression shortly after ACS were
significantly related to all other psychological variables. Anxiety in hospital was only
associated with depression and acute stress symptoms. The acute stress disorder
measure was significantly associated with all other measures with the exception of a
history of depression. Hostility showed a relationship with the other trait measure, type
D, as well as with depression and acute stress disorder symptoms. Subjective pain was
largely unrelated to the other psychological variables, however, an association was
found between this variable and the acute stress disorder measure and depression in
hospital. Type D personality was associated only with a history of depression and
depression in hospital. These correlations suggest that although there are several
different psychological predictors of posttraumatic stress, it is important to note that
many of these are not independent on one another.
TABLE 4.5 CORRELATIONS BETWEEN PSYCHOLOGICAL PREDICTOR VARIABLES
Psychological
BDI
Anxiety
variable†
Acute
Hostility
Stress
Subjective
Type
Pain
D
disorder
symptoms
Anxiety
r
.352
p
.000
Acute stress
r
.526
.434
disorder symptoms
p
.000
.000
Hostility
r
.394
.104
.259
p
.000
.169
.002
r
.284
-.076
.277
.086
p
.004
.440
.005
.385
r
.424
.113
.268
.192
.083
p
.000
.134
.001
.011
.402
History of
r
.364
.069
.085
.114
.057
.232
Depression
p
.000
.361
.295
.132
.535
.002
Subjective Pain
Type D
†Correlations between psychological variables measured at baseline among patients who completed 12-month
posttraumatic stress symptom follow up.
108
The multiple regression on 12 month posttraumatic symptoms indicated that
demographic and clinical factors together accounted for 21.5% of the variance, with
social deprivation and recurrence of cardiac symptoms being the strongest
independent predictors (Table 4.6, Model 1). The psychological factors (Model 2, step
2) accounted for an additional 29.8% of the variance, and the complete model
explained 51.3% of the variance in 12 month posttraumatic symptoms. The BDI
measured in hospital was the only independent predictor from among the psychological
measures. None of the variables included in the final model showed multicollinearity
according to variance inflation factor and tolerance values.
TABLE 4.6 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT 12
MONTHS
Model 1
Standardised
regression
coefficients
P
Model 2
Standardised
regression
coefficients
P
Age
-.083
.585
-.064
.611
Gender
.152
.052
.083
.210
Ethnicity
.085
.248
.060
.327
Social deprivation
.211
.004
.079
.190
GRACE score
-.173
.245
-.093
.447
.269
<.001
.178
.004
BDI (hospital)
.471
<.001
Anxiety
.088
.167
Hostility
.116
.093
Type D
-.027
.676
History of depression
.078
.202
Recurrence of cardiac
symptoms
R²
R²
.215
.513
Figure 4.1 Illustrates the relationship between depression in hospital and later
posttraumatic stress symptom severity, where patients were grouped according to the
cut-off scores for ‗none‘, ‗mild-to-moderate‘, ‗moderate-to-severe‘ and ‗severe‘
symptoms as described in chapter 3, section 3.4.3.1. Scores are shown for both
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unadjusted and adjusted means (co-variates; age, gender, ethnicity, social deprivation,
GRACE score, recurrence of symptoms). A strong linear relationship is apparent.
FIGURE 4.1 THE RELATIONSHIP BETWEEN DEPRESSION SCORES AT BASELINE AND 12 MONTH
POSTTRAUMATIC STRESS SYMPTOMS
4.1.6 Predictors of posttraumatic stress symptoms at 36 months post ACS
Posttraumatic symptom intensity at 36 months was greater in patients from
ethnic minorities (F (1,177) = 8.87, p = .003, η2 = .048), patients who had previously
had an MI (F (1,177) = 5.04, p = .026, η2 = .028) and those experiencing recurrent
cardiac symptoms (F (1,1556) = 13.98, p < .001, η2 = .083), as shown in Table 4.4.
None of the other clinical measures or demographic characteristics obtained at
baseline were predictive of later posttraumatic stress symptoms. All psychological
variables (BDI measured in hospital, anxiety, acute stress disorder symptoms, hostility,
Type D personality, subjective pain ratings and a history of clinical depression) were
110
strongly associated with higher posttraumatic stress symptom scores (all p < 0.05)
(Table 4.4).
The multiple regression analyses indicated that demographic, clinical factors
and posttraumatic symptoms at 12 months together accounted for 61.2% of the
variance in 36 month posttraumatic symptom intensity. This was primarily due to the
strong influence of 12 month posttraumatic symptoms in the model, this variable being
the only independent predictor in this step (Table 4.7, Model 1). Nevertheless,
psychological factors measured in hospital (Model 2, step 2) accounted for an
additional 2.3% of the variance, and the complete model explained 63.5% of the
variance in 36 month posttraumatic symptoms. The BDI measured in hospital was the
only independent predictor from among the psychological measures. None of the
variables included in the final model showed multicollinearity according to variance
inflation factor and tolerance values.
Because the inclusion of the 12 month symptom level in the predictive model for
36 month posttraumatic symptom intensity could possibly obscure other interesting
associations, I repeated the multiple regression on 36 month posttraumatic symptom
intensity omitting the 12 month symptoms. In this model, one additional factor, ethnicity
( = .159, p = .037), made an independent contribution to the final model but the BDI
measured in hospital continued to be the only independent predictor among the
psychological measures ( = .602, p < .001) (data not shown).
111
TABLE 4.7 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT 36
MONTHS
Model 1
Standardised
regression
coefficients
P
Model 2
Standardised
regression
coefficients
P
Age
-.123
.353
-.122
.369
Gender
-.029
.660
-.044
.516
Ethnicity
.077
.214
.098
.137
GRACE score
.121
.353
.107
.423
Previous MI
.093
.127
.089
.143
.083
.188
.051
.443
.726
<.001
.598
<.001
BDI (hospital)
.205
.041
Anxiety
-.027
.689
Hostility
-.067
.363
Type D
.002
.982
.053
.422
Recurrence of cardiac
symptoms
Posttraumatic symptoms
(12 months)
R²
.612
History of depression
R²
.635
4.2 Discussion
This study investigated the prevalence and predictors of posttraumatic stress
symptoms 12 and 36 months following admission for an acute coronary syndrome. The
findings indicate that 12.2% of patients at 12 months and 12.8% of patients at 36
months met criteria for PTSD. Other studies that examined the occurrence of PTSD in
cardiac patients over shorter periods reported comparable rates (Spindler & Pedersen,
2005; Gander & von Känel, 2006; Shemesh et al., 2006; Ginzburg et al., 2003) (see
Table 2.2, chapter 2). It has been suggested that MI related posttraumatic stress may
comprise an acute reaction to a life-threatening event, and therefore may abate with
time (Owen et al., 2001). Although one previous study showed that the prevalence of
PTSD dropped by 40% between 4-6 weeks and 9 months (Pedersen et al., 2004), a
recent review found no convincing evidence of diminishing PTSD 4-6 weeks to 14
112
months following a cardiac event (Gander & von Känel, 2006). I also found that the
prevalence of posttraumatic stress symptoms had not diminished by 36 month follow
up, but remained stable and comparable to levels recorded at earlier times. This result
adds to the findings from studies of posttraumatic stress disorder from other traumatic
experiences that symptoms persisting beyond 12 months may become chronic
(Freedman et al., 1999).
Whether it is appropriate to regard posttraumatic stress symptoms above a
certain threshold to constitute PTSD as a diagnostic entity is open to debate. DSM-IV
criteria specify not only that the person experiences or witnesses a traumatic event, but
that the person‘s response involves intense fear, helplessness, or horror. In common
with much other research on PTSD following MI or ACS (Bennett & Brooke, 1999;
Chung et al., 2007; Ginzburg, 2006a; Doerfler et al., 1994), data were not
systematically collected on these acute emotional responses in all patients at the time
of admission (acute stress disorder measure). In the general PTSD literature, there are
doubts about the value of criterion A2 as an indicator, provided A1 is fulfilled (Weathers
& Keane, 2007). Additionally, only a minority of studies have included criterion F (the
disturbance should cause clinically significant distress or impairment in social,
occupational, or other important areas of functioning) as part of the diagnosis
(Pedersen et al., 2003). Narrow et al (2002) have emphasised that an accurate
understanding of clinical impact is required in order to plan treatment need. In this
study, I assessed physical, social and emotional functioning using the SF36 health
status measure, and found that ratings were markedly impaired in patients exceeding
the threshold on the PSS-SR, suggesting that criterion F was fulfilled.
More generally, the posttraumatic nature of some of the symptoms is open to
question.
As Kangas et al (2002) have pointed out in relation to PTSD following
cancer, the stressor for patients following ACS is not in the past, since they are at
increased risk for future events. Further, the stressor is triggered by an internal cardiac
event rather than an external threat, so cannot be physically avoided in the same way
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as other traumas. There may be ambiguity in the extent to which patients complete
assessment instruments with respect to the index ACS, or take into account current
health and symptomatology. For these reasons, the primary focus of this study was on
severity of posttraumatic symptoms, rather than PTSD as a diagnosis.
Consistent with other research (Kutz et al., 1994; Whitehead et al., 2006), the
clinical severity of the cardiac event was not related to posttraumatic stress symptoms.
In other words, cardiac disease severity and the extent of myocardial damage could not
explain why some patients‘ experienced greater posttraumatic symptoms post ACS
than others. Recurrence of cardiac symptoms, however, strongly predicted
posttraumatic stress symptoms at 12 months. These cardiac symptoms may have
served as reminders of the traumatic event, activating re-experiencing symptoms and
contributing to the persistence of posttraumatic stress symptoms. Unlike the situation
when posttraumatic symptoms arise from external traumas such as natural disasters,
war or violence inflicted by others, ACS involves internal trauma, and it may therefore
be more difficult to avoid reminders of the ACS when interoceptive stimuli such as
chest pain or shortness of breath are present to trigger memories of the event.
Posttraumatic symptoms at 12 months were predicted by high social deprivation
scores, while ethnic minority status was associated with symptom levels after 36
months (model omitting 12 posttraumatic symptom level). Low socioeconomic status is
a consistent predictor of PTSD in trauma-exposed adults (Brewin et al., 2000). Low
socioeconomic status is also associated with depression (Lorant et al., 2003), so it is
interesting in this study that the association between social deprivation and 12 month
posttraumatic symptoms was no longer significant after depressed mood in hospital
was included in the model (Table 4.6). This suggests that depressed mood may have
mediated the social deprivation effect. Ethnic minority status is a predictor of PTSD
following external traumatic events (Brewin et al., 2000), and ethnicity predicted PTSD
following acute MI in a study in Israel (Kutz et al., 1994).
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Previous research indicates that posttraumatic stress symptoms in cardiac
patients are associated with higher levels of anxiety, depression and anger (Doerfler &
Paraskos, 2004). A number of studies indicate that posttraumatic symptoms are
influenced by negative affective states during and following the acute event (Bennett &
Brooke, 1999; Pedersen et al., 2003; Bennett et al., 2001; Whitehead et al., 2006). In
line with my predictions, I found depressed mood in hospital, anxiety, hostility and type
D personality, were predictive of posttraumatic symptoms. Depressed mood was the
strongest predictor of posttraumatic symptoms at 12 and 36 months following ACS.
This finding is not surprising considering the high levels co-morbidity of depression and
posttraumatic stress (Ginzburg, 2006a). Type D (distressed) personality has previously
been found to be associated with posttraumatic symptoms at 3 months following ACS
(Pedersen & Denollet, 2004). The present findings show that although type D was
associated with symptom severity at both 12 and 36 months, it did not independently
predict posttraumatic stress symptoms at 12 months after depressed mood had been
taken into account.
Hostility has been found to be an independent predictor of posttraumatic stress
3 months after ACS (Whitehead et al., 2006), as well as other traumatic events (Brewin
et al., 2000). I replicated this finding at 12 and 36 months, when hostility was a
significant predictor after age, gender, GRACE risk score, ethnicity, social deprivation
and recurrence of symptoms had been taken into account (data not shown). However,
it did not survive as an independent predictor when depressed mood in hospital was
included in the model (tables 4.6 and 4.7). Similarly, I found anxiety to be an
independent predictor of posttraumatic stress at 12 months, though not at 36 months,
after age, gender, GRACE risk score, ethnicity, social deprivation and recurrence of
symptoms had been taken into account. However, once depression was included in the
model at 12 months, anxiety no longer made an independent contribution to the
prediction of posttraumatic symptoms.
115
One explanation may be that hostility and depressed mood were positively
correlated (r = .394, p <.001), and this shared variance reduced the independent
influence of hostility. This may also be the reason why Type D personality and anxiety
did not survive as independent predictors.
Posttraumatic stress symptoms were highly stable between 12 and 36 months,
with a correlation of .79. Consequently, 12 month symptom level was the major
predictor of symptoms at 36 months. Nonetheless, depressed mood following
hospitalization remained a significant independent predictor of posttraumatic symptom
severity at 36 months, further reinforcing the long-term significance of early emotional
responses to acute cardiac events. Despite the stability in average posttraumatic
symptom levels, a small proportion of patients who did not fulfill criteria for PTSD at 12
months moved into the positive category at 36 months, and a similar number improved.
Unfortunately, the numbers were insufficient to carry out robust analyses of predictors
of this pattern. These effects merit a larger investigation, since it may be important from
the clinical perspective that some patients deteriorate over the long-term in
posttraumatic symptomatology. In particular, previous MI was associated with
worsening of symptoms from 12 to 36 months, and some research (non-medical
trauma) suggest that exposure to previous trauma is a vulnerability factor for
developing PTSD following new traumatic events (Breslau et al., 1999), in particular
among those who went on to develop PTSD previously (Breslau et al., 2008).
4.2.1 Strengths and limitations
The strengths of this study include its prospective design, relatively large
sample, and the length of the follow up period. There are however a number of
limitations. Posttraumatic symptoms were assessed with self-report measures rather
than gold standard clinical interviews. A full diagnostic interview was not possible within
the confines of the study, and although a well standardized measure was used, results
116
might have been different with diagnosis by interview. However, it is interesting that in
a recent study that diagnosed PTSD by clinical interview, the prevalence was 9.4%,
similar to the level observed in this study (Wiedemar et al., 2008). Second, the dropout
rate raises the concern of selective attrition. Non-completers were of lower
socioeconomic background, were more likely to be unmarried but had lower scores on
the GRACE index, suggesting that they had experienced less severe ACS. I do not
know how many of these individuals experienced posttraumatic symptoms, so whether
their inclusion would have increased or reduced the prevalence of severe PTSD
remains unclear. It is conceivable that posttraumatic symptom levels would have been
lower in mild cases, leading to an overestimation of the incidence of PTSD. Third, I did
not measure other factors that are thought to relate to the development of
posttraumatic symptoms following ACS, including dissociative responses (Ginzburg,
2006b) and personality traits (Chung et al., 2007).
Finally, the sample was
predominantly male and of white ethnic origin, so it cannot be assumed that the
present findings generalize to the entire population of patients after ACS. Additionally,
patients recruited for the study were selected on the basis of being able to identify a
distinct time of onset of symptoms, and patients with co-morbidities that potentially
influenced cardiac enzyme levels or mood were excluded. In practice, this meant that
few patients with co-morbidities participated, so the sample was not representative of
ACS patients in general.
4.2.2 Summary
Despite these limitations, the present findings show that posttraumatic
symptoms are a problem for some patients who experience an ACS and that we can
begin to predict who is at increased risk for this condition. Although the experience of
posttraumatic stress in patients after major cardiac events is low in comparison with
traumatic events such as war, natural disasters or assault, it is nevertheless associated
117
with significant psychological disability and poorer quality of life. Elevated symptom
levels, regardless of whether they meet criteria for diagnosis, are related to distress
and poor functioning (Doerfler et al., 2005). The prevalence rates of 12.2% and 12.8%,
at 12 and 36 months suggest that symptoms may become chronic. Assessment of
posttraumatic stress symptoms may be helpful in identifying patients who experience
psychological distress. Appropriate treatment for these patients is important as they are
more likely to experience other psychosocial impairments as well as increased risk of
recurrent cardiac events.
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CHAPTER 5. Methodology TRACE study
5.1 Introduction and hypotheses
5.1.1 Acute post-ACS emotional responses and their relationship with short (2 week)
and long term (six months) posttraumatic stress reactions
Emotional reactions identified early after an acute cardiac event can be
predictive of long-term psychosocial adaptation. In my first study, described in chapters
3 and 4, the significance of early emotional reactions for persistence of longer term
posttraumatic stress symptoms was assessed. Approximately 12% of patients meet
diagnostic criteria for PTSD at both 12 and 36 months following admission for ACS,
suggesting a chronic course of symptoms. These analyses further supported the
importance of early emotional distress, in particular depressive symptoms experienced
in the immediate aftermath of the acute event (independent of clinical and demographic
variables), in the prediction of posttraumatic symptomatology up to three years later. In
addition, those patients who reported experiencing recurrent cardiac symptoms also
reported greater posttraumatic stress symptoms, suggesting that ‗internal reminders‘ of
patients‘ cardiac condition can contribute to the maintenance of posttraumatic
symptoms.
Building on the results reported in chapter 4, the first objective of this study was
to assess patients‘ acute emotional reactions to ACS and how these predict early (2
week) posttraumatic symptoms. Secondly, I aimed to establish the prevalence and
severity of ACS related posttraumatic stress symptoms at six months follow up. Thirdly,
I aimed to investigate the predictors of longer-term (six month) posttraumatic stress
symptoms. Based on previous work and the results presented in chapter 4 the
following hypotheses were addressed;
119
i)
Negative emotional state during admission, in particular negative
mood and acute distress in response to the acute cardiac event, will
be associated with greater posttraumatic stress symptomatology
shortly after hospital discharge.
ii)
Negative emotional state, in particular depressed mood, assessed
shortly after hospital discharge for ACS will be predictive of
posttraumatic stress symptoms at six months, independent of clinical
and demographic variables.
iii)
Patients‘ psychological disposition, in particular type D personality and
hostility, will also be predictive of posttraumatic stress independent of
clinical and demographic variables.
5.1.1.1 Introduction to illness representations
Although most patients ultimately have successful adjustment following an
acute cardiac event, the literature reviewed previously and the results presented in
chapter 4 demonstrate that a significant minority have persistent psychosocial distress
that can have negative consequences for physical and psychological well-being, reintegration into usual work and for social, leisure, sexual and domestic activities. There
is relatively little known about the mechanisms underlying emotional reactions to acute
medical trauma such as MI, with most studies in this area focusing on chronically ill
populations. In addition to the importance of early emotional reactions as a
consequence of the acute event in predicting later psychosocial adjustment, an
individuals‘ perception of their illness or condition could also have an effect. The
concept of illness perceptions could provide a useful theoretical framework for
exploring this process in ACS patients.
120
An individual‘s beliefs, thoughts, attributions, cognitive schemas and general
attitudes all provide meaning to life events and contribute to emotional arousal. Beliefs
structure meaning and affect emotion. Four cognitive or belief aspects have been
proposed to have a central role in the development of PTSD: (1) the appraisal of the
event that it is harmful; (2) general beliefs about personal vulnerability; (3) attempts to
assign meaning to the event; and (4) beliefs about the amount of individual control
(Parrot & Howes, 1991). One approach which may prove useful in understanding the
development and persistence of posttraumatic stress following an ACS focuses on the
patients‘ own model of their illness. Leventhal and colleagues (1980) proposed a model
of self-regulation in which individuals regulate both their emotional and behavioural
reactions to illness based on symptoms attributed to the illness (identity), beliefs about
what caused their illness (cause), their belief in the curability or controllability of the
illness (cure/control), the perceived consequences (consequences) and the expected
duration of the illness (time-line). The five components of the self-regulation model;
identity, cause, cure/control, consequences and time-line represent an individuals‘ own
understanding or perception of a situation. It is important to take these into account
when trying to understand health related outcomes, in particular because these often
differ greatly from the cognitive models of patients‘ health care professionals or from
medical fact. It has been shown that the illness perceptions held by the patient can
account for variations in emotional reactions to symptoms of physical disease.
5.1.1.2 Causal attributions and CHD
There have been a number of studies investigating causal attributions of CHD.
Causal attributions are the beliefs that people hold about the causes of their illness or
condition. A wide range of causal attributions have been identified in cardiac patients,
including beliefs about psychological causes (stress, overwork, etc), lifestyle (physical
inactivity, smoking) and hereditary factors (Perkins-Porras et al., 2006). There are
121
strong associations between traditional risk factors, such as smoking, hypertension,
obesity, and the causal attributions endorsed by CHD patients (Perkins-Porras et al.,
2006). For example, patients who are overweight are more likely to attribute the cause
of their cardiac problem to being overweight. Patients‘ attributions have important
implications for recovery. When someone experiences an acute cardiac event,
identifying causes may give them a greater sense of predictability and control, thus
aiding the coping process. Some studies suggest that illness perceptions are predictive
of behaviour change following MI. Weinman and colleagues (2000) demonstrated that
patients who rated poor health habits as the main cause of their MI were also more
likely to make changes to their diet than were those who rated stress or family history
as causal factors. In addition, causal attributions of poor health habits, by the spouses
of MI patients, were predictive of increase in exercise levels at 6 months in the patients.
However, in a more recent reanalysis of the data reported by Weinman and colleagues,
French et al (2005) showed that these attributions were no longer associated with
behaviour change when controlling for pre-MI behaviour. The reanalysis did
nevertheless indicate that spousal attributions may be important in predicting patient
behaviour change, in particular reductions in smoking. In the same paper, French et al
also describe an additional study in which causal belief measures again did not predict
behaviour change 6 months post the event.
Some studies highlight gender differences in CHD related causal attributions.
Aalto et al (2005) investigated illness perceptions and their correlates in CHD in a
sample of 3130 men and women. The results showed that men were more likely to
attribute their illness to internal and behavioural factors, whereas women more often
saw their illness as a result of stress. Women in this study also reported more
perceived CHD-related symptoms and more serious consequences of CHD disease
than did men. These gender differences may be related to the general observation that
women tend to report more psychological morbidity post MI than men (Brezinka &
Kittel, 1996). Although severity of cardiac disease tends not to be related to later
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emotional distress, such disease-related factors appear to exert influence on patients‘
perceptions. For example in the study by Aalto and colleagues, disease severity was
related to change in illness perceptions at one year follow up. In addition, stronger
psychosocial resources (e.g. perceived competence, social support) were related to
weaker illness identity, stronger belief in control/cure, and less severe perceived
consequences.
5.1.1.3 Illness representations and post-MI recovery
Much less research has been conducted focusing on illness perceptions and
their role in the successful recovery following MI. There are some data from studies
using the theoretical framework of illness perceptions. For example, Petrie and
colleagues (1996) reported that illness representations were better predictors of return
to work following MI than was severity of illness. In this sample of 143 first time MI
patients, return to work within six weeks was predicted by the perception that the
illness would last a short time and that it would have less grave consequences.
Rehabilitation attendance was strongly predicted by patients‘ stronger beliefs during
admission that the illness could be cured or controlled. Patients who anticipated
serious consequences [of their MI] were slower to regain social and domestic duties.
These authors argued that the early emergence of such coherent illness
representations suggests they may be largely formed by pre-MI information. In a
sample of post-MI women, MacInnes (2005) found that at 3 months post the acute
event, cardiac rehabilitation attendance was influenced by beliefs of a known cause
and a higher level of perceived control over the illness. In this study, women most
commonly endorsed stress as the causal attribution, and a belief in the illness being
inevitable was more common among older women. These perceptions may have
contributed to the lack of lifestyle changes reported in this sample. Cooper and
colleagues (2007) assessed patient‘s beliefs about cardiac rehabilitation in particular
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and demonstrated that those who attended were more likely to believe that cardiac
rehabilitation was necessary and to understand its role compared with non-attenders.
There was a non-significant trend suggesting that patients who expressed concerns
about exercise or who reported practical barriers to attendance were less likely to
attend. Earlier work has shown non-attenders to be more likely to hold misconceptions
regarding rest and avoiding exerting themselves (Wyer et al., 2001). These studies
demonstrating strong associations of illness representations and cardiac rehabilitation
uptake suggest that screening patients‘ perceptions, and intervening to modify beliefs,
at an early stage may be valuable in increasing participation in such programs.
Petrie at al (2002) demonstrated that a brief hospital intervention was
successful in changing post-MI patients‘ negative illness perceptions about their MI and
resulted in improved recovery and reduced disability at 3 months follow up. 65 patients
were randomized either to the intervention group or usual care (i.e. cardiac
rehabilitation nurse in-hospital visits and standard MI educational material). The
intervention consisted of three 30 – 40 minute sessions conducted by a psychologist in
hospital. Patients‘ understanding of the physiology of MI, their causal beliefs, timeline
and consequences were addressed during these sessions, as well as providing
explanations for symptoms that can be attributable to MI. These authors found that
before leaving hospital the intervention group had significantly modified their
perceptions about the duration of their illness, the personal consequences on their life,
and were more optimistic their illness could be cured or controlled than the control
group. These findings were maintained at the 3 month follow up assessment. Patients
in the intervention group also returned to work sooner than did the controls. There was
a non-significant trend of increased rehabilitation attendance for intervention compared
with control patients. These results support the usefulness of adopting the illness
representation model as a theoretical framework within which in-patient rehabilitation
efforts can be directed and evaluated. A recent study by Broadbent et al (2009a)
further supports these findings. In a sample of 103 MI patients, the group that received
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the three half-hour sessions and one half-hour session including the patient‘s spouse,
had a significantly faster rate of return to work at six month follow up than did those in
the control group (standard post-MI care). This study replicated the findings of Petrie et
al (2002) increasing the generalizability of the intervention to the current broader
definition of MI and to patients who also have experienced a previous MI.
Others have found that illness perceptions are predictive of complications
shortly after the acute cardiac event. Cherrington et al (2004) found that as illness
representations became more negative, the odds of experiencing a cardiac
complication in hospital increased by 1.05. In other words, for each unit increase in the
illness perception measure, the odds of a complication increased by 5%. In this study,
neither anxiety nor depression added significantly to the prediction of recurrent
complications whilst in hospital. However, the authors offer no explanation of the
possible physiological mechanisms underlying this relationship.
5.1.1.4 The relationship between illness representations and post-MI depression and
quality of life
Among CAD patients, illness beliefs have predominantly been investigated in
relation to their impact on health behaviours. The association with depression has
received little attention. Although numerous associations with disease related factors
and socio-demographic variables and depression have been established, the inability
to modify these variables to improve outcome is problematic. Therefore, the
identification of modifiable determinants of depression in CAD patients is important.
Patients‘ illness perceptions are also strongly related to global health status and quality
of life. In particular, patients who associate a greater number of symptoms with their
cardiac disease, perceive more severe consequences, and regard their illness as
uncontrollable report poorer health status and quality of life (Aalto et al., 2006).
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A recent study by Stafford and colleagues (2009) found an association
between illness representations and depressive symptomatology in CAD patients at
three and nine months following hospital admission. Stafford et al (2009) reported that
negative illness beliefs, in particular more severe perceived consequences of the
condition were predictive of higher levels of depression at three and nine months follow
up. However, the relationship between illness representations and depression at nine
months was no longer significant after the three month symptoms were controlled for,
suggesting that the association observed at nine months might entirely reflect a crosssectional relationship between illness representations and depression at three months.
In this study, positive illness beliefs were related to better quality of life at both time
points, as well predicting the change in mental quality of life between the two
assessment points.
Another study found negative illness beliefs following MI was associated with
new onset depression six or 12 months post the acute event (Dickens et al., 2008). In
particular, beliefs that their illness would last a long time were associated with a 2.7 –
fold increased risk of developing new-onset depression. Anticipation that the heart
condition could be controlled or cured was associated with half the incidence of
depression. The cognitive model of depression proposes that negative thoughts and
beliefs can predispose an individual to the development of depression (Kovacs & Beck,
1978). If in fact negative health beliefs do contribute to the development of depression
after MI, they may be amenable to psychological interventions, which in turn could
reduce depression incidence, and therefore, hold potential to improve medical
outcomes.
Women tend to experience greater depressive symptomatology following ACS
than do men (Grace et al., 2005; Frasure-Smith et al., 1999). One way to address this
gender difference in psychosocial adjustment may be through the examination of
illness belief held and their relationship with depression. Grace et al (2005) found that
women, compared with men, were more likely to attribute their cardiac condition to
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causes beyond their control and to perceive CVD as a chronic, untreatable condition.
Depressive symptomatology was associated with younger age, lower activity status
and perceiving a chronic time course among women. For men, being non-white,
reporting lower activity status, less exercise behaviour, perceiving a chronic course,
greater consequences and lower treatability was associated with greater depressive
symptomatology. In this study there was no significant difference in depressive
symptoms between men and women. The explanation for this finding is unclear. Men
were more likely to view their illness as within their personal control, and to view
treatment as effective in controlling or curing their condition. This may have important
ramifications for depressive symptomatology as previous research suggests that
helplessness and external locus of control are associated with depression (Moser &
Dracup, 1995).
Murphy et al (1999) found that depression in the chronic condition rheumatoid
arthritis was most strongly correlated with the negative illness perception components
consequences, suggesting patients view their condition as serious, and control/cure,
which suggest a belief in limited control over their illness. These effects are
independent of disease severity. The findings that patients control beliefs are
associated with depressed mood are in line with other studies measuring health locus
of control, suggesting that perceived control may be conducive to psychological well
being.
Regaining a sense of control has been postulated to be a core process in the
adjustment following an MI. Moser and Dracup (1995) found that when controlling for
sociodemographic and clinical variables, only perceived control contributed significantly
to the prediction of differences in psychosocial recovery. Patients with perceptions of
high control at baseline had significantly lower anxiety, depression, hostility and
psychosocial adjustment (total score) to illness at 6 month follow up, compared with
those with low control. It is important to note that it is the perception of control that has
been associated with positive outcomes, control need not be exercised nor real to be
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effective (Litt, 1988). The findings presented in this section further support the
designing and testing of brief interventions to optimize psychological well-being
following acute cardiac events.
5.1.1.5 Posttraumatic stress and illness representations
Illness perceptions are not rigid and may very well change over time as
patients‘ knowledge and experience of their illness changes. As patients recover,
changes occur in physical ability, affect, social environment and comprehension of their
illness, therefore it can be expected that illness perceptions will change accordingly.
One recent study investigated the association between patients‘ illness perceptions
and posttraumatic stress symptoms following MI (Sheldrick et al., 2006). These authors
found that PTSD levels varied over time. Posttraumatic symptoms were assessed in MI
patients within 2 weeks of admission, then between 5 – 7 weeks, and finally 11 –14
weeks post admission. There was a non-significant increase in symptoms between
baseline and the second assessment. However, symptoms significantly decreased
between the second and third follow up. Increased emotional representations,
decreased illness coherence, increased consequences and decreased treatment
control beliefs measured at 5 to 7 weeks were predictive of posttraumatic stress at 11
to 14 weeks. The multiple regression model explained 62% of variance. The
component of emotional representations was the strongest predictor of posttraumatic
symptoms, accounting for approximately 47% of the total variance. These results
suggest that the experience of PTSD following MI may be mediated by patients‘
emotional responses to the trauma, their confidence in their understanding of the
illness, their perception of its impact upon their lives and their confidence in subsequent
medical treatment. These authors argue that through appropriate information provision
it may be possible to reduce some of the distress experienced by MI patients. Through
appropriate information particular illness beliefs could be targeted, such as
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encouraging and increasing patients‘ confidence in their knowledge and understanding
of their condition, generating realistic expectations of the consequences and reassuring
patients of the efficacy of treatment.
I aimed to assess patients‘ illness beliefs regarding their cardiac condition and
how these relate to levels of posttraumatic stress at six months post ACS. The
following hypothesis was tested;
iv)
Patients who hold more negative illness representations at time 2 will
also report greater posttraumatic stress symptoms at six month follow
up. In particular, beliefs that the condition will have more serious
consequences, a lack of understanding of the condition, experiencing
more negative emotional representations and lower control/cure
beliefs at 2 weeks post admission for ACS will predict posttraumatic
symptom severity at follow up.
5.1.2 Biological determinants of early emotional responses to ACS
5.1.2.1 Cortisol
Cortisol is a glucocorticoid hormone produced in the adrenal cortex both
spontaneously and in response to stressors. Cortisol can be measured in blood, urine
and saliva. Measurement of cortisol is an important tool in assessing HPA axis
function. Cortisol shows a natural diurnal pattern in healthy adults, which peaks at
approximately 20 – 45 minutes after waking, and subsequently decreases through the
day, reaching the lowest levels at night and in the early hours of the morning before
rising again. The sharp increase in cortisol levels post awakening is referred to as the
cortisol awakening response (CAR). Exaggerated increases in cortisol after waking has
been associated with chronic stress (Chida & Steptoe, 2009). Further, studies have
129
found a greater CAR to be related to depressive symptoms and reduced positive affect
(Pruessner et al., 2003; Steptoe et al., 2007). The decline in cortisol levels following the
CAR is referred to as the slope of decline, and is calculated as the difference between
awakening levels and evening [bedtime] values. Total cortisol output over the day is
measured as the area under the curve (AUC).
A growing body of evidence suggest an association between HPA axis activity
and coronary atherosclerosis. A prospective association between cortisol and future
CHD has been documented in middle aged men (Smith et al., 2005), while acute
cortisol elevation after ACS predicts adverse cardiac outcomes (Bain et al., 1989;
Tenerz et al., 2003). Positive correlations between morning plasma cortisol levels and
the degree of coronary artery disease (CAD) have been demonstrated (Troxler et al.,
1977; Koertge et al., 2002; (Alevizaki et al., 2007). Though, others have failed to find a
relationship between morning levels and number of diseased coronary vessels (e.g.
Whitehead et al., 2007). Otte et al (2004) reported significantly increased total output
levels of cortisol in patients with stable CAD compared with age and gender matched
controls. One limitation of these studies is that neither single measures of morning
cortisol, nor collection of 24-hour urinary cortisol, provide information on cortisol
reactivity or daily profiles. In fact, these measures are unable to assess the diurnal
slope. This is important as dysfunction of the HPA axis can take the form of a smaller
decline in levels across the day, that is, a flatter slope. Although it is still unclear what
the determinants or consequences of having a flatter cortisol profile are, it is generally
considered a result of long-term HPA overstimulation (Nijm & Jonasson, 2009).
Findings from a population based cross-sectional study, in which six salivary cortisol
samples were collected over the course of one day, from awakening to bedtime,
demonstrated an increased likelihood of any coronary calcification the flatter the slope
throughout the day (Rosmond et al., 2003).
Some reports also demonstrate associations between HPA dysfunction and
clusters of recognised CAD risk factors. For example, a study by Rosmond and
130
Bjorntorp (2000) showed strong associations between low diurnal cortisol variability
and a poor lunch-induced cortisol response and a cluster of established risk factors.
Other studies suggest a relationship between subtle alterations in cortisol secretion and
separate CAD risk factors such as smoking, abdominal adiposity, and hypertension
(Gluck et al., 2004; Duclos et al., 2005; Rohleder & Kirschbaum, 2006; Wirtz et al.,
2007). Overall, these studies suggest a role of HPA axis dysfunction in inflammatory
disease activity. A flatter diurnal cortisol profile has been found in both preclinical and
clinical CAD samples and the dysregulated cortisol secretion in CAD patients appear to
be associated with a systemic inflammatory activity. HPA axis dysfunction may,
thereby, have implications for CAD progress.
A flatter slope across the day has also been found to be associated with
depression (Weber et al., 2000). Depressed mood is associated with a blunting of the
normal reduction in cortisol levels over the course of the evening (Kirschbaum &
Hellhamner, 1989a). Whereas some findings suggest a relationship between
depression and elevated values in the morning (e.g. Yehuda et al., 1996), others report
strong associations of evening levels with depression (Gold et al., 1988). There is a
lack of evidence directly linking depression and cortisol in patients with CAD. HPA axis
dysfunction, particularly hypercortisolaemia, may play a role in the pathogenesis and
progression of CAD through their association with established physiological risk factors
such as hypertension, hyperlipidaemia, and insuline resistance (Girod & Brotman,
2004). A study by von Kanel and colleagues (2007) showed independent associations
of elevated morning cortisol levels and prothrombotic activity in a sample of women,
however no relationship with mood was observed.
A
relationship
between
psychological
factors
and
cortisol
has
been
demonstrated by Whitehead and colleagues (2007) in sample of 72 ACS patients.
Salivary cortisol was assessed over a 24 hour period within five days of admission,
while patients still remained in the cardiac ward. Cortisol was not associated with
severity of ACS or underlying CAD, nor with depression scores (BDI). However, the
131
CAR was positively associated with type-D personality independently of age, and BMI.
Molloy et al (2008) assessed the association of cortisol and type-D personality in same
sample of patients 4 months post ACS. These authors observed a typical diurnal
pattern of cortisol, with low levels in the evening and high levels early in the day.
However, type-D was not associated with CAR, but a higher total cortisol output was
higher in type-D compared with non type-D ACS patients. These results contrast those
reported from earlier analyses of these data (Whithead et al., 2007). However, in the
earlier report, cortisol was assessed in-hospital, where numerous factors may have
influenced the results. For example, patients were in an unfamiliar environment, where
sleep was likely to be disrupted, and wake up times tend to be early. There is evidence
that basal cortisol levels are increased in hospital settings (Scheer et al., 2002), which
might partly explain the differing pattern of results in the study by Molloy and
colleagues (2008). This study also showed that concurrent depressed mood as
measured by the BDI was not associated with cortisol. This finding is in contrast to
previous work, which has demonstrated links between depression and 24-hour urinary
cortisol in patients with stable CAD (Otte et al., 2004). Bhattacharyya et al (2008)
observed a relationship between cortisol levels and depression in CHD patients. These
authors found a flatter cortisol slope over the day in more depressed patients with
CHD, but no relationship was observed between cortisol slope and depression in
patients without CHD.
There is accumulating evidence linking cortisol to the pathophysiological
processes contributing the cardiovascular disease (Girod & Brotman, 2004; Brotman et
al., 2007). Heightened cortisol output is partly responsible for vascular endothelial
dysfunction in depressed individuals (Broadley et al., 2005), and this effect is reversed
by inhibition of cortisol (Broadley et al., 2006). These results suggest that endothelial
dysfunction may play a role in the increased CHD risk associated with depression.
There is emerging evidence for a distinct pathophysiology of PTSD. The notion
that negative emotional states such as depression and PTSD may confer an
132
atherogenic risk suggests involvement of behavioural factors (e.g. smoking, nonadherence to medications) as well as biological mechanisms (e.g. inflammation,
sympathetic overactivity, and endocrine dysfunction) in underpinning pathways (see
chapter 2, section 2.9). However, teasing apart the unique contributions of these
intertwined variables remains a challenge. Whereas the literature on depression and
cortisol tends to suggest hypercortisolaemia, with a blunted diurnal profile, results from
studies of PTSD and cortisol are more varied. Clearly, more work needs to be
undertaken to address these difference in results, and to further our understanding of
the role of cortisol in the development of posttraumatic stress.
Cortisol in relation to posttraumatic stress in a sample of MI patients was
investigated in a recent study by von Känel and colleagues (in press). In bi-variate
correlation analyses, no significant associations were observed between PTSD or
posttraumatic symptoms and cortisol levels. However, patients with PTSD had
significantly lower mean cortisol levels than patients without PTSD when controlling for
depressive symptoms. These results suggest that depression may disguise
associations between posttraumatic symptoms and cortisol profiles.
For the TRACE study, cortisol was assessed in saliva. Salivary cortisol has
proven to be an accurate reflection of plasma cortisol, and this type of salivary
sampling is a non-invasive and convenient method for assessing diurnal patterns in a
naturalistic ambulatory setting (Kirschbaum & Hellhamner, 1989b). However it is
important to note that responses can be affected by e.g. gender, smoking, use of oral
contraceptive and a range of other factors. For example, some studies show trait
negative affect is associated with a higher overall total cortisol concentration and a
greater morning rise in men, even after controlling for wakening levels (Polk et al.,
2005). This collection method relies on participants themselves taking the samples,
and reporting the time of assessment, resulting in a significant loss of control available
in the laboratory setting in order to gain ecological validity.
133
Mood state in the immediate aftermath of admission for ACS is an important
indicator of future quality of life, morbidity and mortality. One aim of the TRACE study
was to assess the relationship between psychological status and HPA-axis function by
assessing salivary cortisol over the course of one single day in ACS patients shortly
after discharge and again at 12 months. There are relatively few studies investigating
the cortisol profiles of patients in the immediate aftermath of ACS. The analyses
presented in chapter 6 were undertaken to investigate the biological underpinnings of
early distress and mood in patients two weeks following admission for ACS. Based on
previous literature I hypothesized;
v)
Patients who report greater posttraumatic stress symptoms will show
evidence of cortisol dysregulation following hospital discharge.
5.1.2.2 Heart rate variability
Heart rate variability (HRV) refers to the beat-to-beat (or R-R) alterations in
heart rate. HRV is a useful non-invasive tool for the assessment of cardiac autonomic
control. Resting heart rate (HR) is one index of autonomic imbalance (Levy et al., 1990)
and a large positive dose response relationship between resting HR and all cause
mortality has been observed previously (Habib, 1997). In particular, elevated HR has
been shown to predict future CHD, independent of other established CHD risk factors
(Kannel et al., 1987).
One particularly salient reason for the increasing interest in the measurement of
HRV stems from its ability to predict survival after heart attacks. Numerous prospective
studies have demonstrated that reduced HRV predicts sudden death in patients with
MI, independent of other prognostic indicators such as left ventricular ejection fraction
134
(LVEF1) (e.g. Cripps et al., 1991; Bigger et al., 1993; Quintana et al., 1997). Moreover,
findings from population studies suggest that decreased HRV has predictive value for
mortality also among healthy adults, and is a well established risk factor for arrhythmic
events, cardiovascular disease, and sudden cardiac death (Simpson & Wicks, 1988).
Several studies also suggest a link between negative emotions (such as
anxiety, depression and hostility) and reduced HRV. Kawachi et al (1995) reported a
cross-sectional association between anxiety and reduced HRV in 581 men. Offerhaus
(1980) observed lower HRV in individuals who were ‗highly anxious‘. Yeragani et al.
(e.g., 1991; 1993) have published a series of reports indicating reduced HRV (using
both time domain and spectral measures) among DSM-III diagnosed panic disorder
and major depression patients. In turn, at least three prospective epidemiologic studies
(Haines et al, 1987; Kawachi et al., 1994a; Kawachi et al., 1994b), and one casecrossover study (Mittleman et al., 1995) have suggested a relationship between high
levels of anxiety and risk of CHD. Sloan et al (1994) reported reduced high-frequency
power among 33 healthy volunteers who scored high on the Cooke-Medley Hostility
scale. Carney et al (2001) demonstrated that depressed mood is associated with
reduced HRV in patients following MI. The association between negative affect and
reduced HRV may thus provide a potential mechanism linking chronic stress to disease
outcomes (e.g., risk of CHD).
There are three distinct rhythms identified within the beat-to-beat modulation of
the heart; a high, low, and a very low frequency component (Task force, 1996).
* High Frequency band (HF): range between 0.15 and 0.4 Hz. HF is driven by
respiration and appears to derive mainly from vagal (parasympathetic) tone.
1
Ejection fraction refers to the fraction of blood pumped out of the ventricle with each heart beat. The ejection fraction is
one of the most important predictors of prognosis; those with significantly reduced ejection fractions typically have
poorer prognoses. Ejection fractions typically range between 50% and 65% in healthy individuals, lower levels indicate
ventricular dysfunction.
135
* Low Frequency band (LF): between 0.04 and 0.15 Hz. LF is mediated by both
parasympathetic and sympathetic activity.
* Very Low Frequency band (VLF): band between 0.0033 and 0.04 Hz. The
origin of VLF is not well known, but it had been attributed to thermal regulation of the
body's internal systems.
The ratio between HF and LF can be measured, and the higher the ratio, the
greater the vagal tone. LF and HF are strongly related and it is postulated that LF
power reflects substantial parasympathetic influence (Wang et al., 2005). Originally,
HRV was assessed manually from calculation of the mean R-R interval and its
standard deviation measured on short-term (e.g., 5 minute) electrocardiograms. The
smaller the standard deviation in R-R intervals, the lower is the HRV. There are many
different types of arithmetic manipulations of R-R intervals, and time domain measures
include standard deviation of normal mean R-R interval obtained over 24 hour holter
recordings (called SDNN index) and the root-mean square of the difference of
successive R-R intervals (the RMSSD index). The various methods of expressing HRV
are potentially equivalent, with no evidence that one method is superior to another,
providing measurement windows are 5 minutes or longer (Bigger et al., 1992a, 1992b).
The measurement of HRV has been standardised (Task Force, 1996).
A number of studies have found post trauma assessed heart rate to predict
subsequent development of PTSD (see chapter 2, section 2.9.2). One explanation
suggests that the development of PTSD may be facilitated by an atypical biological
response (such as heart rate) in the immediate aftermath of a traumatic event, which in
turn leads to a maladaptive psychological state. In this study, patients‘ heart rate and
HRV were assessed at the time 2 home interview. Based on previous literature I
hypothesized that;
136
vi)
HRV at 2 weeks post ACS will be reduced in those patients exhibiting
greater posttraumatic stress responses to the acute cardiac event.
5.1.3 The relationship between posttraumatic stress responses and post ACS
adaptation
Chronic conditions, such as CHD, often involve complicated treatment regimens
or medications and lifestyle adjustments. One of the challenges these patients may
encounter is finding a balance between the demands and restrictions posed by their
cardiac condition on the one hand and the various challenges and demands of
everyday life on the other. As survival rates following ACS have increased, more and
more patients live with the consequences of such conditions. Post-ACS patients are
encouraged to facilitate their recovery by engaging in diverse lifestyle modifications
such as smoking cessation, dietary change, weight loss and increased exercise.
Improvements of health behaviours are associated with improved prospects for
recovery following ACS (Daubenmier et al., 2007; Pischke et al., 2008). One aspect of
recovery involves cardiac rehabilitation. Participation in comprehensive cardiac
rehabilitation programmes reduces cardiac mortality by 26% (Jolliffe et al., 2004), all
cause mortality by 13% and non-fatal MI by 38% (Clark et al., 2005). As reviewed in
chapter 2, past research has demonstrated that ACS related posttraumatic stress is
associated with a range of adverse consequences such as increased smoking and
alcohol intake, poor adherence to medication regimens and reduced participation in
rehabilitation programmes, lower social support and reduced quality of life. In addition,
many of these outcomes have also been identified as independent risk factors for
cardiac prognosis. The aim of this section is to establish post ACS adjustment and the
influence of posttraumatic stress symptomatology on recovery behaviours. Based on
past research I hypothesized that;
137
vii)
Higher levels of posttraumatic symptoms reported 2 weeks following
discharge from hospital will have a negative influence on health
behaviours and quality of life at six months.
5.1.4 Influence of partner distress on patient posttraumatic stress reactions
Many of the traumatic aspects of an ACS are not experienced by the patient
alone. Family members and friends may feel great concern and even intense anxiety at
the possibility of losing a significant other. For those who are responsible for the care
and wellbeing of the patient (often a spouse), the anxiety experienced might even
reach the traumatic levels associated with PTSD. Previous research has found that
partners of ACS patients experience substantial distress, and examination of affect in
cardiac couples have revealed that partners tend to be more anxious, distressed and
depressed than patients (Bennett & Connell, 1999; Conway et al., 2008; Mayou et al.,
1978; Moore, 1994; Moser & Dracup., 2004; Rose et al., 1996). This difference has
been found to persist beyond hospitalisation and may also impact upon the patient‘s
level of depression and experience suggesting emotional contagion effects (Conway et
al., 2008, Moser & Dracup, 2004). High anxiety and depression in partners may
increase anxiety and depression experienced by the patient, and may negatively
influence provision of social support from the partner to the patient, thereby imposing
greater emotional demands upon the patient. Most research of couple distress
following cardiac events has focused on a female partner, and findings suggest the
increased distress observed among partners may be an artefact of gender. Though,
there is some limited evidence suggesting that these effects may be independent of
gender (Moser & Dracup, 2004). Based on this literature I hypothesized that;
138
viii)
Partners of the ACS patients will show high levels of distress, in
particular anxiety, depression and posttraumatic stress symptoms and
this will in turn negatively influence patients‘ emotional recovery.
5.2 Study design
The TRACE study is a prospective longitudinal study incorporating four
assessment time points conducted over the course of one year following admission for
ACS (fig. 5.1). Time 1 assessment was conducted in-hospital within two days of
admission, Time 2 interviews were conducted approximately 10 to 12 days post
discharge, Time 3 follow-up assessment was conducted at six months post admission
and Time 4 follow-up was completed 12 months following the initial hospital admission.
For the purpose of this thesis, only data from time 1, 2 and 3 are included.
FIGURE 5.1 TRACE STUDY DESIGN
139
5.2.1 My role in study design, data collection and analysis
The TRACE study was undertaken by a team of researchers. In collaboration
with these colleagues, I was actively involved in the study design, including
questionnaire selection and development of the procedure. My main responsibilities
during the data collection phase involved the arranging and undertaking of the home
assessments of patients (interviewing and collection of biological measures), as well as
the 12-month follow up interviewing of patients (telephone interviewing and postal
questionnaire follow up). I undertook a minority of interviews at six month follow up. In
addition, I was responsible for data entry for all patient data from time 2, 3 and 4. The
statistical analyses included in this thesis were undertaken by myself, with additional
guidance from my thesis supervisor.
5.3 Participants
Patients were recruited from one south London hospital following admission for
ACS. Presence of chest pain and verification by electrocardiographic changes, and/or
elevated cardiac enzymes (for details see section 3.2), were required for a diagnosis of
ACS. Patients were eligible for participation if they met the following criteria; admitted
for ACS, aged 18 years or over, able to complete the in-hospital interview and
questionnaire measures in English. Patients with co-morbid conditions, which could
influence either symptom presentation or mood state (such as severe psychiatric
illness, unexplained anaemia, ongoing infection or inflammatory conditions, neoplasia
and renal failure), and conditions that might cause false troponin positivity, were
excluded. In addition, patients who were too unwell or clinically unstable (e.g. those
experiencing
on-going
chest
pain
and
critical
ischaemia
or
ventricular
tachyarrhythmias) were also excluded.
140
Hospital data collection began in June 2007 and was completed in October
2008. 693 potentially eligible patients were admitted on the days recruitment was
conducted during this period. Of these, 125 patients (18%) had been discharged or
transferred on to a different hospital before the researchers could conduct the inhospital interview, 58 patients (8.4%) declined to participate in the study, and 7 patients
(1%) died in hospital. A further 205 patients were excluded for various reasons (see
table 5.1).
Of the 298 patients who completed the time 1 interview, 222 participated at time
2 (74.5%), however, an additional 4 patients completed a postal version of the
interview making the total sample 226 (75.8%) at time 2. The reasons for attrition at
time 2 are outlined in table 5.1 below.
Six-month follow up was completed in March 2009. A total of 200 (67%)
patients were re-contacted at time 3. At this follow up point, 3 patients were deceased
(1%), 32 patients had withdrawn since recruitment (10.7%) and a further 63 patients
(21.1%) were un-contactable. 12 month follow up of the sample is ongoing and will be
completed by October 2009, therefore data from this assessment point are unavailable
for use in this thesis.
141
TABLE 5.1 REASONS FOR EXCLUSIONS AND REFUSALS
n
Target population
%
693
Deceased before inclusion
7
1
Discharged or transferred
125
18
Declined to participate
58
8.4
Patients cardiac event not ACS
3
0.4
Recruitment break (Christmas)
24
3.5
Adverse situation
1
0.1
Patient too unwell/unable to communicate
90
13
Not able to speak English
27
4
Serious psychiatric problem
10
1.4
Confused
23
3.3
Cardiac event occurred as in-patient
13
1.9
Patient in isolation
5
0.7
Patient did not live within recruitment area
9
1.3
Exclusions
Total patients time 1
298
Failed to contact
21
7
Health reason (re-admissions, patient too unwell, memory problems)
12
4
Declined to participate in interview
40
13.4
Deceased
3
1
222 (226*)
74.5 (75.8)
Total patients time 2 interview
Follow up assessment
Time 3
200
*4 additional patients did not complete the time 2 home interview; however, they did complete a postal version instead
(3 of these patients belonged to the ‘failed to contact’ interview, 1 patient declined a home visit, preferring a postal
version).
5.4 Procedure
5.4.1 Time 1 assessment
Patients were recruited from the Coronary Care Unit at St. George‘s Hospital,
London, based on the inclusion/exclusion criteria outline in section 5.3 above. Patients
were approached by a member of the research team as soon as possible after
admission for ACS, at which point the study was explained fully and a detailed
142
information sheet was provided (Appendix III). Patients who opted to participate were
asked to complete a consent form (Appendix IV), which was collected by the
researcher at the time of the interview. A blood sample was drawn within 24 hours for
the assessment of CRP, neutrophil counts, IL-6 and IL-10. The in-hospital interviews
focused primarily on the circumstances surrounding symptom onset and hospital
admission, patients‘ acute fear response and distress during ACS as well as quality of
life prior to admission was also obtained. Current mood state was also recorded at this
time. A second blood sample was taken at this point in time. Information concerning
indicators such as history of heart failure and arrhythmia on admission was obtained
from patients clinical notes. Clinical risk was calculated using the composite measure
developed in the Global Registry of Acute Coronary Events (GRACE) trial (see chapter
3, section 3.4.2) and the Simple Risk Index (SRI; Morrow et al., 2001). The
management strategy for the patient (medical, revascularisation etc.) was recorded,
and angiography results obtained when available.
5.4.2 Time 2 assessment
Following discharge, patients were contacted by telephone by one of the
researchers to arrange a suitable time for the second phase of the study (the home
interview). During this phone call patients‘ spouse/partner was also invited to
participate in the study. Time 2 home interviews took place on average 21 days (range
8 – 51) following the original admission date for ACS, and were conducted by a team of
two researchers. During the home interview patients completed a number of
psychosocial measures and a structured clinical interview (DISH) was completed to
assess depression and psychiatric history. In addition, samples of salivary cortisol were
collected at four points during the interview; at the start of the interview, prior to the
DISH interview, immediately following the DISH, and a final sample at the end of the
interview. Patients‘ heart rate and heart rate variability was also monitored throughout
143
using an ambulatory device (Actiheart). Participating spouses completed a number of
psychosocial measures. The patient and spouse interviews were conducted in
separate rooms when possible. At the end of the interview patients were given a set of
questionnaires for self-completion to be returned via freepost to the research team.
Patients and participating spouses were also asked to complete a saliva sampling task
over the course of one single day following the interview date [not on the same day],
and to return these with the completed self-report questionnaires.
5.4.3 Time 3 follow up assessment
At six (time 3) months following ACS, patients were re-contacted by telephone.
Time 3 interviews took place on average 193 days (range 137 – 281) following the
original admission date for ACS. The follow-up calls included a semi-structured
interview assessing recurrence of symptoms, other health problems, adherence to
medications and cardiac rehabilitation, health behaviours and return to work.
Participants and participating spouses were also mailed a battery of self-completion
standardized questionnaires, to return by post.
5.5 Measures
This section describes the measures obtained at each assessment point. For
an overview of the time at which data were obtained and measures administered
please refer to table 5.2 at the end of this chapter. This table also displays which
measures were administered to partners and at which time point. A selection of these
was used for the purpose of this thesis and only these measures are described in
detail. Some of the questionnaires used in this study were administered as interviews
to facilitate completion and to enhance data collection. All interview materials and
questionnaires used are listed in Appendices V - VII. A variety of measures of
144
emotional state, well-being and psychological traits were utilised. Questionnaires
included in this study have been widely used with cardiac samples previously. Several
of these scales have been introduced in chapter 3, section 3.4.3. Those not previously
discussed in this thesis are presented in the following section. Table 5.3 show the
Cronbach‘s alpha for each of the measures used.
5.6 Measures – Time 1
5.6.1 Socio-demographic information
Socio-demographic information including age, marital status, ethnicity,
employment status at admission, educational qualifications and income were obtained
at the time 1 interview. Patients were categorised as ‗low‘, ‗medium‘ or ‗high‘ on a
social deprivation index (described fully in section 3.4.1). The level of reported
education included no educational qualifications, up to school certificate, CSE‘s,
GCSE‘s, A level, Degree and Other. For the purpose of statistical analyses, these were
reclassified into a four level variable; ‗none‘, ‗basic‘, ‗secondary‘ and ‗degree‘. Socioeconomic status (SES) was derived from patients‘ income and educational
qualifications. Educational attainment is an indicator of socio-economic position that is
easily measured, applicable to people not in the active labour force as well as those in
stable employment over time.
5.6.2 Clinical data
Clinical information was obtained from the hospital admission records. Clinical
variables of interest included admission ECGs and troponin T or creatine kinase levels
for review by a cardiologist in order to classify patients as presenting with ST-elevation
myocardial infarction (STEMI), non ST- elevation myocardial infarction (NSTEMI) or
145
unstable angina (UA). This information was subsequently categorised as a binary
variable (STEMI vs NSTEMI/UA). Clinical risk indices used included the GRACE index
(Eagle et al., 2004: see section 3.4.2) and the Simple Risk Index (SRI). The SRI uses
age, heart rate and systolic blood pressure (SBP) for predicting mortality over 30 –
days [(heart rate x [age/10]²)/SBP]. This risk index is a robust predictor of very early
events including death by 24h (Morrow et al., 2001). Subjective pain experienced at
symptom onset was recorded at the in-hospital interview. Pain was rated on a 10-point
Likert scale ranging from 0 (no pain) to 10 (worst pain ever).
5.6.3 Psychosocial measures
In addition to the psychosocial measures described in this section patients selfreported acute stress (fear, helplessness and horror) experienced at time of ACS (see
section 3.4.3.7 for a description of this measure).
5.6.3.1 Profile of Mood States (POMS)
The POMS was developed by McNair and colleagues (1981) to identify and
assess transient, fluctuating affective mood states. This measure has been widely used
in medical patients, athletes, and normal populations. The POMS has 65 items that
measures six identifiable moods or feelings: Tension-Anxiety (T), Depression-Dejection
(D), Anger-Hostility (A), Vigor-Activity (V), Fatigue-Inertia (F), and ConfusionBewilderment (C). Items include key words such as unhappy, tense, careless, and
cheerful. For each statement, participants state how they feel at that moment, or how
they felt over the previous day, few days, or week, by choosing one of the following
responses: not at all (0); a little (1); moderately (2); quite a lot (3); extremely (4). In our
study participants were asked to rate their mood on each item ‗at the moment‘.
146
Numerous research studies have provided evidence for the predictive and
construct validity of the POMS, finding it to exhibit a highly satisfactory level of internal
consistency, and reasonable test-retest reliability. Factor analytic replications provide
evidence for the validity of a 6 – factor model, and examination of individual items
defining each mood state support the content validity of the factor scores. In this study,
a shortened version of the POMS was used, with the 6 highest loading items
contributing to each mood factor. This shortened version has been used in many other
studies in order to reduce respondent burden. The internal consistency of the 6
subscales ranged from .72 to .91 in the present study. Six additional somatic symptoms
are included in the questionnaire as filler items.
5.6.3.2 Medical Outcome Short Form 12 (SF-12)
The SF-12 measures perceived health related quality of life. This scale consists
of 12 items drawn from the SF-36, a product of the US Medical Outcomes Study (Ware
et al., 1996). The SF-12 reproduces the eight-scale profile of the SF-36 (see section
3.4.3.4 for a description), the scores for each scale are coded, summarized and
transformed into a scale ranging from 0 (worse possible health) to 100 (best possible
health), and as with the SF-36, total scores on two summary components are
calculated. The SF-12 physical and mental health summary measures are referred to
as PCS-12 and MCS-12, respectively.
The brevity and the simple design of the SF-12 is a benefit over the SF-36, and
it appears to be a robust and efficient alternative to the SF-36 for the assessment of
health related quality of life of patients with coronary heart disease (e.g. (Melville et al.,
2003) (Muller-Nordhorn et al., 2004).
147
5.7 Measures – Time 2
5.7.1 Psychosocial measures
In addition to the measures described in this section, the following were also
administered at time 2: Beck Depression Inventory (BDI: see section 3.4.3.1);
Posttraumatic Stress Symptoms – Self Report Scale (PSS-SR: see section 3.4.3.2);
Hospital Anxiety and Depression Scale (HADS-A: see section 3.4.3.3); Cook and
Medley Hostility Scale (Ho: see section 3.4.3.5) and; type D personality (see section
3.4.3.6). For a full description of these scales please refer to the relevant sections in
chapter 3.
5.7.1.1 DISH
The DSM-IV Depression Interview and Structured Hamilton (DISH; Freedland et
al., 2002) is a semi-structured psycho-diagnostic interview that is used to diagnose
current major and minor depressive episodes and dysthymia in medically ill persons
according to DSM-IV criteria. It also assesses the past history and longitudinal course
of depressive disorders, including partial and full remissions, relapses and recurrences.
It determines the 17-item Hamilton Rating Scale for Depression severity score for the
past week, and screen for other neuropsychiatric disorders. This measure was
developed for use in the ENRICHD clinical trial, in which participants‘ eligibility required
a unipolar depressive disorder and/or low social support. The DISH was specifically
developed for use among post-MI patients (and other medically ill patients). Enrolment
for ENRICHD took place shortly after the acute event, in order to reduce the risk of
reinfarction or death, therefore the measure was designed to minimize respondent
burden, without sacrificing the thoroughness or accuracy of the interview. This is a
suitable measure for use by well-trained research interviewers and health clinicians.
148
Symptoms of depression are scored and coded as absent, sub-threshold,
present or present but due entirely to direct physiological effects of medical illness or its
treatment. Symptom duration in weeks (or days if present less than two weeks) is
specified in an adjacent column. The interviewer uses mandatory and optional probes
to determine the presence or absence of DSM-IV depressive disorder. From this
interview a total depression score is generated (Hamilton), as well as frequency of
current major or minor depression lasting 1-2 weeks, or lasting 2 weeks or longer.
Frequency of a history of depression was also calculated. This instrument is efficient in
yielding both a DSM-IV diagnosis and a 17-item Hamilton score, and correlates highly
with other established diagnostic instruments such as the BDI (Freedland et al., 2002).
5.7.1.2 Social Network
Small social networks have been associated with increased risk of coronary
heart disease and mortality (Ruthledge et al., 2004). More to the point, smaller
networks are predictive of poor outcomes following ACS (Lett et al., 2005). In this study
patients social network was assessed using the Social Network Index (Cohen et al.,
1997). This scale measures 12 sets of contacts (e.g. children, friends, work colleagues
etc.) and works as an index of the diversity of social interactions. A score of 1 is
assigned for each type of relationship (range 0 – 12) for which respondents indicate
that they speak to (in person or on the phone) at least once every two weeks (items
scored from never to everyday). Greater values on this measure represent more
diverse social networks.
5.7.1.3 Social Support
Not only the number of social interactions is important for health, but also the
quality of social support has been identified as a particularly important factor
149
(Greenwood, 1996). The ENRICHD social support inventory (ENRICHD Writing
Committee, 2003) was used in this study to assess the quality of social support
available. This measure was selected for use because it was specifically developed for
use among cardiac patients, and has been found to be a reliable and valid measure of
support in this population (Vaglio et al., 2004) and is a useful short measure with good
internal consistency.
This measure includes six questions concerning the amount of instrumental and
emotional support available to the participant (e.g. ‗Is there someone available to whom
you can count on to listen to you when you need to talk?‘; ‗Is there someone available
to help with daily chores?‘) and one final question assessing marital status (yes = 1, no
= 0). Each question is rated on a five-point scale, ranging from none of the time (1) to
all the time (5). Totalled scores gave an overall social support value between 1 and 30,
with higher values showing increased levels of social support.
Three additional
questions were incorporated in this measure, to assess support for health behaviours
(medication adherence, diet and exercise). These were scored in the same way as the
first six items, described above.
5.7.1.4 Causal Beliefs
Patients‘ beliefs concerning the causes of their heart problem and heart disease
symptoms were measured using the Illness Attribution Questionnaire which is based
on the major categories of causal attribution described by French et al (2001) and
Gudmundsdottir et al (2001). This measure has previously been used in cardiac
populations (Perkins-Porras et al., 2006). The scale consists of 16 items scored as
‗yes‘ (2), ‗maybe‘ (1) and ‗no‘ (0) and load on 4 factors (State of mind/over
exertion/stress; Traditional risk factors; Genetic factors; Other medical causes).
150
5.7.1.5 Illness Perception Questionnaire – Revised
According to Leventhal, the initial response to the onset of illness is to identify
the nature of the threat faced, so that appropriate coping strategies can be engaged.
This process is represented along five dimensions; identity, consequences, timeline,
control/cure and cause. The illness perception questionnaire is a quantitative
assessment tool developed to capture the five components of the illness representation
model (Leventhal et al., 1997, 2003).
The revised illness perception questionnaire (IPQ-R: Moss-Morris et al., 2002)
was developed in response to problems of internal consistency of specific sub-scales in
the previous version (cure/control; timeline), and the previous oversight of not including
an emotional representation component (an important dimension of Leventhal‘s
model). A final addition to the scale was the assessment of the extent to which a
patients‘ illness representation provide a coherent understanding of the illness (the
illness coherence sub-scale).
The IPQ-R is divided into three sections, with the identity and causal
dimensions presented separately from the remaining dimensions. For the TRACE
study, the causal dimension of the IPQ-R was not included. Instead, participants‘
causal beliefs were assessed using the measure described in section 5.7.1.4 above.
The use of this measure was deemed more appropriate as the causal attributes
included are better associated with heart disease than are some of the causal
attributes included in this dimension of the IPQ-R.
The identity scale contains 14 commonly experienced illness symptoms,
patients respond whether they have experienced the symptom (yes or no), and
whether they believe the symptom to be specifically related to their illness (yes or no).
At the 12 month follow up (TRACE), the identity scale was not included for reasons of
patient acceptability. In the second section the consequences, timeline (acute/chronic),
timeline (cyclical), control (personal), control (treatment), illness coherence and
151
emotional representations are rated on a 5-point Likert type scale: strongly disagree
(1), disagree (2), neither agree nor disagree (3), agree (4), strongly agree (5). Total
scores for each scale range between 1 and 5. The IPQ-R show good internal reliability
of all sub-scales and has acceptable levels of stability over three weeks and six months
(Moss-Morris et al., 2002).
5.7.2 Health behaviours
5.7.2.1 Smoking
Patients were asked to provide information on their smoking status (current
smoker, ex-smoker, never smoker, nr of cigarettes/cigars/pipe per day).
5.7.2.2 Alcohol consumption
Weekly alcohol consumption was assessed at the time 2 interview. A unit of
alcohol was defined as one measure of sprit, a small glass of wine, or a half pint of
beer. A weekly total score was generated.
5.7.2.3 Diet
The healthy eating assessment (Little et al., 1999) was used to collect dietary
information at time 2. Patients reported the average number of pieces of fruit
consumed per [typical] day and also rated how often they would consume less than this
average figure reported per week. The same was obtained for average portions of
vegetables (excluding potatoes). An average daily fruit and vegetable intake score was
generated using patients responses indicating number of daily vegetables eaten and
the frequency of eating less than the reported amount per week.
152
Fat intake was assessed by nine diet questions relating to the frequency of
intake of certain fatty foods (e.g. type of milk used, cheese intake, meat, ready meals,
take away food, snacks etc.) Greater scores on this measure reflect a diet higher in fat.
5.7.2.4 Physical activity
Patients reported past week physical activity at the time 2 interview. ‗Walking
per day‘ in minutes, ‗Brisk walking per day‘ in minutes, ‗Cycling per day‘ in minutes
scores were generated. Patients also reported the number of times per week they
performed vigorous physical activity, enough to make them out of breath, prior to their
ACS.
5.7.2.5 Adherence to medications
Self-reported adherence to medications was assessed using the Medication
Adherence Report Scale (MARS; Horne & Weinman, 1999) at all follow up assessment
points. Patients rate their adherence on five questions (‗forget medication‘, ‗alter dose‘,
‗stop medication‘, ‗decided to miss dose‘, ‗take less than instructed‘) on a 5-point Likert
scale. Items are scored from Never (4) to Always (0), total scores range from 0 to 20
with higher scores indicating greater adherence.
5.7.3 Biological measures
5.7.3.1 Salivary cortisol
During the time 2 interview a set of four salivary cortisol samples were obtained.
Sample 1 was collected at the start of the interview, sample 2 immediately prior the
clinical interview for assessing depression followed by sample 3 directly after
153
completing the clinical assessment. The final sample was collected at the end of the
home interview. Patients were left with a saliva sampling kit and diary to complete over
the course of one single day following the interview day to complete and return to the
research group by post. Patients were required to provide six 2-minute saliva samples
at the following times: sample 1 – immediately upon waking; sample 2 – 30 minutes
post waking; sample 3; between 10.00 – 10.30 AM; sample 4 – between 14.00 – 14.30
PM; sample 5 – between 19.00 – 19.30 and the final sample 6 – at bedtime. Patients
were instructed not to brush their teeth, eat or drink caffeinated products before
providing the waking samples (1 – 2). A sampling diary was to be completed at the time
of collecting the saliva sample. Patients completed three mood questions at the time of
each sample (happy/stressed/depressed or gloomy) as well as providing information on
time of sleep the night prior to sampling day, time of waking on the sampling day, and
meal times during the sampling day. At the end of the day patients reported on
smoking, drinking and exercise undertaken during the day, as well as any stressful
encounters. Samples were returned via mail and stored at -80 degrees Celsius.
5.7.3.2 Heart rate variability
At the start of the time 2 interview patients were fitted with an Actiheart
(Cambridge Neuroscience Ltd) monitor to record their heart rate and heart rate
variability throughout the interview session. Actiheart is a compact, chest-worn device
that records heart rate, inter-beat-interval (IBI), and physical activity. Actiheart digitizes
the ECG signal and determines the IBI from the R-to-R interval. The Actiheart is worn
on the chest and consists of two electrodes connected by a short lead which simply clip
onto two standard ECG pads. Once the recording session is completed, the data are
transferred to a computer for storage, viewing, and analysis. From the IBI recording
files, the heart rate and heart rate variability parameters are calculated. The reliability
154
and validity of this monitor for recording activity and heart rate has been scientifically
validated (Brage et al., 2005).
The HRV sequences were screened for data quality, and RR intervals were
excluded if the current beat and the two beats preceding the current beat were not in
sinus rhythm, as recommended by the manufacturers. Specifically, we excluded RR
intervals <300 ms or >3000 ms, any RR intervals <80% or >120% of the previous RR,
and any intervals >3 times the SD of the preceding period. The interview recording
sequence was analyzed in 10-minute segments. Periods with <80% valid data were
excluded. The following variables were computed: total power (ms2), VLF power
between the limits of 0.003 Hz and 0.04 Hz (ms2), LF power in the range of 0.04 Hz
and 0.15 Hz (ms2), and HF power in the range 0.15 Hz and 0.40 Hz (ms2). The HF
component is thought to reflect parasympathetic cardiac control, whereas the LF
component reflects sympathetic/parasympathetic balance. In the time domain, we
computed the square root of the mean square difference between successive RR
intervals (RMSSD) and pNN50. The indices used for analysis in Chapter 6 were, heart
rate, mean RMSSD, normalised high frequency power, low frequency power and very
low frequency power. With the exception of heart rate, the other variables were log
transformed due to the data being skewed.
5.8 Measures – Time 3
5.8.1 Psychosocial measures
At the time 3 follow-up the following measures were re-administered; BDI;
HADS-A; PSS-SR; Ho; DS-14; SF-12; Social Support; IPQ-R; Causal attributions.
155
5.8.2 Health behaviours
Patients‘ smoking status was reassessed during the telephone interview, and if
relapse was reported, reasons for this were recorded. Diet, alcohol intake and physical
activity were also reassessed. Patients also reported current weight and their
adherence to cardiac medications.
5.10 Data storage
Data collected as part of this study were treated as confidential. Information
obtained from interviews and questionnaires from all data collection time points were
kept separate from consent forms, and kept in locked filing cabinets with restricted
access. Data entered into a database were anonymised, and personal information was
stored separately.
5.11 Statistical analyses
All statistical analyses were performed using the statistical programme SPSS
17.0 (SPSS Inc). Specific details on the analyses conducted are presented in the
relevant results sections.
156
TABLE 5.2 MEASURES OBTAINED AT EACH TIME POINT
Time 1
Time 2
Time 3
Time 4
Location
Hospital
Home
Tel Int / Post
Tel Int / Post
Time post Admission for ACS
6-28 hrs
10 – 14 Days
6 Months
12 Months
MEASUREMENTS
Pt Only
Pt
Pt
Part
Pt
Part
Socio-Demographics
CN
Clinical data
CN
Health Details
INT
Triggers (2 hours pre-ACS)
INT
Triggers (2 hours pre-ACS previous day)
INT
Acute fear
INT
Events surrounding heart problem / Delay
INT
Part
INT
INT
1. Emotional Distress
Hospital Anxiety and Depression Scale – Anxiety
INT
SR
SR
SR
SR
SR
Beck Depression Inventory
INT
SR
SR
SR
SR
SR
PTSD / Acute stress
INT
SR
SR
SR
SR
SR
Depression Interview and structured Hamilton
Profile of Mood States
INT
INT
2. Behaviours
Medication Adherence Report Scale
INT
INT
INT
SR
INT
SR
Physical Activity
INT
INT
INT
SR
SR
SR
SR
Diet
INT
INT
SR
SR
SR
SR
Smoking / Alcohol
INT
INT
INT
SR
INT
SR
Jenkins Sleep Scale [1]
INT
INT
SR
SR
SR
SR
Cardiac Rehabilitation Attendance
SR
SR
3. Health Status
Quality of Life – SF-12
INT
INT
INT
Cortisol
INT HM
INT/HM
Heart Rate and Heart Rate Variability
INT
SR
SR
SR
SR
4. Biological
Blood
INT
HM
5. Psychosocial Measures
Social Network Scale
SR
SR
ENRICHD Social Support Inventory
SR
SR
Support with recovery behaviours
SR
SR
Illness Perceptions Questionnaire – Revised
SR
Illness Perceptions Questionnaire – Partner [2]
SR
SR
SR
SR
SR
SR
SR
SR
SR
Causal Attributions
SR
SR
SR
SR
SR
SR
Self-efficacy for Recovery Behaviours [3]
SR
SR
SR
SR
SR
SR
Cardiac Denial of Impact Scale [4]
SR
Type – D Personality
SR
SR
SR
Cook Medley Hostility Scale
SR
SR
SR
Life Orientation Test – Optimism
SR
SR
Benefit Finding Scale [5]
SR
SR
Coping Inventory of Stress Situations [6]
SR
Seattle Angina Questionnaire [7]
SR
SR
SR
SR
SR
SR
Key: CN – taken from clinical notes, INT – Interview measure or questionnaire by interview, SR – Self-Report questionnaire, HM – home
based collection NB. Measures not previously described are listed numerically in the measures reference section of the Chapter 9.
157
TABLE 5.3 CRONBACH‘S ALPHA FOR MEASURES ADMINISTERED IN TRACE
Measure
Time 1
Time 2
.82
--
.77 - .91
--
Causal Beliefs
--
.34 - 85
BDI
--
.86
PTSS-SR
--
.89
HADS-A
--
.88
Ho-Scale
--
.88
Negative affectivity scale
--
.86
Social inhibition scale
--
.85
Social Support
--
.85
IPQ-R
--
.65 - .90
Acute stress
POMS
Type D
158
CHAPTER 6. Results TRACE Study I
In this chapter, the results from the time 1 and time 2 assessment points of the
TRACE study are presented. The sample characteristics are outlined, followed by an
examination of the prevalence and acute predictors of posttraumatic stress symptoms
at 3 – 4 weeks post ACS. The relationship between patients‘ illness cognitions and
concurrent posttraumatic stress symptoms is presented next. This is followed by an
evaluation of patients‘ post-ACS biological reactions (salivary cortisol and heart rate
variability) in relation to posttraumatic symptoms. This chapter concludes with a
discussion of the results presented in the following sections.
6.1 Data analysis
693 patients were approached in hospital, of these 125 declined to participate,
205 were excluded, 7 died in hospital and 58 were transferred to another hospital
before time 1 could be completed (see table 5.1, chapter 5). Of the 298 patients
recruited in hospital (time 1), 226 were assessed at time 2 (home interview) and the
principal analyses of the time 2 data were carried out this sub-sample. Of these, 222
patients had valid data for a total score on the PTSD measure at time 2.
The prevalence of PTSD and severity of posttraumatic stress symptoms were
examined. Associations between posttraumatic stress and time 1 clinical, demographic
and psychological variables were analyzed using Pearson correlations for continuous
variables and non-parametric Spearman correlations for categorical variables. To
identify early independent predictors of time 2 posttraumatic stress multiple regression
analyses were conducted. The variables included in these models were selected based
on previous work as well as the results from the univariate analyses. Multiple
regressions were also conducted to assess the independent contribution of
psychosocial risk factors as well as the relationship of time 2 cortisol levels and heart
159
rate variability with posttraumatic symptoms. Standardized regression coefficients (β)
are presented along with the standard errors for these.
6.2 Patient characteristics
The baseline (time 1) clinical, demographic and psychological characteristics of
the complete sample and the sub-set of patients who completed time 2 interviews are
presented in table 6.1. The majority of patients were men, and mostly of white
European decent, aged 60 years on average at the time of ACS. More than half of
patients participating in the study were employed at the time of their admission, and the
majority were married. Patients had low educational attainment with only a small
proportion reporting education above secondary school. 87% of the patients had
experienced a STEMI rather than an NSTEMI/UA. ACS severity as defined by the
GRACE score was moderate, 13% had experienced a previous MI, and 9%
experienced heart failure in hospital.
Patients who completed time 1 interviews only were similar to those who
completed interviews at both time 1 and time 2 on all clinical and psychological
variables. However, those who did not complete time 2 were more likely to be
unmarried (χ² = 5.46, p < .05) and in the moderate and high deprivation categories than
completers (χ² = 7.94, p < .05).
160
TABLE 6.1 PATIENT CHARACTERISTICS
Time 1 Sample
Mean (SD)
N
Demographic factors
Age
298
Gender
298
Educational attainment
297
n (%)
60.15 (11.57)
N
Time 2 Sample
Mean (SD)
n (%)
226
59.74 (11.75)
Men
250 (83.9)
Women
48 (16.1)
226
190 (84.1)
None
84 (28.23)
Basic
74 (24.9)
61 (27.0)
Secondary
Degree
93 (31.3)
46 (15.5)
68 (30.1)
33 (14.6)
36 (15.9)
225
63 (28.0)
Marital status (married)
298
203 (68.1)
226
162 (71.7)
Ethnicity (white)
298
247 (82.9)
224
188 (83.2)
Social deprivation
294
188 (63.9)
70 (23.8)
36 (12.2)
221
152 (68.2)
45 (20.2)
26 (11.7)
Employed (yes)
296
169 (57.1)
224
127 (56.7)
Clinical factors
ACS type
298
STEMI
260 (87.2)
226
199 (88.1)
NSTEMI/UA
38 (12.8)
Low
Medium
High
27 (12.1)
GRACE score
298
92.85 (27.72)
226
91.81 (26.45)
SRI
298
21.74 (11.82)
226
20.93 (10.15)
Heart failure
298
28 (9.4)
226
20 (8.8)
Previous MI (yes)
297
39 (13.1)
226
28 (12.4)
Family history of CHD (yes)
298
189 (63.4)
225
141 (62.4)
Smoker (current)
298
118 (39.6)
226
85 (37.6)
BMI
292
Psychosocial factors
SF12
SF12
260
Physical
Mental
Acute stress
280
Low
Medium
High
POMS negative
278
3.69 (2.81)
220
3.66 (2.74)
Subjective pain during ACS
295
7 (2.54)
226
6.98 (2.43)
History of depression (yes) –
assessed at time 2
27.60 (4.93)
210
27.52 (40.7)
46.27 (8.39)
50.16 (9.93)
205
46.29 (8.30)
50.02 (10.26)
67 (23.9)
151 (53.9)
62 (22.1)
203
212
56 (25.5)
120 (54.5)
44 (20.0)
67 (31.6)
161
6.3 Posttraumatic stress symptoms 3 – 4 weeks post ACS (time 2)
At the time 2 interview patients‘ posttraumatic stress responses to their ACS
were assessed. Although a diagnosis of PTSD can only be established (according to
DSM-IV guidelines) one month following the trauma at the earliest, I felt that it would be
informative to assess early posttraumatic reactions at this stage. Furthermore, the
timing of time 2 interviews (average 21 days post ACS) did approach the one month
mark. Symptoms of posttraumatic stress prior to one month post trauma are usually
referred to as acute stress disorder or symptoms. However, the validity and usefulness
of this concept has been questioned (Marshall et al., 1999). At this early post ACS
assessment point, I chose to use the same PTSD measure as for the follow up
assessments at six months to aid comparison of results (Professor Chris Brewin;
personal communication). Patients were asked to think back over the last few weeks
since their ACS and report on symptoms experienced since the acute event. The
symptoms reported by patients at time 2 show a similar pattern to those reported by
patients at 12 and 36 months in the ACCENT study (see data presented in chapter 4),
with avoidance symptoms endorsed most frequently, followed by arousal then reexperiencing symptoms (table 6.2). Already at this early stage, 5.8% of patients met
diagnostic criteria for potential PTSD, using the modified criteria as outlined in sections
3.4.3.2 and 4.1.2. Using the original scoring method yielded a prevalence level for
PTSD of 22.2% at this early stage, a rate that appears highly inflated compared with
rates observed in previous studies.
162
TABLE 6.2 PSS-SR SCORES AT TIME 2
Time 2
N
Means (SD)
Range
PSS-SR Total score
222
7.70 (7.73)
0 – 41
PSS-SR Avoidance
222
3.43 (3.52)
0 – 19
PSS-SR Arousal
222
2.56 (3.12)
0 – 16
PSS-SR Re-experiencing
223
1.70 (2.01)
0 – 10
PTSD Diagnosis (positive) – modified
223
5.8%
PTSD Diagnosis (positive) – original
223
22.2%
In line with DSM-IV requirements for PTSD classification, patients reported on
acute distress and fear (acute stress) experienced during their ACS, with the majority
of patients reporting moderate distress (53.9%), intense distress was reported by
22.1% and a further 23.9% reported low distress during ACS.
6.3.1 Acute admission predictors of posttraumatic stress symptoms at time 2
Greater posttraumatic stress symptomatology at time 2 was associated with
greater acute distress at time of ACS, a history of depression, younger age, female
gender, and greater social deprivation assessed at time 1. In hospital negative mood,
prior lower mental health status (mental quality of life) and lower GRACE risk scores
were associated with higher posttraumatic symptoms at time 2. There was a nonsignificant trend for more intense pain (self-report) also relate to higher posttraumatic
stress at time 2 follow up (table 6.3).
163
TABLE 6.3 CORRELATIONS BETWEEN BASLINE VARIABLES (TIME 1) AND TIME 2
POSTTRAUMATIC STRESS SYMPTOMS
Time 2
posttraumatic
symptoms
N
r
P
Age
222
-.183
.006
Gender (female)
222
.140
.038
Educational attainment
221
-.115
.089
Marital status (married)
222
.078
.247
Ethnicity (white)
222
.076
.262
Social deprivation
219
.259
< .001
ACS type (STEMI)
222
-.031
.641
GRACE score
222
-.151
.024
SRI
222
-.058
.387
Heart failure
222
.043
.520
Previous MI
222
-.051
.446
Family history of CHD
222
-.036
.589
Smoker (current)
222
.043
.527
BMI
221
.016
.808
SF12 – physical
201
-.098
.168
SF12 – mental
201
-.460
< .001
POMS negative
216
-.393
< .001
Acute stress
216
.269
< .001
Subjective pain during ACS
222
.126
.061
History of depression
209
.261
< .001
Demographic factors
Clinical factors
Psychosocial factors
6.3.2 Multivariate predictors of posttraumatic stress symptoms at time 2
The factors identified in the previous section as being related to posttraumatic
symptoms may not be independent of one another. Time 1 clinical, demographic and
psychological variables were therefore entered into a multiple regression model in
order to establish the early predictors of shorter-term posttraumatic stress reactions in
response to an acute cardiac event. These variables were selected on the basis of the
correlations described in the previous section as well as previous work by myself
(chapter 4) and others (chapter 2). The following variables were tested as predictors in
164
the multiple regression; age, gender, ethnicity, social deprivation, GRACE score,
subjective pain (at time of ACS), negative mood in hospital (POMS), acute stress, SF12 mental quality of life, and concurrent pain (since discharge, assessed at time 2).
The multiple regression on time 2 posttraumatic symptom severity indicated that
the variables included in the model together accounted for 38.3% of variance (table
6.4). Social deprivation, negative mood in hospital, acute stress reactions, mental
quality of life and current (time 2) pain emerged as independent predictors of
posttraumatic stress symptom severity at time 2. None of the variables included in the
final model showed multicollinearity according to variance inflation factor and tolerance
values. It is notable that posttraumatic stress symptoms were not associated in multiple
regression analyses with the clinical severity of the ACS as defined by the Grace
algorithm, or with other clinical factors such as history of MI, type of ACS, heart failure,
or cardiac arrest (Spearman‘s correlations, p > .05).
This model was re-run including the history of depression variable, though this
resulted in a slight reduction in the numbers included in the model. A history of
depression predicted posttraumatic stress symptoms at time 2. Social deprivation,
negative mood, acute stress, mental QoL and current pain remained independent
predictors of posttraumatic reactions also (table 6.5). These variables accounted for
38.4% of the variance in posttraumatic stress symptom severity at time 2.
165
TABLE 6.4 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME 2
Model 1*
Standardised
regression
Standard error
P
coefficients
Age
.069
.110
.527
Gender
.028
.064
.666
Ethnicity
.070
.059
.240
Social deprivation
.205
.062
.001
GRACE score
.002
.105
.988
Subjective pain
-.025
.061
.678
Negative mood (POMS)
.258
.064
<.001
Acute stress
.184
.067
.006
SF-12 mental QoL
-.240
.068
.001
Current pain (time 2)
.134
.061
.030
R²
.383
* n = 197
TABLE 6.5 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME 2
Model 1*
Standardised
regression
Standard error
P
coefficients
Age
.038
.113
.735
Gender
-.057
.066
.383
Ethnicity
.076
.062
.221
Social deprivation
.181
.063
.005
GRACE score
.047
.109
.663
Subjective pain
-.006
.064
.929
Negative mood (POMS)
.287
.066
<.001
Acute stress
.158
.069
.023
SF-12 mental QoL
-.221
.071
.002
Current pain (time 2)
.135
.063
.034
History of depression
.122
.062
.051
R²
.384
* n = 186
166
6.3.3 Psychosocial predictors of posttraumatic stress at time 2
Time 2 posttraumatic stress symptoms were associated with hostility and type
D personality. Lower reported social support was associated with greater posttraumatic
stress symptoms. However, there was no relationship with patients‘ social network
(table 6.6).
TABLE 6.6 CORRELATIONS BETWEEN PSYCHOSOCIAL RISK FACTORS AND TIME 2
POSTTRAUMATIC STRESS SYMPTOMS
Time 2
Posttraumatic stress symptoms
r
p
Hostility
.324
<.001
Type D
.418
<.001
Social support
-.234
.003
Social network
-.136
.083
Psychosocial factors
The influence of hostility, type D personality, social support and social network,
although measured at time 2, were considered previously existing psychosocial
characteristics and were therefore entered into a separate multiple regression model to
that including acute emotional reactions to the ACS. This model explained 28.2% of the
variance, with hostility and type D personality emerging as independent predictors of
posttraumatic stress reactions at time 2. Social support or network did not contribute to
the explained variance (table 6.7). None of the variables included in the final model
showed multicollinearity according to variance inflation factor and tolerance values.
167
TABLE 6.7 PSYCHOSOCIAL PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME 2
Model 1*
Standardised
regression
Standard error
P
coefficients
Age
-.035
.134
.795
Gender
.155
.080
.055
Ethnicity
.022
.073
.762
Social deprivation
.086
.075
.254
GRACE score
-.043
.133
.749
Hostility
.264
.076
.001
Type D personality
.294
.075
<.001
Social support
.002
.075
.979
Social network
-.076
.082
.354
R²
.282
*n = 156
Figure 6.1 illustrates the relationship between type D personality and
posttraumatic stress symptom severity at time 2, after controlling for the other variables
included in the regression model above.
14
12
10
8
6
4
2
0
Type D positive
Type D negative
FIGURE 6.1 TYPE D PERSONALITY AND POSTTRAUMATIC STRESS SYMPTOMS
168
6.3.4 Illness representations and current mood state in relation to posttraumatic stress
reactions at time 2
Total posttraumatic stress scores at time 2 were correlated with concurrent
depression, anxiety, and quality of life scores. At time 2 patients reporting higher
posttraumatic stress symptoms also had significantly greater depression and anxiety
symptoms, and reported worse physical and mental quality of life (table 6.8).
TABLE 6.8 CORRELATIONS BETWEEN TIME 2 PSYCHOLOGICAL VARIABLES AND TIME 2
POSTTRAUMATIC STRESS SYMPTOMS
Time 2
Posttraumatic stress symptoms
r
p
BDI
.761
<.001
Anxiety
.770
<.001
SF12 physical QoL
-.226
.001
SF12 mental QoL
-.778
<.001
Psychological factors
TABLE 6.9 ILLNESS REPRESENTATION CHARACTERISTICS AT TIME 2
N
Means
SD
Median
Timeline
158
2.99
1.02
3.0
Timeline – cyclical
160
2.26
0.78
2.0
Consequences
159
3.28
0.69
3.3
Personal control
160
3.94
0.65
4.0
Treatment control
158
3.92
0.55
4.0
Illness coherence
160
3.77
0.77
4.0
Emotional representations
162
2.55
0.85
2.5
Illness representation
169
Patients‘ beliefs about their condition were also assessed at time 2 and the
relationship between posttraumatic stress symptoms and cognitive representations
were tested. Illness representation scores for the seven dimensions assessed are
presented in table 6.9. High scores denote a more chronic and cyclical timeline, greater
perceived negative consequences, greater perceived personal control and belief in
treatment, a sense of having a more coherent understanding of their illness and more
negative emotional responses. The patients viewed their illness as chronic and cyclical,
but reported understanding their condition quite well. They considered having quite
strong personal control and treatment control. However, at the same time, the
perceived negative consequences of the disease upon their lives were perceived as
considerable, as was their emotional response. Table 6.10 shows the intercorrelations
between the illness representation dimensions at time 2 after adjusting for patient age
and gender.
TABLE 6.10 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATION DIMENSIONS
Time 2 Illness representations
Time 2 Illness
representations
1. Timeline
r
p
2. Timeline – cyclical
r
p
3. Consequences
r
p
4. Personal control
r
p
5. Treatment control
r
p
6. Illness coherence
r
p
7. Emotional
representations
r
p
2
3
4
5
6
7
.274
.001
.476
<.001
-.150
.066
-.410
<.001
-.149
.070
.307
<.001
.364
<.001
-.255
.002
-.359
<.001
-.447
<.001
.489
<.001
-.009
.908
-.237
.004
-.221
.007
.481
<.001
.506
<.001
.238
.003
-.130
.114
.311
<.001
-.288
<.001
-.349
<.001
170
Overall the pattern of associations appears as one would expect. For example,
stronger timeline – cyclical beliefs were associated with stronger timeline beliefs,
greater consequences, less personal and treatment control, worse illness coherence
and greater emotional representations. Patients who rate the consequences of ACS as
more severe also have stronger emotional representations, and lower beliefs in the
effectiveness of treatment. Beliefs in personal control are less consistently associated
with the other illness representations, and appear to be unrelated to timeline beliefs
and consequences in this sample of patients. A lack of understanding of the illness (low
illness coherence) is accompanied by the belief that the condition will last a long time,
has serious consequences, and is not controllable.
Correlations between illness perceptions and posttraumatic stress symptoms
and the sub-scales of intrusion, avoidance and arousal were examined using partial
correlations controlling for age and gender (table 6.11). Greater posttraumatic stress
symptoms were associated with perceptions of a more chronic timeline, a cyclical
timeline, serious consequences, poor understanding of the condition, poor treatment
control and more negative emotional representations. The correlation with personal
control was non-significant. A similar pattern of results was observed for each of the
individual sub-scales, with the exception of the correlations between arousal symptoms
and timeline and between intrusive symptoms and timeline, which were non-significant.
171
TABLE 6.11 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 2
POSTTRAUMATIC STRESS SYMPTOMS
Posttraumatic
stress
symptoms
Intrusion
sub-scale
Avoidance
sub-scale
Arousal
sub-scale
Illness representation
Timeline
r
p
.182
.027
.154
.062
.191
.020
.123
.136
Timeline – cyclical
r
p
.424
<.001
.328
<.001
.387
<.001
.369
<.001
Consequences
r
p
.328
<.001
.263
.001
.347
<.001
.227
.005
Personal control
r
p
-.059
.475
-.089
.284
-.039
.638
-.042
.611
Treatment control
r
p
-.207
.012
-.168
.042
-.176
.033
-.190
.020
Illness coherence
r
p
-.307
<.001
-.282
.001
-.259
.001
-.260
.001
Emotional representations
r
p
.560
<.001
.482
<.001
.542
<.001
.442
<.001
A multiple regression analysis was conducted to test whether the effect of
illness representations on time 2 posttraumatic stress symptoms were independent of
clinical and demographic factors. Variables entered on step one of the model were age,
gender, ethnicity, social deprivation, and GRACE score. The seven illness
representation dimensions were entered on step two (table 6.12). Demographic and
clinical characteristics accounted for 11.1% of the variance in time 2 posttraumatic
stress symptoms. The illness representations accounted for an additional 31.6% of
variance, together the model accounted for 42.7% of variance. Social deprivation and
emotional representations were the only independent predictors of posttraumatic
symptoms.
172
TABLE 6.12 COGNITIVE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME 2
Model 1
Standardised
regression
coefficients
-.033
Age
Model 2*
Standard
error
P
.148
.823
Standardised
regression
coefficients
-.069
Standard
error
P
.126
.584
Gender
.144
.083
.084
.082
.072
.257
Social deprivation
.203
.083
.015
.167
.072
.021
Ethnicity
.085
.080
.290
.065
.069
.350
GRACE score
-.124
.148
.404
.014
.127
.911
Timeline
-.057
.085
.503
Timeline – cyclical
.137
.085
.108
Consequences
.067
.088
.449
Personal control
.085
.086
.323
Treatment control
-.016
.087
.854
Illness coherence
-.054
.079
.499
.460
.086
<.001
R²
.111
Emotional representations
R²
.427
*n = 146
6.4 Posttraumatic stress symptoms and salivary cortisol
Patients provided six samples of saliva for cortisol analyses across the course
of one single day from awakening to bedtime. Mean values for cortisol for all
participants showed that a typical diurnal pattern was present throughout the
assessment day, with high level of cortisol early in the morning and with the lowest
levels at night (fig. 6.2)
173
FIGURE 6.2 PROFILE OF SALIVARY CORTISOL THROUGHOUT THE DAY AT TIME 2
I conducted multiple regression analyses adjusted for age, gender, BMI,
smoking and time of waking in the morning to examine potential relationships between
posttraumatic stress symptoms and patients cortisol profiles. Four cortisol parameters
were tested: the cortisol value on waking in the morning, the cortisol awakening
response, the total output of cortisol as defined by area under the curve, and cortisol
slope across the day. No significant associations between posttraumatic symptoms at
time 2 and any of these measures of cortisol were observed. The regression
coefficients derived from multiple regression analyses including age, gender, BMI,
smoking and time of waking are presented in table 6.13. The study therefore failed to
identify any associations between posttraumatic stress symptoms 3-4 weeks post-ACS
and cortisol profiles.
174
TABLE 6.13 POSTTRAUMATIC STRESS SYMPTOMS AND SALIVARY CORTISOL AT TIME 2
Cortisol measure
Standardised
regression
coefficients
Standard
Error
p
n
Cortisol waking value
102
-.066
.098
.502
Cortisol awakening response
91
.055
.103
.597
Total cortisol output (area
under the curve)
100
.130
.101
.202
Cortisol slope
102
-.110
.097
.263
It was noted in chapter 5 (section 5.1.2.1) that depression may disguise
associations between posttraumatic symptoms and cortisol profiles.
I therefore
replicated the analytic strategy described by Von Känel and colleagues (in press).
When the regression model was re-run including depressed mood (depressed [BDI >
10] vs non-depressed [BDI < 10]) as a co-variate, a significant association between
patients posttraumatic stress symptoms at time 2 and total cortisol output across the
day (table 6.14) was observed. Depression also emerged as an independent predictor
of cortisol levels in model 2. The full model accounted for 16.7% of variance in total
cortisol output over the day, assessed following the time 2 interview. An illustration of
these results is shown in figure 6.3, where lines represent the upper and lower tertiles
of posttraumatic stress symptoms, after controlling for age, gender, grace, BMI,
smoking, time of waking and presence or absence of depression. This graph shows
that total area under the curve appears elevated in the higher posttraumatic stress
tertile throughout the day.
175
TABLE 6.14 POSTTRAUMATIC STRESS SYMPTOMS AND TOTAL CORTISOL OUTPUT AT
TIME 2
Model 1
Standardised
regression
coefficients
.206
Age
Model 2*
Standard
error
P
.188
.276
Standardised
regression
coefficients
.172
Standard
error
P
.184
.352
Gender
.030
.101
.770
-.042
.103
.682
GRACE score
.120
.178
.501
.221
.178
.218
BMI
.036
.101
.723
.074
.099
.457
Smoker
.025
.100
.804
.023
.097
.815
Time of waking
.014
.103
.892
-.045
.103
.666
-.091
.102
.374
-.279
.126
.029
.315
.129
.017
Depression
R²
.113
Posttraumatic stress
symptoms
R²
.167
*n = 100
FIGURE 6.3 THE RELATIONSHIP BETWEEN POSTTRAUMATIC STRESS SYMPTOMS AND TOTAL
CORTISOL OUTPUT CONTROLLING FOR DEPRESSION
176
In addition, a significant relationship between acute distress experienced at time
of ACS and cortisol levels at time 2 was observed. Patients who reported lower acute
stress scores had significantly higher cortisol total output (area under the curve) during
the day compared with those reporting higher distress (r = -.254, p = .002). This
relationship remained when excluding the waking response from the area under the
curve value (r = -.248, p = .002). No other relationships were observed between cortisol
values and psychological variables (subjective pain, negative mood) measured at time
1.
Analysis of co-variance was performed to test the differences in total cortisol
values across the day and the acute stress categories (low, medium, high) assessed at
time of ACS, controlling for age, gender, GRACE, BMI and smoking as covariates. This
analysis indicate that patients with low acute stress had significantly higher cortisol
output throughout the day than did those reporting moderate and high distress (F
(2, 134)
= 4.72, p = .010, η2 = .066). For an illustration of these results see figure 6.4.
177
FIGURE 6.4 THE RELATIONSHIP BETWEEN ACUTE STRESS AND CORTISOL AT TIME 2
Further analysis of co-variance revealed that those patients who reported low
acute stress also had a greater cortisol awakening response than those reporting
moderate and high acute distress, controlling for age, gender GRACE risk score, BMI
and smoking. Time of waking was also included as a co-variate (F
(2, 84)
= 3.15, p =
.034, η2 = .077). Analysis of co-variance between acute stress category and the timing
of saliva sampling, with age, gender, GRACE, BMI and smoking as covariates, was
performed to test whether patients with different acute stress responses to their ACS
vary in when they performed the actual sampling in the home testing at time 2. The
variables used in this analysis were computed from the timing of samples provided by
patients and re-calculated as ‗time after midnight sample taken‘ (in minutes) to aid
comparison. The results demonstrated that those reporting moderate acute stress
(mean 7:05 am, SD 59 min) at time 1 also reported waking significantly earlier than
those reporting low (mean 7:59 am, SD 59 min) or high acute stress (mean 8:00 am,
178
SD 81 min) (F
(2, 97)
= 3.56, p = .032, η2 = .068). There were no other significant
differences between the acute stress categories in the timing of samples across the
day. Consequently, the differences identified in figure 6.4 were not secondary to
differences in the timing of cortisol samples.
6.5 Posttraumatic stress symptoms and heart rate variability at time 2
Heart rate (HR) and heart rate variability (HRV) were analyzed in terms of heart
rate, LF-HRV, HF-HRV and VLF-HRV. Although heart rate variability was initially
averaged into 10 minute segments during the interview (see chapter 5 section 5.7.3.2),
the analyses were limited to the means for the sampling period rather than the
repeated measures data from the individual 10 minute sections. This decision was
based on the following reasons; firstly, patients had varying numbers of 10 minute
sections, some with 3 (i.e. 3 x 10 min) sections others with as many as 6 (i.e. 6 x 10
min), secondly, I was unable to control for the duration of the interview and the timing
of the questionnaires completed. The mean values for the heart rate variability
measures are summarized in table 6.15.
TABLE 6.15 INDICES OF HEART RATE VARIABILITY AT TIME 2
HRV measure
n
mean
SD
High frequency HRV
151
4.16
1.50
Low frequency HRV
151
4.89
1.28
Very low frequency HRV
151
4.58
1.10
Heart rate
151
66.66
12.20
* adjusting for age, gender and cardiac medications.
179
The relationship of heart rate variability with posttraumatic stress at time 2 was
analyzed both using continuous posttraumatic stress symptom levels, and categorically
comparing the PTSD positive and PTSD negative patients. All comparisons were
controlled for age, gender and cardiac medication (beta-blockers, ACE inhibitors and
statins), since these may influence heart rate variability measures. As can be seen
from table 6.16 no significant associations between posttraumatic symptom scores and
heart rate variability measures were observed.
TABLE 6.16 ASSOCIATIONS BETWEEN TIME 2 HEART RATE VARIABILITY AND TIME 2
POSTTRAUMATIC STRESS
Posttraumatic stress
symptoms
HRV measure
n
r
p*
High frequency HRV
137
-.098
.262
Low frequency HRV
137
-.119
.175
Very low frequency HRV
137
-.091
.299
Heart rate
137
-.059
.498
* adjusting for age, gender and cardiac medications.
However, an interesting effect emerged when heart rate and heart rate
variability were compared between the PTSD positive and PTSD negative patients (n =
138) using analysis of covariance. There was significantly reduced HF HRV among
PTSD positive patients (mean 3.66, SD 1.33) compared with those who scored below
the diagnostic threshold (mean 4.60, SD 1.47) (F (1, 130) = 4.33, p = .039, η2 = .032). This
effect was independent of patient age, gender, and cardiac medication (beta-blocker,
ACE inhibitor and statins). It should be noted that the number of PTSD positive patients
was very small. Nevertheless, the finding does suggest that parasympathetic control is
reduced in patients with severe PTSD symptoms.
180
There was no difference in either LF HRV or VLF HRV between the diagnostic
groups, nor were there any differences in mean heart rate between the two groups
(table 6.17). Whereas elevated heart rate has previously been found to predict PTSD
following trauma (Bryant, 2006), in the TRACE study, there were no differences in
heart rate between those who scored positive (mean 69.94, SD 10.84) on the PTSD
measure compared with those who did not (mean 66.20, SD 11.86), after controlling for
age, gender, cardiac medications and BMI (p = .291).
TABLE 6.17 PTSD AND HEART RATE VARIABILITY AT TIME 2
PTSD positive
PTSD negative
HRV measure
n
Mean
SD
n
Mean
SD
P*
High frequency HRV
6
3.66
1.33
132
4.60
1.47
.039
Low frequency HRV
6
4.15
1.42
132
4.87
1.25
.059
Very low frequency HRV
6
4.07
1.05
132
4.55
1.07
.108
Heart rate
6
69.94
10.84
132
66.20
11.86
.291
* controlling for age, gender and cardiac medications.
6.6 Discussion
6.6.1 Predicting short term posttraumatic stress symptoms from patients’ acute postACS emotional responses
This study investigated the role of acute emotional reactions to an ACS in the
prediction of short-term (3-4 week) posttraumatic stress symptoms in response to the
cardiac event. The findings indicate that even at this early assessment point 5.8% of
the patients met criteria for a diagnosis of PTSD. According to DSM-IV criteria, a
diagnosis of PTSD cannot be made until one month post trauma at the earliest, and
diagnosis of acute stress disorder (ASD) should instead be applied 1 – 4 weeks post
181
trauma. However, some research supports the usefulness of early assessed
posttraumatic stress reactions, and challenges the proposition that an acute stress
disorder diagnosis is an adequate tool to predict chronic PTSD.
O‘Donnell et al (2007) showed that significantly higher re-experiencing, arousal,
and avoidance symptoms assessed at eight days post trauma were predictive of 12month PTSD. Bryant and colleagues (2008) conducted a large-scale multisite study
assessing the relationship between ASD and PTSD in a sample of 597 patients
admitted to hospital for major trauma. All patients were randomly selected for screening
during hospital admission, which occurred within one month of trauma exposure, and
reassessed for PTSD at three months following the initial assessment (n=507). Among
patients who met criteria for PTSD at follow-up, the authors studied how many also
presented with acute stress disorder at first assessment to determine predictive rates
between the disorders. Within one month of trauma 6% (n=33) met criteria for ASD. At
three month follow up 10% (n=49) met diagnostic criteria for PTSD. Fifteen patients
(7%) diagnosed with acute stress disorder and 34 (45%) patients who were not
diagnosed with acute stress disorder were later diagnosed with PTSD at follow-up. The
authors concluded that the majority of patients who develop chronic PTSD do not
initially present with acute stress disorder. These results are similar to results found in
other studies and illustrate that presence of acute stress disorder may not be an
effective tool to predict chronic PTSD. One study suggests that identification of
potential PTSD cases as early as eight days post trauma is warranted and that it can
facilitate treatment matching (O‘Donnell et al., 2007). In addition, average follow up of
patients in the TRACE study for time 2 assessment was 21 days post admission,
approaching the one month mark for appropriate DSM-IV diagnosis of PTSD.
For a diagnosis of PTSD, a person‘s response to trauma should involve intense
fear, helplessness and horror. This emotional reaction forms criterion A2 in the DSM-IV
diagnostic manual for PTSD. However, some argue that provided that criterion A1
(trauma must involve actual or threatened death or serious injury, or a threat to the
182
physical integrity of self or others) is met, the value of criterion A2 is significantly
reduced (Weathers & Keane, 2007). Other research suggests an important role for
such acute emotional reactions. In the present study I was able to systematically collect
information on patients‘ acute stress reactions in response to their ACS. This was a
limitation of the study presented in chapters 3 and 4. Intense distress was reported by
22.1% of the sample, and 53.9% of the sample reported experiencing moderate
distress at the time of their ACS. These figures are highly comparable to those reported
by Whitehead et al (2005) from analyses of a sample of 184 patients recruited as part
of the ACCENT study (see chapters 3 and 4), where 21.7% and 51.6% reported
intense and moderate distress, respectively. Whitehead et al (2005) found that inhospital distress was predictive of short-term emotional responses. Patients who
reported more intense distress and fear of dying also reported greater depression and
anxiety one week post ACS. This relationship was independent of age, gender and
negative affect. These authors concluded that acute distress during the initial stages of
an ACS might serve as a trigger for subsequent emotional distress, in particular
depression and anxiety, in turn promoting poorer prognosis and greater morbidity with
time. Further analyses from the ACCENT study showed that acute stress symptoms,
depression, negative affect, hostility, and subjective chest pain at admission were
independent predictors of three-month PTSD symptoms (Whitehead et al., 2006).
In the present study I assessed patients‘ acute emotional reactions shortly after
admission for ACS. In line with the results presented by Whitehead and colleagues
(2005, 2006) acute stress symptoms were predictive of emotional distress shortly after
hospital discharge. Patients who reported more intense acute distress also reported
greater posttraumatic stress symptoms. I hypothesized that negative mood state in
hospital would be predictive of short-term posttraumatic symptoms. This hypothesis
was supported by the findings. Clinical variables were unrelated to subsequent
posttraumatic stress, although a univariate correlation was observed between lower
GRACE risk scores and greater posttraumatic stress symptoms. This relationship may
183
be explained by the associations between age, GRACE scores and posttraumatic
symptoms. Closer examination of the univariate associations suggests that younger
patients have lower GRACE risk scores, but also report higher posttraumatic stress
symptoms. The relationship between lower GRACE risk and posttraumatic symptoms
was no longer significant in the multivariate analyses once age and gender were
controlled for.
Posttraumatic stress symptoms at time 2 were also predicted by higher social
deprivation. In the general PTSD literature, low socioeconomic status is a consistent
predictor of PTSD (Brewin et al., 2000). Consistent with previous research, negative
mood in hospital and poorer mental quality of life also emerged as independent
predictors of posttraumatic stress symptoms severity. These findings support previous
research demonstrating that posttraumatic symptoms are influenced by negative
affective states during and following the acute event (e.g. Bennett & Brooke, 1999;
Pedersen et al., 2003; Bennett et al., 2001; Whitehead et al., 2006, Wikman et al.,
2008).
One particularly interesting finding was that patients who reported experiencing
current pain (cardiac event related) at the time 2 assessment also reported more
intense posttraumatic reactions. This finding is in line with the results presented in
chapter 4, where recurrence of symptoms was strongly predictive of 12 posttraumatic
stress symptoms. The experience of recurrent cardiac symptoms, chest pain or other
cardiac event related pain, as in the present study, may serve as a constant reminder
of the traumatic event, thereby intensifying the posttraumatic reaction.
Hostility and type D personality have previously been found to predict
posttraumatic stress. As these variables were considered pre-existing psychosocial
characteristics, together with patients social network and perceived social support,
these were entered into a separate multiple linear regression model to that of patients
acute emotional reactions at this stage of the analyses. In this separate analysis both
type D personality and hostility emerged as independent predictors of posttraumatic
184
stress symptoms at time 2. Although a lack of social support is a risk factor for PTSD in
the general literature (Brewin et al., 2000), in this study neither social network nor level
of social support were predictive of posttraumatic stress at time 2.
Individuals high in hostility may be more likely to interpret people and events in
a negative way and may be a vulnerability factor in developing PTSD in response to a
traumatic event. Those trauma victims high in trait hostility may in fact have different
responses to trauma than those with low or no trait hostility. For example, a study by
Beckham et al (2002) compared the cardiovascular response of 118 male Vietnam
veterans with a PTSD diagnosis (n=62) to Vietnam veterans without a PTSD diagnosis
(n=56) on a relived anger task that did not involve trauma. The results from the study
found that the veterans with PTSD experienced anger more quickly, had greater
diastolic blood pressure (DBP) response, had greater anxiety and anger, and had
greater magnitude DBP response during anger recovery. Additionally, the veterans with
PTSD also reported greater trait covert hostility, and these scores were more predictive
of DBP response, reported anger, and DBP and SBP (systolic blood pressure)
recovery. According to the investigators, these results suggest ―covert hostility may
affect reactivity in a different way among individuals with PTSD‖ (p.232). Furthermore,
the authors hypothesized that individuals with PTSD may have intensified negative
emotion as it ―may be encoded as part of a trauma memory structure‖ (p. 232). Another
hypothesis posited by the authors is that individuals with PTSD may have difficulty
regulating negative emotional responses.
Similarly the personality trait type D, may be a vulnerability factor for developing
PTSD in response to trauma, by the way in which type D individuals may react to the
event. Type D represents a personality profile characterized by both the tendency to
experience negative emotions (NA) and the propensity to inhibit self-expression in
social interaction (SI) (Denollet & Van Heck, 2001). NA refers to the tendency of
experiencing negative emotions across different times and situations. Individuals that
score high on NA experience more feelings of dysphoria, anxiety and irritability; have a
185
negative view of self; and scan the world for signs of impending trouble. SI refers to the
tendency to inhibit the expression of emotions/behaviors in social interactions to avoid
disapproval by others. High-SI individuals tend to feel inhibited, tense and insecure
when with others (Denollet, 1998). Miller (2003) claimed that the broad-band trait of
high negative emotionality/neuroticism is the primary risk factor for developing fullPTSD. If this is the case, it would support the notion that Type D personality is a
vulnerability factor for PTSD, as the negative affectivity aspect of Type D constitute
partly the characteristics of neuroticism.
Another aspect investigated in this early post ACS period was patients‘ illness
representations. I found that more negative illness perceptions were associated with
greater posttraumatic stress symptoms, in particular, duration of illness, greater
consequences of the illness, lack of understanding, poorer treatment control and
greater negative emotional representations were associated with posttraumatic
symptoms severity at time 2. However, the association between patients‘ beliefs in
personal control and posttraumatic stress was not significant. This finding appears
contradictory to previous research, which has demonstrated the importance of a belief
in personal control (Moser & Dracup, 1995; Doerfler et al., 2005). The concept of
illness representations refers to the cognitive models and explanations a patient may
hold regarding their illness or condition. Previous research has shown that illness
perceptions held by patients can account for variations in emotional reactions to
symptoms of physical disease (e.g. Murphy et al., 1999) as well as variations in selfcare behaviours (Petrie et al., 1996). Illness perceptions may partly explain the mental
processes underlying emotional reactions to acute cardiac events, and provide a useful
theoretical framework for understanding post ACS distress. When an individual face a
health threat such as myocardial infarction, they form a representation of the illness.
Internal and external variables, such as health history, personality, social environment,
and demographic factors, contribute to the formation of illness perceptions. Illness
perceptions may change over time as patient‘s knowledge and experience of the
186
condition evolve. They are not therefore fixed beliefs, and are amenable to change,
offering one potential approach to improve adjustment following ACS. The role of
illness perceptions in the development of longer term (six month) posttraumatic stress
symptoms will be addressed in the following chapter.
6.6.2 Salivary cortisol and heart rate variability in the immediate aftermath of ACS –
predictors of acute emotional reactions
Although posttraumatic stress has previously been associated with cortisol
dysfunction, at this point in time, shortly after hospital discharge for ACS, no significant
relationships were observed between posttraumatic stress symptoms measured at time
2 and total cortisol output across the day, the slope of decline in cortisol levels or with
the cortisol awakening response. The literature on cortisol profiles in PTSD is rather
varied, with some reporting increased levels of cortisol, while others provide evidence
of blunted HPA function. Attempts to identify biological markers of patients at high risk
for developing PTSD following trauma have emerged from models postulating a
progressive neural sensitization in the immediate aftermath of trauma, leading to
increased activation of the sympathetic nervous system and the subsequent
development of PTSD (Pitman et al., 2000). The lack of any observed relationships at
this stage may be explained by the assessment of cortisol at this early post-ACS point.
It may be that the lower cortisol levels observed in other PTSD samples are examples
of ‗burn out‘ of the HPA axis, something that may occur after a long period of time of
HPA axis over-activation. If this is the case, any potential cortisol dysfunction may
become apparent with time, and the cortisol samples collected at the 12 month follow
up would offer the opportunity to assess the cortisol levels over time in relation to
posttraumatic stress symptoms (data not available for use in this thesis). Interestingly,
when depression was entered into the model as a co-variate, total cortisol output
187
across the day was independently predicted by posttraumatic symptom severity at time
2.
However, it is also interesting that that overall pattern of cortisol output over the
day was not unusual (fig. 6.2) compared with that observed in healthy samples
(Kirchbaum & Hellhammer, 2000). The mean cortisol awakening response was 4.27 
9.7 nmol/l, comparable with that of non-cardiac men and women of similar age
assessed on non-working days (e.g. 3.7  10.9 in Kunz-Ebrecht et al., 2004).
Cortisol output at time 2 was however significantly associated with the level of
acute stress experienced at the time of ACS. This is a particularly intriguing finding,
since in my earlier analyses (see section 6.3.2) acute stress emerged as an
independent predictor of posttraumatic symptom severity. Patients who reported lower
levels of acute stress at the time of their ACS had higher total cortisol output over the
day, and a significantly greater cortisol awakening response than did those with
moderate and high acute stress. One explanation for these results may be that in those
with higher levels of acute stress, what is observed here is a blunting of the normal
cortisol response. This explanation is in line with prospective research suggesting that
low cortisol levels at the time of exposure to trauma are predictive of subsequent PTSD
(e.g. Resnick et al., 1995; Yehuda et al., 1998). Further, low levels of cortisol may in
fact be a pre-existing risk factor associated with maladaptive stress responses to
trauma resulting in PTSD. However, since cortisol was only measured post ACS in the
present sample I cannot make any inferences about pre trauma levels.
Further evidence of post ACS biological dysfunction was demonstrated by the
HRV data collected during the time 2 home interviews. Although there were no
differences in either low frequency or very low frequency HRV activity between PTSD
positive and negative groups, the PTSD positive group showed significantly lower high
frequency activity, indicative of reduced parasympathetic control. Reduced heart rate
variability has been identified as a prognostic factor for cardiac mortality (e.g. Bigger et
188
al., 1993, Janszky et al., 2004, Nolan et al., 1998). PTSD has been associated with
increased risk for mortality in cardiac patients and low HRV may act as an intermediary
in this association. The finding that PTSD is associated with reduced HRV suggests
that treating PTSD could increase HRV and, thus, decrease mortality post-ACS.
However, HRV was only assessed post ACS in this sample, and it is not known
whether the posttraumatic reaction to ACS caused the reduced HRV, or whether
reduced HRV was a pre-existing state in these patients, potentially making them more
vulnerable to developing PTSD. The result must also be interpreted very cautiously,
since it only emerged in the comparison of patients with very high PTSD levels and the
remainder, rather than in the multiple regression on the total PTSD scores. Further
information may emerge from analyses of the 6 month data.
Additionally, the heart rate observed in this sample at 3 – 4 weeks post ACS
appeared, on the surface, lower than previously observed in trauma samples who later
went on to develop PTSD. Shalev and colleagues (1998) assessed HR in 86 trauma
survivors in the emergency department (ED), and reassessed these patients‘ HR one
week, one month, and four months after their admission to the ED. Patients who
displayed PTSD at four months had higher resting HR than those without PTSD at one
week (95.5 beats per minute [bpm] versus 83.3 bpm) and one month (77.8 bpm versus
73.0 bpm). Bryant and Harvey (2002) found that patients who developed delayed onset
PTSD (83.0 bpm) had comparable HR to those with continuous PTSD since the trauma
(82.2 bpm), and both these groups had higher HR than those groups that did not
develop PTSD (74.31 bpm). Zatzick and colleagues (2005) assessed HR in 161 injured
surgical patients at the ED, and subsequently assessed them for PTSD at one month,
4–6 months, and 12 months post-trauma. Adopting an HR cutoff of ≥95 bpm, this study
found that those patients with HR ≥95 bpm had higher PTSD scores at each of the
follow-up assessments than those with the lower HR levels. Regression analyses
indicated that this HR cutoff significantly predicted PTSD at 4–6 months and 12 months
independently of injury, clinical, and demographic characteristics.
189
6.6.3 Summary
The results presented in this chapter demonstrate the importance of post ACS
acute emotional reactions in the prediction of subsequent posttraumatic responses, as
well as the role of patients‘ illness cognitions. Although the prevalence rates of PTSD
were lower than those observed in chapter 4, this significant minority of patients
experience psychological distress which deserves further attention. The identification of
predictors of posttraumatic stress is important as it could offer one potential pathway
for developing interventions to reduce distress, in particular since these patients [high
on posttraumatic stress] are also more likely to experience other psychosocial
impairments and recurrent health problems.
190
CHAPTER 7. Results TRACE Study II
The results from the second part of the TRACE study are presented in this
chapter. The prevalence of PTSD at six months is introduced to start, followed by the
examination of psychosocial and cognitive predictors of six-month posttraumatic stress
symptoms. Patients‘ psychosocial adjustment is subsequently assessed, and the role
of posttraumatic stress in predicting recovery behaviours is explored. This section is
followed by the results showing a role of partner posttraumatic reactions for
posttraumatic stress among patients six months following ACS. The chapter concludes
with results addressing the issue whether time 2 (3-4 weeks post-ACS) cortisol and
HRV data predict time 3 posttraumatic stress symptom severity.
7.1 Data analysis
At six month follow up 200 of the 226 patients (88.4%) who completed time 2
were re-contacted. Although 200 patients completed the telephone interview (for a
description see section 5.4.3), only 160 of these returned the follow up questionnaires.
As the PTSD measure was included in the postal questionnaire only, not in the
telephone assessment, the analyses were therefore limited to this number (n = 160).
174 patients completed both time 2 and time 3 follow up. The prevalence of PTSD and
severity of posttraumatic stress symptoms were examined. Associations between
posttraumatic stress and time 1 clinical, demographic and psychological variables were
analyzed using Pearson correlations for continuous variables and non-parametric
Spearman correlations for categorical variables. Multiple regression analyses were
conducted to assess independent psychological, cognitive and biological predictors of
time 3 posttraumatic symptom severity. Standardized regression coefficients (β) are
presented along with the standard error for these.
191
7.2 Patient characteristics
The baseline (time 1) clinical, demographic and psychological characteristics of
the patients who completed time 3 interviews are presented in table 7.1.
TABLE 7.1 PATIENT CHARACTERISTICS AT TIME 3
Time 3 Sample
Mean (SD)
N
n (%)
Demographic factors
Age
200
Gender
200
Educational attainment
199
60.85 (10.88)
Men
172 (86.0)
Women
28 (14.0)
None
51 (25.6)
Basic
57 (28.6)
Secondary
Degree
62 (31.2)
29 (14.6)
Marital status (married)
200
141 (70.5)
Ethnicity (white)
200
170 (85.0)
Social deprivation
197
Employed (yes)
199
Low
Medium
High
139 (70.6)
40 (20.3)
18 (9.1)
110 (55.3)
Clinical factors
ACS type
200
STEMI
175 (87.5)
NSTEMI/UA
25 (12.5)
GRACE score
200
94.18 (25.87)
SRI
200
21.71 (10.45)
Heart failure
200
14 (7.0)
Previous MI (yes)
200
31 (15.5)
Family history of CHD (yes)
200
129 (64.5)
Smoker (current)
200
76 (38.0)
BMI
188
Major cardiac event since
hospital
193
27.53 (4.62)
36 (18.7)
192
Time 3 Sample
Mean (SD)
N
n (%)
Psychosocial factors
SF12
SF12
181
Physical
Mental
45.84 (8.49)
50.82 (10.13)
Acute stress
193
Low
Medium
High
POMS negative
192
3.55 (2.60)
Subjective pain during ACS
200
6.73 (2.57)
History of depression (yes) –
assessed at time 2
162
49 (25.4)
109 (56.5)
35 (18.1)
45 (27.8)
Non-completers at time 3 were younger, more likely to be married, less socially
deprived, and less likely to have had a history of depression than were those who
completed time 3 follow up (p < .05). At six month follow up approximately 19% of
patients had experienced some form of major cardiac event since hospital discharge.
This included a having had a new ACS, cardiac surgery (including CABG), angiogram,
new stent, and persistent chest pain or angina.
7.3 Posttraumatic stress symptoms six months (time 3) post ACS
At six months follow up, 11 patients (7%) met the diagnostic criteria for PTSD.
The posttraumatic stress symptom severity score was 8.19 (SD 8.08). Table 7.2 shows
the total symptom scores, as well as the scores on each of the three sub-scales.
Avoidance symptoms were most frequently endorsed, followed by arousal symptoms
and finally intrusions/re-experiencing symptoms. The prevalence rate at six months
was calculated using the modified scoring criteria, outlined in chapter 3, section 3.4.2.2.
The original scoring method yielded a prevalence of 24.4% at six months.
193
TABLE 7.2 PSS-SR SCORES AT SIX MONTHS POST ACS
Time 3 – 6 months
N
Means (SD)
Range
PSS-SR Total score
156
8.19 (8.08)
0 – 44
PSS-SR Avoidance
156
3.66 (3.55)
0 – 19
PSS-SR Arousal
155
3.15 (3.44)
0 – 17
PSS-SR Re-experiencing
156
1.38 (1.89)
0 – 10
PTSD Diagnosis (positive) – modified
157
7%
PTSD Diagnosis (positive) – original
158
24.4%
At six months there were four new cases of PTSD that had not scored above
the diagnostic threshold at time 2, while two cases showed improvements from time 2
follow up and were no longer classified as having PTSD at six months. 139 patients
provided scores on the posttraumatic stress measure at both time 2 and time 3. The
change in symptom severity between time 2 and time 3 follow up was examined by
creating a posttraumatic severity change score. This score was generated by
subtracting the total severity score at six months from the total score at time 2 for those
patients who provided data at both follow up points. The mean symptom severity
change score was -.69 (SD 4.93), range -21 – 13, the correlation between time 2 and 3
scores was .79 (p < .001), indicating very little change on average in symptom severity
over time. Repeated measures ANOVA revealed no significant differences in symptom
severity between time 2 and time 3 (F (1, 138) = 2.69, p = .103, η2 = .019).
A diagnosis of PTSD, according to DSM-IV criteria, requires patients also to
experience PTSD associated impairments in other important areas of functioning
(criteria F). Patients with posttraumatic stress symptoms above threshold at six months
also reported significantly impaired physical and mental health status, assessed using
the SF-12 quality of life scale, averaging 35.50 (SD 10.34) and 30.68 (SD 10.53) for the
two scales, compared with 44.76 (SD 9.94) and 54.52 (SD 7.82) for physical and
mental health status respectively in the non-PTSD patients (both p < .05).
194
7.3.1. Multivariate predictors of posttraumatic stress symptoms at time 3
Table 7.3 shows associations between demographic, clinical and psychological
variables assessed at time 1 and 2 and posttraumatic stress symptom intensity at six
months post ACS. There were no significant associations between any of the
demographic or clinical variables assessed at baseline and the posttraumatic stress
symptom severity at six months. Patients‘ acute emotional reactions (acute stress
symptoms, negative mood) in hospital were significantly associated with greater
posttraumatic stress symptoms at time 3 (both p < .001). All psychosocial factors
measured at time 2 (depression, anxiety, low social support, hostility, type D
personality, and a history of depression) were associated with higher posttraumatic
stress symptom scores at six months (all p < .001, history of depression p = .003), with
the exception of pain experienced since ACS (p = .078). Greater posttraumatic
symptom intensity was also associated with having experienced a new major cardiac
event in the six months since hospital discharge (F (1,150) = 4.05, p = .046, η2 = .026),
as well as having had a non-cardiac health problem in the past six months (F (1,150) =
7.93, p = .006, η2 = .050). Non-cardiac health problems reported by patients included
having had a broken arm, receiving a diabetes diagnosis, deep vein thrombosis, and
having had a cataract operation.
195
TABLE 7.3 PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS
6 month posttraumatic
symptoms
Means (SD) or
r
P
Demographic factors
Age
-.123
.125
Gender
Men
Women
7.85 (8.06)
10.51 (7.99)
.169
Education
None
Basic
Secondary
Degree
8.76 (8.45)
9.73 (7.96)
7.01 (7.63)
6.66 (8.55)
.301
Ethnicity
White
Other
8.00 (8.00)
9.42 (8.65)
.457
Social deprivation
Low
Medium
High
7.49 (7.46)
10.07 (10.30)
11.87 (6.99)
.098
Marital status
Married
Not married
6.94 (6.16)
11.29 (11.02)
.060
STEMI
NSTEMI/UA
8.29 (8.40)
7.44 (5.35)
.669
GRACE score
-.116
.151
SRI
-.076
.346
BMI
.021
.797
Clinical factors
ACS type
Smoker
Yes
No
9.88 (10.48)
7.27 (6.28)
.054
Previous MI
Yes
No
7.00 (7.26)
8.44 (8.25)
.401
Major cardiac event since
hospital discharge
Yes
No
11.69 (8.59)
7.69 (7.84)
.046
Other health problems
since hospital discharge
Yes
No
14.90 (12.70)
7.69 (7.41)
.006
Psychosocial factors
Depression (BDI)
.656
<.001
Anxiety
.700
<.001
Acute stress symptoms
.305
<.001
Subjective pain at ACS
.037
.648
Pain since discharge (T2)
.150
.078
POMS negative
.393
<.001
Social support
-.310
<.001
Hostility
.345
<.001
196
6 month posttraumatic
symptoms
Means (SD) or
r
P
Type D
Yes
No
13.25 (9.42)
5.77 (5.98)
<.001
History of depression
Yes
No
10.96 (9.16)
6.49 (6.83)
.003
The intercorrelations between the psychological predictor variables shown in
table 7.4, highlight the importance of understanding that although there may be a
number of different psychological predictors of posttraumatic stress, these are not
always independent of each other.
TABLE 7.4 CORRELATIONS BETWEEN PSYCHOLOGICAL PREDICTOR VARIABLES
Psychological
BDI
Anxiety
variable
Acute
Type D
Hostility
Stress
Anxiety
Acute stress
Type D
r
.722
p
<.001
r
.300
.380
p
<.001
<.001
Subjective
Depression
pain
History
r
.403
.496
.261
p
<.001
<.001
.001
r
.234
.288
.173
.267
p
.002
<.001
.027
.001
Subjective
r
.150
.199
.266
.290
.144
pain at ACS
p
.025
.003
<.001
<.001
.062
History of
r
.219
.226
.107
.163
.148
.017
Depression
p
.001
.001
.124
.043
.066
.801
POMS negative
r
.427
.477
.286
.304
.161
.167
.082
p
<.001
<.001
<.001
<.001
.039
.005
.240
Hostility
A hierarchical multiple regression model was performed. Age, gender, social
deprivation, ethnicity, GRACE and having had a major cardiac event since hospital
discharge were entered in step one. Demographic and clinical variables accounted for
197
9.1% of the variance, with social deprivation and experiencing a major cardiac event
following hospital discharge emerging as independent predictors (Table 7.5, model 1).
The psychological predictor variables were entered in the second step (Model 2).
These variables accounted for an additional 51.9% of the variance, with the complete
model explaining 61% of the variance in six-month posttraumatic symptom severity.
Depressed mood and anxiety measured at time 2 were the only independent predictors
from among the psychological variables. Though, there was a trend towards
significance for the experiencing a new major cardiac event since hospital discharge.
None of the variables included in the final model showed multicollinearity according to
variance inflation factor and tolerance values. The model was re-run including a history
of depression (independent predictors remained the same), though this resulted in a
slight reduction in numbers included in the model and therefore the final model
excluded this variable (data not shown).
198
TABLE 7.5 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX
MONTHS
Model 1
Standardised
regression
coefficients
-.053
Age
Standard
Error
P
.150
.722
Standardised
regression
coefficients
.130
Model 2*
Standard
Error
P
.102
.207
Gender
.061
.089
.497
.061
.061
.318
Social deprivation
.191
.089
.034
-.098
.065
.138
Ethnicity
.057
.087
.514
-.041
.059
.487
GRACE score
-.003
.151
.987
-.037
.101
.712
.188
.087
.033
.111
.060
.066
.114
.070
.103
Acute stress
-.079
.067
.237
Depression time 2
.319
.089
<.001
.514
.090
<.001
Major cardiac event since
hospital discharge
R2
.091
Negative mood in hospital
(POMS )
Anxiety time 2
2
R
.610
*n= 128
TABLE 7.6 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX
MONTHS
Model 1
Standardised
regression
coefficients
.005
Age
Standard
Error
P
.088
.951
Standardised
regression
coefficients
.040
Model 2*
Standard
Error
P
.089
.652
Gender
-.057
.053
.289
-.014
.054
.789
Social deprivation
-.080
.055
.149
-.110
.057
.055
Ethnicity
.052
.051
.311
.018
.052
.734
GRACE score
.060
.089
.501
.033
.088
.706
.082
.052
.118
.083
.052
.111
.850
.056
<.001
.635
.099
<.001
.043
.061
.480
Acute stress
-.088
.058
.132
Depression time 2
.077
.086
.371
.218
.090
.017
Major cardiac event since
hospital discharge
Posttraumatic symptoms
time 2
R2
.686
Negative mood in hospital
(POMS )
Anxiety time 2
R2
.711
*n= 128
199
Table 7.6 shows the regression analysis including the posttraumatic symptom
severity at time 2. Posttraumatic stress symptom severity at time 2 emerged as the
strongest independent predictor of six-month symptoms, with model 1 accounting for
68.6% of the variance. The acute emotional reactions at time 1 and emotional
responses at time 2 accounted for an additional 2.5% of variance in six-month
posttraumatic stress. From among these variables, anxiety at time 2 was the only
independent predictor of posttraumatic symptoms at time 3. A non-significant trend for
social deprivation was observed. The complete model accounted for 71.1% of
variance.
A model including type D personality and hostility was also tested. However,
these variables did not emerge as independent predictors when negative mood in
hospital, acute stress, depression and anxiety were included in the model. Two models
were tested including the clinical and demographic variables on step one, and the type
D personality variable and hostility variable on step two, respectively, where these
variables emerged as independent predictors of posttraumatic symptoms (data not
shown).
7.3.2 Cognitive predictors of posttraumatic stress symptoms at time 3
Patients‘ beliefs about their condition were re-assessed at time 3. Illness
representation scores for the seven dimensions assessed are presented in table 7.7.
Patients viewed their illness as chronic and cyclical, but reported understanding their
condition quite well. They had quite strong personal and treatment control beliefs,
indicating that they thought that their medical condition could be helped by their own
actions and by clinical treatments. However, at the same time, the negative
consequences of the disease upon their lives were perceived as considerable, as was
their emotional response.
200
TABLE 7.7 TIME 3 ILLNESS REPRESENTATIONS
N
Means
SD
Median
Timeline
151
3.37
0.91
3.5
Timeline – cyclical
151
2.29
0.84
2.0
Consequences
152
3.13
0.74
3.2
Personal control
151
3.88
0.66
4.0
Treatment control
151
3.74
0.61
3.8
Illness coherence
154
3.85
0.79
4.0
Emotional representations
153
2.50
0.87
2.3
Illness representations – Time 3
Table 7.8 shows the correlations between each of the illness representation
dimension for ratings provided at time 2 and time 3 (controlling for age and gender). As
can be seen in the table, all seven dimensions at time 2 correlated significantly with the
corresponding dimension at time 3, indicating moderate stability (r = 0.44 to 0.64) in
these cognitive representations.
TABLE 7.8 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 3
ILLNESS REPRESENTATIONS
Time 3 Illness representations
Time 2 Illness
representations
1
2
3
4
5
6
7
1. Timeline
r
p
.583
<.001
.115
.230
.386
<.001
-.116
.227
-.247
.009
.052
.586
.271
.004
2. Timeline – cyclical
r
p
.094
.327
.610
<.001
.136
.156
-.235
.013
-.231
.015
-.341
<.001
.281
.003
3. Consequences
r
p
.241
.011
.134
.160
.443
<.001
.067
.483
-.175
.067
-.157
.100
.247
.009
4. Personal control
r
p
.011
.909
-.168
.078
-.057
.553
.440
<.001
.473
<.001
.200
.035
-.118
.219
5. Treatment control
r
p
-.143
.135
-.231
.014
-.132
.168
.349
<.001
.506
<.001
.087
.363
-.219
.021
6. Illness coherence
r
p
-.015
.872
-.441
<.001
-.114
.235
.040
.674
.180
.059
.529
<.001
-.245
.010
7. Emotional
representations
r
p
.118
.216
.370
<.001
.399
<.001
-.090
.345
-.282
.003
-.265
.005
.638
<.001
201
Repeated measures ANOVA were conducted to assess any potential change in
illness beliefs between time 2 and six month follow up. These analyses showed that
beliefs in timeline were significantly higher at time 3 (mean 3.36, SD 0.93) than at time
2 (mean 3.00, SD 1.01), suggesting that patients more strongly believed their illness
would last a long time or that it was of a permanent nature at time 3 than they did at
time 2 (F (1,120) = 23.79, p < .001, η2 = .165). Consequences were perceived as
significantly less severe at time 3 (mean 3.11, SD 0.71) than at time 2 (mean 3.27, SD
0.69) (F (1,122) = 6.84, p = .010, η2 = .053), but beliefs in the control of the illness by
treatment were also significantly lower at time 3 (mean 3.73, SD 0.56) than at time 2
(mean 3.93, SD 0.56) (F (1,121) = 16.03, p < .001, η 2 = .117). There were no
significant change for timeline – cyclical, personal control, illness coherence and
emotional representations assessed at both time points.
The relationship between each of the illness representation dimensions
measured at time 2 and six-month posttraumatic stress symptom severity, as well as
the relationship between illness representations and the three PTSD sub-scales
(intrusion, avoidance and arousal) were examined. Table 7.9 shows that greater
posttraumatic stress symptoms were associated with stronger beliefs in timeline (illness
will last a long time), timeline – cyclical (illness is unpredictable, symptoms will come
and go), greater consequences, less personal control, less treatment control, poorer
illness coherence, and greater emotional representations. This pattern of significant
correlations was largely observed across the three sub-scales of the PTSD measure,
with the exception of the non-significant associations between consequences and
intrusions, and between personal control and avoidance symptoms.
202
TABLE 7.9 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 3
POSTTRAUMATIC STRESS SYMPTOMS
Time 2
Illness representation
Posttraumatic
stress
symptoms
Time 3
Intrusion
sub-scale
Time 3
Avoidance
sub-scale
Time 3
Arousal
sub-scale
Time 3
Timeline
r
p
.290
.002
.223
.016
.279
.002
.260
.005
Timeline – cyclical
r
p
.373
<.001
.240
.010
.357
<.001
.359
<.001
Consequences
r
p
.339
<.001
.169
.070
.328
<.001
.346
<.001
Personal control
r
p
-.198
.033
-.228
.014
-.163
.080
-.169
.070
Treatment control
r
p
-.335
<.001
-.229
.001
-.313
.001
-.289
.002
Illness coherence
r
p
-.452
<.001
-.221
.017
-.466
<.001
-.336
<.001
Emotional representations
r
p
.580
<.001
.419
<.001
.561
<.001
.522
<.001
A multiple regression analysis was conducted to investigate the predictive value
of illness representations held by patients at time 2 on the posttraumatic stress severity
at six months follow up (table 7.10). After controlling for patient age, gender, ethnicity,
social deprivation, GRACE and the posttraumatic symptom severity at time 2 on step
one, the seven dimensions of illness representations added in step two explained an
additional 5.4% of variance, with the complete model accounting for 63.3% of the
variance in six-month posttraumatic stress severity. The posttraumatic symptoms at
time 2 were, unsurprisingly, the strongest predictor of time 3 posttraumatic symptoms.
However, patients‘ emotional representations also emerged as an independent
predictor of posttraumatic stress severity at six months. In addition, there was a near
significant trend for poorer illness coherence predicting posttraumatic symptoms in the
second step. None of the variables included in the final model showed multicollinearity
according to variance inflation factor and tolerance values.
203
TABLE 7.10 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS
AT SIX MONTHS
Model 2*
Model 1
Standardised
regression
coefficients
-.024
Age
Standard
Error
P
.111
.829
Standardised
regression
coefficients
-.093
Standard
Error
P
.115
.425
Gender
-.040
.065
.543
-.058
.067
.387
Social deprivation
-.064
.068
.345
-.077
.070
.279
Ethnicity
.116
.063
.069
.098
.063
.126
GRACE score
.043
.111
.698
.060
.113
.595
Posttraumatic stress time 2
.774
.069
<.001
.617
.086
<.001
Timeline
.060
.084
.472
Timeline – cyclical
-.035
.079
.663
Consequences
-.072
.082
.382
Personal control
-.099
.083
.233
Treatment control
.018
.087
.835
Illness coherence
-.148
.077
.058
.201
.086
.021
R2
.578
Emotional representations
R2
.633
*n= 114
This model was re-run excluding the time 2 posttraumatic symptom level as its
inclusion in the model could potentially obscure other more interesting relationships.
However, this reduced regression model did not demonstrate any strikingly different
effects, with the illness representation dimension of emotional representations still
being the only independent predictor of six-month posttraumatic stress (data not
shown).
The relationship between patients‘ illness representations and the three
posttraumatic stress sub-scales was also investigated. As for the total posttraumatic
stress severity score, age, gender, social deprivation, ethnicity, GRACE and
posttraumatic symptoms (in this case the appropriate sub-scale) at time 2 were entered
on to step one. The seven illness representation dimensions were entered on step two.
These models are presented in tables 7.11 to 7.13 below.
204
TABLE 7.11 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC INTRUSION
SYMPTOMS AT SIX MONTHS
Model 2*
Model 1
Standardised
regression
coefficients
-.064
Age
Standard
Error
P
.133
.632
Standardised
regression
coefficients
-.127
Standard
Error
P
.141
.369
Gender
-.014
.079
.861
-.041
.083
.622
Social deprivation
.034
.079
.672
.003
.083
.975
Ethnicity
.099
.075
.193
.100
.078
.200
GRACE score
.063
.134
.638
.057
.139
.685
Intrusion symptoms time 2
.606
.081
<.001
.531
.093
<.001
Timeline
.026
.102
.803
Timeline – cyclical
-.014
.097
.881
Consequences
-.144
.101
.156
Personal control
-.137
.101
.176
Treatment control
-.053
.106
.615
Illness coherence
.010
.094
.919
.201
.099
.046
R2
.393
Emotional representations
R2
.451
*n= 114
TABLE 7.12 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC AVOIDANCE
SYMPTOMS AT SIX MONTHS
Model 2*
Model 1
Standardised
regression
coefficients
.092
Age
Standard
Error
P
.120
.445
Standardised
regression
coefficients
-.017
Standard
Error
P
.120
.885
Gender
-.005
.070
.945
-.047
.070
.498
Social deprivation
.053
.071
.461
.033
.071
.643
Ethnicity
.188
.068
.006
.149
.066
.026
GRACE score
-.077
.120
.522
-.029
.118
.803
Avoidance symptoms time 2
.666
.071
<.001
.507
.084
<.001
R2
.512
Timeline
.030
.087
.731
Timeline – cyclical
-.069
.083
.403
Consequences
-.083
.086
.334
Personal control
-.085
.087
.328
Treatment control
.010
.090
.909
Illness coherence
-.231
.080
.005
Emotional representations
.245
.087
.006
R2
.603
*n= 114
205
TABLE 7.13 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC AROUSAL
SYMPTOMS AT SIX MONTHS
Model 2*
Model 1
Standardised
regression
coefficients
-.125
Age
Standard
Error
P
.127
.327
Standardised
regression
coefficients
-.133
Standard
Error
P
.133
.321
Gender
-.043
.075
.565
-.036
.076
.636
Social deprivation
-.156
.079
.050
-.168
.082
.043
Ethnicity
.020
.072
.777
.018
.073
.803
GRACE score
.120
.127
.348
.136
.130
.297
Arousal symptoms time 2
.718
.079
<.001
.549
.093
<.001
Timeline
.094
.097
.334
Timeline – cyclical
.019
.091
.837
Consequences
.014
.095
.881
Personal control
-.050
.094
.599
Treatment control
.029
.099
.769
Illness coherence
-.110
.089
.220
.187
.096
.055
R2
.447
Emotional representations
R2
.514
*n= 114
These regression models show some interesting differences in the prediction of
the three posttraumatic stress symptom clusters. Intrusive symptoms were predicted by
the intrusion symptoms at time 2 (table 7.11, model 1), and the emotional
representation variable was the only significant predictor from among the illness
representation dimensions (Model 2, step 2). This suggests that patients with stronger
emotional representations at time 2 had more intrusive symptoms after 6 months. The
full model accounted for 45.1% of variance in six-month intrusion symptoms.
Avoidance symptoms at six months were predicted by non-white ethnicity and
avoidance symptoms at time 2 (table 7.12, model 1). Patients‘ illness representations
accounted for an additional 9.1% of variance. Emotional representations and illness
coherence emerged as independent predictors, indicating that patients with stronger
emotional representations and poorer illness coherence at time 2 also had more
intense avoidance reactions at time 3. The complete model accounted for 60.3% of
206
variance in avoidance symptoms at six months. None of the illness representations
assessed at time 2 independently predicted arousal symptoms at six months. Instead
only social deprivation and arousal symptoms experienced at time 2 independently
predicted the variance in six-month posttraumatic arousal symptoms. However, it is
noteworthy that there was a near significant trend for stronger emotional
representations.
The analyses presented above suggest that early cognitive representations
predict future posttraumatic disturbances. One reason for this may be that early
cognitive representations predict later representations, and that it is the cross-sectional
association between cognitive representations and posttraumatic symptoms at six
months that is important. I therefore carried out analyses to assess the association
between concurrent illness beliefs (assessed at time 3) and posttraumatic symptom
intensity at six months.
These results are presented in table 7.14 below. After controlling for patient
age, gender, ethnicity, social deprivation, GRACE and the posttraumatic symptom
severity at time 2 on step one, the seven dimensions of illness representations
measured at six months accounted for an additional 8.4% of variance, with the
complete model accounting for 73.9% of the variance in six-month posttraumatic stress
severity. Posttraumatic symptoms at time 2 were, unsurprisingly, the strongest
predictors of time 3 posttraumatic symptoms. However, timeline – cyclical, personal
control, treatment control and emotional representations also emerged as independent
predictors. These results suggest that patients who had stronger beliefs in personal
and treatment control, believed that their illness was more unpredictable with
symptoms coming and going, as well as having stronger emotional representations at
six months, also reported more intense posttraumatic stress symptoms. In addition,
social deprivation emerged as an independent predictor in the second step. None of
the variables included in the final model showed multicollinearity according to variance
inflation factor and tolerance values.
207
TABLE 7.14 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX
MONTHS – CONCURRENT ILLNESS BELIEFS
Model 2*
Model 1
Standardised
regression
coefficients
-.075
Age
Standard
Error
P
.097
.438
Standardised
regression
coefficients
.000
Standard
Error
P
.091
.999
Gender
-.054
.057
.344
-.057
.053
.287
Social deprivation
-.086
.059
.146
-.112
.055
.044
Ethnicity
.097
.054
.076
.048
.050
.337
GRACE score
.133
.097
.170
.126
.089
.162
Posttraumatic stress time 2
.841
.059
<.001
.649
.065
<.001
Timeline time 3
-.012
.058
.838
Timeline – cyclical time 3
.182
.064
.005
Consequences time 3
.038
.069
.580
Personal control time 3
.174
.073
.019
Treatment control time 3
-.204
.076
.008
Illness coherence time 3
.045
.060
.460
R2
.654
Emotional representations time
.171
3
R2
.075
.024
.739
*n= 127
In the same way as for the total posttraumatic stress severity score, regressions
on the posttraumatic symptom sub-scales were carried out, with age, gender, social
deprivation, ethnicity, GRACE and intrusion, arousal or avoidance symptoms
(respectively) at time 2 being entered at step one. The seven illness representation
dimensions assessed at time 3 were entered on step two (tables 7.15 to 7.17).
208
TABLE 7.15 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC INTRUSION SYMPTOMS AT SIX
MONTHS – CONCURRENT ILLNESS BELIEFS
Model 2*
Model 1
Standardised
regression
coefficients
-.228
Age
Standard
Error
P
.124
.068
Standardised
regression
coefficients
-.118
Standard
Error
P
.117
.313
Gender
.033
.073
.648
.004
.069
.954
Social deprivation
.042
.073
.566
-.016
.069
.817
Ethnicity
.165
.069
.019
.102
.064
.115
GRACE score
.261
.125
.038
.216
.116
.065
Intrusion time 2
.611
.073
<.001
.411
.075
<.001
Timeline time 3
-.049
.075
.518
Timeline – cyclical time 3
.199
.082
.017
Consequences time 3
.027
.088
.761
Personal control time 3
.100
.094
.288
Treatment control time 3
-.169
.098
.086
Illness coherence time 3
.128
.078
.103
.301
.094
.002
R2
.431
Emotional representations time 3
R2
.569
*n= 127
TABLE 7.16 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC AVOIDANCE SYMPTOMS AT SIX
MONTHS – CONCURRENT ILLNESS BELIEFS
Model 2*
Model 1
Standardised
regression
coefficients
.054
Age
Standard
Error
P
.112
.631
Standardised
regression
coefficients
.118
Standard
Error
P
.106
.268
Gender
-.069
.065
.292
-.071
.063
.262
Social deprivation
-.006
.067
.926
-.046
.063
.472
Ethnicity
.098
.062
.120
.050
.058
.393
GRACE score
-.007
.112
.947
.011
.105
.917
Avoidance time 2
.740
.066
<.001
.523
.075
<.001
Timeline time 3
-.056
.068
.413
Timeline – cyclical time 3
.162
.075
.034
Consequences time 3
.063
.081
.436
Personal control time 3
.227
.086
.010
Treatment control time 3
-.233
.089
.010
Illness coherence time 3
.013
.071
.850
Emotional representations time 3
.208
.087
.019
R2
R2
.538
.640
*n= 127
209
TABLE 7.17 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC AROUSAL SYMPTOMS AT SIX
MONTHS – CONCURRENT ILLNESS BELIEFS
Model 2*
Model 1
Standardised
regression
coefficients
-.135
Age
Standard
Error
P
.109
.218
Standardised
regression
coefficients
-.055
Standard
Error
P
.106
.604
Gender
-.025
.063
.699
-.035
.062
.581
Social deprivation
-.148
.068
.032
-.174
.066
.009
Ethnicity
.037
.061
.549
-.007
.058
.903
GRACE score
.155
.109
.158
.148
.105
.161
Arousal time 2
.795
.067
<.00
.660
.071
<.001
Timeline time 3
.042
.068
.535
Timeline – cyclical time 3
.167
.075
.028
Consequences time 3
.045
.080
.574
Personal control time 3
.137
.086
.112
Treatment control time 3
-.181
.089
.044
Illness coherence time 3
.031
.071
.660
.122
.086
.155
R2
.558
Emotional representations time 3
R2
.641
*n=127
The regression models above show some interesting differences in the
prediction of the three posttraumatic stress symptom clusters by the concurrent time 3
illness representations. Intrusive symptoms were predicted by non-white ethnicity and
the intrusion severity at time 2 (table 7.15, model 1), accounting for 55.8% of the
variance in six-month symptoms. However, when the illness representations were
entered into the model, only the intrusion symptoms at time 2 remained an independent
predictor, as did the timeline – cyclical and emotional representations from among the
illness representations variables (Model 2, step 2). These results suggest that patients
who had a stronger belief in the unpredictability of their cardiac condition (believing that
symptoms would come and go), as well as stronger concurrent emotional
representations, were more likely also to have more intense intrusive reactions at time
3. When the illness representations were added to the model, these accounted for a
further 13.8% of the variance, with the whole model accounting for 56.9% of variance in
six-month intrusion symptoms.
210
Avoidance symptoms at six months was predicted by the avoidance symptoms
at time 2 (table 7.16, model 1, accounting for 53.8% of variance). Once patients‘ illness
beliefs were entered into the model, posttraumatic avoidance symptoms at time 2,
timeline – cyclical, personal control, treatment control and emotional representations
emerged as independent predictors, accounting for an additional 10.2% of variance.
The complete model explained 59.9% of variance in avoidance symptoms at six
months. These results indicate that patients who had a stronger belief in the
unpredictability of their cardiac condition believing that symptoms would come and go,
stronger beliefs in personal control, weaker beliefs in treatment control and stronger
concurrent emotional representations were more likely also to have more intense
avoidance reactions at time 3.
Arousal symptoms at six months were predicted by social deprivation and
arousal symptoms reported at time 2 (table 7.17, model 1). The illness representations
accounted for an additional 8.3% of variance in arousal symptoms at six months, with
timeline – cyclical and treatment control emerging as the only independent predictors
from among the illness representations at time 3. These results indicate that patients
who had a stronger belief in the unpredictability of their cardiac condition believing that
symptoms would come and go as well as weaker beliefs in treatment control were
more likely also to report greater arousal symptoms at time 3. The full model explained
64.1% of variance.
211
7.3.3 Multivariate predictors of posttraumatic stress symptoms at six months
A final multiple regression model was run incorporating those variables
emerging as independent predictors in previous sections, in order to test for
independence of these predictor variables. The following variables were tested in this
model: age, gender, social deprivation, ethnicity, GRACE score, major cardiac since
hospital discharge, depressed mood at time 2, anxiety time 2, posttraumatic stress
symptoms at time 2, and emotional representations time 2. Demographic and clinical
characteristics were entered on step 1. Patients‘ acute emotional reactions, mood and
illness cognitions at time 2 were entered on to step 2. This model is shown in table 7.18
below.
TABLE 7.18 COMBINED MULTIVARIATE PREDICTOR MODEL OF SIX MONTH POSTTRAUMATIC
STRESS SYMPTOMS
Model 2*
Model 1
Standardised
regression
coefficients
-.012
.163
.939
Standardised
regression
coefficients
.128
.096
.183
Gender
.131
.093
.160
-.022
.052
.675
Social deprivation
.160
.093
.087
-.092
.054
.092
Ethnicity
.093
.090
.303
.005
.052
.929
GRACE score
-.073
.163
.656
-.050
.093
.593
.188
.090
.040
.085
.051
.099
.569
.099
<.001
Depression time 2
.103
.091
.263
Anxiety time 2
.196
.091
.033
Emotional representations
.102
.069
.145
Age
Major cardiac event since
hospital discharge
R2
time 2
R
P
Standard
Error
P
.111
Posttraumatic stress symptoms
2
Standard
Error
.743
*n=118
The complete regression model accounted for 74.3% of variance in six-month
posttraumatic stress symptoms (Table 7.18). In model 1, having experienced a major
212
cardiac event since hospital discharge emerged as an independent predictor of
posttraumatic stress. However, once the posttraumatic stress symptoms experienced
at time 2 were included in the model, only the time 2 anxiety symptoms remained
independent predictors of posttraumatic symptom intensity at six months.
When the model was re-run omitting the time 2 posttraumatic symptom level a
slightly different picture emerged. In model 2 (Table 7.19), older age and major cardiac
since discharge emerged as independent predictors from among the clinical and
demographic variables. Patients‘ emotional reactions to the ACS as well as their
emotional representations (near-significant) of their condition now also emerged as
independent predictors of six-month posttraumatic symptoms. Anxiety was the
strongest predictor from among these variables. This model accounted for 66.4% of
variance in six-month posttraumatic stress.
TABLE 7.19 UNADJUSTED COMBINED MULTIVARIATE PREDICTOR MODEL OF SIX MONTH
POSTTRAUMATIC STRESS SYMPTOMS
Model 2*
Model 1
Standardised
regression
coefficients
-.012
.163
.939
Standardised
regression
coefficients
.249
.106
.021
Gender
.131
.093
.160
.028
.059
.633
Social deprivation
.160
.093
.087
-.076
.062
.221
Ethnicity
.093
.090
.303
-.048
.058
.406
GRACE score
-.073
.163
.656
-.135
.104
.198
.188
.090
.040
.128
.057
.028
Depression time 2
.383
.088
.000
Anxiety time 2
.405
.095
.000
Emotional representations
.152
.078
.054
Age
Major cardiac event since hospital
discharge
R2
2
R
Standard
Error
P
Standard
Error
P
.111
.664
*n=118
213
7.4 Posttraumatic stress symptoms, health behaviour and psychosocial
adjustment at time 3
Information about patients‘ health behaviours at time 2 and time 3 is displayed
in table 7.20. A change score was calculated to show the difference in these health
behaviours over time, by subtracting the time 2 scores from the time 3 scores. A
positive value on the change score variables indicates improvement over time.
Repeated measures ANOVA was conducted to investigate whether the scores at time
2 and time 3 were significantly different.
Patients were less adherent to medications at time 3 than at time 2 (using the
continuous score), but reported a significant increase in their fruit and vegetable intake
and walking (in minutes) per day (table 7.20). Patients were categorised as adherent or
non-adherent based on their scores on the MARS scale (see section 5.7.2.5). At time
2, 79.8% of patients reported being adherent, whereas at time 3 adherence was
reported by 46% of the patients. 125 patients provided information on their adherence
levels at both time 2 and time 3. Almost half of the patients (48.1%) who reported being
adherent at time 2, reported being non-adherent at six month follow up (χ² = 4.22, p <
.040). Posttraumatic symptoms at time 2 were unrelated to adherence in logistic
regression analysis (Odds ratio [OR] = .999, p = .982, CI = .947 – 1.055), controlling for
age, gender, social deprivation and ethnicity.
At time 2 39.6% of patients were smokers (n=118) but at time 3 only 12.4% of
patients reported smoking (n=18). 145 patients provided information on their smoking
status at both time points. A significant proportion of patients (67.3%) who reported
smoking at baseline (smoking status assessed in hospital interview – time 1), reported
they no longer smoked at time 3. Whereas, 32.7% of patients who smoked at time 1
also reported smoking at time 3 (χ² = 30.66, p < .001). A logistic regression analysis
was conducted to assess the effect of time 2 posttraumatic stress symptoms on time 3
smoking status, controlling for age, gender and social deprivation. This analysis
214
showed a near significant effect of posttraumatic stress on the increased likelihood of
smoking at time 3 (adjusted OR = 1.07, p = .055, CI = .999 – 1.151). When social
deprivation was removed from the analysis, greater posttraumatic stress symptoms at
time 2 was significantly associated with an increased likelihood of smoking at time 3 (p
= .036), suggesting shared variance between social deprivation and posttraumatic
stress.
TABLE 7.20 HEALTH BEHAVIOUR CHANGE TIME 2 TO TIME 3
N
Time 2
Time 3
Score
Score
Means (SD)
Means (SD)
Change
p
Score Means
(SD)
Range
Health Behaviour
Medication adherence
125
19.72 (1.04)
19.14 (1.28)
<.001
-.58 (1.51)
-6 – 9
Fruit and vegetable
intake
Exercise (walking in
min)
137
3.54 (1.98)
4.72 (2.37)
<.001
1.18 (2.38)
-4.29 – 12
129
35.73 (40.53)
80.58 (92.48)
<.001
44.82 (93.41)
-128.57 – 507.14
N
(%)
(%)
Smoker (yes)
298/145
118 (39.6)
18 (12.4)
Adherent (yes)
223/139
178 (79.8)
64 (46.0)
Health Behaviour
Cross-sectional associations between posttraumatic symptoms and health
behaviour at six months were also tested. Posttraumatic stress symptoms at time 3
were not associated with or daily fruit and vegetable intake (r = -.044, p = .627)
reported at time 3. However, patients reporting more intense posttraumatic stress
symptoms reported significantly less walking (r = -.198, p = .041), after controlling for
age, gender, ACS severity and physical health status. In addition, those patients who
scored positively on the PTSD measure at time 3 were significantly more likely to
smoke than those in the PTSD negative group (χ² = 13.96, p < .001). There was no
significant association between time 3 posttraumatic symptoms and adherence (χ² =
.906, p = .341).
215
Associations between posttraumatic stress symptoms and the change in health
behaviours between time 2 and 3 were tested. After controlling for patient age, gender,
social deprivation, ethnicity and GRACE score no significant associations emerged
between posttraumatic symptoms at time 2 and change fruit and vegetable intake (r = .135, p = .127), or between posttraumatic stress symptoms at time 2 and change in
walking per day [additionally controlling for current physical health status] (r = -.105, p =
.268). Change in levels of medication adherence between time 2 and six months were
not significantly associated with time 2 posttraumatic stress (r = -.032, p = .733), nor
was there an association between posttraumatic stress symptoms and change in
smoking status by six months (r = .057, p = .557).
At six month follow up patients reported significantly improved physical health
status (SF12) than at time 2 (means 40.13, SD 9.43 and 44.53, SD 9.85 respectively, F
[1,150] = 20.51, p < .001, η2 = .143). However, there was no difference in patient
mental health status (SF12) between the two time points (table 7.21).
TABLE 7.21 PHYSICAL AND MENTAL HEALTH STATUS CHANGE TIME 2 TO TIME 3
Time 2
N
Time 3
Score
Score
Means (SD)
Means (SD)
Change
p
Score Means
(SD)
Range
SF-12 Physical QoL
124
40.13 (9.43)
44.53 (9.85)
<.001
4.40 (10.81)
-22.95 – 29.20
SF-12 Mental QoL
124
53.56 (9.48)
53.06 (9.87)
.484
.50 (7.86)
-27.46 – 21.17
After controlling for patient age, gender, social deprivation, ethnicity and
GRACE score, the change in physical health status from time 2 to time 3 was
significantly associated with posttraumatic stress symptoms at time 2 (r = -.214, p =
.021), suggesting higher posttraumatic stress symptom severity at time 2 is associated
with a worsening in physical health status by six months. There was no significant
216
association between time 2 posttraumatic stress and change in mental health status by
six months (table 7.22).
TABLE 7.22 THE RELATIONSHIP BETWEEN TIME 2 POSTTRAUMATIC STRESS SYMPTOMS AND
PHYSICAL AND MENTAL HEALTH STATUS CHANGE
Posttraumatic stress
symptoms at time 2
n
SF-12 Physical QoL change score
114
SF-12 Mental QoL change score
114
r
-.214
p
.021
r
.084
p
.369
105 patients provided data on their employment status at both baseline and
time 3 follow up. Of these, 53 reported having returned to work, and the remaining 52
had not. After controlling for age, gender, ACS severity (GRACE), and having had
major cardiac event since discharge, those who had returned to work by six month
follow up did not score differently on the posttraumatic stress measure at time 2 than
did those that did not return to work by six months (F [1,86] = .587, p .446, η2 = .007).
However, as reasons for non-employment at baseline were unknown (for example,
patients may not have worked prior to hospitalization [time 1] due to cardiac related
health problems, but may have returned to work subsequently), a logistic regression
excluding working status at time 1 was conducted. This analysis included patient age,
gender, social deprivation, ACS severity (GRACE), major cardiac event since hospital
discharge and posttraumatic stress symptoms at time 2. The results showed that male
patients were more likely to work at six month follow up (adjusted OR = .066, p = .008,
CI = .009 – .500, with men as the reference group), as were patients who had
experienced a major cardiac event since discharge were more likely to work at six
months (adjusted OR = 13.04, p < .001, CI = 3.54 – 48.10). In addition, patients who
reported stronger posttraumatic stress symptoms at time 2 were less likely to be
217
working at time 3 (adjusted OR = .919, p = .013, CI = .859 – .986). For illustrative
purposes, the proportion of patients in the highest posttraumatic symptom tertile who
were working at time 3 was 22.5%, compared with 33.5% in the middle and 28.4% in
the lowest tertile.
7.5 Partner posttraumatic stress reactions and post ACS patient emotional
recovery
Patient and partner emotional reactions in the weeks following admission for
ACS (time 2) were significantly correlated (across all three measures, as illustrated in
table 7.23), whereas at six month follow up (time 3) patient emotional distress
(posttraumatic stress, depressive symptoms, anxiety) was associated with partner
posttraumatic stress but not with depression. However, partner anxiety at time 3 was
significantly associated with patient posttraumatic symptoms, but not with patient
depression and anxiety (table 7.24).
218
TABLE 7.23 CORRELATIONS BETWEEN PATIENT AND PARTNER EMOTIONAL REACTIONS AT
TIME 2
Partners
Posttraumatic
stress
symptoms
r
p
n
Posttraumatic
stress
symptoms
.367
.001
74
Depression
r
p
n
.288
.013
74
.280
.004
106
.193
.046
108
Anxiety
r
p
n
.414
.000
74
.351
.000
106
.346
.000
108
Patients
Depression
Anxiety
.370
.000
106
.248
.010
108
TABLE 7.24 CORRELATIONS BETWEEN PATIENT AND PARTNER EMOTIONAL REACTIONS AT
TIME 3
Partners
Posttraumatic
stress
symptoms
r
p
n
Posttraumatic
stress
symptoms
.388
.001
72
Depression
r
p
n
.389
.001
71
.156
.195
71
.208
.081
71
Anxiety
r
p
n
.445
.000
72
.175
.141
72
.186
.118
72
Patients
Depression
Anxiety
.157
.188
72
.246
.037
72
Table 7.25 below show the PSS-SR scores for the partners at time 2 and 3. The
symptom severity score at time 2 was 10.34 (SD 7.58), and 13% of the sample met
diagnostic criteria for PTSD. At six month follow up the posttraumatic symptom severity
score was 10.25 (SD 7.86), and the prevalence of PTSD was 11.5%. Unfortunately,
only 48 partners completed the measures at both time 2 and 3, so within-person
219
changes could only be assessed on a small sample. A severity change score was
calculated by subtracting the posttraumatic stress scores at time 3 from posttraumatic
stress scores at time 2, whereby a negative score indicate a worsening of symptoms
over time. Comparison of posttraumatic symptom severity among those partners who
provided scores at both time 2 and time 3 (n = 48) suggested an improvement in
symptom severity over time. The mean symptom severity change score was 1.91 (SD
6.33). Repeated measures ANOVA confirmed that posttraumatic symptom severity
decreased between time 2 (mean 11.35, SD 8.28) and six month follow up (mean 9.44,
SD 7.92) (F (1, 47) = 4.38, p = .042, η2 = .085), in those partners who provided data at
both time points.
TABLE 7.25 PARTNER PSS-SR SCORES AT TIME 2 AND TIME 3
Time 2 – 3-4 weeks
Time 3 – 6 months
N
Means (SD)
Range
N
Means (SD)
Range
PSS-SR Total score
77
10.34 (7.58)
0 – 30
77
10.25 (7.86)
0 – 31
PSS-SR Avoidance
77
3.82 (3.22)
0 – 15
77
4.11 (3.65)
0 – 14
PSS-SR Arousal
77
3.49 (2.94)
0 – 11
77
3.58 (2.75)
0 – 12
PSS-SR Re-experiencing
77
3.00 (2.48)
0 - 10
77
2.57 (2.21)
0–9
PTSD Diagnosis (positive) – modified
77
13%
77
11.5%
Using paired-samples t-test, posttraumatic symptom scores were compared
within couples (n=74) to determine whether there was a significant difference in the
posttraumatic stress reaction between patients and their partners at time 2. This
analysis showed that partners reported significantly greater posttraumatic stress
symptoms (mean 10.42, SD 7.60) than did the patients (mean 6.27, SD 6.07) (t (73) = 4.586, p < .001). Similarly, partners reported higher intrusion (means 1.30, SD 1.73;
3.05, SD 2.47, respectively) (t (73) = -5.558, p < .001), avoidance (means 3.88, SD
3.24; 2.81, SD 2.52, respectively) (t (73) = -2.801, p = .007), and arousal symptoms
(means 3.47, SD 2.96; 2.17, SD 2.75, respectively) (t (73) = -3.210, p = .002) than did
the patients. Similarly, at time 3, within-couples, partners reported significantly higher
220
total posttraumatic symptoms, intrusions, avoidance and arousal symptoms than did
the patients (data not shown).
A multiple regression analysis was conducted to assess the association of
partner posttraumatic stress (assessed at time 2) and patients‘ posttraumatic stress
reactions at six months (table 7.26). This analysis could only be carried out on 53
patients. Partners‘ posttraumatic stress symptom severity at time 2 (Model 2, step 2)
accounted for an additional 7.5% of the variance, after patient clinical and demographic
variables were included. The complete model explained 39.5% of the variance in
patients‘ six-month posttraumatic symptoms. Partner posttraumatic stress symptoms at
time 2 significantly predicted patient symptoms at six months, suggesting that the
posttraumatic reaction of the partners can have a significant impact of patient
reactions. None of the variables included in the final model showed multicollinearity
according to variance inflation factor and tolerance values.
TABLE 7.26 PARTNER POSTTRAUMATIC STRESS AS A PREDICTOR OF PATIENT
POSTTRAUMATIC STRESS AT SIX MONTHS
Model 2*
Model 1
Standardised
regression
coefficients
-.197
.177
.273
Standardised
regression
coefficients
-.109
.173
.532
Gender
-.093
.123
.452
-.027
.120
.821
Social deprivation
.293
.122
.020
.240
.118
.048
Ethnicity
.420
.123
.001
.399
.118
.001
GRACE score
.151
.177
.397
.038
.175
.830
.301
.125
.020
Age
2
R
R
P
Standard
Error
P
.320
Partner posttraumatic stress time 2
2
Standard
Error
.395
*n= 53
221
7.6 Posttraumatic stress at six months and salivary cortisol
I conducted multiple regression analyses adjusted for age, gender, BMI,
smoking and time of waking in the morning to examine potential relationships between
six-month posttraumatic stress symptoms and patients‘ cortisol profiles measured at
time 2. Four cortisol parameters were tested: the cortisol value on waking in the
morning, the cortisol awakening response, the total output of cortisol as defined by
area under the curve, and cortisol slope across the day. No significant associations
between posttraumatic symptoms at time 3 and any of these measures of cortisol were
observed (table 7.26). The study therefore failed to identify any associations between
posttraumatic stress symptoms six months post-ACS and cortisol profiles at time 2.
TABLE 7.27 POSTTRAUMATIC STRESS SYMPTOMS AT TIME 3 AND SALIVARY CORTISOL AT
TIME 2
Standardised
regression
coefficients
Standard
Error
P
Cortisol measure
n
Cortisol waking value
82
-.011
.116
.923
Cortisol awakening response
72
.039
.120
.744
Total cortisol output (area
under the curve)
80
.206
.116
.080
Cortisol slope
82
-.079
.113
.486
7.7 Time 2 heart rate variability and six-month posttraumatic stress
Heart rate and heart rate variability assessed at time 2 was compared between
the patients who scored as PTSD positive or PTSD negative at time 3. HRV data from
time 2 were available for 91 of the patients at time 3. Between groups analysis of
variance on the time 2 HRV frequency data showed no significant differences in HF, LF
222
or VLF HRV between the PTSD groups after controlling for patient age, gender, and
cardiac medication (beta-blocker, ACE inhibitor and statins) (table 7.27).
TABLE 7.28 HEART RATE VARIABILITY (TIME 2) AND PTSD AT TIME 3
PTSD positive
PTSD negative
HRV measure
N
Mean
SD
n
Mean
SD
P*
High frequency HRV
5
4.82
0.51
86
4.62
1.53
.831
Low frequency HRV
5
4.92
0.60
86
4.94
1.27
.516
Very low frequency HRV
5
4.68
0.47
86
4.56
1.11
.524
Heart rate
5
67.16
4.62
86
65.36
11.13
.970
* controlling for age, gender and cardiac medications.
These analyses were re-run using the original scoring criteria for PTSD. The
results showed no significant differences emerged on any of the HRV measures
between PTSD positive and PTSD negative patients (table 7.28).
TABLE 7.29 POSTTRAUMATIC STRESS SYMPTOMS (ORIGINAL CRITERIA – TIME 3) AND HEART
RATE VARIABILITY AT TIME 2
PTSD positive – original
criteria
PTSD negative – original criteria
HRV measure
n
Mean
SD
n
Mean
SD
P*
High frequency HRV
19
4.61
1.08
71
4.61
1.58
.666
Low frequency HRV
19
4.91
1.05
71
4.93
1.29
.552
Very low frequency HRV
19
4.59
0.91
71
4.55
1.13
.625
Heart rate
19
65.25
11.06
71
65.44
10.95
.826
* controlling for age, gender and cardiac medications.
223
7.8 Discussion
7.8.1 Predicting six-month posttraumatic stress symptoms from patients’ emotional and
cognitive post-ACS reactions
This study investigated the role of psychological and cognitive factors in the
prediction of longer-term posttraumatic stress symptoms. Patients‘ posttraumatic stress
symptoms were assessed at six months following their admission to hospital for ACS.
The findings indicate that 5.8% of patients at six months met diagnostic criteria for
PTSD. Though this rate of PTSD is markedly smaller than that presented in chapter 4
at 12 and 36 months, other studies assessing PTSD in cardiac patients have reported
comparable rates (e.g. Doerfler et al., 1994, Doerfler et al., 2005; O‘Reilly et al., 2004;
Wiedemar et al., 2008). Prevalence of PTSD varies widely across studies, due in part
to the wide variety of PTSD instruments used, study design, and timing of
measurement. On the surface, the two samples (ACCENT and TRACE) included in this
thesis appear very similar (for patient characteristics see tables 4.1 and 6.1), both
being prospective studies of ACS patients, utilizing similar or the same measures at
repeated follow up assessment points. The marked difference in prevalence is
puzzling. However, the prevalence rates may have been artificially reduced if patients
who were more distressed in hospital did not opt to participate in the study, possibly in
order to avoid talking about their condition. One potential explanation could be that the
participants in the TRACE study compared with the participants in the ACCENT study
reacted with greater relief to ‗have come through‘ the life-threatening trauma, and
rather than experiencing a deterioration in emotional state, perhaps subsequent
distress may have been buffered. Previous longitudinal research suggests that the
tendency to hold dysfunctional beliefs [about self and the world] prior to trauma
exposure increases the vulnerability to develop PTSD post trauma (Bryant & Guthrie,
2005). Conversely, strongly optimistic beliefs [about self and the world] can serve as a
224
buffer against the effects of trauma (Ali, Dunmore, Clarke & Ehlers., 2002). There were
no measures of ‗relief of having come through‘ the ACS, therefore no conclusion about
a buffering effect can be made. Another factor which could have influenced the
prevalence rates differently in the two studies is patients‘ social deprivation. In
ACCENT a larger percentage of patients (29.6%) were categorized as high deprivation
at baseline, whereas in TRACE this figure was much lower at time 1 (12.2%). Previous
research has found that low socioeconomic status is a consistent predictor of PTSD in
trauma-exposed adults (Brewin et al., 2000).
As reported in chapter 4, I also found that there was little change in
posttraumatic symptom severity in this study between those who completed both time 2
and six-month follow-up. Though there was some movement between the diagnostic
categories, there was no significant difference in symptom severity. Chronicity of PTSD
has been observed in samples where symptoms persist beyond 12 months (Freedman
et al., 1999; Wikman et al., 2008), however, there have been no other studies reporting
stabilization of symptoms within six months of the acute event. This finding suggests
that intervening at an earlier stage may be appropriate.
In line with my predictions, clinical characteristics of the ACS were unrelated to
six-month posttraumatic stress. This finding is generally supported by the literature on
post ACS PTSD (chapter 2). Patients‘ acute emotional reactions (acute stress
responses and negative mood in hospital), type D personality and hostility predicted
subsequent posttraumatic stress, however, the association between these variables
and posttraumatic stress at six month no longer remained significant after depressed
mood at time 2 and symptoms of anxiety at time 2 were entered into the model. In this
study, anxiety emerged as the strongest predictor of posttraumatic symptoms. Though
contrasting with the non-significant effect of anxiety observed in the ACCENT study
(chapter 4, sections 4.1.5 and 4.1.6) and others (Spindler & Pedersen, 2005), this
finding is not surprising, considering that PTSD belongs to the group of anxiety
225
disorders, and others have observed a positive association between anxiety and
subsequent posttraumatic stress (e.g. Pedersen et al., 2003; Rocha et al., 2008).
Patient‘s cognitive and emotional representations of their illness were also
assessed 3 – 4 weeks post ACS. I hypothesized that patients‘ illness perceptions
would partly account for the variation in subsequent emotional reactions to an acute
cardiac event. Specifically, beliefs that the condition will have more serious
consequences, a lack of understanding of the condition, experiencing more negative
emotional representations and lower control/cure beliefs will be related to posttraumatic
stress at six months. This hypothesis was only partly supported by the findings, though
the results demonstrate a significant role for illness representations in the prediction of
longer term posttraumatic stress.
Illness representations are by nature dynamic (Leventhal, 1997). Post ACS
recovery entails changes in many areas of life, such as physical ability, affect, social
relationships and knowledge of the illness and so on, therefore, patients‘ illness models
may change accordingly. Patients‘ illness representations were re-assessed at sixmonth follow up. Patients reported significantly higher timeline beliefs, significantly
lower consequences and treatment control beliefs at time 3 than at time 2. These
findings suggest that patients reported increasing uncertainty regarding the control of
their physical well-being, together with stronger beliefs in chronicity of the disease,
which could have contributed to emotional distress experienced. These findings are
similar to those reported by Sheldrick et al (2006) where in a combined sample of
myocardial infarction patients and patients who had suffered subarachnoid
haemorrhage, timeline beliefs increased over time, while timeline – cyclical, personal
control and treatment control decreased with time. Patients in the present study,
however, reported fewer consequences of their ACS at six-month follow-up than at 2 –
3 weeks following admission for ACS. This may suggest that over time patients learnt
to accept the impact of their illness and thereby perceived fewer consequences.
226
Sheldrick et al (2006) reported that greater emotional representations, reduced
illness coherence, greater consequences and stronger treatment control beliefs
independently predicted posttraumatic stress at 5 – 7 weeks post admission. In this
study the emotional representation variable was the strongest predictor, accounting for
approximately 47% of variance in the posttraumatic stress scores. Analyses of the
TRACE data found that only the emotional representation dimension from among the
illness representations included in the model was predictive of posttraumatic stress
intensity at six months, after controlling for posttraumatic symptoms at time 2.
However, there was a near significant effect of the illness coherence variable as well.
The emotional representation dimension involves aspects of depressed mood, anxiety,
emotional upset, anger, worry and fear in relation to illness. The finding that greater
emotional representations was the only significant independent predictor of
posttraumatic stress symptom severity at time 3 corresponds well with the research
showing a relationship between various measures of negative affect shortly after ACS
and subsequent posttraumatic stress (see chapter 2 for a review of these studies). This
is consistent with my hypothesis that negative affective states and cognitions shortly
after ACS would be predictive of later posttraumatic stress. Patients‘ beliefs in
consequences, treatment control and illness understanding were not related to
posttraumatic stress in TRACE. One reason for this non-significant result may partly be
explained by the anecdotal reports provided by some patients that they viewed their
ACS as a single health event that occurred in the past and that they did not consider
their ACS to be an ‗illness‘.
I also assessed the role of illness perception in the prediction of the three
posttraumatic stress sub-scales. Patients‘ intrusion symptoms at six months were
predicted only by the emotional representations variable. Avoidance symptoms were
predicted by emotional representations at time 2 as well as reduced illness coherence.
None of the illness representation dimensions were predictive of six-month arousal
symptoms. These findings are particularly interesting, as it seems that illness
227
representations may only be relevant to intrusion and avoidance symptoms. None of
the illness representations predicted time 3 arousal symptoms, though one may not
consider this a surprising finding as this dimension of posttraumatic symptoms are of a
more physiological nature, such as sleep disturbances, irritability and physical
reactivity. However, one might have expected to see an association with illness
coherence. Patients who do not understand their illness might be more likely to react
more strongly to unexpected physiological signs and symptoms, in turn leading to
greater arousal. The relationship between illness coherence and avoidance symptoms,
in addition to greater emotional representations, suggest that a lack of understanding of
ones illness may lead to inappropriate avoidance of trauma related stimuli.
Patients‘ illness representations at time 2 and 3 were moderately correlated,
suggesting that time 3 illness representations may be driven in part by patients‘ illness
representations at time 2, as well as their experiences in the months between the two
assessment points. A slightly different picture emerged when concurrent illness beliefs
were entered into the prediction model for six-month posttraumatic stress symptoms
compared with when the time 2 representations were tested. Posttraumatic symptom
severity was predicted by greater emotional representations, timeline – cyclical,
personal and treatment control. These results suggest that patients who reported more
intense posttraumatic reactions at six months also perceived their illness as more
emotionally upsetting, more strongly believed in the unpredictability of their condition,
had weaker beliefs in treatment control, but stronger beliefs in personal control.
Whereas previous research have suggested that weaker beliefs in personal control is
associated with poor emotional adjustment (e.g. Doerfler et al., 2005), in this sample,
the association between personal control beliefs and posttraumatic stress was positive.
The direction of this result was surprising, however, it may suggest that patients‘ felt
more responsible for the outcome of their illness, but may have been impeded if they
also experienced stronger emotional upset (emotional representations), did not believe
228
their illness could be controlled through treatment and that symptoms of their illness
varied much and were of a unpredictable nature.
The model including all independent predictors identified from the various
models tested in this chapter was tested to determine their independence. The
psychological predictors entered into the model, appeared to be independent of one
another. In the model excluding the posttraumatic symptom intensity at time 2,
depression, anxiety, emotional representations and having experienced a major
cardiac event since hospital discharge all emerged as significant independent
predictors of posttraumatic symptoms at six months. This model suggest that rather
than being a single factor of ‗distress‘, these psychological factors may in fact operate
though different pathways to precipitate posttraumatic stress reactions, and may
require different intervention strategies. However, when posttraumatic symptom
severity at time 2 was included, only the anxiety experienced shortly after
hospitalization for ACS independently predicted patients posttraumatic stress level from
among the psychological predictors identified in this study. In the majority of cases,
conventional PTSD is accompanied by another condition, such as major depression,
an anxiety disorder, or substance abuse (McFarlane, 2000; Kessler et al., 1995).
Whereas negative affect in the immediate aftermath of ACS has been found to strongly
predict subsequent posttraumatic reactions, in this sample, anxiety symptoms at time 2
were strongly positively correlated with six-month posttraumatic stress, suggesting that
highly anxious responses post ACS, increased vulnerability to posttraumatic stress
reactions.
7.8.2 Adjustment
PTSD is associated with a variety of adverse consequences. In this study I
investigated the influence of posttraumatic stress at time 2 on later health behaviours
and adjustment (medication adherence, diet, exercise, smoking, QoL, return to work).
229
Patients reported being significantly less adherent to medication over time, while they
increased fruit and vegetable intake and time spent walking per day. However, none of
these were significantly associated with posttraumatic stress symptoms at time 2.
These results suggest that in this sample of patients, early posttraumatic stress
reactions do not predict later health behaviours. Physical health status seemed to
improve from time 2 to time 3, however, greater posttraumatic stress was significantly
associated with poorer physical health status change between the two time points. In
cross-sectional analyses, greater posttraumatic stress symptoms at time 3 were also
associated with less walking. One explanation may be that patients who were more
traumatized by their ACS, intentionally reduced their activity levels (in this case
walking) as increased physical exertion may have served as a reminder (such as
shortness of breath, increased heart rate, sweating) of their cardiac event. Patients
who at time 3 met diagnostic criteria for PTSD were also more likely to smoke than
those who did not have PTSD, and time 2 posttraumatic stress symptoms showed a
near-significant trend for increased likelihood of smoking at time 3. These findings are
in line with previous research that has found posttraumatic stress following MI to be
associated with increased smoking [and alcohol intake] (Op den Velde et al., 2002).
One explanation for the non-significant effect of posttraumatic stress at time 2
on later (six-month) health behaviours, may be the low prevalence of PTSD observed
in this sample. The total scores on the PTSD measure and its sub-scales were
relatively low. Thus patients may not have experienced distress to such a level that it
would affect these outcomes. Another issue relates to the use of self-report for health
behaviours. For example, the adherence levels reported were very high (means 19.72,
SD 1.04; 19.14, SD 1.28, at time 2 and 3 respectively, with a score of 20 indicating
complete adherence), and it is not possible to know whether these are in fact the true
levels of medication adherence. However, concurrent posttraumatic stress was
associated with lower reported adherence, although earlier levels of posttraumatic
stress (time 2) did not predict six-month adherence.
230
At time 3 patients also reported on their employment status. Male patients,
those who had experienced a major cardiac event since hospital discharge, and those
who reported less intense posttraumatic stress symptoms at time 2 were more likely to
be working at time 3. These findings support the literature that PTSD is associated with
impairments in psychosocial adjustment. However, the finding that having had a major
cardiac event since discharge was associated with work at six months is puzzling. One
explanation might be that the patients reporting major cardiac events may have
undergone a revascularization procedure, thus improving their functional status,
allowing them to work again. Missing data on the timing of return to work limited the
analyses to the use of a binary variable of working vs not working at six months.
Analysing the effect of posttraumatic stress at time 2 on the actual timing of return to
work could also be interesting.
7.8.3 The influence of post ACS biological dysfunction on patients posttraumatic stress
responses at six months
No relationship was observed with any of the cortisol measures assessed at
time 2 and patients‘ posttraumatic stress symptom levels at time 3. Although, cortisol
dysfunction in response to trauma may be more likely to be observed over time, in this
study patient posttraumatic stress symptoms remained stable between time 2 and 3,
with very little movement between PTSD positive vs negative groups. This will result in
little power to detect change between these assessment points. Similarly, at time 2,
PTSD positive patients had significantly lower HF-activity than those patients in the
PTSD negative category. At time 3 follow up, this relationship was no longer significant.
231
7.8.4 The association of partner distress with patients’ posttraumatic stress reactions
Emotional reactions to ACS were also assessed in the partners of patients
participating in this study. Anxiety, depression and posttraumatic stress symptoms in
partners were significantly associated with patients‘ anxiety, depression and
posttraumatic stress levels at time 2. At time 3, only the posttraumatic stress response
of the partners was significantly associated with the overall emotional state of the
patients (anxiety, depression, posttraumatic stress). However, higher anxiety in
partners at six months was also associated with higher posttraumatic stress symptoms
in patients.
In line with my predictions, the findings from the multivariate analyses
demonstrated a significant impact of partner posttraumatic symptom severity on
patients‘ posttraumatic response at six months. Partner posttraumatic stress at time 2
independently predicted patient posttraumatic stress at six months. A model including
the depression and anxiety experienced by partners at time 3 was also run (data not
shown), however, these variables made no independent contribution to the prediction
of posttraumatic stress symptoms. One explanation for this may be that symptoms of
anxiety and depression in partners at this point in time are unrelated to the acute
cardiac event. The finding that partner posttraumatic reactions were predictive of
patient posttraumatic symptoms is particularly interesting, as previous research has
found that patients‘ adjustment to illness is worse when distress is greater in the
partners than when distress is greater in patients (Moser & Dracup, 2004). Bennett and
Connell (1999) found two contrasting processes to influence anxiety and depression in
partners of MI patients. The physical health consequences of the MI, was one of the
primary causes for partner anxiety, in particular the perceived physical limitations
imposed on their spouse by their MI. In contrast, partner depression was strongly
predicted by the emotional state of their spouse [MI patient]. Also the quality of the
232
marital relationship and the wider social support available to them influenced
depression in the partners.
Partner posttraumatic stress symptoms at six months were significantly
positively associated with patient posttraumatic stress symptoms at six months.
Previous research into couples and couple interactions have demonstrated an
‗emotional contagion‘ effect, whereby the emotional state and reaction of one partner
influences the emotional reactions of the other (e.g. Bookwala & Schulz, 1996;
Cutrona, 1990). There is growing evidence of emotional similarity within couples and
contagion effects with regards to depressive symptomatology (e.g. Butterworth &
Rodgers, 2006; Holahan et al., 2007; Kim et al., 2008).
An alternative approach suggests that ‗Expressed Emotion‘ (EE) can have a
significant and negative impact on patients with PTSD. EE refers to an emotional style
characterized by criticism/critical comments, hostility, and marked emotional overinvolvement (Leff & Vaughn, 1985). EE is thought to reflect the quality of the emotional
atmosphere in the home environment. The measure of EE has been most widely used
in prospective studies to predict outcome in schizophrenia (e.g. Butzlaff & Hooley,
1998). Living with a high EE partner can result in chronic and accumulating stress, and
the evidence that social support has an ameliorating action on the long-term effect of
trauma (e.g. Green et al., 1985; Keane et al., 1985) and that psychiatric morbidity in
traumatized populations is predicted by family instability and the quality of the
relationship with the partner (e.g. Romans et al., 1995; King et al., 1996) suggest that
EE can be considered pertinent to PTSD research. High EE partners also tend to
behave in a more negative and intrusive way towards the patient compared with low
EE partners (Hahlweg et al., 1989). Assessing the attributions held by partners of ACS
patients may help explain differences in their responses to the patients‘ illness. High
EE partners tend to view patients‘ behaviour as problematic and to attribute the cause
of behaviour as due to factors that are personal and internal to the patient. This may
lead to attempts by the partner to coerce the patient back to a normal level of functions.
233
However, this coercive or over-controlling response style of partners can be detrimental
to the recovery of patients suffering from PTSD (Tarrier et al., 1999). This suggests that
the quality of the emotional environment in patients homes can be a barrier for
successful treatment of PTSD (Tarrier et al., 1999).
Further, for PTSD, the DSM-IV now defines the stressor event to include
learning of a trauma occurring to a loved one. Thus, secondary exposure can lead to
an extreme reaction, such as the development of the full PTSD syndrome. Research
has shown that caring for people who have experienced highly stressful, negative life
events puts the caregivers (e.g. the partner) at risk for developing stress-related
symptoms similar to those of the victims (Barnes, 1998; Figley, 1995; Stamm, 1995).
This phenomenon is known as secondary traumatic stress (STS). Not only did partners
in this study report high levels of distress in response to the patient ACS, within
couples partners reported significantly higher levels of posttraumatic symptoms than
did patients themselves. However, it is important to note that this may have been a
gender effect, as the majority of partners were female. Overall, these findings are
particularly important as the presence of high levels of distress in partners can have
significant implications for patient recovery, by the reduced ability of partners to provide
support, as well as increasing the levels of distress experienced by the patients
themselves, this in itself being a risk factor for poorer post ACS adjustment and
prognosis.
7.8.5 TRACE: Study strengths and limitations
The strengths of this study include its prospective design and the inclusion of
the full range of ACS rather than just MI. Data were collected from a single site (St
George‘s Hospital) which is a strength of the study, whereas, multi-site data collection
poses various challenges in research design, communication, and comparability.
Further, the measures used to assess patients‘ emotional state were well-established
234
validated scales, used in previous studies on cardiac patients. The design of the study,
which included the home assessment at time 2, is a particular strength of the study.
This method may have been useful in increasing the response rate at time 2, as well as
allowing for the assessment of posttraumatic symptoms in a ‗safe‘ or ‗familiar‘
environment for the patient.
However, the study has a number of limitations. It involved a sample of patients
with a higher proportion of STEMI than NSTEMI/UA and included a greater number of
men than women. Exclusion of patients with co-morbidities likely to influence symptom
presentation or mood may have restricted the sample to individuals with lower risk
profiles. A substantial minority of eligible patients (13.4% and 11.5%) declined to take
part at time 2 and 3, respectively, and it is possible that more severely distressed
individuals chose not to participate. The majority of participants were men of white
European decent, and it would have been desirable to recruit a wider range of ethnic
minority groups and a larger percentage of women. The results from this study are
therefore difficult to generalize. Further, the loss of (approximately) 24% between time
1 and time 2 might have influenced the pattern of results.
Another issue is that posttraumatic stress was assessed by questionnaire rather
than by clinical interview. A full diagnostic interview was not possible within the
confines of the study, given the sample size and multi-phase design. It is important to
note that PTSD diagnosed by questionnaire tends to yield higher prevalence rates than
by clinical interview. However, the prevalence rates observed in this study are similar to
those observed in other samples using structured clinical interview diagnosis (e.g.
O‘Reilly et al., 2004). Additionally, modified more conservative criteria for a diagnosis of
PTSD was applied, and therefore only patients endorsing symptoms more frequently
were assigned to the PTSD positive group. This is of particular importance as this
method avoids confounding by high scores on somatic items, which is problematic
when assessing mood state in samples with chronic illness. For the majority of
analyses I chose to examine the effect on posttraumatic symptoms rather than on
235
PTSD positive vs PTSD negative groups. Posttraumatic reactions occur on a
continuum of severity, hence sub-threshold symptoms may also exert detrimental
effects on post ACS patients. Further, of the 200 patients that completed the telephone
interview at time 3 follow up, only 160 returned completed questionnaires. It may have
been better to assess PTSD in the telephone interview at six month follow up instead of
within the postal questionnaire, thereby increasing the response rate for this measure.
A number of issues relate to the assessment of the biological variables. Firstly,
cortisol was recorded only over the course a single day, and repeated measures are
likely to generate findings that are more robust. Secondly, compliance with the
sampling times was assessed by self-report and it was not possible to assess the
reliability of these reports objectively. The heart rate variability data was limited by the
issue of the interviews at time 2 not being of the same length, thus rather than being
able to use the 10-minute recording sections of the interviews only differences in
means across the whole session were suitable for use.
7.8.6 Summary
Posttraumatic stress symptoms were experienced by approximately 7% of
patients six months following ACS. This prevalence was similar to the rate reported at 2
– 3 weeks post admission in the same sample, suggesting persistence of symptoms as
early as six months post the acute event. The findings presented in this chapter
support previous research, demonstrating the importance of early emotional reactions
to ACS in the prediction of longer-term posttraumatic stress. This study also showed
that the assessment of patients‘ illness beliefs deserve further attention as significant
relationships emerged between a number of illness representation dimensions and
posttraumatic symptomatology. The inclusion of patients‘ partners was a particular
strength of this study. Results showed that the emotional state of the partners strongly
influenced the posttraumatic reactions of the patients. This area of research deserves
236
further attention, as patients‘ adjustment often takes place in the context of a
partnership, and the quality of the emotional atmosphere in the home has been linked
with posttraumatic stress.
237
CHAPTER 8. General discussion of research carried out in this thesis
Emotional states and reactions to acute cardiac events have been linked with
later emotional distress and difficulties in psychosocial adjustment. This thesis presents
a series of three analyses (chapters 4, 6 and 7) investigating the role of acute
emotional reactions to ACS, as well as emotional state in the weeks following
discharge, in the development of subsequent posttraumatic stress symptoms. This
thesis has also considered the importance of patients‘ cognitive models of their illness
in understanding later distress, as well as the influence of acute biological reactions to
trauma. The existing literature generally lacks studies of posttraumatic stress reactions
in ACS patients over longer periods of time, and most focus on a narrower range of
predictors than have been included in this thesis. A discussion of the results has
already been presented in chapters 4, 6 and 7. Therefore in this chapter I intend to
discuss the findings of this thesis in a broader context. A summary of the aims,
hypotheses and findings will be presented first, followed by a discussion of the
comparability of the two study samples in relation to the findings. The general
limitations of the studies included here will be presented next, followed by the clinical
implications of my findings. Finally directions for future research will be outlined. The
contribution that the two studies (ACCENT and TRACE) have made to the overall
understanding of post ACS posttraumatic stress will be highlighted.
8.1 ACCENT and TRACE studies – Aims
The three analyses included in this thesis were carried out with the following
aims:
a)
To evaluate the influence of acute post ACS emotional reactions in the
prediction of posttraumatic stress reactions 2 weeks following admission.
238
b)
To evaluate the influence of emotional responses measured in the
aftermath of ACS on longer-term posttraumatic stress symptoms (6, 12
and 36 months).
c)
To evaluate the influence of illness representations on subsequent
posttraumatic stress.
d)
To evaluate the relationship between biological reactions following ACS
and posttraumatic stress.
e)
To evaluate the influence of partner posttraumatic stress reactions on
patients‘ posttraumatic stress symptoms at six months post ACS.
f)
To evaluate the influence of posttraumatic stress symptoms on health
behaviours at six months following admission for ACS.
8.1.1 Accent – the relationship between emotional reactions to ACS and long-term
posttraumatic stress
My predictions for the ACCENT study were as follows:
-
Depressed mood following hospital admission will predict posttraumatic
stress symptoms 12 and 36 months later, independently of clinical and
sociodemographic factors.
In addition I hypothesized that:
-
Patients’ psychological disposition, in particular type D personality and
hostility, will also be predictive of posttraumatic stress symptoms 12 and
36 months following hospital admission for ACS.
Finally I hypothesized that:
-
Patients’ posttraumatic stress symptoms will show chronicity, remaining
stable between 12 and 36-month follow up.
239
The findings in chapter 4 largely supported these hypotheses. The prevalence
rates at 12 and 36 months of 12.2% and 12.8%, respectively, are in accordance with
previous research. Further, there was also very little movement between PTSD
categories (positive vs negative) between the follow up time points, suggesting that
once PTSD has established beyond 12 months it may remain chronic when left
untreated. Negative emotional state shortly after ACS was predictive of posttraumatic
symptom intensity at both 12 and 36 months. Specifically, depressed mood in hospital
and the recurrence of cardiac symptoms by 12 month follow up strongly predicted
posttraumatic symptom severity. Depressed mood remained a significant predictor of
posttraumatic stress at 36 months, however, recurrence of cardiac symptoms was no
longer significantly associated with symptom severity.
8.1.2 TRACE – the relationship between acute emotional reactions to ACS and
posttraumatic stress 3 – 4 weeks and six months post trauma
My predictions for the TRACE study were as follows:
-
Acute negative mood (in hospital) and distress will predict short-term
posttraumatic stress reactions (time 2).
-
Negative emotional state, in particular depressed mood, at time 2 will
predict
posttraumatic
stress
symptom
severity
at
six
months,
independently of clinical and demographic variables.
-
Patients’ psychological disposition (type D and hostility) will also be
predictive of posttraumatic stress symptoms.
-
Negative illness representations will be predictive of six-month
posttraumatic symptom intensity, specifically strong beliefs in negative
consequences,
poor
illness
coherence,
greater
emotional
representations and lower control/cure beliefs.
240
-
Cortisol dysregulation following ACS (assessed at time 2) will be
associated with greater posttraumatic stress reactions.
-
Heart rate variability will be reduced in those patients reporting greater
posttraumatic stress reactions shortly after the acute cardiac event.
-
Posttraumatic symptom intensity will predict poor medication adherence
and uptake of healthy behaviours at six months follow up.
-
Partner posttraumatic stress symptoms will have an influence on patient
posttraumatic symptom severity.
These hypotheses were partly supported by the findings reported in chapters 6
and 7. In line with my predictions, patients‘ acute negative mood in hospital and acute
stress experienced at the time of ACS predicted posttraumatic stress symptoms at 3 –
4 weeks following admission. In addition, social deprivation, mental quality of life,
current pain and a history of depression emerged as independent predictors of
posttraumatic stress symptom intensity. As with the results from the ACCENT study,
type D and hostility emerged as independent predictors, though not once depressed
mood was included in the models.
At 3 – 4 weeks post ACS patients‘ illness beliefs were assessed. Stronger
beliefs in chronicity of the illness, beliefs that symptoms were of a cyclical nature (i.e.
coming and going), more serious consequences, beliefs that the illness was poorly
controlled by treatment, poorer understanding of the illness and greater emotional
responses were significantly associated with more intense posttraumatic stress
symptoms. In multivariate analyses, the emotional representation dimension of the
illness representations emerged as the strongest significant predictor of concurrent
(time 2) posttraumatic stress symptoms. Similarly, at six-month follow up, greater
posttraumatic stress symptoms were associated with perceptions of a more chronic
and cyclical timeline, more serious consequences, poor understanding of the condition,
poor treatment control, greater personal control and more negative emotional
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representations assessed at time 2. Stronger emotional representations emerged as
the only significant independent predictor of six-month posttraumatic stress symptom
intensity in multivariate analyses. Further, depressed mood and anxiety assessed at
time 2 predicted posttraumatic stress symptoms at six months. Interestingly, in this
study anxiety emerged as the strongest predictor.
Other results were not consistent with my hypotheses. Patients‘ cortisol levels
at time 2 were generally unrelated to posttraumatic stress symptoms, though a
relationship was observed between total cortisol output across the day and
posttraumatic stress symptoms once depression was included in the model. In
multivariate analyses, patients who reported more intense depressive symptoms at
time 2 had lower total cortisol output across the day, whereas patients who reported
more intense posttraumatic stress symptoms at time 2 had higher total cortisol output
across the day. Further, an interesting relationship emerged between acute stress
symptoms at time of ACS and cortisol at time 2. Patients who reported lower acute
stress in response to the ACS had higher total cortisol output over the day, and a
significantly greater cortisol awakening response than those reporting medium or high
acute stress. Patients who were classified as PTSD positive at time 2 also had
significantly reduced heart rate variability, in terms of reduced high frequency activity.
There were no significant associations between cortisol and six-month posttraumatic
stress or with heart rate variability and six-month posttraumatic stress.
Unexpectedly, posttraumatic stress at time 2 did not predict adherence at time
3. However, greater posttraumatic stress symptoms at time 2 were predictive of
smoking status at six months. However, when the analysis included social deprivation,
this association was rendered just non-significant, suggesting shared variance between
posttraumatic stress and social deprivation status. Patients reporting more intense
posttraumatic stress symptoms at time 2 were also less likely to be working at six
months. In cross-sectional analyses at time 3, patients who met diagnostic criteria for
PTSD were more likely to smoke than were those without PTSD. Greater posttraumatic
242
stress symptoms were also associated with less daily walking, and a significant
worsening of physical health status between time 2 and 3. Finally, an interesting
relationship with partner distress was observed. Partner posttraumatic stress severity
at time 2 was predictive of patients‘ posttraumatic stress symptom intensity at sixmonths.
8.1.3 Comparability of ACCENT and TRACE findings
The work undertaken in this thesis has added to the literature on PTSD
following cardiac events in a number of important ways. Firstly, the ACCENT study
(chapter 4) is one of the few longitudinal studies to date assessing prevalence, severity
and predictors of posttraumatic stress beyond the first year after the acute event.
Although some prospective studies report on PTSD prevalence as long as 26 months
post trauma, the prevalence and predictors of PTSD in this group of patients at 3 years
follow up has not been demonstrated previously.
Overall the prevalence of PTSD observed in these studies is in line with those
reported previously (for a review of studies see chapter 2, section 2.7.1). For both the
ACCENT and TRACE studies, prevalence rates between follow up points remained
stable, and were not statistically significantly different over time. However, whereas the
ACCENT prevalence was approximately 12% at both 12 and 36 months, in TRACE this
rate was markedly reduced. In this sample only approximately 6% and 7% met
diagnostic criteria for PTSD at 3 – 4 weeks and six months post ACS, respectively. It
will be of great interest to assess the prevalence of PTSD in this sample at the 12month follow up (data not currently available).
For an overview of some of the main features of the two samples see table 8.1.
The severity of posttraumatic stress symptoms and prevalence at all follow up points
appear much lower in TRACE than in ACCENT. There may have been some
unmeasured protective factor in the TRACE study, which could have acted as a buffer
243
against more severe posttraumatic stress reactions. However, this would be surprising,
as the two studies appear very similar. Both ACCENT and TRACE included relatively
large samples. With a few exceptions, the number of patients included in previous
studies of PTSD in MI survivors has generally ranged from 20 to 120 (see table 2.2,
chapter 2).
The salience of acute emotional reactions in the prediction of later
posttraumatic stress reactions is supported by the findings presented in this thesis.
However, in order to get a more complete picture, and to more fully understand the
complex nature of post-ACS posttraumatic stress, the TRACE study (chapters 6 and 7)
was undertaken as an extension of the work conducted as part of the ACCENT project.
It therefore incorporated measures of illness cognitions as well as biological
measurement in the immediate aftermath of ACS. This approach enabled me to assess
the contribution of these potential predictors in a more multivariate manner, whereas in
previous studies the focus has been on a much narrower range of predictors. Further,
patients‘ spouses were included in this study, allowing me to assess the influence of
partner distress on levels of posttraumatic stress in patients.
One of the major issues with this research is that PTSD may in fact not be the
right model to represent the anxiety and distress that ACS patients feel after the acute
event, because distress appears to be experienced differently than when considering
more traditionally studied traumatic stressors. In addition, medical stressors (such as
MI) may be characterized by intrusions centered on future-oriented events. If one
considers that the intrusions are more future oriented, generalized anxiety disorder
(GAD) could offer a better fit for the symptom presentation. The defining features of
GAD are excessive, pervasive, and uncontrollable worry characterized by anxious
apprehension. Anxious apprehension refers to a future-oriented mood state in which
one becomes ready or prepared to attempt to cope with upcoming negative events.
This mood state is associated with a state of high negative affect and chronic
overarousal, a sense of uncontrollability, and an attentional focus on threat-related
244
stimuli (e.g., high self-focused attention, hypervigilance for threat cues) (Brown et al.,
2001). Some patients in both ACCENT and TRACE reported experiencing recurrent
cardiac-related symptoms or major cardiac events, both which independently predicted
intensity of their posttraumatic stress reaction following ACS. Such recurrence may
contribute to a sense of anxious apprehension, and in fact, symptoms reported may be
more anxiety specific than posttraumatic in nature.
It may be possible to distinguish whether medical patients presenting with
intrusive thoughts and anxiety have PTSD versus GAD using physiological
assessments. GAD is the one anxiety disorder in which somatic presentation involves
inhibition of the sympathetic nervous system, a restriction in the range of system
variability, and resulting physiological inflexibility at rest and when challenged (HazlettStevens, 2008). In contrast, trauma survivors with PTSD exhibit greater sympathetic
reactivity than trauma survivors without PTSD (Blanchard et al., 1996; Orr et al., 1998).
Alternatively, a new diagnosis may need to be formulated surrounding medical lifethreatening illnesses if the intrusive thoughts are both focused on the discrete past
event and future-oriented events.
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TABLE 8.1 OVERVIEW ACCENT AND TRACE STUDIES
Baseline sample
ACCENT
TRACE
284
298
(3), 12 (n=213)
Follow up and sample size
& 36 (n=179)
months
Design
Mood measures in hospital
2 -3 weeks (n=226), 6 (n=200)
& 12 months (follow up not yet completed)
Prospective
Prospective
Depression, Anxiety, Acute stress disorder
[Profile of] Mood states, Acute stress
(available only for part of the sample)
symptoms
Severity of posttraumatic
12 month severity mean 12.65 (SD 10.40)
Time 2 (2-3 weeks) mean 7.70 (SD .73)
symptoms (what time?)
36 month severity mean 11.78 (SD 10.27)
Time 3 (6 months) mean 8.19 (SD 8.08)
12 month prevalence 12.2%
Time 2 (2 – 3 weeks) prevalence 5.8%
36 month prevalence 12.8%
Time 3 (6 months) prevalence 7%
Home assessment
No
Yes
Biological measures
No
Yes
Spouse inclusion
No
Yes
Prevalence of PTSD
As illustrated in table 8.1 above, both studies were prospective in nature.
However, the timing of the assessment points varied greatly. Both studies comprised in
hospital interviews, however, only TRACE involved a home assessment following
hospital discharge. This was a particular strength of the study, allowing for in-depth
interviewing of patients to assess depression (utilizing the DISH, see section 5.7.1.1).
Also, it allowed participants to complete this part of the study in a familiar environment.
However, some patients may have declined to participate if they did not wish the
researchers to visit their homes. Though, overall one would expect that completion of
questionnaires at this stage would have been improved compared with a postal
assessment only. In ACCENT, patients only had face-to-face contact with the
researchers in the hospital setting. This may have influenced response rates, and
responses differently between the two studies. The assessment of acute stress
responses to the ACS was more complete in TRACE than it was in ACCENT, where
246
the acute stress measure was only introduced once hospital recruitment was
underway. This allowed me to examine the importance of acute stress reactions for the
prediction of shorter and longer-term posttraumatic responses in the TRACE sample.
Negative mood in hospital was assessed using the Beck Depression Inventory (BDI:
see section 3.4.3.1) in ACCENT. In TRACE the Profile of Mood States (POMS: see
section 5.6.3.1) measure was utilized. The BDI requires patients to rate their mood
over the past couple of weeks (including the present day) whereas the POMS asks
patients to rate only their present mood state. The use of the POMS in TRACE is an
advantage as it may be more representative of current mood. Patients thinking back
and rating past week mood may have coloured the ratings of present mood.
Both studies had multiple follow up points, allowing closer examination of PTSD
trajectories. In ACCENT, patients were followed up at three months, 12 months and 36
months following the hospital assessment point. In TRACE, patients were followed up
at 3 – 4 weeks, six months and 12 months (currently ongoing) following the initial
hospital assessment point. A particular advantage of ACCENT over TRACE is the long
follow up of patients (up until 36 months), which allowed for the assessment of longterm posttraumatic stress symptom severity and prevalence. Long-term studies such
as this one are generally lacking in the post-ACS PTSD literature. However, it may
have been sufficient to assess patients over relatively shorter periods, reducing
participant burden, as in TRACE (up until 12 months), since previous work and the
work presented in this thesis suggest that prevalence rates of PTSD appear to stabilize
over time. The inclusion of biological measurements in TRACE is an important addition.
With the exception of one recent study (von Känel et al., in press), there is a lack of
studies exploring the unique relationship between posttraumatic stress and cortisol in
this population.
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8.1.3.1 Predictor variables – ACCENT and TRACE
Although some of the demographic variables were predictive of posttraumatic
stress in the analyses presented in chapters 4, 6 and 7, in this section the focus will be
on the emotional and psychological predictor variables identified. Many of the
psychological factors assessed emerged as predictors of later posttraumatic stress in
both studies. However, there were a number of differences also. The predictor
variables of posttraumatic stress are summarized in table 8.2. Depressed mood was
identified as a significant independent predictor of later posttraumatic stress symptoms
(multivariate analyses) in both ACCENT (depressed mood in hospital) and TRACE
(negative mood in hospital and depressed mood at 3 – 4 weeks post ACS). Anxiety
additionally emerged as a significant independent predictor of posttraumatic stress
symptoms at six months in the TRACE study. Acute emotional reactions to the ACS,
assessed in hospital, were predictive of short term posttraumatic symptoms (TRACE),
however, these associations were no longer significant in the prediction of long term
(six-month) posttraumatic stress reactions once time 2 emotional state was included in
the regression models.
In both studies, type D personality and hostility were identified as significant
predictors of posttraumatic symptoms. However, these associations were rendered
non-significant once the negative mood measures were included in the models both in
ACCENT and TRACE. Recurrence of cardiac symptoms or the experience of new
major cardiac events since discharge emerged as significant predictors in both studies.
With the inclusion of biological and cognitive measures in TRACE, additional predictor
variables of importance were identified from this study. In multivariate analyses,
posttraumatic stress at six months was predicted by stronger emotional representations
(illness representation dimension describing emotional upset and distress). Cortisol
was associated with the acute stress experienced in hospital. A significant relationship
was also observed between cortisol and posttraumatic stress, once depressive
248
symptoms were controlled for. Finally, high frequency heart rate variability appeared
reduced in PTSD positive patients at 3 – 4 weeks post ACS. As this section describes,
the results from both studies are remarkably similar. Differences appear to be largely
due to different measures, but in both studies mood in hospital (POMS or BDI), cardiac
symptom recurrence, type D and hostility emerge as significant. This suggests that the
effects observed are robust.
TABLE 8.2 SUMMARY OF PREDICTOR VARIABLES ACCENT AND TRACE
ACCENT
Predictor variable
12 months
36 months
TRACE
3 – 4 weeks
6 months
Negative mood in hospital
Depression in hospital
Depression at 3 – 4 weeks
post ACS
Anxiety
(3-4
weeks
post
ACS)
Acute stress (in hospital)
Recurrent cardiac symptoms
Type D
Hostility
Emotional representations
Cortisol
HRV
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8.1.3.2 Cognitive factors – TRACE
Illness
representations
were
measured
to
investigate
the
cognitive
representations and explanations patients held regarding their cardiac disease. A
number of interesting associations between illness representations and posttraumatic
stress
were observed.
Cross-sectional associations between
time 2 illness
representations and time 2 posttraumatic symptoms, as well as time 2 illness
representations and time 3 posttraumatic stress symptom, showed that those who had
more intense posttraumatic reactions also believed their illness was unpredictable and
would last a long time, that it had more serious consequences and was poorly
controllable by treatments. They also reported poorer understanding of their illness, as
well as greater emotional responses. At time 3, stronger posttraumatic stress reactions
were also associated with weaker beliefs in personal control.
The moderate correlations observed between time 2 and time 3 illness
representations suggest that early cognitive representations may predict later
representations, and that it is the cross-sectional association between cognitive
representations and posttraumatic symptoms at six months that is important.
Concurrent stronger beliefs in chronicity of illness, poor treatment control, and stronger
emotional representations predicted six-month posttraumatic stress. Greater personal
control was also predictive of greater posttraumatic symptoms. Particularly interesting
is the direction of this association. Whereas lower personal control has been found to
be related to poorer psychosocial recovery (e.g. Moser & Dracup, 1995), in this
sample, stronger beliefs in personal control predicted more intense posttraumatic
reactions. Stronger personal control beliefs also predicted posttraumatic avoidance
symptoms, though not intrusions or arousal symptoms. Patients may have felt more
personally responsible for the outcome of their cardiac illness, and personal control
could have been exerted by the active avoidance of cardiac event related reminders,
situations or activities.
250
There are few studies investigating the relationship between illness
representations and posttraumatic stress in ACS patients. In a study by Oxlad and
Wade (2006) of pre-CABG patients, more negative illness representations (total score
on IPQ-R) had a direct effect on PTSD symptomatology and also acted indirectly,
mediated by increased use of avoidance coping. The results of post hoc analyses
suggested that the illness representation dimensions of personal control and emotional
representations may best explain the relationship between illness representation,
avoidance coping and PTSD symptomatology.
A study of MI patients found that poorer treatment control beliefs, weaker illness
coherence, stronger emotional representations and greater consequences predicted
subsequent PTSD (Sheldrick et al, 2006). However, this was not the case in TRACE
where stronger emotional representations (at time 2) emerged as the only independent
predictor of six-month posttraumatic stress in multivariate analyses. That emotional
representations were the strongest predictor of later posttraumatic stress is not
surprising, since this illness representation dimension could be regarded as another
measure of distress, and that distress at one time generally predicts distress at
another. The fact that none of the other illness representations emerged as
independent predictors of posttraumatic stress following ACS may be explained by the
reports by some patients that they did not consider their ACS to be an ‗illness‘. Rather,
these patients described their ACS as a one time event that occurred in the past and
did not consider it a chronic illness. Thus, the use of the word ‗illness‘ to describe the
acute cardiac event in this sample may have been inappropriate, and could have
influenced patients‘ responses on the illness representation measure. If the majority of
patients held this view, it may explain why beliefs in negative consequences, poorer
control and worse illness coherence did not emerge as independent predictors of
posttraumatic stress. If the ACS was, in fact, not viewed as a ‗continuous‘ illness, it is
not surprising that patients did not think it would have serious consequences, requiring
251
to be controlled (either by treatments of personal control) or that it needed to be
understood in any way.
An alternative cognitive approach may be more appropriate in this particular
population than the illness representation perspective. Cognitive appraisals can
increase the likelihood of developing PTSD following the traumatic event (i.e. ACS). As
with the self-regulation model, Ehlers and Clark‘s (2000) cognitive-behavioural
explanation for the persistence of PTSD symptomatology also focuses on the role of
cognitive and emotional processes. These authors particularly emphasized the role of
perceived threat and cognitive appraisals about the event or trauma sequelae.
Modifying relevant cognitive appraisals is of paramount importance in cognitive therapy
for PTSD following this model. Cognitive appraisals are not the by-product of cognitive
reasoning, but are presumed to be relatively effortless, intuitive, and automatic
evaluations that are sensitive to events related to survival and opportunities (Peters et
al., 2004).
This argument is in line with the cognitive model (Beck, 1995), which is based
on the idea that our emotions and behaviours are influenced by our perceptions of
events. Cognitive theory posits it is not the situations themselves that determine what
we feel, but rather it is the way we think about the situation. The thoughts that go
through our minds in any given situation are automatic and cause us to have different
emotional responses. These automatic thoughts reflect the way in which we interpret
and think about a situation, and have a major effect on the way we feel. Automatic
thoughts spring up like a reflex; they are rapid and often very brief. In fact, many people
are barely aware of their automatic thoughts and are more likely to notice the emotions
that follow. As a result, many people accept their automatic thoughts as being true,
without evaluating the validity of the thought.
Negative cognitive appraisals can lead to negative emotional responses.
Schachter and Singer‘s (1962), bi-factorial theory of emotion argue that emotional
experience is a consequence of the interaction between cognition and physiological
252
arousal. It has been evident that bodily arousal is present in individuals with a history of
trauma and that hyperarousal symptoms in PTSD patients may also trigger cognitions
of poor physical health (Falsetti & Resnick, 1997). One could argue that
misinterpretation of bodily arousal, during the experience of certain negative emotions
could account for the generation of the perceptions of ill health. By identifying and
challenging maladaptive cognitive appraisals about one‘s body about posttraumatic
symptoms maybe one can achieve a decrease in physiological arousal and in negative
health behaviours and subsequent positive health outcomes.
8.1.4 Biological dysfunction post ACS and later posttraumatic stress
8.1.4.1 Cortisol
Cortisol responds to a range of psychosocial factors, such as acute and chronic
stressors, and for this thesis more relevant, affective disorders (McEwen, 1998;
Steptoe & Ayers, 2004; Steptoe & Brydon, 2005; Williams et al., 2005). Cortisol in
relation to short-term posttraumatic stress reactions was investigated in chapter 6.
These analyses were conducted as an exploratory exercise to assess one potential
pathway between experiencing an ACS and the increased risk of developing PTSD.
Although there are numerous investigations of cortisol profiles in PTSD sufferers, these
tend to focus on individuals exposed to interpersonal violence, war or motor vehicle
accidents. Few studies have previously explored whether post-MI PTSD is associated
with altered peripheral neuroendocrine activity. This seems a significant gap in the
literature, considering findings suggesting an association between cortisol and
proinflammatory activity which can sustain the atherothrombotic process in turn leading
to recurrent cardiac events (Gander & von Känel, 2006).
However, one recent article (von Känel et al., in press) has addressed this issue
comparing post-MI PTSD patients (n=15) with post-MI patients without PTSD (n=29).
253
These authors suggest that due to the co-morbidity of PTSD and depression, and the
fact that there are a number of commonalities in symptomatology, such as levelling of
affect, sleep disturbances, irritability etc, distinct cortisol findings in PTSD samples can
potentially be obscured by co-morbid depression. Von Känel and colleagues found that
in unadjusted analyses there were no significant associations between PTSD and
cortisol levels. However, when depressive symptoms were included as a co-variate,
mean cortisol levels were significantly lower in patients with PTSD than in those without
PTSD. These findings fit in well with the classical theory of divergent HPA axis
dysfunction in PTSD and depression which posits that individuals with PTSD have
reduced cortisol levels whereas those with depression exhibit elevated cortisol levels
(Yehuda, 2002a). These findings are also in line with a recent meta-analysis showing
that levels of cortisol were lower in PTSD patients when depression was absent relative
to when depression was present, though, this difference was not significantly different
(Meewisse et al., 2007). However, it is important to note that cortisol was assessed in
this sample approximately 2.5 years following the acute cardiac event, and pre-trauma
levels are not known. Thus these observed lower cortisol levels in the PTSD group may
have reflected a pre-existing vulnerability predisposing these patients to go on to
develop PTSD in response to the MI. Alternatively, a blunted cortisol response could
have developed over time, in response to having experienced the traumatic cardiac
event, as a consequence of persistent over-activation leading to exhaustion and
depletion of the HPA system.
Similar to the findings of von Känel et al (in press), I found no significant
univariate associations between posttraumatic stress and cortisol, nor between
depression and cortisol. However, a significant association emerged between
posttraumatic stress symptoms and total cortisol output at time 2 after controlling for
depression in the regression model. Interestingly, the direction of my findings is in
direct contrast to that of von Känel and colleagues. Whereas PTSD (both as a
categorical variable and a continuous variable) was associated with lower cortisol
254
levels in their study, in my sample, higher posttraumatic stress symptoms were
associated with higher cortisol levels in multivariate analyses after controlling for the
presence or absence of depression.
It is, however, interesting to consider these findings within the context of the
results presented in chapter 6, section 6.4, where those patients who experienced low
acute stress at time of ACS had significantly elevated cortisol levels at time 2, as well
as an increased cortisol awakening response (CAR). Conversely, patients with high
levels of acute stress showed a blunted total cortisol output across the day, and a
decreased cortisol awakening response. This is particularly interesting when
considering my earlier findings where higher acute stress symptoms emerged as an
independent predictor of greater posttraumatic symptoms at time 2. These findings
may suggest lower cortisol in those who experience high acute stress at the time of
ACS and who later go on to develop PTSD. Additionally, there is evidence suggesting
that exhaustion of the HPA axis is associated with an attenuated CAR (Pruessner et
al., 1999).
Whether this cortisol profile emerged in response to trauma, or whether it is a
pre-existing vulnerability factor is not known. Of course, cross-sectional analyses such
as these cannot demonstrate causality and therefore, the associations found cannot
confirm the psychological factor as the predicting variable. It is possible that the
presence of biological dysfunction may promote negative psychological responses to
trauma. I will be able to address this issue more specifically upon completion of data
collection in TRACE, since cortisol is being re-assessed at 12 months post ACS.
8.1.4.2 Heart rate variability
Heart rate variability (HRV) is an indicator of cardiac autonomic control, and in
particular the balance between the sympathetic and parasympathetic nervous system.
Impaired HRV is a risk factor for adverse health outcomes in patients following ACS,
255
cardiac morbidity (La Rovere et al., 1998), and is also sensitive to psychosocial factors
(Hemingway et al., 2001). It may therefore provide another potential mechanism for the
association between psychosocial factors and cardiac disease. Several studies have
found post-trauma heart rate to predict subsequent PTSD. Shalev et al (1998b) found
that elevated heart rate (which is typically associated with reduced HRV) in the
emergency department (immediately post trauma) predicted PTSD at 4 months follow
up. However, no differences in heart rate were observed at either the one-month and
four month follow up. Zatzick et al (2005) found that elevated admission heart rate
(95), in a sample of severely injured surgical inpatient, predicted posttraumatic stress
symptom over the course of one year follow up.
PTSD has a strong physiologic component, principally of hyperarousal, as
demonstrated by peripheral physiologic parameters (e.g. Tachycardica, excessive
sweating etc; Kolb, 1987). PTSD patients demonstrate hyperalertness, and higher
basal heart rate and blood pressure than controls (Kosten et al., 1987), suggesting
autonomic function is altered in those with PTSD. Various studies in small samples
have found reduced heart rate variability and increased sympathetic activity at rest
(Blechert et al., 2007; Cohen et al., 2000; Cohen et al., 1997; Sahar et al., 2001), with
parasympathetic activity blunted in response to challenge or trauma reminder among
PTSD patients compared with healthy individuals without PTSD (Cohen et al., 1997;
Sack et al., 2004; Sahar et al., 2001). However, there are no studies investigating HRV
in post-ACS patients in relation to posttraumatic stress, where the traumatic event was
the ACS.
I found no relationship between patients‘ heart rate and posttraumatic stress
reactions 3 – 4 weeks following ACS. However, it is important to note that previous
studies that have found positive associations between heart rate and PTSD have
assessed heart rate in the immediate aftermath of trauma. In TRACE, heart rate and
HRV were assessed at the time 2 assessment, at which point all patients were also on
beta-blocker medication. Beta-blockers reduce sympathetic cardiac drive, leading to
256
lower heart rate, and HRV is also modified. Interestingly, I did find significantly reduced
high frequency (HF) activity among patients with PTSD compared with patients without
PTSD at time 2 in the TRACE study, indicating reduced parasympathetic control.
However, these findings were based on a very small sample of patients with PTSD (n =
6), and so need replication to be truly credible.
HF power is firmly acknowledged as an indicator of parasympathetic influences.
There is, however, some confusion in the literature as to whether elevated low
frequency or reduced low frequency power is beneficial and whether low frequency and
high frequency are positively or negatively correlated (e.g. Thayer & Lane, 2007).
Although there is evidence relating alterations in HRV with post-MI prognosis, it should
be emphasized that interpreting HRV results is not straightforward. Particular caution
must be taken when interpreting the HRV results of the TRACE study as a number of
PTSD positive patients did not have adequate HRV recordings, reducing the power to
detect potentially significant effects. However, these findings suggest that HRV in
relation to posttraumatic stress in ACS patients warrants further study.
8.2 General thesis limitations
Study specific strengths and limitations have already been presented in the
discussions of chapters 4, 6 and 7. The following section outlines some additional
general thesis limitations, which must be taken into consideration.
8.2.1 Study design
Both ACCENT and TRACE were of an observational nature, thus no
experimental manipulations were undertaken, which would have allowed for cause and
effect relationships to be evaluated and characterized. The use of cross-sectional data
257
is a major disadvantage in research as no causal inferences can be made. Although
some of the analyses in this study (i.e. cortisol assessment at time 2 in relation to time
2 posttraumatic stress reactions) were of a cross-sectional nature, the studies included
in this thesis were by design longitudinal and the majority of the analyses were
conducted on these longitudinal data. The assessment of posttraumatic stress up to
three years post-trauma is a particular strength of the ACCENT study, contributing
significantly to the understanding of PTSD trajectories in post-MI patients. However,
the lack of pre-trauma assessment is a weakness of these studies as pre-ACS
measures of affect, cortisol and HRV would allow for control of baseline levels, thus
allowing analyses to isolate the effect of the cardiac event itself on subsequent
posttraumatic stress. This is difficult to achieve in this particular population of cardiac
patients since acute cardiac events are typically unpredictable, compared with, for
example, coronary artery bypass graft surgery. Further, both studies were carried out
on patients who were admitted to hospital, thus individuals who were not admitted were
automatically excluded from participation. Also, some cardiac events may have been
missed, some patients who experience an ACS may refuse admission, or the clinical
event may not have been considered sufficiently severe to warrant hospitalization.
8.2.2 Measurement issues
The length of the questionnaire could itself have been a limiting factor,
potentially deterring patients from participating in the follow up. Inevitably there was a
limit to the size of the questionnaire included at each assessment point, and other
interesting variables were omitted. For example, assessing the emotional atmosphere
in patients‘ homes through the ‗Expressed Emotion‘ of partners could have offered
useful insights into patients‘ posttraumatic symptomatology. Further, including
assessment of prior [to ACS] traumatic exposure or adverse life events in the months
between admission for ACS and follow up could aid the understanding of vulnerability
258
to subsequent PTSD or the course of posttraumatic symptomatology post ACS.
Although I used well-established standardized questionnaires, as with any research
conducted using self-report measures, there is always the possibility of reporting bias,
as participants may respond to questions in a way which they would like to be
perceived, particularly when measuring health behaviours. For example, in the TRACE
sample, the adherence levels reported were incredibly high, which may be an artifact of
self-report bias. In order to increase questionnaire completion the interviewer was
present not only for the interview (at time 2) but also for the completion of some of the
self-report measures. Thus, social desirability bias could have influence the responses
of some patients at this assessment point. Further, questionnaire responses may be
influenced by patients‘ mood. For example, in the TRACE study, 19.3% of patients
reported above mild-to-moderate depression at the time 2 interview, and this could
have coloured their responses to measures of illness representations and social
factors.
8.2.2.1 PTSD assessment
PTSD was assessed using the PSS-SR scale by Foa and colleagues (1993).
Although this scale assesses all three symptom clusters of PTSD, and is a validated
scale, the gold standard would have been to conduct a diagnostic interview to establish
the presence or absence of PTSD. The evaluation of PTSD post-ACS is problematic,
especially when assessed by self-report, as the nature of the posttraumatic intrusions
cannot be established. Intrusions may be of a future oriented ruminative nature (such
as worrying about potential recurrence of cardiac symptoms), rather than truly intrusive
trauma related memories (such as reliving aspects of the ACS as if they were
happening in the present). Particular difficulties arise from the common co-morbidity
with depression. In the TRACE study, there were no cases of ‗pure‘ PTSD, that is,
depressive symptoms were present in all cases that also met diagnostic criteria for
259
PTSD. Whereas PTSD is frequently associated with other emotional disturbances
(such as comorbid depression and anxiety) in the literature, some studies have
reported cases of post MI PTSD positive patients without comorbid depression (e.g.
Ginzburg, 2006a).
Comorbidity [of PTSD and depression] is associated with greater symptom
severity and lower levels of functioning (Ginzburg, 2006a; Shalev et al., 1998). One
major issue is that many of the symptoms of depression overlap with symptoms of
posttraumatic stress, and some argue that the PTSD observed may be a depressive
sub-type of ‗pure‘ PTSD (e.g. Constans et al., 1997). An important question is whether
the intrusions in PTSD differ from those in depressed patients? Studies of matched
samples (e.g. Reynolds & Brewin, 1997) suggest that PTSD patients are somewhat
more likely to have intrusive memories than are depressed patients, and the memories
tend to be somewhat more prominent and more frequent. When intrusive memories are
present, however, there seem to be few obvious differences. Both the depressed and
PTSD groups are likely to experience very vivid, highly distressing memories that on
average occur several times a week and last between several minutes and one hour.
The majority of the memories are accompanied by physical sensations and the feeling
of reliving the event. One of the few ways in which they differ is that PTSD patients are
more likely to report feeling helpless and to have a dissociative experience such as
feeling they were leaving their body or seeing themselves as an object in their memory.
Another issue with the assessment of PTSD in this thesis is that PTSD was
considered a consequence of the ACS, and the questionnaire specifically referred to
the acute event. However, other potential sources of PTSD symptoms are present,
such as those relating to the coronary care unit treatment, any invasive procedures
performed, cardiac arrest, and resuscitation. Further, a history of previous trauma or
PTSD was not assessed. A number of studies have demonstrated that previous
exposure to trauma is a risk factor for developing PTSD in a variety of samples (e.g.
Bremner et al., 1993; Breslau et al., 1999; Galea et a., 2003; Yehuda et al., 1998). One
260
approach suggests that prior negative life events (as stressors) can deplete an
individual‘s resources, inhibiting adequate coping with further traumatic experiences,
thereby rendering the individual more vulnerable to subsequent adjustment difficulties.
A number of studies have shown that negative life events impair persons‘ emotional,
somatic and psychosocial adjustment (e.g. Lane et al., 2005; Pagano et al., 2004).
However, in contrast to these multiple-trauma studies, other studies suggest that
individuals exposed to negative life events or traumatic experiences demonstrate
adaptive responses (Corneil et al., 1999; Falsetti & Resick, 1995), with some data
suggesting that trauma exposed adults respond to repeat trauma with greater
confidence and enhanced coping skills (Aldwin et al., 1996). Thus, a lower prevalence
of PTSD could be expected if major life events that superseded the trauma intensity of
ACS, had been experienced prior to participation in this study. One general issue with
the literature on PTSD in cardiac patients is that it is not sufficiently integrated with
current theories and models of PTSD. There is a general lack of a conceptual
framework in this research.
8.2.3 Cortisol assessment
One issue that may have complicated the interpretation of the cortisol results in
this sample of patients (TRACE) is that the timing of assessment was rather close to
the actual trauma itself. Alterations in the HPA system may only become apparent over
longer time periods, if in fact the traumatic event is causally related to dysfunction of
cortisol. One major advantage of the TRACE study is the longitudinal follow up of
cortisol at 12 months post ACS. However, assessment of cortisol in the ACCENT study
(data not included in this thesis) was conducted in hospital, and results showed a
significant positive relationship between cortisol awakening response and type D
personality (Whitehead et al., 2007). However, type D is considered a personality trait
and may therefore be associated with cortisol dysfunction unrelated to the ACS. That
261
is, dysfunction of the HPA axis may have preceded the assessment point, whereas the
posttraumatic response in TRACE was specific to the acute cardiac event and any
subsequent cortisol dysfunction may emerge over time.
Compliance with the timing of cortisol samples is a problem with studies set in
the natural environment. Although participants in the study were given clear
instructions to follow about the timing of samples, almost 36% reported a delay of 10
minutes or more in the timing of the first sample after waking. Without this information
on compliance, the cortisol awakening response may be misinterpreted as blunted
(Dockray et al., 2008), a profile sometimes associated with poor health (Stone et al,
2001; Kudielka & Kirschbaum, 2003). Timing of the first sample of the day was
controlled for in the analyses conducted to avoid confounding by this delay. Another
confounding variable may be sleep disturbance. For example, some patients may have
suffered from fatigue or poor sleep, in turn affecting time of waking. It should be noted
that the measure of compliance with the timing of cortisol sampling used in this
investigation was based on self-report, and this may not be completely accurate. For
example, some participants may have stated that they took samples within a few
minutes of waking, when in fact they did not. The use of electronic devices that time
saliva samples are beneficial (Kudielka et al, 2003), however this was beyond the
scope of this project. Also, although cortisol response to awakening is relatively stable,
measurement over several days would have increased the reliability of these results. In
the TRACE study, cortisol was measured over the course of a single day shortly after
the time 2 assessment. This may also have contributed to the magnitude of the cortisol
awakening response (Williams et al, 2005). Therefore, the cortisol findings must be
interpreted with caution.
262
8.2.4 Response rate, sample size and power
A satisfactory follow up rate is important to ensure the findings are not biased
and are representative of the population. Only 8.4% of the eligible patients [during the
recruitment period] declined to participate, but the representativeness of the sample
was limited by the exclusion of patients with co-morbid conditions. However, exclusion
of patients for these reasons is important as some comorbidities can have an influence
on symptom presentation or mood state. The sample also consisted of mainly male
patients of white European descent. The response rate at time 2 was good with over
74% of patients agreeing to participate in the home interview. Non-responders at time 2
were more likely to be unmarried and more socially deprived. This finding is not
unexpected. It has been shown previously that married people are more likely to
participate in health research. At six month follow up, 88% of the patients that
completed time 2 were re-contacted and completed the telephone interview; however,
18% of these did not return the postal questionnaire which contained the majority of
measures. At six month follow up patients who were married, less socially deprived and
without a history of depression were less likely to participate. It may be that these
patients had greater demands in daily life and therefore would not spend time
completing the questionnaires. It is also possible that non-responders at time 1 were
more distressed than those who chose to participate in the study. The data from
ACCENT were limited to the original sample recruited and at 12 months as I was only
involved in the 36 month data collection. This means that the true extent of the
relationship between ACS and emotional responses could have been attenuated.
Estimated power for the TRACE study was based upon the power calculations
for the ACCENT study (detailed in Professor Steptoe's BHF programme grant
application and approved by the BHF Chairs & Programme Grants committee in 2005).
Assuming that the frequency of acute distress would be similar to that found in
ACCENT, there would be 80% power to detect the same difference in Beck Depression
263
Inventory scores one week after discharge between intense distress and no distress
groups (effect size 0.45), and 94% power for differences at 6 and 12 months. Power to
confirm the predicted associations with psychobiological indicators (e.g. cortisol
profiles) would be >90%. However, the prevalence of PTSD in the TRACE sample was
much lower than that observed in ACCENT, this will likely have reduced power to
detect between group differences.
8.3 Clinical implications
The finding that between 6 and 12% of post ACS patients go on to develop
PTSD suggest that this is a significant patient group which requires the attention of
clinicians involved in the care of cardiac patients. Interventions aimed at reducing
posttraumatic stress symptoms can carry significant health benefits considering the
findings that posttraumatic stress is associated with increased non-adherence to
medications (chapter 7), poorer quality of life (chapters 4, 6 & 7), and marked
emotional distress (chapters 4, 6 & 7)
The finding that acute stress symptoms in response to the ACS predicted
subsequent
posttraumatic
symptoms
suggests
that
decreasing
feelings
of
helplessness, fear and horror in-hospital could be a reasonable means to decrease
clinically significant posttraumatic symptom levels in the immediate aftermath of the
cardiac event. However, some research suggest that formal interventions attempting to
help individuals cope with PTSD or distressing intrusive thoughts (whether symptoms
reach diagnostic threshold for PTSD or not) should not be conduced in the immediate
aftermath of the trauma. In fact, interventions conducted at this time may worsen rather
than improve long-term prognosis (Bisson et al., 1997). Further, as some studies
suggest (e.g. Pedersen et al., 2004), ACS related PTSD may comprise an acute
reaction and therefore symptoms may abate with time, it may be appropriate to focus
interventions towards patients who have experienced posttraumatic symptomatology
264
for a period of time. However, the findings from both ACCENT and TRACE suggest
that posttraumatic stress symptoms do not abate with time in this population of cardiac
patients. Further work is needed to identify predictors of these varied posttraumatic
symptom trajectories.
A recent randomized control trial of a brief in-hospital illness perception
intervention demonstrated that patients in the intervention group had improved rates of
return to work than control patients receiving standard care. The illness perception
intervention was moderately intense, consisting of 4 x 30 minute sessions, but resulted
in some good effects. At six month follow up the intervention group also reported better
understanding of information given in hospital, higher intentions to attend cardiac
rehabilitation as well as lower anxiety about returning to work. The intervention group
also showed greater increases in exercise levels and had contacted their general
practitioner about their cardiac condition less often (Broadbent et al., 2009a).
Broadbent and colleagues (2009b) also assessed whether the intervention would
reduce anxiety in the spouses of the MI patients. In addition to standard care, spouses
attended the final of the 4 30-minute sessions with the MI patient. At one-week post
discharge, spouses were less anxious about patients doing physical activity, and had
lower anxiety about the patient‘s medications, as well as lower distress about the
patient‘s symptoms. At three month follow up, spouses in the intervention group
reported being less worried about the illness. This finding suggests that an illness
perception intervention can also be beneficial for spouses of cardiac patients in
reducing their anxiety levels.
Although specific interventions for ACS patients with PTSD are only just
beginning to emerge (e.g. Shemesh et al., 2006), there are established treatments
available for PTSD from other sources, including counselling, cognitive-behavioural
and interpersonal psychotherapy, and psychopharmacology (Davidson, 2006; Yehuda,
2002b). However, conducting such formal interventions in the busy hospital
environment does not seem feasible. What may be important is the screening and early
265
identification of those at increased risk of subsequent PTSD. For example, screening
patients as they are invited to attend cardiac rehabilitation could be advantageous.
Alternative approaches could also include undemanding interventions aimed at
reducing emotional distress (e.g. leaflet information, short-term cognitive restructuring)
in the acute phase post-ACS. In fact, reassuring the patient that the emotional distress
experienced in response to the ACS is normal, as well as providing an explanation for
commonly experienced symptoms, could be a useful strategy. However, randomized,
controlled treatment studies would need to be conducted before any firm treatment
recommendations could be made.
8.4 Directions for future research
The findings from the studies reported in this thesis highlight a number of
possibilities for future research. The first and foremost recommendation would be for
additional longitudinal research to be conduced with numerous PTSD assessment
points. Secondly, it would be interesting to obtain data from more diverse samples. In
both the ACCENT and TRACE studies the majority of ACS patients were men. It would
be important to include a larger number of women in future research, as they appear to
have more difficulties with adjustment following cardiac illness (Wiklund et al., 1989;
Brezinka & Kittel, 1995; Herlitz et al., 1999). Women may be more susceptible to
developing PTSD following a traumatic event such as ACS compared with men
(Breslau et al., 1997). However, more research is warranted to determine whether
gender per se constitutes a risk factor for PTSD in this population (Wolfe & Kimerling,
1997).
More conclusive evidence of the association between post-ACS PTSD and poor
medical prognosis is needed. Additional work may wish to examine the exact duration
or chronicity of PTSD necessary to initiate pathophysiological processes. Moreover, it
is unknown whether the cardiotoxic effects of PTSD can be reversed if PTSD is
266
successfully treated. Future work may compare long-term cardiac outcomes between
individuals with PTSD who were successfully treated and those whose PTSD was
refractory to treatment.
A more careful examination of biological mechanisms is also required. There
has been relatively little research on the unique relationship between PTSD and
peripheral stress hormones in post-MI patients with PTSD. Although, one recent study
(von Känel et al., in press) has addressed this issue, more research is warranted. In
TRACE, cortisol was assessed at more than one time point in a larger sample of post
ACS patients. Although this provided some preliminary findings, it would be interesting
to examine the relationship of post ACS cortisol levels and 12 month posttraumatic
symptomatology, as well as cortisol values at 12 month follow up. Further, I feel that
the TRACE analyses would have benefited from the inclusion of measures of acute
inflammatory markers such as interleukin-6, cystatin C and C-reactive protein. In
particular, I would be interested in assessing the relationship between post ACS
inflammation and cortisol dysfunction in the shorter (3 – 4 weeks post ACS) and longer
term (12 months post ACS). Data on inflammation in the TRACE participants will
become available in the near future, which will enable me to further evaluate this
relationship.
The earlier finding (see chapter 7) that anxiety predicts PTSD suggests that
anxiety may not simply be a symptom of PTSD but may instead predispose people to
experience the disorder after a trauma. This is a subtle but important distinction.
Although PTSD is technically considered to be an anxiety disorder, the symptoms of
anxiety and posttraumatic stress are not synonymous. It is important to disentangle the
ways in which other primary anxiety disorders can be differentiated from the anxiety
that is associated with PTSD. Replicating the predictive relationship between these two
constructs is the next step in this process. Similar examinations of the relationship
between posttraumatic stress and other psychological symptoms could further illustrate
the ways in which PTSD symptoms place patients at risk for adjustment difficulties.
267
Finally, I would be interested in identifying factors that determine resilience to PTSD,
rather than placing all the focus on the risk factors for PTSD. This may be used to
create interventions also, and is one approach that can potentially inform treatment
options.
8.5 Key message of thesis
The key findings of this thesis are summarized in figure 8.1 below.
ACCENT
Type D personality and hostility may
Depressed mood in hospital is predictive
of posttraumatic stress symptoms at 12
and 36 months follow up.
Recurrent cardiac related symptoms may
pre-dispose patients to react in more
serve as reminders thus increasing
negative emotional manner post ACS,
posttraumatic symptom intensity at 12 and
predictive of posttraumatic stress, though
36 months post ACS.
not once depressed mood and anxiety is
accounted for.
TRACE
Acute emotional
reactions are
predictive of
posttraumatic
stress symptoms,
in particular
negative mood
and acute stress.
Depressed mood
and anxiety in the
weeks following
discharge is
predictive of 6
month
posttraumatic
stress.
Concurrent cardiac
Patient‘s illness
representations
may serve to
influence post ACS
emotional reactions,
and patients
emotional
representations is
predictive of 6
month posttraumatic
stress.
related pain might
serve as a reminder
thus increasing
posttraumatic
symptom intensity at
2 – 3 weeks post
ACS. Having
experience a major
cardiac event in the
past 6 months
predicted 6 month
posttraumatic stress
symptoms.
Cortisol is not
Type D personality
related to
and hostility may
posttraumatic
pre-dispose
stress, unless
patients to react in
depression is
more negative
included in the
emotional manner
model. The
post ACS,
relationship is
predictive of
complex, but there
posttraumatic
is some indication
stress, though not
of a blunted profile
once depressed
in those who report
mood and anxiety
high acute stress
is accounted for.
and later go on to
HF HRV appear
reduced 2 -3
weeks post ACS in
patients who
report more
intense
posttraumatic
stress reactions.
develop PTSD.
FIGURE 8.1 KEY FINDINGS OF ACCENT AND TRACE INVESTIGATIONS
268
8.6 Conclusion
The studies presented in this thesis shows that a PTSD perspective can be
productive in conceptualizing the process of adjustment to a serious cardiac event.
This is of clinical significance because such symptoms may have an influence on health
care behaviors. Previous studies of the relative contribution of PTSD to medical
morbidity, as compared with comorbid depressive and general distress or anxiety
symptoms in patients with cardiovascular illnesses have found that PTSD symptoms
are stronger predictors of poor outcome than are symptoms of depression or general
distress (e.g. Shemesh et al., 2001; Shemesh et al., 2004). The findings presented in
this thesis, along with the growing body of studies that assess PTSD among MI
patients (see chapter 2 for a review of studies), support the view that acute cardiac
events, such as ACS, like other life threatening illnesses, may be considered a
traumatic event that may entail stress reactions.
PTSD has been identified as a marker of extreme distress in response to a
potentially traumatic event such as ACS, and may also be indicative of a chronic stress
reaction. Diagnosis of PTSD is often difficult because PTSD symptoms overlap with
those of anxiety and affective disorders, both of which are generally more recognized.
However, unlike depressive and anxiety disorders, PTSD is defined by the combination
of exposure to a potentially traumatic event and the occurrence of three types of
symptoms: re-experiencing, avoidance and hyperarousal. The time course of
posttraumatic symptoms can follow one of several patterns, where high levels of
symptoms after traumatic exposure are followed by recovery, chronic symptoms persist
over time, or symptoms relapse and remit.
The results presented here also underscore the importance of identifying
cardiac patients with elevated levels of posttraumatic stress symptoms, particularly in
light of the evidence that PTSD has implications for prognosis. For this end, a simple
269
screening procedure may be sufficient. If PTSD is demonstrated to have significant
cardiotoxic effects, there are numerous implications for both prevention and treatment.
270
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