Prediction of Symptomatic Embolism in Infective Endocarditis a Multicenter Cohort

Journal of the American College of Cardiology
2013 by the American College of Cardiology Foundation
Published by Elsevier Inc.
Vol. 62, No. 15, 2013
ISSN 0735-1097/$36.00
http://dx.doi.org/10.1016/j.jacc.2013.07.029
Heart Valve Disease
Prediction of Symptomatic Embolism
in Infective Endocarditis
Construction and Validation of a Risk Calculator in
a Multicenter Cohort
Sandrine Hubert, MD,*y Franck Thuny, MD, PHD,*zx Noemie Resseguier, MD,k
Roch Giorgi, MD, PHD,k Christophe Tribouilloy, MD, PHD,{# Yvan Le Dolley, MD,*
Jean-Paul Casalta, MD,** Alberto Riberi, MD,y Florent Chevalier, MD,{ Dan Rusinaru, MD,{
Dorothée Malaquin, MD,{ Jean Paul Remadi, MD,yy Ammar Ben Ammar, MD,zz
Jean Francois Avierinos, MD,* Frederic Collart, MD,y Didier Raoult, MD, PHD,z** Gilbert Habib, MD*
Marseille and Amiens, France
Objectives
The aim of this study was to develop and validate a simple calculator to quantify the embolic risk (ER) at admission
of patients with infective endocarditis.
Background
Early valve surgery reduces the incidence of embolism in high-risk patients with endocarditis, but the quantification
of ER remains challenging.
Methods
From 1,022 consecutive patients presenting with definite diagnoses of infective endocarditis in a multicenter
observational cohort study, 847 were randomized into derivation (n ¼ 565) and validation (n ¼ 282) samples.
Clinical, microbiological, and echocardiographic data were collected at admission. The primary endpoint was
symptomatic embolism that occurred during the 6-month period after the initiation of treatment. The prediction
model was developed and validated accounting for competing risks.
Results
The 6-month incidence of embolism was similar in the development and validation samples (8.5% in the 2
samples). Six variables were associated with ER and were used to create the calculator: age, diabetes, atrial
fibrillation, embolism before antibiotics, vegetation length, and Staphylococcus aureus infection. There was an
excellent correlation between the predicted and observed ER in both the development and validation samples. The
C-statistics for the development and validation samples were 0.72 and 0.65, respectively. Finally, a significantly
higher cumulative incidence of embolic events was observed in patients with high predicted ER in both the
development (p < 0.0001) and validation (p < 0.05) samples.
Conclusions
The risk for embolism during infective endocarditis can be quantified at admission using a simple and accurate
calculator. It might be useful for facilitating therapeutic decisions. (J Am Coll Cardiol 2013;62:1384–92) ª 2013
by the American College of Cardiology Foundation
Embolic events are frequent and life-threatening complications occurring in more than 50% of patients with infective
endocarditis (1–5). They are believed to be caused by fragmentation of vegetations, but they are also dependent on the
prothrombogenic conditions associated with or related to the
infection (6,7). These events are factors of poor prognosis,
especially with involvement of the cerebral circulation bed
(2). Two types of embolic events must be differentiated: 1)
embolic events occurring before the initiation of antibiotic
therapy, which could be prevented by earlier diagnosis and
From the *Département de Cardiologie Hôpital Universitaire de la Timone,
Assistance Publique Hôpitaux de Marseille, Aix-Marseille Université, Marseille,
France; yService de Chirurgie Cardiaque, Hôpital Universitaire de la Timone,
Assistance Publique Hôpitaux de Marseille, Aix-Marseille Université, Marseille,
France; zURMITE, UM63, CNRS 7278, IRD 198, Inserm 1095, Aix-Marseille
Université, Marseille, France; xDépartement de Cardiologie, Hôpital Universitaire
Nord, Assistance Publique Hôpitaux de Marseille, Aix-Marseille Université,
Marseille, France; kLERTIM and UMR 912 INSERM-IRD, Faculté de Médecine,
Aix-Marseille Université, Marseille, France; {Département de Cardiologie, Hôpital
Universitaire Sud, Amiens, France; #INSERM, ERI 12, Amiens, France;
**Laboratoire de Microbiologie, Centre Hospitalier Universitaire, Hôpital de la
Timone, Assistance Publique Hôpitaux de Marseille, Aix-Marseille Université,
Marseille, France; yyDépartement de Chirurgie Cardiaque, Hôpital Universitaire
Sud, Amiens, France; and the zzDépartement d’Anesthésie, Hôpital Universitaire
Sud, Amiens, France. The authors have reported that they have no relationships
relevant to the contents of this paper to disclose.
Manuscript received May 18, 2013; revised manuscript received July 4, 2013,
accepted July 12, 2013.
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rapid initiation of antibiotics (8); and 2) embolic events
occurring during and after antibiotic therapy, which could be
prevented by valve surgery (9,10). Thus, the evaluation of
the embolic risk (ER) at admission is crucial in the
management of endocarditis to avoid such potential catastrophic events. Several factors have been associated with
ER, such as the length and localization of vegetations, the
causative microorganism, and the presence of previous
emboli (11,12). Thus, the current international guidelines
recommend early valve surgery according to these predictors
(13,14). However, these recommendations are based on a
low level of evidence, and the rate of embolic events remains
high despite their routine use (10,15). Recently, a randomized trial demonstrated that early surgery significantly
reduced the risk for systemic embolism (10). However, this
study included only patients with very low operative risk,
which may limit its application in clinical practice, in which
the benefit/risk ratio for surgery must be evaluated. Thus,
an appropriate and accurate quantification of ER associated
with a correct evaluation of operative risk would allow a
more reliable assessment of the benefit/risk ratio of valve
surgery to prevent embolism and help standardize endocarditis management.
Currently, there is no simple method to accurately quantify ER. Moreover, the effects of competing events, such as
valve surgery or death, have always been ignored in previous
studies aimed at evaluating ER. Indeed, valve operation or
death, regardless of the cause, may preclude the occurrence of
embolic events.
Therefore, the objective of this multicenter study was to
develop and validate a model to quantify ER at admission,
accounting for competing risks.
Methods
Patients. This study took place in the departments of
cardiology of 2 university-affiliated tertiary care hospitals for
adults in France (Marseille and Amiens), which are referral
centers for their regions on infective endocarditis. At these
centers, more than half of the patients are referred for
complicated endocarditis with a potential indication for
surgery. A specific database was created to prospectively
collect information on patients with a diagnoses of suspected
infective endocarditis. To ensure that all endocarditis
episodes were enrolled, suspected patients were screened
weekly by cardiologists and microbiologists. For this study,
we reviewed all consecutive patients with definite diagnoses
of infective endocarditis determined at each center, according to the modified Duke criteria (16), from January 2000 to
June 2011. The exclusion criteria were isolated pacemaker or
defibrillator lead endocarditis and patients already included
in the study but rehospitalized for recurrent episodes of
endocarditis. Thus, this study relied on incident cases of
endocarditis. Written informed consent was obtained from
all participating patients under an approved protocol, as
required by the institutional review board.
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Baseline data. The following
Abbreviations
and Acronyms
data were collected at admission
and during hospital stay: age, sex,
CI = confidence interval
Charlson comorbidity index (17),
ER = embolic risk
diabetes, history of cancer, intravenous drug use, underlying heart
disease, chronic renal insufficiency, aspirin and/or anticoagulant therapy, causative pathogen (determined by blood
cultures, serology testing, valve culture, or polymerase chain
reaction on a valve specimen according to international
guidelines [14]), heart failure, and indications for valve
surgery. History of paroxysmal, persistent, or permanent
atrial fibrillation was also collected at admission according
to international guidelines (18). Transthoracic and transesophageal echocardiographic studies were performed in all
patients within 24 h of admission, and the data from the
first echocardiographic study were collected as previously
described (12). Briefly, echocardiographic data included the
presence and maximal length of vegetations. Vegetation
length was measured in various planes, and the maximal
length was used. For multiple vegetations, the largest length
was used for analysis. Periannular complications were defined
as an abscess, pseudoaneurysm, or fistula, according to
accepted definitions (14). The severity of valvular regurgitations was assessed according to international guidelines (19).
Embolic events occurring before the initiation of antibiotic therapy (previous embolism) were systematically
screened at admission by clinical examination. Moreover,
cerebral, thoracic, and abdominal computed tomographic
scans were also performed in the absence of severe renal
insufficiency or hemodynamic instability. Data were electronically stored and used as noted at the time of the original
examination without alteration.
Endpoint. Patients were treated according to each center’s
local recommendations and followed for 6 months after
the initiation of antibiotic therapy. Follow-up was obtained
through clinical records and telephone calls to patients and
their physicians. The primary endpoint was symptomatic
embolic events that occurred during the 6-month period after the initiation of antibiotic therapy and before valve surgery. These events were defined as sudden clinical symptoms
of cerebral ischemia or peripheral or pulmonary embolism.
Peripheral and pulmonary emboli were systematically confirmed by computed tomography, and/or magnetic resonance
imaging, and/or lung ventilation-perfusion scintigraphy,
and/or arteriography. A specific diagnosis of cerebral embolism was confirmed by an experienced neurologist and
by imaging. Cutaneous manifestations were not included.
Data on valve surgery or death were also collected.
Statistical analysis. First, a descriptive analysis of recorded
data was performed among the total cohort. Continuous
variables are expressed as mean SD or median (interquartile
range), and categorical data are expressed as number (percent).
Then, the prognostic model for ER was constructed using
the following procedure: 1) the total cohort was randomized
into 2 groups, resulting in development (two-thirds of the
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Table 1
JACC Vol. 62, No. 15, 2013
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Baseline Characteristics of Patients With Infective Endocarditis
Total Cohort
(n ¼ 847)
Development Sample
(n ¼ 565)
Validation Sample
(n ¼ 282)
Marseille
598 (70.6%)
401 (71.0%)
197 (69.9%)
Amiens
249 (29.4%)
164 (29.0%)
85 (30.1%)
Center
p Value*
0.73
Clinical data
62.3 15
62.7 15
61.6 15.6
0.31
606 (71.6%)
402 (71.2%)
204 (72.3%)
0.72
Diabetes
140 (16.5%)
95 (16.8%)
45 (16.0%)
0.75
History of cancer
126 (14.9%)
87 (15.4%)
39 (13.8%)
0.54
42 (5.0%)
27 (4.8%)
15 (5.3%)
0.80
Previous heart disease
493 (58.2%)
331 (58.6%)
162 (57.5%)
0.75
Atrial fibrillation
121 (14.3%)
80 (14.2%)
41 (14.5%)
0.88
Age (yrs)
Men
Intravenous drug use
Heart failure
317 (37.4%)
221 (39.1%)
96 (34.0%)
0.15
Previous embolism
318 (38.2%)
212 (38.3%)
106 (38.0%)
0.93
Ischemic stroke
180 (21.3%)
117 (20.7%)
63 (22.3%)
0.58
47 (5.6%)
28 (5.0%)
19 (6.7%)
0.28
0.19
Cerebral hemorrhage
3.2 2.4
3.3 2.4
3.1 2.4
Aspirin
10.8 (12.8%)
72 (12.8%)
36 (12.8%)
0.99
Anticoagulant therapy
155 (18.3%)
108 (19.1%)
47 (16.7%)
0.39
0.77
Charlson comorbidity index
Localization
Prosthetic valve
226 (26.7%)
149 (26.4%)
77 (27.3%)
Multivalvular
172 (20.3%)
119 (21.1%)
53 (18.8%)
0.44
Aortic
517 (61.0%)
351 (62.1%)
166 (58.9%)
0.36
Mitral
474 (56.0%)
318 (56.3%)
156 (55.3%)
0.79
22 (2.6%)
15 (2.7%)
7 (2.5%)
0.88
11.7 9
11.6 8.9
11.9 9.3
0.64
136 (16.0%)
88 (15.6%)
48 (17.0%)
Tricuspid
Echocardiography
Vegetation length (mm)
Vegetation length (stratified)
Absence of vegetation
0.86
Vegetation 10 mm
220 (26.0%)
148 (26.2%)
72 (25.5%)
Vegetation >10 mm
491 (58.0%)
329 (58.2%)
162 (57.5%)
Periannular complications
224 (26.5%)
157 (27.8%)
67 (23.8%)
67 (7.9%)
47 (8.3%)
20 (7.0%)
0.53
423 (49.9%)
294 (52.0%)
129 (45.7%)
0.08
0.23
Left ventricular ejection fraction <45%
Severe regurgitation
Causative microorganism
Oral streptococci
135 (15.9%)
96 (17.0%)
39 (13.8%)
Streptococcus bovis
148 (17.5%)
91 (16.1%)
57 (20.2%)
0.14
Staphylococcus aureus
145 (17.1%)
99 (17.5%)
46 (16.3%)
0.66
Coagulase-negative staphylococci
72 (8.5%)
42 (7.4%)
30 (10.6%)
0.12
105 (12.4%)
72 (12.7%)
33 (11.7%)
0.66
Fungi
9 (1.1%)
6 (1.1%)
3 (1.1%)
0.99
Others
104 (12.3%)
73 (12.9%)
31 (11.0%)
0.42
No identification
129 (15.2%)
86 (15.3%)
43 (15.3%)
0.34
Enterococci
Values are n (%) or mean SD. *Comparison between the development and validation samples.
total cohort) and validation (one-third of the total cohort)
samples; and 2) a prognostic model was constructed and
validated on the basis of the development and validation
samples, respectively. However, in this clinical context, the
occurrence of embolic events may be altered or precluded
by cardiac surgery or death, regardless of the cause, creating
a context of competing risks (20). Therefore, our analysis
accounted for this potential bias, as previously described
(21). The analysis was limited to the first occurring event
in the competing risks framework using the following
quantities commonly used to summarize outcomes by event
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type: 1) the cause-specific hazard function, which for
embolic events can heuristically be thought of as the probability of embolism in a small interval of time, given that no
cardiac surgery or death occurred before; and 2) the cumulative incidence function, which for embolic events corresponds to the probability of embolism in the presence of
competing cardiac surgery or death events. The delay from
the initiation of antibiotic therapy to the first observed event
was calculated in each case, and follow-up was restricted to
the first 6 months after the initiation of antibiotic therapy,
a time when patients still at risk were censored.
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Incidence of Embolic Events After Initiation of
Antibiotic Therapy in the Total Cohort
Figure 1
Accounting for the competing risks, the incidence of embolic events was highest
during the first 2 weeks after the initiation of antibiotic therapy (44.9 embolic
events per 1,000 patient-weeks in the first week and 21.3 embolic events per
1,000 patient-weeks in the second week) and then decreased rapidly and significantly to low in the sixth week (2.4 embolic events per 1,000 patient-weeks).
More precisely, patients were randomly assigned to 1 of
the samples according to the type of event of interest (i.e.,
embolism, cardiac surgery, and death) and the medical
center. Clinical and pre-clinical characteristics of the
patients were described for the 2 samples and compared. For
qualitative variables, chi-square tests were performed when
valid, and Fisher exact tests were performed otherwise. For
quantitative variables, Student t tests were performed when
valid, and Mann-Whitney tests were performed otherwise.
To assess the quality of the randomization, we estimated
the cumulative incidence of embolic events, accounting
for competing risks, and we compared these incidences
Table 2
Predictive Variables for Embolic Events Determined
Using the Fine and Gray Model (Development Sample)
Multivariate Analysis
Univariate
Analysis
p Value
Hazard Ratio
(95% Confidence
Interval)
p Value
Age
0.15
1.01 (0.99–1.03)
0.18
Diabetes
0.05
1.30 (0.61–2.80)
0.50
Previous embolism
0.04
1.40 (0.74–2.65)
0.30
Atrial fibrillation
0.07
1.66 (0.81–3.41)
0.17
Vegetation length (mm)
(stratified)*
0.001
Variable
>0 to 10
1.26 (0.24–6.69)
0.79
>10
4.46 (1.06–18.88)
0.04
1.78 (0.85–3.76)
0.13
Staphylococcus aureus
0.07
*Absence of visible vegetation was the reference state.
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according to the samples (development or validation) using
a Gray test (22). To construct the prognostic model for
embolic events, univariate analyses were first performed with
the development sample to identify prognostic factors of
embolic events. The following variables, established at
diagnosis, were tested as potentially predictive of ER: age,
sex, diabetes, comorbidity index, atrial fibrillation, previous
embolism, heart failure, aspirin, anticoagulant therapy, valve
localization, presence of vegetation, vegetation length,
periannular complications, left ventricular ejection fraction,
causative microorganism, and calendar year. Subdistribution
hazard ratios and their 95% confidence intervals (CIs)
were estimated using the Fine and Gray model (23). The
proportional hazards assumption was assessed by testing
covariate interactions with quadratic function of time and
checked graphically using Schoenfeld-type residuals (24).
All variables with p values <0.15 were included in the
multivariate model to compute the ER. Their effect was
studied in the multivariate model, but no variable selection
was performed. The predictive accuracy of the prognostic
model was assessed by studying calibration (agreement
between predicted and observed risks) and discrimination
(adapted C-index [21,25] and Royston and Sauerbrei’s
measure D [26] with bootstrap 95% CI). Moreover, in
the development and validation samples, patients were
classified according to the median of the distribution of the
predicted probability of the occurrence of embolic events.
Then, cumulative incidences for embolic events were estimated in each of these subgroups and compared using the
Gray test (22).
All of the tests were 2-sided. A p value <0.05 was
considered to be significant. Statistical analysis was performed using R version 2.14.0 (R Foundation for Statistical
Computing, Vienna, Austria). The R package cmprsk was
used for competing risk analyses.
Results
Baseline characteristics. During the study period, 1,022
patients with definite diagnoses of infective endocarditis
were treated at the 2 centers. Among these, 135 were
excluded because of endocarditis episodes that involved only
a pacemaker or defibrillator lead. An additional 40 patients
were excluded because of previous endocarditis episodes for
which they were already included. Data are thus presented
for 847 patients. The mean age of the patients was 62 15 years, and 71.5% were men. Streptococci were the most
frequent causative pathogens (33.6%), and Staphylococcus
aureus was the cause in 17.1% of the patients. In total, 226
patients (26.7%) had prosthetic valve endocarditis. Computed tomography could be performed at admission in 823
patients (97%). Of the 318 patients with previous embolism,
134 had symptomatic events, 217 had silent events, and 33
had both symptomatic and silent events. Four hundred
ninety-three patients underwent valve surgery after a median time of 10 days (interquartile range: 3 to 27 days) after
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Figure 2
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JACC Vol. 62, No. 15, 2013
October 8, 2013:1384–92
Comparison of Predicted Versus Observed Risk (Calibration) of Embolic Events for the Development Sample
and the Validation Sample
The predictive accuracy of the calculator was assessed by studying the agreement between predicted embolic risk and observed embolic risk. The figure illustrates
that the calibration of the model was excellent, as shown by the excellent correlation between predicted and observed embolic risk in both the development (A) and
validation (B) samples.
the beginning of antibiotic therapy. Among them, the indications of surgery were acute heart failure in 227 (46%),
periannular complications in 172 (35%), and large vegetations with or without previous embolism in 185 (36%).
Eighty-nine patients (18% of operated patients and 11% of
all patients) were operated only on the basis of the vegetation length, with or without previous embolism. During the
Figure 3
study period, there was no significant modification of the
overall rate of surgery (p for trend ¼ 0.09) and the rate of
surgery for a high ER (p for trend ¼ 0.99).
The total cohort was randomly divided into the development sample (n ¼ 565) and the validation sample (n ¼ 282).
The 2 samples were similar with respect to the main baseline
characteristics (Table 1).
Cumulative Incidence of Embolic Events According to Predicted Embolic Risk in the Development Sample
and the Validation Sample
All patients of each sample (derivation and validation samples) were classified according to the value of their embolic risk predicted by the calculator. Thus, 2 groups were
formed: less than or equal to or greater than a cutoff value, which was the median value of predicted embolic risk in each sample. Then, this cutoff value was tested as to
whether it correctly identified patients who experienced an excess incidence of embolic events. A significantly higher cumulative incidence of embolic events was observed in
patients with high predicted embolic risk in both the derivation (A) and validation (B) sample.
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Figure 4
Cumulative Incidence of Embolic Events According to
Predicted Embolic Risk in the Total Cohort After the
Exclusion of Patients With Reasonable Indications for
Early Surgery
The analysis (see Fig. 3 legend for the method) was performed in the total cohort
after the exclusion of patients with severe valvular regurgitation or periannular
complications or both. A significantly higher cumulative incidence of embolic
events was observed in patients with high predicted embolic risk.
Incidence of embolic events according to competing
risks. In the total cohort, embolic events occurred in
72 patients a median of 6.5 days (interquartile range: 3 to
14 days) after the initiation of antibiotic therapy. The sites
of embolization were the central nervous system (n ¼ 36),
peripheral arteries (n ¼ 16), spleen (n ¼ 10), kidneys
(n ¼ 4), eyes (n ¼ 2), coronary circulation (n ¼ 2), and
mesenteric circulation (n ¼ 2). At 6 months, valve surgery
was performed in 61.7% of patients (95% CI: 58.1% to
65%), and 20% of patients (95% CI: 17.2% to 22.7%) died.
Accounting for the competing risks, the 6-month cumulative incidence of embolic events was 8.5% (95% CI: 6.7% to
10.4%) in the total cohort and was similar in the development
and validation samples (p ¼ 0.99). The incidence of embolic
events was highest during the first 2 weeks after the initiation
of antibiotic therapy (44.9 embolic events per 1,000 patientweeks in the first week and 21.3 embolic events per 1,000
patient-weeks in the second week) and then decreased rapidly
and significantly to low in the sixth week (2.4 embolic events
per 1,000 patient-weeks) (Fig. 1).
The ER prediction model. In the development sample, the
variables associated with embolic events were age, diabetes,
atrial fibrillation, previous embolism, vegetation length, and
Staphylococcus aureus. Using these variables, a multivariate ER
prediction model was developed (Table 2). The calibration
of the model was excellent, as shown by the excellent correlation between the predicted and observed risk for embolic
events (Fig. 2A). The predictive accuracy of this prediction
model was excellent, with a C-index of 0.72 (95% CI: 0.65 to
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0.81), Royston and Sauerbrei’s measure D of 1.26 (95% CI:
0.81 to 1.70), and a significantly higher cumulative incidence
of embolic events observed in patients with high predicted ER
(p < 0.0001) (Fig. 3A).
The good predictive accuracy of the model was retained
when it was tested in the validation sample. In this sample,
there was also an excellent correlation between the predicted
and observed ER (Fig. 2B). Moreover, the C-index was
0.65 (95% CI: 0.55 to 0.77), Royston and Sauerbrei’s
measure D was 0.82 (95% CI: 0.22 to 1.41), and there was
a significantly higher cumulative incidence of embolic events
in patients with high predicted ER (p < 0.05) (Fig. 3B).
After the exclusion of patients who already had reasonable
indications for early surgery (severe valvular regurgitations
and/or periannular complications), all patients remained
correctly classified by the calculator (p ¼ 0.01) (Fig. 4).
Figure 5 illustrates the method to calculate a patient’s ER.
This model can be programmed into a handheld device to
make risk calculation automatic once the individual variables
have been entered (Online Appendix).
Discussion
We have developed and validated a simple bedside prediction system that can be used to quantify the ER at admission
of patients with endocarditis. By using a large multicenter
cohort and focusing on clinically simple and relevant variables, we believe that this tool will be usable and reliable for
clinicians and will help them make treatment decisions.
A recent randomized trial demonstrated the benefit of
early surgery on the risk for embolism. Although this result
is of crucial importance, it was limited by the fact that
it was obtained in a population with very low operative risk.
Thus, we now have strong evidence that early surgery
reduces ER, but there remains a need for better risk
stratification to evaluate accurately the benefit/risk ratio of
this procedure. Indeed, for high ER associated with low or
intermediate predicted operative mortality (computed by
scoring systems [27]), the benefit of early surgery would be
greater.
The vegetation length and the presence of a previous
embolism are the only 2 parameters included in the present
international guidelines to assess ER and indicate preventive
valve surgery (13,14). These recommendations do not
provide a precise quantification of ER and do not take into
account other potentially important predictors. Our embolic
prediction model is the only current method to accurately
quantify this ER. This model is robust because it was
developed in a large multicenter sample and validated in an
independent second sample. Moreover, it takes into account
predictors both related to the disease and associated with
patient characteristics.
We demonstrated that vegetation length and the presence
of Staphylococcus aureus were significantly and nearly significantly, respectively, associated with ER, as previously
found (11,12). These results emphasize the role of
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Figure 5
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JACC Vol. 62, No. 15, 2013
October 8, 2013:1384–92
Embolic Risk Calculator (Embolic Risk French Calculator)
This figure illustrates an example of the embolic risk calculation for a 75-year-old man with diabetes in sinus rhythm who experienced Staphylococcus aureus infective
endocarditis with a >10-mm vegetation and a previous embolic event. Note that embolic risk is particularly high during the first days after the initiation of antibiotic therapy,
reaching 24% at day 14. The additional embolic risk is very low, reaching a cumulative risk of only 29% at 6 months.
echocardiography in the prediction of embolisms and the
absolute necessity of a precise and fast microbiological diagnosis (1). Although vegetation mobility was previously
demonstrated to be a potential predictor of ER, this variable
was not tested in our model because the interobserver variability of its evaluation is known to be low (28). Indeed,
vegetation mobility is not a criterion recommended in the
international guidelines to indicate surgery. The occurrence of
an embolism before medical therapy was included in our
prediction model. According to previous studies, we
confirmed the impact of silent embolism screening in the
prediction of ER (5,11). Systematic computed tomographic
scanning was used to detect these asymptomatic embolic
events but was limited by its low sensitivity for small cerebral
damage and the risk for renal failure related to iodine injection.
More sensitive imaging modalities, such as magnetic resonance imaging (3,5) and 18-fluorine fluorodeoxyglucose
positron emission tomography, could be relevant alternatives
to improve our predictive model (29,30).
In addition, our prediction model included variables that
were not related to the disease itself but to prothrombogenic
conditions directly related to patient characteristics, namely,
advanced age, diabetes, and atrial fibrillation. Indeed, these
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are conditions that may influence the formation and adhesion
of thrombi to endothelial cells and influence ER (31,32).
Several studies have demonstrated that host humoral factors,
such as those involved in the coagulation and fibrinolysis
phenomena, are additional determinants of ER during
infective endocarditis (6,7,33,34). Although the influence of
these factors on ER is lower than that of vegetation length,
their presence associated with large vegetation would
increase the risk for embolism and thus could influence
the decision to remove surgically the main risk factor of
embolism, namely, the vegetation. Moreover, future medical
therapeutic strategies could be developed to decrease the
global thrombotic activity involved in the pathophysiology
of endocarditis.
The incidence of embolic events after the initiation of
medical therapy was estimated to be from 6% to 21% in
previous studies (10,14). This heterogeneity is due mainly to
differences in selection criteria and in the frequency of valve
surgery, which vary from center to center. This therapeutic
procedure has never been appropriately addressed in the
assessment of ER. Thus, we considered this bias using
a statistical method that accounted for competing risks (i.e.,
valve surgery and death), which may preclude the occurrence
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Embolic Risk in Endocarditis
JACC Vol. 62, No. 15, 2013
October 8, 2013:1384–92
of embolic events. Thus, we demonstrated that the incidence
of embolic events was the highest during the first 2 weeks
after the initiation of antibiotic therapy and decreased
thereafter. This observation most likely illustrates the beneficial effect of antibiotics on ER by modifying the biological
constitution of vegetations and confirms the previous uncontrolled studies (8). Therefore, our calculator should be
used as soon as possible after diagnosis so that valve surgery
can be performed in an urgent setting if ER is high (10).
However, further studies will be needed to determine at
which level of ER a significant benefit of valve surgery can be
observed. Nevertheless, this tool will help clinicians in the
standardization of endocarditis management (35). Moreover,
it could be used in future studies to investigate the impact of
new treatment strategies on the occurrence of embolic
events.
Study limitations. First, the 2 samples were assembled
retrospectively; thus, the study was exposed to a detection bias
for the determination of the baseline characteristics. However,
we have been developing a common database for several years
that includes consecutive patients, with rigorous definitions of
variables collected.
Second, in our prediction model, we considered only the
predictors evaluated at admission, without taking into account
their modifications after the initial evaluation. However,
because the objective of the study was to provide support
for rapid therapeutic decisions to avoid embolic events, data
collected at admission were the most important.
Third, left atrial dimensions and glycosylated hemoglobin
are parameters that could influence the prothrombotic state
but were not collected in the present study.
Fourth, the relatively small number of events could create
the risk for overfitting the model when adding covariates.
However, we tested only the few clinically relevant variables
known to be potentially important predictors of the
outcome. This strategy could decrease the probability to
include predictors by chance.
Fifth, although the predictive properties of proposed
model was assessed in an internal validation step, it needs to
be assessed in external data to increase its generalizability.
Finally, the study was subject to a referral bias because it
was performed at referral centers.
Conclusions
The evaluation of ER is crucial in the management of
infective endocarditis. We thus developed and validated
a new prediction system to quantify the ER at admission:
the Embolic Risk French Calculator (Online Appendix),
which can be used by clinicians from a dedicated Web site or
as a program installed on a handheld device. This risk index
may be useful for facilitating management decisions and may
allow future researchers to address critical and difficult
problems about weighing the risks and benefits of early
surgery in individual patients with infective endocarditis.
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Reprint requests and correspondence: Prof. Franck Thuny,
Département de Cardiologie, Hôpital de la Timone, Assistance
Publique Hôpitaux de Marseille, Aix-Marseille Université, Boulevard Jean Moulin, 13005 Marseille, France. E-mail: franck.thuny@
gmail.com. OR Prof. Gilbert Habib, Département de Cardiologie,
Hôpital de la Timone, Assistance Publique Hôpitaux de Marseille,
Aix-Marseille Université, Boulevard Jean Moulin, 13005 Marseille,
France. E-mail: [email protected].
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Key Words: embolism
-
endocarditis
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prognosis.
APPENDIX
For the risk calculator for 6-month embolic risk for infective endocarditis,
please see the online version of this article.