Depression, Healthcare Utilization, and Death in Heart Failure: A

Transcription

Depression, Healthcare Utilization, and Death in Heart Failure:
A Community Study
Moraska et al: Depression in Heart Failure
Amanda R. Moraska, BAa; Alanna M. Chamberlain, PhD, MPHb; Nilay D. Shah, PhDb;
Kristin S. Vickers, PhDc; Teresa A. Rummans, MDc; Shannon M. Dunlay, MD, MScd;
Downloaded from http://circheartfailure.ahajournals.org/ by guest on November 19, 2016
John A. Spertus, MD, MPHe; Susan A. Weston, MSb; Sheila M. McNallan, MPHb;
Margaret M. Redfield, MDd; Véronique L. Roger, MD, MPHb,d
a
Mayo Medical School;
ool;; bDepa
Department
p rtment of Health Sciences Research,
h cDepartment
D
t
t of Psychiatry
and Psychology; and
d dD
Division
ivisionn of
of Cardiovascular
Card
Ca
rdiova
rd
vasc
va
sccular
ar Diseases,
Disea
easses
ea
e , Mayo
M yo
Ma
yo C
Clinic,
liniic, R
Rochester,
ocches
MN;
e
Department of Medicine,
d
dicine,
Division of
of Cardiology,
Card
Ca
diolo
ogy
g , University
Univ
Un
ver
ersity
y of
of Missouri at Kansas
Kan
n City, Kansas
City, MO
Correspondence to:
Véronique L. Roger, MD MPH
Department of Health Science Research
Mayo Clinic
200 First St. SW
Rochester, MN 55095
Phone 507-293-3247
Fax 507-284-1516
Email [email protected]
DOI: 10.1161/CIRCHEARTFAILURE.112.000118
Journal Subject Codes: [110] Congestive; [163] Resource Utilization
Abstract
Background—The increasing prevalence of heart failure (HF) and high associated costs have
spurred investigation of factors leading to adverse outcomes in HF patients. Studies to date
report inconsistent evidence regarding the link between depression and outcomes with only
limited data on emergency department (ED) and outpatient visits.
Methods and Results—Olmsted, Dodge, and Fillmore county, MN residents with HF were
prospectively recruited between October 2007 and December 2010, and completed a one-time 9item Patient Health Questionnaire (PHQ-9) for depression categorized as: none-minimal (PHQ-9
score 0-4), mild (5-9), or moderate-severe (10). Andersen-Gill models were used to determine
if depression predicted hospitalizations and ED visits while negative binomial regression models
explored the association of depression with outpatient visits. Cox proportional hazards regression
o ta
or
tali
l ty
li
ty.. A
monn 402 HF
mo
characterized the relationship between depression and all-cause m
mortality.
Among
depr
pres
pr
essi
es
siion
sion
on,, 26%
2 % mild, and
26
patients (mean age 73±13, 58% male), 15% had moderate-severee depression,
Over a mean
meean follow-up of 1.66 years,
years, 7811 hospitalizations,
hos
o pitalizations, 1000
os
1
59% none-minimal. Over
ED visits,
sits
si
ts, and 744 dea
eathss occurr
rrred
d. Af
fterr ad
adj
just
sttmeent,, m
ode
deraate-s
-sev
v depression
15,515 outpatient visits,
deaths
occurred.
After
adjustment,
moderate-severe
nearlly a 2-fold increased
in
ncrreased risk
ris
isk of hospitalization
is
hos
ospi
os
pitali
i ization (HR 1.79, 95
5 CI 1.30was associated with nearly
95%
( R 11.83,
(HR
.83
83, 95
83
95%
% CI 11.34-2.50),
.34344-22.50
50),
50
), a m
odes
od
estt in
es
incr
crea
cr
ease
ea
se iin
n ou
outp
tpat
tp
atie
at
ienn visits (RR
ie
2.47) and ED visits (H
modest
increase
outpatient
1.20, 95% CI 1.00-1.45), and a 4-fold increase in all-cause mortality (HR 4.06, 95% CI 2.357.01).
Conclusions—In this prospective cohort study, depression independently predicted an increase
in the use of healthcare resources and mortality. Greater recognition and management of
depression in HF may optimize clinical outcomes and resource utilization.
Key Words: cardiovascular outcomes; depression; healthcare utilization; heart failure;
psychosocial factors
2
Heart failure (HF) affects nearly 6 million Americans1 and remains the most common cause of
hospitalization in the Medicare population,2 with readmission rates approaching 50% within 6
months after initial hospitalization.2-4 As such, HF is one of the most costly health care problems
in the U.S., with estimated direct and indirect costs of $39.2 billion in 2010,1 an increase of
163% compared with 2000.2 These costs continue to rise with the aging population and
improvements in survival after cardiovascular events.
Due to its high prevalence, associated costs, and relatively poor prognosis, factors
contributing to adverse outcomes in HF are targets of investigation. Among such factors,
diagnosed andd po
ppotentially
depression has been given special attention, as it is often under-diagnosed
haan th
thee ggeneral
modifiable.5-7 Although depression rates are consistently higher in HF tthan
population, estimated
ed at
ed
at 5%-10%,
5%--100%, the
the exact
exac
ex
actt prevalence
ac
p ev
pr
eval
a en
encee and
and
n severity
sev
ever
erit
er
i y of depression
dep
pre
ress
ssio
ss
io in HF varies
considerably across studies,
sttud
udie
i s,
ie
s ranging
ran
angi
an
ging
gi
ng from
fro
ro
om 11
11%11%-25%
%-25
%25%
25
% in
n ooutpatients
utpa
ut
pati
pa
t en
ti
ents
ts tto
o 35
35%35%-70%
% 70
%7 % aamong
inpatients.2, 8, 9 These
se di
disc
discrepancies
scre
sc
repa
re
p nc
pa
ncie
iess leave
ie
leav
le
avee uncertainties
av
unce
un
cert
ce
rtai
rt
aint
ai
ntie
nt
iess about
ie
abo
bout
ut the
the importance
imp
por
orta
tanc
ta
ncee of ddepression in
nc
HF, from both a clinical and public health standpoint.
Several studies have reported an increased risk of death and hospitalizations in patients
with HF and depressive symptoms,2, 5, 10-17 while others have failed to identify significant
associations with rehospitalizations17, 18 or mortality.19, 20 Still others have demonstrated mixed
results, with significant associations only in the most severely depressed.8, 21, 22
As most studies have focused on mortality and rehospitalization, the impact of depression
on other forms of healthcare utilization such as outpatient or emergency department (ED) visits
is unknown. This study was designed to address, in a prospective community-based cohort
study, the aforementioned knowledge gaps by examining the prevalence and severity of
3
depression among HF patients, as well as its impact on hospitalizations, ED visits, outpatient
visits and survival.
Methods
Study Setting
This was a prospective cohort study conducted in southeast Minnesota, which constitutes a
unique environment because there are a small number of medical providers including Mayo
Clinic, Olmsted Medical Center, and a few private practitioners. Each provider uses
comprehensive medical records, which are indexed through the Rochester Epid
Epidemiology
Project
de
(REP).23 This record linkage system, enables virtually complete cap
capture
ptu
ture
re ooff ou
out
outcomes
t co
tc
and
healthcare utilization
n iin
n Olms
Olmsted
mste
ms
ted
te
ed Co
Coun
County.
unty
un
ty.. T
ty
This
h s sy
hi
syst
system
tem
m ccaptures
aptu
ap
ure
r s ou
ooutcomes
tccom
omes
es aand
nd uutilization
t
for nonOlmsted County residents
iden
id
ents
t who
ts
who are
aree seen
seeen by an
an Olmsted
Olms
Ol
mste
ms
tedd County
te
Coun
Co
unty
un
ty provider,
pro
rovi
vide
vi
der,
de
r bbut
u nnon-residents
ut
oon
n
may
also seek care in their
ir own
own counties
coun
co
unti
un
tiees with
ti
with providers
pro
rovi
vide
vi
ders
de
rs that
tha
hatt do not
not participate
par
arti
tici
ti
cipa
ci
p te in
pa
in the
the REP.
R
Therefore,
capture of utilization for these patients may be less comprehensive than that of Olmsted County
residents. This study was approved by the Mayo Clinic and Olmsted Medical Center
Institutional Review Boards.
Identification of the Study Cohort
Patients with either incident or prevalent HF during an inpatient or outpatient visit were
identified using natural language processing of the electronic medical record and the diagnoses
were manually validated by trained nurse abstractors using the Framingham criteria, as described
previously.24 Participants meeting the following criteria were prospectively recruited between
October 2007 and December 2010: 1) aged 18 years, 2) residents of Olmsted, Dodge, or
4
Fillmore counties, MN, and 3) completed a 9-item Patient Health Questionnaire (PHQ-9) for
depression upon study enrollment. Written informed consent was obtained from all participants
prior to enrollment.
Clinical Data Collection
Patient characteristics including age, gender, education, marital status, current or former cigarette
smoking status, and medications at index were obtained from patient records. Body mass index
(BMI) was calculated from the outpatient weight and height recorded in the medical record
closest to study enrollment. Hypertension was defined as systolicc blood pressur
pressure
ure 140 mm Hg,
ur
diastolic blood pressure 90 mm Hg, or a physician diagnosis of hyp
hypertension.
per
erte
tens
te
nsio
ns
ion.
io
n.25 Prevalent
diabetes was defined
d by
by the American
Amer
Am
eric
er
ican
ic
an D
Diabetes
iabe
ia
bete
be
t s Association
A so
As
sociiat
atio
ionn criteria.
io
c itter
cr
eria..26 A clinical
clin
cl
inic
in
icall diagnosis
ic
i al re
ica
rrecord
cord
co
rd w
aass uused
seed to iidentify
den
enti
tify
ti
fy
y hhyperlipidemia
yper
yp
erli
er
lipi
li
pide
pi
demi
de
miaa an
mi
andd pr
rev
evio
io
ouuss myocardial
recorded in the medical
was
previous
infarction (MI). Glomerular
o er
omer
om
erul
ular
ul
ar ffiltration
iltr
il
trat
tr
atio
at
ionn ra
io
rate
te (G
(GFR
(GFR)
FR)) wa
FR
wass es
esti
estimated
tima
ti
mate
ma
tedd us
te
usin
using
ingg th
in
thee cl
clos
closest
oses
os
ess serum
creatinine value within 1 year of HF diagnosis using the Modification of Diet in Renal Disease
Study (MDRD) equation.27 Comorbidities abstracted from patient records were used to calculate
an overall comorbidity score for each patient using the Charlson comorbidity index.28 Left
ventricular ejection fraction (LVEF) (%) was obtained using the closest value from an
echocardiogram within 6 months prior to 2 months after HF date.
Depression Measurement
Depression was assessed once using a 9-item Patient Health Questionnaire (PHQ-9)
administered by a registered nurse during a face-to-face interview conducted during a return visit
within 6 weeks of consent. This brief questionnaire has been validated and assesses each of the 9
5
criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) for clinical
depression on a scale from “0” being “not at all” to “3” being “nearly every day”.29 According
to Kroenke and colleagues (2001), PHQ-9 scores of 10 were 88% sensitive and 88% specific
for detecting major depression and scores of 5, 10, 15, and 20 corresponded well to mild,
moderate, moderately severe, and severe depressive symptoms, respectively, as measured by
diagnostic interview.29 Taking the results of this study into consideration, depressive symptoms
were categorized as none-minimal (PHQ-9 score 0-4), mild (5-9), or moderate-severe (10 or
greater).
Ascertainment of Healthcare Utilization and Death
llow
ll
o ed ffrom
ro
om st
stud
udyy en
ud
enro
roll
ro
l me
ll
ment
n tthrough
hrou
ouugh D
ough
e em
ec
embeer 31
31,, 20
2010
10 fo
o death and
Participants were followed
study
enrollment
December
for
n Deaths
n.
Dea
eath
thss were
th
w re obtained
we
obt
btai
aiined from
from iinpatient
npat
np
atie
at
ient
ie
nt aand
nd ooutpatient
u pa
ut
pati
tien
ti
ent me
medi
d c records and
di
healthcare utilization.
medical
cei
eive
vedd annually
ve
annu
an
nual
nu
ally
al
ly
y from
fro
rom
m Olmsted
Olms
Ol
mste
ms
tedd County
te
Coun
Co
untty and
un
and the
the state
stat
st
atee of Minnesota.
at
Min
inne
nee
death certificates received
Hospitalizations, ED visits, and outpatient office visits were ascertained through the Olmsted
County Healthcare Expenditure and Utilization Database, which contains healthcare utilization
information from 1987 to present. For patients enrolled during hospitalization, only subsequent
hospitalizations were included in the analysis. In-hospital transfers or transfers between Olmsted
Medical Center and Mayo Clinic were analyzed as a single hospitalization. ED visits that
resulted in admission and hospitalization were counted as both an ED visit and a hospitalization.
Outpatient visits for psychiatric care, tests, imaging, or outpatient procedures were not included.
6
Statistical Analysis
Baseline patient characteristics are reported as a frequency (%) for categorical variables and
mean (standard deviation) for continuous variables. Mantel-Haenszel chi-square tests and
generalized linear models were used to test differences in baseline characteristics between
depression categories. Follow-up was calculated from HF date until death, last follow-up visit,
or December 31, 2010, whichever came first.
Kaplan-Meier plots were constructed to illustrate the association of depression severity
with all-cause mortality. Cox proportional hazards regression was used to describe associations
between severity of depressive symptoms and mortality after adjustment
age,
ustment for age
ge, sex, Charlson
ge
comorbidity index, and incident vs. prevalent HF status. The proportional
oporrti
t on
onal
al hazards
haz
azar
a d assumption
was tested using Schoenfeld
residuals
found
valid.
Andersen-Gill
modeling,
which
hoe
hoe
oenfeldd re
resi
s du
dual
alss an
al
andd fo
ounnd to bbee va
vali
lid.
li
d Ande
An
nd rs
rsen
en-G
en
-Gil
-G
illl mo
il
m
odd
allows for modeling off m
multiple
outcome
was
associations
ullti
tipl
plee outc
pl
ou
utc
t om
me eevents,
ven
ents
ts, w
ts
wa
as us
uused
ed tto
o ex
eexplore
plor
pl
oree as
or
asso
sooci
ciat
attio
ionn of depressive
symptoms with hospitalizations
may
pit
ital
aliz
al
izat
iz
atio
at
ions
io
ns and
and ED
ED visits.
visi
vi
sits
si
ts. Since
ts
Sin
ince
ce outpatient
out
utpa
p ti
pa
tien
entt visits
en
visi
vi
sits
si
ts during
dur
urin
ingg follow-up
in
foll
fo
cluster together (e.g. multiple outpatient visits on a given day or within in a span of several days
as part of the diagnostic process or for yearly physical examinations), a time-to-event analysis
such as the Andersen-Gill model, is not appropriate. Thus, the association between depression
and outpatient office visits was evaluated by calculating the number of visits per person-year for
each patient. A likelihood ratio test for overdispersion in the Poisson regression model
examining the association between depression severity and rate of outpatient visits indicated that
overdispersion existed; therefore, the negative binomial regression model was used.
A number of sensitivity analyses were also conducted. First, in an attempt to reduce the
possible impact of residual confounding on the association of depression with healthcare
utilization and mortality, we additionally adjusted for LVEF, eGFR, serum sodium,
7
hypertension,use of antidepressants or heart failure medications, and hospitalizations within the
last year in our fully adjusted models. Second, because responses to 2 of the questions of the
PHQ-9 may be reflective of a patient’s symptoms due to their HF and not due to depression, we
deleted the responses to questions 4 and 5, which asked participants how often they were
bothered by ‘feeling tired or having little energy’ and ‘poor appetite or overeating.’ The totals
were re-calculated after deleing these 2 questions, but the same cutpoints were used to define
none-minimal, mild, and moderate-severe levels of depression. All analyses were then repeated
to determine the robustness of our results. Analyses were conducted using SAS statistical
software, version 9.2 (SAS Institute Inc., Cary, NC).
Results
Baseline Patient Characteristics
h ra
hara
ract
c er
ct
erisstiics
Between October 2007
which 402
0007
07 aand
nd D
December
ecem
ec
embe
em
berr 20
be
2010
2010,
10,, we iidentified
10
dent
de
ntif
nt
ifie
if
iedd 90
ie
9022 HF ppatients,
atie
at
ient
ie
ntss, ooff w
nt
patients (mean age 73±13 years, 58% male, 41% incident HF), were enrolled and completed all
necessary components of this study (Figure 1). Of these 402 patients, 189 (47%) were identified
during an inpatient visit, whereas 213 (53%) were identified during an outpatient visit.
Furthermore, nearly 60% of the HF patients enrolled had prevalent HF, with a median duration
of HF of 4.9 years. Greater than 50% of all patients, regardless of depression status, had a
reduced EF (<50%). Sixty-two (15%) had moderate-severe depressive symptoms, 104 (26%)
mild symptoms, and 236 (59%) none-minimal symptoms. Among the inpatients, 11%, 28%, and
61% had moderate-severe, mild, and none-minimal depression; the respective proportions were
19%, 24%, and 56% in the outpatients. No significant differences in baseline characteristics
were observed between depression categories, with the exception of an increasing proportion of
8
diabetes and current or former smoking status in those with more severe depression (Table 1).
As expected, there was an increased use of antidepressants among patients with more severe
depression. Interestingly, only 37.1% of patients with moderate-severe depression were on
antidepressant medication.
Depression and Healthcare Utilization
Over a mean follow-up of 1.6 years, 781 hospitalizations, 1000 ED visits, and 15,515 outpatient
office visits occurred. Hospitalizations after HF ranged from 0-18 (median 1) per person, ED
visits ranged from 0-32 (median 2), and outpatient office visits ranged
anged from 1-189
1 18
1 (median 30).
Sixty-seven percent of hospitalizations were preceded by ED visits,
its, while
whi
hile
lee 53%
53% of
o ED visits
resulted in hospitalizations.
There
were
zations.
zat
atiions. T
at
h re w
he
eree mo
er
more
r hospitalizations
re
hhos
o pittalizations
os
taliiza
zati
tion
ti
onss an
on
andd ED visits
vis
isit
sit
itss with
wi h increasing
severity of depressive
with
modest
effect
outpatient
office
v ssymptoms,
ve
ympt
ym
ptom
pt
om
ms,
s ccontrasting
ontr
tras
asti
as
ting
ti
gw
itth a mo
ith
more
re m
odes
od
esst effe
est
ef
ffe
fect
ct oon
n outp
ou
utp
pa
visits (Table 2). Compared
with
none-minimal
with mild
mpared
mpar
mp
ared
ar
ed tto
o pa
ppatients
tien
ti
ents
en
ts w
ithh no
it
none
ne-m
ne
-min
-m
inim
in
imal
im
al ddepression,
ep
pre
ress
ssio
ss
ion,
io
n,, ppatients
atie
at
ient
ie
ntss w
nt
depressive symptoms demonstrated a marginal increase in hospitalizations (hazard ratio (HR)
1.16, 95% CI 0.88-1.53), ED visits (HR 1.35, 95% CI 1.00-1.83), and outpatient visits (risk ratio
(RR) 1.04, 95% CI 0.89-1.21) after adjustment for age, sex, comorbidity, and incident vs.
prevalent HF status. Patients with moderate-severe depression demonstrated nearly a 2-fold
increased risk of hospitalizations (HR 1.79, 95% CI 1.30-2.47) and ED visits (HR 1.83, 95% CI
1.34-2.50) compared to those with none-minimal depression (P for trend, 0.001 and <0.001
respectively). A modest increase in outpatient office visits was also apparent among moderateseverely depressed HF patients, keeping in mind that psychiatric care related visits were
excluded (RR 1.20, 95% CI 1.00-1.45, P for trend =0.068).
9
Depression and Mortality
Within the 402 study participants, 74 deaths occurred. There was a strong positive and graded
association between depressive symptom severity and mortality (P for trend <0.001, Figure 2).
In fully-adjusted models, moderate-severe depression was associated with a 4-fold increased risk
of all-cause mortality compared to none-minimal depression (HR 4.06, 95% CI 2.35-7.01). Mild
depression was associated with a more modest increased risk of mortality (HR 1.59, 95% CI
0.89-2.83, Table 3).
Sensitivity Analyses
Additional adjustment for LVEF, eGFR, serum sodium, hypertension,
within the
nsion,
n, hhospitalizations
osspi
pita
tali
ta
liz
li
last year, and the usee of
including
betaof antidepressants
antiide
deppreess
ssan
ants
an
tss or
or heart
h arrt failure
he
f iluuree related
fa
rela
re
l te
tedd medications,
mediica
me
cati
tion
ti
ons,
on
s, in
incl
cu
blockers, angiotensin
converting
n co
conv
n er
nv
erti
ting
ti
ng eenzyme
nzy
z me
m iinhibitors,
nhib
nh
ibit
ib
itor
it
o s,
or
s aangiotensin
n io
ng
iote
tens
te
nsin
ns
in rreceptor
e ept
ec
ptor
o bblockers,
or
lo
ock
cker
er or statins, did
not alter results for he
all-cause
mortality.
After
hhealthcare
heal
alth
al
thca
th
care
ca
re uutilization
tili
ti
liza
li
zati
za
tion
ti
on oorr al
alll-ca
lcaus
ca
usee mo
us
mort
rtal
rt
alit
al
ity.
it
y A
y.
fter
ft
er tthe
he rremoval
emov
em
ovv of 2
questions on the PHQ-9 that may be related to symptoms of HF, 306 people were classified as
none-minimal depression, 69 had mild depression, and only 27 patients remained in the
moderate-severe depression category. While the associations of depression with each measure of
healthcare utilization remained the same, the hazard ratio for all-cause mortality became higher
for those categorized as mildly depressed (HR 3.07, 95% CI 1.80-5.22 for mild and HR 3.72,
95% CI 1.92-7.23 for moderate-severe in the fully-adjusted model).
10
Discussion
Depression Prevalence in HF
In this community-based HF cohort, depression was frequent in HF patients, with 26% of
patients reporting mild depressive symptoms and 15% reporting moderate-severe depressive
symptoms, and its presence and severity was not related to HF characteristics. Although
reported prevalence of depression in HF are variable,5 our findings are similar to the 15-20% rate
of major depression cited in studies of coronary artery disease (CAD).30, 31 Importantly, the rate
of depression in our HF patients was 2 to 3 times the estimated rate in the general population.2
tients, of those
see rreporting mild
Interestingly, despite the high prevalence of depression in HF patients,
and moderate-severe depressive symptoms at study enrollment, onlyy approximately
app
ppro
roxi
ro
xima
xi
m t 1/3 were on
ma
antidepressant medications
cat
atiions at
a tthat
hatt time.
ha
time
ti
me.. Although
me
Alttho
h ug
u h it
it iss possible
pos
ossi
siibl
b e that
th some
som
omee patients
pati
pa
tien
ti
en were receiving
a non-pharmacologicc th
ther
therapy,
eerrapy
ap
py, tthis
h s fi
hi
ffinding
ind
din
ingg ra
raises
ais
ises
es tthe
es
he ppossibility
o si
os
sibi
bili
bi
liity tthat
hat de
ha
depr
depression
pres
pr
esssi
s onn m
might be underrecognized and undertreated
e tr
ertr
er
trea
eate
ea
tedd in these
te
the
hese
se patients.
pat
atie
ient
ie
ntss.
nt
Depression, Healthcare Utilization, and Mortality in HF
Within our cohort, patients with moderate-severe depression had nearly double the rate of
hospitalizations and ED visits, in contrast with only a modest increase in outpatient visits
compared to those with none-minimal depression. Depression is known to have a strong impact
on patient behaviors, particularly among the more severely depressed. Psychosocial stressors
contribute to increased smoking and alcohol abuse, poor diet, physical inactivity, and poor
medication adherence, all behaviors that are risk factors for cardiovascular deterioration.32, 33
These behavioral changes could at least partially explain why these patients have increased
healthcare utilization.
11
In addition, patients with moderate-severe depression had a four-fold increased risk of
all-cause mortality compared to none-minimal depression. Just as depression is associated with
negative behavioral changes, it can also cause deleterious physiologic and hormonal changes.
Depressed states have been shown to induce sympathetic activation, hypercortisolemia and other
metabolic abnormalities, increased heart rhythm disturbances, hypercoagulability, endothelial
dysfunction, and a state of elevated proinflammatory cytokines including interleukin (IL)-6,
tumor necrosis factor (TNF)-alpha, and IL-1ȕ .2, 34, 35 The effects of neurohormonal
dysregulation and a pro-inflammatory state over time have been hypothesized to adversely affect
derlying the increased
inc
nccr
the failing heart,2, 36-38 which could be part of the mechanism underlying
cardiovascular mortality in depressed HF patients. However, other
her causes
cau
ause
s s of mortality
se
mo
were also
increased in these patients,
changes
occurring
atieents, ssuggesting
at
uggge
gest
stin
st
in
ng th
that
at tthe
h pphysiologic
he
h siolo
hy
ogi
gicc ch
chan
an
nggees oc
occu
curr
cu
u rin
ingg in depression
negatively affect other
h r or
her
orga
organ
gaan sy
gan
ssystems
s em
st
ems as
a w
well.
elll. Fi
Finally,
Fina
nall
na
llly,
y aass the
the hypothetical
hypo
hy
poth
po
het
etic
ical
ic
al pathways
patthw
w
are
complex, the elevation
genesis
on of
of cytokines
cy
yto
toki
kine
ki
ness that
ne
that occurs
occ
ccur
urss in
ur
in HF may
may
y play
pla
layy a role
role in
in the
the ge
gene
nee of
depression. In this scenario, depression itself may not have a strong impact on survival as
depression may be a symptom of cytokine activation rather than a cause.
The finding of higher rates of hospitalizations and mortality among depressed HF patients
are congruent with findings from several prior studies of HF as well as CAD patients.5, 8, 13, 19, 39,
40
However, only limited data exist for ED and outpatient office visits.41, 42 Although
hospitalizations are considered to be one of the largest expenditures in HF patients,1 outpatient
and ED visits are also important contributors to the excess cost associated with depression in HF
and ED visits denote care seeking behaviors often leading to hospitalizations.
12
Clinical Implications
In this study, only about 1/3 of patients with mild and moderate-severe depression were on
antidepressants. These data resonate with prior reports suggesting that depression is underrecognized and undertreated in practice.5, 43 We acknowledge that the most effective ways to
assess and manage depression in HF remain to be fully defined as there are limited data
investigating options to treat depression in HF and uncertainties about impact on outcomes.5 A
trial of the antidepressant sertraline showed relative efficacy with 44.3% remission, as well as
decreased hospitalizations and nonfatal cardiovascular events.44 However, non-pharmacologic
ora
rab impact
treatments may also be helpful, as exercise programs have been shown to favo
favorably
cytokines, which are elevated with depression.45, 46 It is likely that
at clinical
cllin
nic
ical
al aapproaches
ppro
pp
r
will be
most effective when
n ta
tai
tailored
ilored
d tto
o in
individual
ndi
d vi
vidu
dual
du
al ppatients’
a ieent
at
n s’ pprofiles.
ro
ofile
file
fi
les.. Rega
Re
Regardless
eg rd
dle
l ss ooff un
unce
uncertainties
c r
surrounding the optimal
i al treatment
ima
t ea
tr
eatm
tm
men
e t approach,
a prroaach
ap
c , the
the present
pres
pr
esen
es
en
nt data
data uunderscore
nder
nd
eerrsccore tthat
hatt de
ha
dep
depression
p
is a key
driver of health care utilization
util
ut
iliz
il
izat
iz
atiion iin
at
n HF tthereby
here
he
reby
re
by
y ddelineating
elin
el
inea
in
eati
ea
ting
ti
ng
g aan
n op
opportunity
ppo
p rt
rtun
unit
un
ityy fo
it
forr a gr
greater
r
emphasis
on managing depression to reduce acute care use.
Limitations and Strengths
Several limitations should be acknowledged. Depressive symptoms were measured only at
enrollment and therefore we cannot account for temporal changes in depressive symptoms.
Likewise, medications were only available at baseline, and treatments for depression other than
anti-depressant medications were unavailable. Some of the symptoms of depression overlap
with common symptoms of HF including fatigue, low energy, psychomotor retardation, and
difficulty sleeping or insomnia,2, 47 a limitation that is common to all studies of depression in HF.
Additionally, participants willing to enroll in the study and complete questionnaires may differ
13
from non-participants. While it is possible that some healthcare utilization may have occurred
outside of Olmsted County, in our experience, such under-ascertainment is minimal and would
not have impacted our results. Finally, the population of southeastern Minnesota is chiefly
white and thus, our results should be examined in other racial groups.
Despite potential limitations, this study has a number of unique strengths including its
defined cohort of HF patients, prospectively recruited from the community with validated HF
diagnoses. In addition, through the record-linkage system of the REP, we captured most
healthcare utilization among participants, allowing us to analyze ED and outpatient office visits,
indicators of health resource use rarely evaluated in prior studies.. This enable
enabled
ed tthe
categorization of healthcare utilization patterns into chronic (outpatient
patie
ieent vvisits)
isit
is
its)
it
s) aand
n acute (ED and
hospitalization) care.
doing
wee id
identified
depression
on acute
e IIn
e.
n doi
ing
g sso,
o, w
o,
iden
enti
en
tiified
fii d th
tthat
at the
at
he sstriking
t ik
tr
ikin
i g im
in
iimpact
pact
pa
ct ooff de
depr
pree
pr
care use contrasted with
This
with a more
wi
morre modest
mode
mo
d st increase
increeas
asee in
in cchronic
hrron
onic
ic ccare.
aree. T
ar
hiss ad
hi
adds
ds ssupport
uppp for recasting
up
care models for depression
primary
rression
essi
es
sion
si
on iin
n HF ttowards
owar
ow
ards
ar
ds m
more
oree pr
or
proa
proactive
oact
oa
ctiv
ct
ivee ma
iv
mana
management
nage
na
geme
ge
ment
me
nt iin
n pr
prim
imar
im
arr care, as had
been done in some settings.48
Conclusions
Depression is frequent among HF patients in the community and is associated with a large
increase in acute healthcare utilization and mortality. Further research is warranted to develop
more effective clinical approaches for management of depressed HF patients.
14
Acknowledgements
We thank Annette L. McNallan, RN, Kay A. Traverse, RN and Ellen E. Koepsell, RN for
assistance with participant enrollment and data collection, Ruoxiang Jiang and Jill M. Killian for
assistance with statistical analysis, and Deborah S. Russell for secretarial assistance.
Sources of Funding
This study was supported by grants from the National Institutes of Health (R01 HL72435) and
the National Institute on Aging (R01 AG034676). The funding sources played no role in the
design, conduct, or reporting of this study.
Disclosures
D scclo
Di
l su
uress
None.
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18
Table 1. Baseline Patient Characteristics by Severity of Depressive Symptoms
Total
None-Minimal
Mild
Moderate-Severe
(N=402)
(N=236)
(N=104)
(N=62)
P-value
73.3 (13.2)
74.4 (12.6)
71.8 (13.8)
71.4 (14.2)
0.054
57.7
56.4
61.5
56.5
0.743
Married
59.1
59.1
61.5
54.8
0.044
Widowed
23.2
26.4
15.4
5.44
24.2
Divorced
110.0
10
.00
64
6.4
14.44
14
16.1
Single
77.7
.77
8.1
8.
8.7
8.
4.8
Socio-demographic variable
Age (years), mean (SD)
Male, %
Marital status, %
Education, %
0.728
Non high school graduate
d
14.22
14
115.22
12.7
12
13.1
High school graduate
38.0
37.1
35.3
45.9
Some college /
37.5
37.5
38.2
36.1
10.3
10.3
13.7
4.9
Hypertension, %
90.3
89.0
93.3
90.3
0.481
Hyperlipidemia,%
81.8
80.5
81.7
87.1
0.266
Current or former smoking,
60.4
55.9
64.4
71.0
0.019
college degree
Graduate school
Cardiovascular risk factors
%
19
Diabetes mellitus, %
Total
None-Minimal
Mild
Moderate-Severe
(N=402)
(N=236)
(N=104)
(N=62)
P-value
39.2
34.0
44.2
50.0
0.010
Body mass index (kg/m2), %
0.852
Normal (<25)
22.3
21.7
24.0
21.3
Overweight (25 to <30)
31.3
31.5
33.7
26.2
Obese (30)
46.5
46.8
42.3
52.5
226.6
26
.66
25 4
25.4
25.0
25
50
33.9
Comorbidities
Myocardial infarction, %
Charlson index score, %
0.268
0.457
0
8.2
99.7
.7
77.77
7.
3.2
1-2
22.66
22
20.88
20
225.00
25.8
3
69.2
69.5
67.3
71.0
57.2 (22.8)
58.6 (22.8)
54.7 (22.2)
56.1 (23.4)
0.267
138.2 (4.1)
138.3 (3.9)
138.0 (4.4)
137.9 (4.1)
0.009
Prevalent heart failure, %
59.5
54.7
64.4
69.4
0.017
Ejection fraction (<50%), %
53.2
50.4
56.7
58.1
0.197
Estimated glomerular
filtration rate, mL/min per
1.73 m2, mean (SD)
Serum sodium, mmol/L,
mean (SD)
Heart failure characteristics
20
Total
None-Minimal
Mild
Moderate-Severe
(N=402)
(N=236)
(N=104)
(N=62)
P-value
Beta blockers, %
84.8
83.9
88.5
82.3
0.911
Angiotensin converting
67.7
68.2
65.4
69.4
0.969
Statins, %
58.7
59.7
54.8
48
61.3
0.908
Antidepressants, %
24.4
24
4.44
18.2
18
30.8
330
0.88
37.1
0.001
Treatments
enzyme inhibitors
/angiotensin II receptor
blockers
21
Table 2. Rates and Hazard Ratios* (95% CI) or Rate Ratios† (95% CI) for Healthcare
Utilization by Severity of Depression
Severity of Depressive Symptoms
None-Minimal
Mild
Moderate-Severe
P for trend
Rate‡
1.05
1.23
2.04
Crude
1.00 (ref)
1.20 (0.91-1.59)
1.90 (1.36-2.64)
<0.001
Fully-Adjusted§
1.00 (ref)
1.16 (0.88-1.53)
1.79 (1.30-2.47)
0.001
Rate‡
11.43
.43
43
1.93
93
22.66
.66
66
Crude
(re
reef)
f)
1.00 (ref)
1.366 (1.02-1.82)
(1
1.0022-1.
1.82
1.
822)
1.36
(1.36-2
2
1.83 (1.36-2.46)
<0.001
Fully-Adjusted§
1.000 (ref)
(ref
(r
ef))
ef
1.00
11..35 (1.00-1.83)
((1.00
11..00
00-1
-1
11.83)
.883)
1.35
11.83
.883 (1
(1.34
(1.3
.3442
(1.34-2.50)
<0.001
Rate‡
23.46
25.66
28.37
Crude
1.00 (ref)
1.05 (0.90-1.23)
1.24 (1.03-1.50)
0.029
Fully-Adjusted§
1.00 (ref)
1.04 (0.89-1.21)
1.20 (1.00-1.45)
0.068
Hospitalizations
ment
Emergency Department
Outpatient Visits
* For hospitalizations and emergency department visits.
† For outpatient visits.
‡ Crude event rate per person-year.
§ Adjusted for: age, sex, Charlson comorbidity index, and incident vs. prevalent HF status.
Outpatient Visits exclude psychiatric care, tests, imaging, and outpatient procedures.
22
Table 3. Rates and Hazard Ratios (95% CI) for All-Cause Mortality by Severity of Depression
Severity of Depressive Symptoms
None-Minimal
Mild
Moderate-Severe
P for trend
Rate*
7.76
11.69
26.36
Crude
1.00 (ref)
1.50 (0.85-2.65)
3.37 (1.97-5.75)
<0.001
Fully-Adjusted†
1.00 (ref)
1.59 (0.89-2.83)
4.06 (2.35-7.01)
<0.001
All-Cause Mortality
* Crude death rate per 100 person-years.
† Adjusted for: age, sex, Charlson comorbidity index, and incident
nt vs.
vs. prevalent
prev
pr
eval
ev
alen
al
entt HF status.
en
23
Figure Legend
Figure 1. Heart Failure Participant Enrollment
Figure 2. All-Cause Mortality in Heart Failure Patients by Severity of Depression
24
Depression, Healthcare Utilization, and Death in Heart Failure: A Community Study
Amanda R. Moraska, Alanna M. Chamberlain, Nilay D. Shah, Kristin S. Vickers, Teresa A.
Rummans, Shannon M. Dunlay, John A. Spertus, Susan A. Weston, Sheila M. McNallan, Margaret
M. Redfield and Véronique L. Roger
Circ Heart Fail. published online March 19, 2013;
Circulation: Heart Failure is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2013 American Heart Association, Inc. All rights reserved.
Print ISSN: 1941-3289. Online ISSN: 1941-3297
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://circheartfailure.ahajournals.org/content/early/2013/03/19/CIRCHEARTFAILURE.112.000118
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in
Circulation: Heart Failure can be obtained via RightsLink, a service of the Copyright Clearance Center, not the
Editorial Office. Once the online version of the published article for which permission is being requested is located,
click Request Permissions in the middle column of the Web page under Services. Further information about this
process is available in the Permissions and Rights Question and Answer document.
Reprints: Information about reprints can be found online at:
http://www.lww.com/reprints
Subscriptions: Information about subscribing to Circulation: Heart Failure is online at:
http://circheartfailure.ahajournals.org//subscriptions/

Depression, Healthcare Utilization, and Death in Heart Failure: A

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