DOI: 10.1161/CIRCEP.113.001705

1

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: gtomaseli@jhmi.edu

Journal Subject Codes: [121] Primary prevention, [22] Ablation/ICD/surgery, [106] Electrophysiology, [5] Arrhythmias, clinical electrophysiology, drugs

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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

(

m

abetes (36.5% vs. 27.6%), hypertension (66.2% vs. 49.2%), and CKD (31.7% vs

e cohort into ttwowww groups, those with a rate cutoff <200 bpmmmm (n 766) and those

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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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429 (50 7) 213 (62 1)

457.3 (42.0)0)0) 466.00 (4(4(4( 6.1) 0.00117.1 (2( 9.8)) 121120.9 (3(3( 1.0))) 0.0

s <0<0<0<0.0161166 (11999.6) 30 ((8.7)))3939393 111 (4(4(4(46.6.6.6 2)22) 13131333333 (3(3( 8.8.88 8))))285 (3(3(333.7)7)7)7) 179999 (5(( 2.2))

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

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100 22220 17 33

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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)