Protein Biomarkers Identify Patients Unlikely to Benefit...
Transcript of Protein Biomarkers Identify Patients Unlikely to Benefit...
DOI: 10.1161/CIRCEP.113.001705
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Protein Biomarkers Identify Patients Unlikely to Benefit from Primary
Prevention ICDs: Findings from the PROSE-ICD Study
Running title: Cheng et al.; Biomarkers and Primary Prevention ICDs
Alan Cheng, MD1; Yiyi Zhang, PhD1; Elena Blasco-Colmenares, MD, PhD1; Darshan Dalal,
MD, PhD1,2; Barbara Butcher, RN1; Sanaz Norgard, BA1; Zayd Eldadah, MD, PhD3; Kenneth A.
Ellenbogen, MD4; Timm Dickfeld, MD, PhD5; David D. Spragg, MD1; Joseph E. Marine, MD1;
Eliseo Guallar, MD, DrPH1; Gordon F. Tomaselli, MD1
1Johns Hopkins Medical Institutions, Baltimore, MD; 3Washington Hospital Center, Washington,
DC; 4Medical College of Virginia, Richmond, VA; 5University of Maryland, Baltimore, MD;2Present: Employee of Novartis Corporation
Correspondence:
Gordon F. Tomaselli, MD
Johns Hopkins Medical Institutions
720 N. Rutland Avenue, Ross 844
Baltimore MD 21205-2196
Tel: 410-955-2774
Fax: (410) 614-3191
E-mail: [email protected]
Journal Subject Codes: [121] Primary prevention, [22] Ablation/ICD/surgery, [106] Electrophysiology, [5] Arrhythmias, clinical electrophysiology, drugs
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DOI: 10.1161/CIRCEP.113.001705
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Abstract:
Background - Primary prevention implantable cardioverter defibrillators (ICDs) reduce all-cause
mortality but the benefits are heterogeneous. Current risk stratification based on left ventricular
ejection fraction has limited discrimination power. We hypothesize that biomarkers for
inflammation, neurohumoral activation and cardiac injury can predict appropriate shocks and all-
cause mortality in patients with primary prevention ICDs.
Methods and Results - The Prospective Observational Study of Implantable Cardioverter
Defibrillators (PROSe-ICD) enrolled 1,189 patients with systolic heart failure who underwent
ICD implantation for primary prevention of sudden cardiac death. The primary endpoint was an
ICD shock for adjudicated ventricular tachyarrhythmia. The secondary endpoint was all-cause
mortality. After a median follow-up of 4.0 years, 137 subjects experienced an appropriate ICD
shock and 343 participants died (incidence rates of 3.2 and 5.8 per 100 person-years,
respectively). In multivariable adjusted models, higher interleukin-6 (IL-6) levels increased the
risk of appropriate ICD shocks. In contrast, C-reactive protein, IL-6, tumor necrosis factor-
receptor II, pro-brain natriuretic peptide, and cardiac troponin T showed significant linear trends
for increased risk of all-cause mortality across quartiles. A score combining these 5 biomarkers
identified patients who were much more likely to die than to receive an appropriate shock from
the ICD.
Conclusions - An increase in serum biomarkers of inflammation, neurohumoral activation and
myocardial injury increased the risk for death but poorly predicted the likelihood of an ICD
shock. These findings highlight the potential importance of serum-based biomarkers in
identifying patients who are unlikely to benefit from primary prevention ICDs.
Clinical Trial Registration - clinicaltrials.gov; Unique Identifier: NCT00733590.
Key words: arrhythmia, sudden cardiac death, inflammation, prevention, implantable cardioverter-defibrillator
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Introduction
Implantable cardioverter defibrillators (ICDs) have become the cornerstone to prevent sudden
cardiac death (SCD) in patients with systolic heart failure.1,2 However, only a minority of
patients with ICDs experience therapy over time with wide variations in all-cause mortality rates
among patients eligible for primary prevention ICD implantation.3,4 A significant shortcoming of
primary prevention ICD therapy is the inadequacy of clinical selection criteria for patients at
greatest risk for arrhythmic SCD. Hence, attention has been focused on identifying novel factors
that may better identify those who may benefit the most.5,6
Serum biomarkers of inflammation, neurohumoral activation and myocardial injury have
established prognostic utility in various forms of cardiovascular disease.7,8 However, their role in
predicting SCD is unclear. We hypothesize that serum markers of inflammation, heart failure
status and cardiac injury can predict ICD shocks and mortality in a large, community-based
cohort of primary prevention ICD recipients with ischemic and non-ischemic cardiomyopathy.
Methods
Study Design and Clinical Data Collection
The Prospective Observational Study of Implantable Cardioverter-Defibrillators (PROSE-ICD)
is a multicenter observational study of patients with systolic heart failure eligible for a primary
prevention ICD. Details of the design and baseline characteristics of study participants have been
described elsewhere; study participants underwent ICD implantation based on current
guidelines.9,10 The study enrolled 1,189 participants. All centers obtained approval from their
respective institutional review boards and all patients provided written informed consent.
PROSE-ICD participants were extensively phenotyped as previously described.9 All
patients underwent a baseline comprehensive history and cardiovascular examination along with
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a digitally-recorded resting 12-lead electrocardiogram (ECG), a five minute three lead ECG, an
echocardiogram and fasting blood collection. Study participants were evaluated twice a year.
ICDs were also interrogated (in person or via remote transmission) to assess arrhythmic events.
Patients who were not seen in clinic underwent a telephone interview to update history and
medication use.
Serum biomarker analysis
Whole blood samples were collected and were kept at room temperature for one hour prior to
centrifugation. Serum was stored at -80°C after quick freezing in liquid nitrogen. All biomarker
measurements were made in serum. The inflammatory biomarkers included C-reactive protein
(CRP), interleukin-6 (IL-6), IL-10, and tumor necrosis factor receptor II (TNF- RII). IL-6
(R&D Systems, Minneapolis MN), IL-10 (R&D Systems) and CRP (ALPCO Diagnostics, Salem
NH) were measured with high-sensitivity ELISAs according to manufacturer’s instructions (see
Supplemental Material to Methods Section).
Study outcomes
The primary outcome in PROSE-ICD was the occurrence of a first appropriate ICD shock for an
adjudicated ventricular tachyarrhythmia. Detailed information from ICDs and patient outcomes
were adjudicated as previously described9. For sensitivity analysis, we also examined the
association of biomarkers with all-cause mortality after censoring participants at their first
appropriate ICD shock.
Statistical Analyses
This was a post-hoc analysis using unpaired t-test, Wilcoxon rank-sum test, and chi-square
analyses as appropriate. Two-sided p <0.05 was considered statistically significant. Nominal p-
values were presented for analysis of each biomarker without adjustment for multiple
comparisons.
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Serum biomarkers were log transformed prior to analyses and values expressed as
medians. To evaluate the associations between serum biomarkers and endpoint events, we
categorized each biomarker into quartiles. For cTnT, cTnI, and myoglobin, since levels were
undetectable in >25% of samples, participants were categorized into four groups (group 1, value
0; groups 2-4, tertiles of the non-zero values). We then used separate Cox proportional hazards
regressions for each biomarker to estimate the hazard ratios comparing quartiles 2-4 to the first
quartile of each biomarker. Cox models were adjusted for age, gender, race, enrollment center,
current smoking, BMI, ejection fraction, atrial fibrillation, hypertension, diabetes, chronic kidney
disease (CKD, defined as a GFR < 60ml/min/1.73 m2), device type (ICD, CRT-D). Tests for
linear trends across quartiles were conducted by including an ordinal variable with the median
biomarker level of each quartile in the regression models. A combined score of the association
between biomarker levels and study outcomes was created by adding the quartile ranks of all
biomarkers that showed significant linear trends with appropriate ICD shocks or mortality. As a
sensitivity analysis, death was treated as a competing risk for appropriate shock, and the results
were virtually the same (data now shown).
To examine whether the biomarker score improves prediction for mortality and shock
beyond conventional clinical variables, we compared the performance of a basic model (a model
with all clinical variables associated with either all-cause mortality or appropriate shock with a p-
value <0.1 in the univariate analysis) with a model incorporating both the clinical variables and
the serum biomarker risk score. Model discrimination was determined by c-statistics, and risk
reclassification was assessed using both net reclassification improvement (NRI) and integrated
discrimination improvement (IDI), all accounted for censoring.11-12 In describing the NRI, 5-year
mortality and shock risk was categorized into three groups (<15%, 15-50%, and >50% for
mortality, and <10%, 10-20% and >20% for appropriate shock) based upon the observed
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distribution of risk in the cohort. Confidence intervals for these statistics were obtained using
boot-strapping. We used the added variable version of Gronnesby and Borgan goodness-of-fit
test for Cox regression, and a p-value of >0.05 from the goodness of fit test indicates that
predicted risk of the model is similar to the observed risk. All analyses were performed using
STATA version 12 (StataCorp LP, College Station, TX).
Results
The average age (SD) of study participants at baseline was 60.6 (12.7) years and detailed
information regarding their clinical characteristics are noted in Table 1. Most participants
received a single chamber ICD (55.1%) with 17.7% receiving dual chamber systems and 27.2%
CRT devices. The average lowest cut off zone for tachycardia therapy was programmed to 185.2
beats per minute (bpm) (14.6).
After a median follow-up of 4.0 years, 137 subjects experienced an appropriate ICD
shock and 343 participants died (incidence rates of 3.2 and 5.8 per 100 person-years,
respectively). The majority of participants who died did not experience an appropriate ICD shock
(294 out of 343, 85.7%). Patients who experienced an appropriate ICD shock during follow-up
were more likely to be male, Caucasian, current or former smokers, and to have higher BMI,
lower resting heart rate, and less hypertensive and less likely to have CKD (Table 1). Patients
who died during follow-up were more often male, current or former smokers, with NYHA Class
III symptoms, longer QTc intervals and QRS durations, ischemic cardiomyopathy, atrial
fibrillation, diabetes, hypertension, CKD, lower BMI, lower ejection fraction, and lower ICD
therapy cutoff rates (Table 2). Participants who died during follow-up were more frequently
taking diuretics, ASA and ACE-I/ARBs compared to those who survived.
Number of events (eg ICD shock and all-cause mortality) by quartile levels of biomarkers
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are noted in Table 3. Median (Q1 to Q3) levels of study biomarkers are shown in Supplemental
Table 1. CRP and IL-6, cTnT and IL-6, and cTnT and cTnI showed pairwise Spearman
correlation coefficients >0.4 (Supplemental Table 2). In multivariable adjusted models, the only
biomarker showing a significant linear trend across quartiles with increased risk of appropriate
ICD shock was IL-6 (Figure 1). The hazard ratio (95% CI) for appropriate ICD shocks
comparing the highest to the lowest quartiles of IL-6 was 2.23 (1.20 to 4.14). When IL-6 was
introduced as a log-transformed continuous variable in Cox models, the hazard ratio for
appropriate ICD shock comparing the 80th to the 20th percentile of the IL-6 distribution was 1.25
(0.98 to 1.60) (Supplemental Table 3).
In contrast to appropriate ICD shocks, CRP, IL-6, TNF- -BNP, and cTnT
showed significant linear trends for increased risk of mortality across quartiles (Figure 1). The
hazard ratios for all-cause mortality comparing the highest to the lowest quartiles of CRP, IL-6,
TNF- -BNP, and cTnT were 1.72 (1.21 to 2.45), 2.39 (1.58 to 3.60), 1.95 (1.34 to 2.84),
3.63 (2.37 to 5.56), and 2.42 (1.74 to 3.37), respectively. The associations with all-cause
mortality were similar when these analyses were repeated after censoring participants at their
first appropriate ICD shock.
In order to understand the combined impact of biomarker levels on risk, we created a
cumulative score by adding the quartile rank for the 5 biomarkers that showed a significant trend
with all-cause mortality. The median score was 12 and ranged from 5 to 20. The proportion of
participants with scores 5 to 9, 10 to 14, and 15 to 20 were 27.2, 38.1, and 27.2%, respectively.
The rates of appropriate ICD shocks for patients with scores 5 to 9, 10 to 14, and 15 to 20 were
2.4, 3.2, and 4.1 per 100 person-years, respectively (Figure 2). The corresponding rates for all-
cause mortality were 1.8, 4.4, and 12.8 per 100 person-years, respectively.
To better understand the added potency of the serum biomarker risk score on
contrast to appropriate ICD shocks, CRP, IL 6 TNF BNP and cTnT
gnificant linear trends for increased risk of mortality across quartiles (Figure 1). T
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discrimination for all-cause mortality, we incorporated the biomarker risk score into a set of
clinical variables associated with an increased risk for death. By adding the biomarker score to
the clinical variables, the c-statistics improved from 0.74 to 0.78 (difference in c-statistics = 0.04,
95% CI 0.02 to 0.06), with an NRI of 15.7% (8.5% to 22.3%) and the p-value of the goodness of
fit tests of 0.29 (Table 4). With respect to appropriate ICD shocks, adding the biomarker risk
score resulted in only minor improvement in the prediction for appropriate shocks with c-
statistics from 0.67 to 0.69 (difference in c-statistics = 0.02, 95% CI -0.008 to 0.04).
Given the recent studies demonstrating improvements in mortality and patient outcomes
when ICDs are programmed with higher rate cutoffs, we performed a secondary analysis
dividing the cohort into two groups, those with a rate cutoff <200 bpm (n=766) and those
>200bpm (n=221). When comparing the baseline characteristics between these two groups,
patients with a lower cutoff rate were more likely to be older (average age 62.8 vs. 52.4), male
(74.6% vs. 64.1%), smoker (68.9% vs. 58.5%), have ischemic cardiomyopathy (58.0% vs.
38.2%), diabetes (36.5% vs. 27.6%), hypertension (66.2% vs. 49.2%), and CKD (31.7% vs.
22.4%). Both the clinical score and the serum biomarker risk score performed better in patients
the serum biomarker risk
score (when added to the clinical variables only) in risk prediction was similar among the two
groups (Supplemental Table 4).
Discussion
In this large cohort study of stable systolic heart failure patients who were candidates for primary
prevention ICD implantation, serum biomarkers did poorly in predicting the likelihood of an
appropriate ICD shock (primary endpoint). However, they did identify patients at increased risk
of dying (secondary endpoint) without experiencing an appropriate ICD shock. Interestingly, IL-
secocondndndndarararary y y y aaaananananalylylylysisiss ssss
e cohort into two groups, those with a rate cutoff <200 bpm (n 766) and those
(
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abetes (36.5% vs. 27.6%), hypertension (66.2% vs. 49.2%), and CKD (31.7% vs
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6 showed a significant trend predicting the occurrence of both appropriate ICD shocks and all-
cause mortality, thus making it potentially less useful in identifying patients likely to experience
an appropriate ICD shock. By utilizing a composite score of these biomarkers, we were able to
identify patients who were much more likely to die than to benefit from an appropriate ICD
shock for a ventricular tachyarrhythmia. Furthermore, adding biomarker data to conventional
clinical risk factors improved discrimination for all-cause mortality. Hence, our results suggest
that inflammation, neurohumoral modulation, and cardiac injury assess complementary
pathophysiological mechanisms that promote the progression of heart failure and ultimately
increase the likelihood of death but they are poor predictors of ventricular arrhythmias that are
effectively treated by ICD shocks. This profile of serum biomarkers allows for prospective
identification of a subgroup of patients with a higher risk of death relative to their risk for an
appropriate ICD shock.
The role of inflammation and neurohumoral activation in cardiovascular disease is well
known and most strongly linked to atherosclerosis and heart failure. These studies have also
provided insight into the predictive power of these biomarkers in the development of atrial and
ventricular arrhythmias.13-15 While these findings may apply to previously healthy subjects,
studies in patients with coronary disease or heart failure have been less definitive.16-19 In fact, the
CAMI-GUIDE study demonstrated marginal predictive power of elevated CRP levels for heart
failure mortality, but not for arrhythmic SCD.19
The role of inflammation in predicting appropriate ICD therapy has also been
inconsistent. While some studies have demonstrated that elevated IL-6 and CRP levels were
associated with ICD shocks,20,21 others have not.22 This inconsistency is most likely explained by
the small size and heterogeneity of the cohorts studied. Our findings demonstrate that CRP and
other markers of systemic inflammation are inadequate predictors of the development of ICD
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shocks but can identify individuals who are at increased risk for non-arrhythmic modes of death.
This latter point is particularly important given the controversy regarding the benefits of primary
prevention ICDs and the need to develop more refined risk-stratification metrics.
The associations between pro-BNP levels, ICD shocks and all-cause mortality, were
similar to the relationship for markers of inflammation. Pro-BNP failed to predict the
development of ICD shocks but did predict death after ICD implantation. Earlier studies
suggested a strong link between BNP levels and ICD events,23 but our findings are aligned with
more recent studies which do not.19,24 From the perspective of overall mortality, our findings
support an earlier study demonstrating that elevated levels of BNP are associated with an
increased risk for death despite patients appearing clinically euvolemic.25 Hence, obtaining
information on BNP may provide additional discriminatory power in those at highest risk for
death despite primary prevention ICD implantation.
Biomarkers for subclinical cardiac injury have long been recognized to predict major
cardiovascular events in patients with cardiomyopathy. A number of these clinical studies
occurred in cohorts where medical therapy for heart failure was not optimal by contemporary
standards. Hence, the prognostic utility of cardiac injury markers in well-treated heart failure
patients remains unclear. Our studies demonstrate that markers for cardiac injury remain an
important predictor of death after ICD implantation. The mechanisms remain unclear but prior
studies have suggested that they may promote inflammation.26
The limited sensitivity and specificity of using the EF as the primary means for SCD risk
stratification has resulted in many individuals receiving ICDs deriving little benefit and also
failing to identify individuals with relatively preserved function but remain at high SCD risk.27 In
fact, the greatest number of SCD events occur in the general population without known heart
disease,28 thus the overall impact of the ICD on the population burden of SCD is relatively small.
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Hence, many have sought to develop a more personalized approach whereby the risk of an
individual for SCD is not based on population-based observations but rather unique features of
the particular individual.29 Our findings add to these efforts by focusing on the biology of heart
failure and shedding more light on potential mechanistic relationships between inflammation,
neurohumoral activation, ongoing myocardial injury and death. It is difficult to determine which
of the three mechanisms predominates but our composite biomarker scoring schema suggests
that they are in fact complementary to each other in predicting patient outcomes.
There are a number of limitations that need to be considered. First, we did not have a
comparison cohort with heart failure who did not undergo ICD implantation. This was not a
clinical trial and randomizing patients for ICD therapy in patients who fulfilled current
guidelines would be unethical. We attempted to account for this by censoring patients who
experienced appropriate ICD shocks prior to death for the all-cause mortality analysis. Despite
this, we cannot exclude the possibility that the ICD prevented bradycardia-induced SCD since all
devices provided pacing support. However, prior studies suggest that this mode of death is
uncommon.30 Second, this was a post-hoc analysis and no analysis was performed on temporal
changes in biomarker levels especially immediately prior to an endpoint event. Hence, these
results alone do not resolve the inadequacy of ICD patient selection but may provide preliminary
data in guiding the design of future prospective studies. Third, we do not have detailed
information on what proportion of patients were “at target” with their heart failure medications
or more detailed information on the detection duration parameters for ICD therapy delivery.
Fourth, we excluded ATP therapy from the primary endpoint in order to identify the best
surrogate for SCD and because of prior reports highlighting the prognostic importance of ICD
shocks on mortality outcomes.31 Fifth, the number of participants with appropriate ICD shocks
was only 137, which limited our ability to identify modest predictors of risk. Lastly, our
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observations on the relationship between biomarkers and appropriate ICD shocks and death may
not be applicable in all populations at risk of SCD including those with preserved left ventricular
function. Despite this, we believe our findings remain relevant to the majority of ICD recipients
given that most have systolic dysfunction and improvements on currently available risk
prediction models are urgently needed to refine application of this therapy to those who would
benefit the most.
Using a limited set of serum biomarkers of inflammation, neurohumoral activation and
myocardial injury obtained at the time enrollment, we identified patients who were likely to die
after primary prevention ICD implantation without receiving ICD shocks for ventricular
tachyarrhythmias. These findings may provide more specific criteria to identify those who are
most likely to benefit from a primary prevention ICD but will need to be further validated in
large prospective clinical studies.
Funding Sources: The Donald W. Reynolds Foundation funded the initial design of the study and patient enrollment. Patient follow-up, data collection and analyses were supported by NIH R01 HL091062 (GFT) and NIH R01 HL103946 (AC).
Conflict of Interest Disclosures: Dr. A Cheng received honoraria from Boston Scientific, Medtronic and St. Jude Medical. Dr. D. Dalal’s contributions to the study pre-dated his current employment with Novartis. Dr. Z. Eldadah received an honorarium from St. Jude Medical. Dr. K. Ellenbogen received honoraria from Medtronic, Boston Scientific, Biotronik, served as a consultant for Medtronic, Boston Scientific, St.Jude Medical and received fellowship support from Medtronic and Boston Scientific. Dr. D. Spragg received honoraria from Biotronik and Medtronic. All other authors have no relevant disclosures to report.
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TR, Turner SJ, Sacrinty MT, Lingle KC, Applegate RJ, Kutcher MA, Sane DC. brinogen compared with C-reactive protein and brain natriuretic peptide for predr
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Table 1: Baseline characteristics of participants, by appropriate ICD shock
Values are number (%), or mean (SD)
Characteristic Total(n = 1189)
No appropriate ICD shock (n=1052)
Appropriate ICD shock (n = 137) p-value
Age (year) 60.6 (12.7) 60.7 (12.8) 59.9 (11.6) 0.46Sex 0.01
Male 867 (72.9) 755 (71.8) 112 (81.8)Female 322 (27.1) 297 (28.2) 25 (18.2)
Race 0.008White 679 (57.1) 584 (55.5) 95 (69.3)Black 477 (40.1) 437 (41.5) 40 (29.2)Other 33 (2.8) 31 (2.9) 2 (1.5)
Smoking 0.02Never 398 (33.5) 367 (34.9) 31 (22.6)Former 541 (45.5) 469 (44.6) 72 (52.6)Current 250 (21.0) 216 (20.5) 34 (24.8)
Body mass index (kg/m2) 29.8 (6.5) 29.6 (6.6) 30.8 (6.2) 0.05Ejection fraction (%) 22.3 (7.4) 22.4 (7.4) 21.6 (7.5) 0.27Heart rate (beats/min) 76.4 (17.1) 76.9 (17.3) 72.3 (14.4) 0.004QTc (ms) 459.8 (43.4) 459.7 (43.8) 460.8 (40.0) 0.77QRS (ms) 118.2 (30.2) 117.9 (30.3) 120.5 (28.8) 0.36NHYA class 0.54
Class I 196 (16.5) 168 (16.0) 28 (20.4)Class II 524 (44.1) 467 (44.4) 57 (41.6)Class III 464 (39.0) 413 (39.3) 51 (37.2)Class IV 5 (0.4) 4 (0.4) 1 (0.7)
Cardiomyopathy 0.71Non-ischemic 547 (46.0) 486 (46.2) 61 (44.5)Ischemic 642 (54.0) 566 (53.8) 76 (55.5)
Atrial fibrillation 312 (26.2) 280 (26.6) 32 (23.4) 0.42Diabetes 414 (34.8) 369 (35.1) 45 (32.8) 0.61Hypertension 747 (62.8) 674 (64.1) 73 (53.3) 0.01Chronic kidney disease 360 (30.3) 331 (31.5) 29 (21.2) 0.03Medications
ASA 781 (65.7) 689 (65.5) 92 (67.2) 0.70ACE-I/ARB 850 (71.5) 748 (71.1) 102 (74.5) 0.41Beta blocker 1061 (89.2) 943 (89.6) 118 (86.1) 0.21Diuretics 857 (72.1) 757 (72.0) 100 (73.0) 0.80Aldosterone antagonist 302 (25.4) 267 (25.4) 35 (25.5) 0.97
Device type 0.74Single 655 (55.1) 574 (54.6) 81 (59.1)BiV (no atrial lead) 26 (2.2) 24 (2.3) 2 (1.5)Dual 211 (17.7) 189 (18.0) 22 (16.1)Dual/BiV 297 (25.0) 265 (25.2) 32 (23.4)
Lowest rate of cutoff (beats/min) 185.2 (14.6) 185.5 (14.7) 182.9 (13.4) 0.05ATP used 694 (58.4) 627 (59.6) 67 (48.9) 0.02
72 (52.6)34343434 ((((24242424.8.8.8.8))))30303030.8.8.88 ((((6.6.6.6.2)2)2)2) 000021111.6.6.6.6 ((((7.7.77 5)5)5)5) 0
beats/min) 76 4 (17 1) 76 9 (17 3) 72 3 (14 4) 0.00
s 0
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beats//mimimin)nn) 76.4 (17.1) 76.9 (17.3) 72.3 (14.4) 0.459.8 (43.4))) 44459.7 (4(( 3.8)))) 460.8 (4(( 0.0)))) 01111111 8.2 22 (33(330...2)) 1117.77.7 999 (3(3(3300.0 3) 12121212000.5 (2(2(228.88 88)8 0
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Table 2: Baseline characteristics of participants, by all-cause mortality
Characteristic Alive(n=846)
Dead(n = 343) p-value
Age (year) 58.8 (12.4) 65.2 (12.2) <0.001Sex 0.003
Male 596 (70.4) 271 (79.0)Female 250 (29.6) 72 (21.0)
Race 0.28White 475 (56.1) 204 (59.5)Black 344 (40.7) 133 (38.8)Other 27 (3.2) 6 (1.7)
Smoking 0.02Never 303 (35.8) 95 (27.7)Former 374 (44.2) 167 (48.7)Current 169 (20.0) 81 (23.6)
Body mass index (kg/m2) 30.1 (6.8) 29.0 (5.9) 0.009Ejection fraction (%) 22.7 (7.3) 21.2 (7.5) 0.001Heart rate (beats/min) 76.1 (16.9) 77.3 (17.4) 0.29QTc (ms) 457.3 (42.0) 466.0 (46.1) 0.002QRS (ms) 117.1 (29.8) 120.9 (31.0) 0.05NHYA class <0.001
Class I 166 (19.6) 30 (8.7)Class II 391 (46.2) 133 (38.8)Class III 285 (33.7) 179 (52.2)Class IV 4 (0.5) 1 (0.3)
Cardiomyopathy <0.001Non-ischemic 417 (49.3) 130 (37.9)Ischemic 429 (50.7) 213 (62.1)
Atrial fibrillation 198 (23.4) 114 (33.2) <0.001Diabetes 255 (30.1) 159 (46.4) <0.001Hypertension 501 (59.2) 246 (71.7) <0.001Chronic kidney disease 198 (23.4) 162 (47.2) <0.001Medications
ASA 536 (63.4) 245 (71.4) 0.008ACE-I/ARB 589 (69.6) 261 (76.1) 0.03Beta blocker 762 (90.1) 299 (87.2) 0.14Diuretics 582 (68.8) 275 (80.2) <0.001Aldosterone antagonist 214 (25.3) 88 (25.7) 0.90
Device type 0.15Single 482 (57.0) 173 (50.4)BiV (no atrial lead) 16 (1.9) 10 (2.9)Dual 141 (16.7) 70 (20.4)Dual/BiV 207 (24.5) 90 (26.2)
Lowest rate of cutoff (beats/min) 187.0 (14.3) 180.8 (14.4) <0.001ATP used 510 (60.3) 184 (53.6) 0.11Values are number (%), or mean (SD)
23.6))((((5.555.9)9)9)9) 0.0.0.0.00000000(7.5) 0.0.0.0.00000017 4) 0000 2
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4444 (0(0(0(0..5) 111 (0(0(0.3.333))))atatathyhyhy <0<0 0.0mic 41414141777 7 (4(4(4(4999.9 3)3)3)3) 1313131 0000 (3(3(3(37.777 9)9)9))
429 (50 7) 213 (62 1)
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Table 3: Number of events and incidence rates (number of events/total person-years at risk) by levels of biomarkers.
BiomarkersAppropriate ICD shock All-cause mortality
Number of events
Person-years
Incidence rate (%)
Number of events
Person-years
Incidence rate (%)
hs-CRP (μg/mL)27 1143.7 2.4 54 1495.1 3.6
2: 1.7-4.4 36 1076.3 3.3 60 1430.5 4.23: 4.5-10.6 36 955.2 3.8 85 1344.6 6.34: >10.6 31 860.4 3.6 115 1297.1 8.8
IL-6 (pg/mL)18 1020.6 1.8 34 1342.5 2.5
2: 1.2-2.0 35 1064.9 3.3 56 1473.5 3.83: 2.1-4.3 35 1048.6 3.3 93 1423.7 6.54: >4.3 42 905.2 4.6 131 1332 9.8
IL-10 (pg/mL)31 1157.1 2.7 69 1607.1 4.3
2: 1.0-1.4 41 984.2 4.2 71 1343.6 5.33: 1.5-2.8 29 899.6 3.2 88 1263.3 6.94: >2.8 28 993.8 2.8 86 1348.2 6.4
TNF-36 1133.3 3.2 48 1589.6 3.0
2: 2318.9-3160.4 33 1099.5 3.0 58 1491.4 3.93: 3160.5-4754.1 36 1014 3.6 86 1386 6.14: >4754.1 25 792.5 3.2 122 1104.7 11.0
Pro-BNP (ng/mL)1: 22 1048.1 2.1 30 1413.8 2.12: 1.8-2.6 40 1122.2 3.6 58 1546.1 3.83: 2.7-4.1 41 1075.3 3.8 79 1455.2 5.44: >4.1 25 772.8 3.2 146 1127.2 12.9
cTnT (ng/mL)1:00 43 1918.3 2.2 75 2469 3.02: 0.001-0.017 37 793.8 4.7 53 1199.6 4.43: 0.018-0.050 23 741 3.1 83 1019.3 8.04: >0.050 26 577.7 4.5 103 869.9 11.8
cTnI (ng/mL)1:00 35 1248.5 2.8 72 1623.7 4.42: 0.001-0.015 26 921.9 2.8 67 1256.1 5.33: 0.016-0.057 30 899.4 3.3 77 1286.1 6.04: >0.057 34 865.2 3.9 89 1246.9 7.1
CK-MB (ng/mL)26 991.8 2.6 62 1365.9 4.5
2: 1.8-2.5 30 968.4 3.1 77 1328.5 5.83: 2.6-4.1 32 1000.9 3.2 75 1388.6 5.44: >4.1 37 973.9 3.8 91 1329.8 6.8
Myoglobin (ng/mL)1:00 22 1070.7 2.1 87 1625.1 5.32: 0.1-21.3 31 876 3.5 46 1163.1 4.03: 21.4-25.2 29 803.8 3.6 74 1067.4 6.94: >25.2 32 835.6 3.8 77 1109.4 6.9
1332 9.9.9.9.88
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Table 4: Predicted 5-year mortality using clinical variables with and without cytokine score
Clinical variables + Cytokine score
<15% 15-50% >50% Total
Patients with events
Clinical variables only *
<15% 23 13 0 36
15-50% 16 126 34 176
>50% 0 9 54 63
Total 39 148 88 275
Patients without events
Clinical variables only *
<15% 297 43 0 340
15-50% 100 220 17 337
>50% 0 15 20 35
Total 397 278 37 712
Net reclassification improvement (NRI): 15.7% (8.5% to 22.3%)Integrated discrimination improvement (IDI): 0.06 (0.04 to 0.07)
* Clinical variables including age, sex, race, smoking, body mass index, ejection fraction, heart rate, QTc, QRS, NYHA class III/IV, ischemic cardiomyopathy, atrial fibrillation, diabetes, hypertension, chronic kidney disease, ASA, ACE-I/ARB, diuretics, lowest rate of cutoff, and ATP used.
0 3343 0
ariables including age sex race smoking body mass index ejection fraction heart rate QTc Q
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Figure Legends:
Figure 1: Multivariable adjusted hazard ratios (HR) for appropriate ICD shocks (left), all-cause
mortality (center) and all-cause mortality censored at the first appropriate ICD shock (right) by
quartiles of biomarkers of inflammation, neurohumoral activation and myocardial injury. Hazard
ratios were adjusted for age, sex, race, study center, smoking status, body mass index, NYHA
class, atrial fibrillation, diabetes, hypertension, chronic kidney disease, and CRT device.
Figure 2: Probability of survival free from appropriate ICD shocks (top) and all-cause mortality
(bottom) as a function of a combined biomarker score. The combined score was created by
adding the quartile rank for CRP, IL-6, TNF- RII, pro-BNP, and cTnT. The median score was
12 (range 5 to 20). The proportion of participants with scores 5 to 9, 10 to 14, and 15 to 20 were
27.2, 38.1, and 27.2%, respectively.
o )p) aandndndnd alalalalllll----cacacacaususususe e ee momomomorrrr
s a function of a combined biomarker score. The combined score was created by
w
5
s a function of f ff a a combined biomarker scoreee. The combined sscore was created by
qqqquaaaartile ranknknkk ffffoor CCCCRPRPRPRP,,,, ILILILIL-666,,,, TNTNTNT FF-F RRRIIII , pproo-BBBNPNPNP, anana d ddd cTcTcTnTnTTnT. ThThThheee mememedidididiaana scscscscorororo e w
5 tototo 2220)0)0)). ThThThThee pppprooopopopoporttttion ofofofo pppparararticccic papapanntnn sss wiwiwithhh sscocococ rereres s 555 ttototo 9999, , , 101010 tooo 14141414,,, anananand d 151515 tooo o 22202
and d d 2727227.2.2.2%,%,%, rrresesesespepepep ctcctivivivellelely.y.y.
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Guallar and Gordon F. TomaselliZayd Eldadah, Kenneth A. Ellenbogen, Timm Dickfeld, David D. Spragg, Joseph E. Marine, Eliseo
Alan Cheng, Yiyi Zhang, Elena Blasco-Colmenares, Darshan Dalal, Barbara Butcher, Sanaz Norgard,Findings from the PROSE-ICD Study
Protein Biomarkers Identify Patients Unlikely to Benefit from Primary Prevention ICDs:
Print ISSN: 1941-3149. Online ISSN: 1941-3084 Copyright © 2014 American Heart Association, Inc. All rights reserved.
Dallas, TX 75231is published by the American Heart Association, 7272 Greenville Avenue,Circulation: Arrhythmia and Electrophysiology
published online October 1, 2014;Circ Arrhythm Electrophysiol.
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SUPPLEMENTAL MATERIAL
Supplemental Material to Methods Sections:
The lower limits of detection (LLOD) for CRP, IL-6, and IL-10 were 0.5 ng/ml, 0.036 pg/ml,
and <0.5 pg/ml, respectively, with inter-assay coefficients of variation (CV) of 2.2%, 5%, and
6.3%, respectively. Soluble TNF-αRII is more stable than TNF-α and was measured to index
levels of the cytokine using an ELISA (R&D Systems) with a LLOD of 0.6 pg/ml and inter-
assay CV of 5.9%. The neurohumoral/cardiac injury biomarkers included pro-brain natriuretic
peptide (pro-BNP), cardiac troponin T (cTnT), cTnI, myoglobin and creatine kinase MB (CK-
MB). Pro-BNP was measured using an ELISA (ALPCO Diagnostics) with a lower limit of
detection (LLOD) of 5 fmol/ml and an inter-assay CV of 5.25%. CK-MB, myoglobin, cTnI and
cTnT were measured using antibody-based electrochemiluminescence detection and patterned
array sandwich ELISA (Meso Scale Discovery, Rockville MD). CK-MB, myoglobin and cTnI
were measured in a multiplex format and cTnT in a monoplex format with LLODs of 0.021
ng/ml, 0.075 ng/ml, 1.24ng/ml and 0.72 pg/ml, respectively. The intra- and inter-assay CVs for
the cardiac injury panels varied from 2.4-8.4% (mean 5.6%) and 4.3-9.5% (mean 7.8%),
respectively.
Supplemental Table 1: Levels of biomarkers of inflammation, neurohumoral activation and myocardial injury by appropriate ICD shock and by all-cause mortality.
Biomarkers
Total
(n = 1189)
Appropriate ICD shock
All-cause mortality
No (n=1052) Yes (n = 137) p-value
No (n=846) Yes (n = 343) p-value
CRP (µg/mL) 4.4 (1.6, 10.6)
4.3 (1.6, 10.7) 4.7 (2.0, 10.5) 0.76
3.5 (1.5, 8.5) 7.5 (2.6, 15.2) <0.001
IL-6 (pg/mL) 2.0 (1.1, 4.3)
2.0 (1.1, 4.1) 2.5 (1.5, 5.1) 0.03
1.7 (1.0, 3.2) 3.5 (1.9, 7.5) <0.001
IL-10 (pg/mL) 1.4 (0.9, 2.8)
1.4 (0.9, 2.8) 1.3 (0.9, 2.4) 0.20
1.4 (0.9, 2.6) 1.6 (1.0, 3.1) 0.02
TNF-αRII (pg/mL) 3160 (2319, 4754)
3182 (2327, 4825) 3086 (2280, 4449) 0.52
2988 (2232, 4257) 4026 (2723, 6000) <0.001
Pro-BNP (ng/mL) 2.6 (1.7, 4.1)
2.6 (1.7, 4.2) 2.8 (1.9, 3.9) 0.75
2.2 (1.5, 3.4) 3.6 (2.4, 6.4) <0.001
cTnT (ng/mL) 0.005 (0, 0.037)
0.005 (0, 0.037) 0.005 (0, 0.037) 0.98
0.001 (0, 0.024) 0.027 (0.001, 0.071) <0.001
cTnI (ng/mL) 0.013 (0, 0.054)
0.013 (0, 0.051) 0.016 (0, 0.060) 0.50
0.011 (0, 0.047) 0.019 (0.001, 0.073) 0.01
CK-MB (ng/mL) 2.5 (1.7, 4.1)
2.5 (1.7, 4.0) 2.7 (1.8, 4.2) 0.24
2.5 (1.7, 4.0) 2.8 (1.9, 4.4) 0.07
Myoglobin (ng/mL) 16.4 (25.9, 20.3) 19.8 (0.0, 24.3) 22.1 (16.4, 25.9) 0.02 19.6 (0.0, 23.6) 22.1 (0.0, 25.9) 0.00
Values are medians (Q1-Q3)
Supplemental Table 2: Spearman correlation matrix of the biomarkers.
CRP IL-6 IL-10 TNF-
αRII Pro-BNP cTnT cTnI CK-MB Myoglobin
CRP 1.00
IL-6 0.64* 1.00
IL-10 0.18* 0.16* 1.00
TNF-αRII 0.36* 0.39* 0.17* 1.00
Pro-BNP 0.21* 0.35* 0.20* 0.34* 1.00
cTnT 0.32* 0.42* 0.15* 0.39* 0.39* 1.00
cTnI 0.14* 0.18* 0.07* 0.11* 0.14* 0.40* 1.00
CK-MB -0.04 0.00 0.01 0.05 0.09* 0.26* 0.24* 1.00
Myoglobin -0.05 0.12* -0.01 -0.02 0.13* -0.01 0.09* 0.21* 1.00
* p-value <0.05
Supplemental Table 3: Hazard ratio (95% CI) for outcomes comparing the 80th to the 20th percentile of each biomarker in log-linear models.
Appropriate ICD shock
All-cause mortality
Model 1 * Model 2 †
Model 1 * Model 2 †
hs-CRP 1.46 (1.08, 1.97) 1.28 (0.92, 1.78)
1.82 (1.50, 2.20) 1.56 (1.26, 1.94)
IL-6 1.35 (1.08, 1.68) 1.25 (0.98, 1.60)
1.74 (1.51, 2.01) 1.56 (1.32, 1.83)
IL-10 0.89 (0.73, 1.09) 0.92 (0.75, 1.13)
1.04 (0.95, 1.14) 1.03 (0.94, 1.12)
TNF-α rec II 1.23 (0.85, 1.77) 1.16 (0.78, 1.72)
2.42 (1.97, 2.98) 1.79 (1.40, 2.28)
Pro-BNP 1.22 (0.91, 1.64) 1.29 (0.90, 1.84)
2.01 (1.62, 2.48) 1.73 (1.42, 2.11)
cTnT 0.99 (0.96, 1.03) 0.98 (0.95, 1.02)
1.04 (1.03, 1.06) 1.04 (1.02, 1.05)
cTnI 1.00 (0.95, 1.05) 0.99 (0.94, 1.05)
1.04 (1.02, 1.08) 1.05 (1.02, 1.08)
CK-MB 1.03 (0.79, 1.35) 1.06 (0.79, 1.41)
1.16 (0.98, 1.37) 1.21 (1.01, 1.43)
Myoglobin 0.96 (0.55, 1.68) 1.04 (0.58, 1.87) 1.24 (0.86, 1.79) 1.20 (0.82, 1.76)
* Model 1: Adjusted for age, sex, race, and study center. † Model 2: Further adjusted for smoking status, body mass index, NYHA class, atrial fibrillation, diabetes, hypertension, chronic kidney disease, and CRT device.
Supplemental Table 4: Model performance stratified by device cutoff rate of 200 beats per minute
Models
All-cause mortality
Appropriate ICD shock
Lowest cutoff rate <200 bpm (n=766)
Model 1: clinical variables 0.73 (0.70, 0.76)
0.65 (0.60, 0.71)
Model 2: clinical variables + cytokine score 0.77 (0.75, 0.80)
0.67 (0.62, 0.72)
Model 2 vs. Model 1 0.04 (0.02, 0.06)
0.02 (-0.01, 0.05)
Lowest cutoff rate ≥200 bpm (n=221)
Model 1: clinical variables 0.78 (0.69, 0.86)
0.82 (0.71, 0.92)
Model 2: clinical variables + cytokine score 0.82 (0.74, 0.89)
0.82 (0.72, 0.92)
Model 2 vs. Model 1 0.04 (-0.01, 0.09) 0.003 (-0.04, 0.05)