Author + information
- Received November 23, 2016
- Revision received February 27, 2017
- Accepted March 17, 2017
- Published online May 22, 2017.
- Kenneth C. Bilchick, MDa,∗ (, )
- Yongfei Wang, MSb,c,
- Alan Cheng, MDd,
- Jeptha P. Curtis, MDb,c,
- Kumar Dharmarajan, MDb,c,
- George J. Stukenborg, PhDe,
- Ramin Shadman, MDf,
- Inder Anand, MDg,
- Lars H. Lund, MDh,
- Ulf Dahlström, MD, PhDi,
- Ulrik Sartipy, MD, PhDj,k,
- Aldo Maggioni, MDl,
- Karl Swedberg, MD, PhDm,n,
- Chris O’Conner, MDo and
- Wayne C. Levy, MDp
- aDepartment of Medicine, University of Virginia Health System, Charlottesville, Virginia
- bCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut
- cDepartment of Internal Medicine, Yale University, New Haven, Connecticut
- dDepartment of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
- eDepartment of Public Health Sciences, University of Virginia, Charlottesville, Virginia
- fSouthern California Permanente Medical Group, Los Angeles, California
- gUniversity of Minnesota, Minneapolis, Minnesota
- hDepartment of Medicine/Cardiology, Karolinska University Hospital, Stockholm, Sweden
- iDepartment of Cardiology and Department of Medical and Health Sciences, Linkoping University, Linkoping, Sweden
- jDepartment of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- kSection of Cardiothoracic Surgery, Karolinska University Hospital, Stockholm, Sweden
- lItalian Association of Hospital Cardiologists Research Center, Florence, Italy
- mDepartment of Clinical and Molecular Medicine, Sahlgrenska Academy, University of Gothenburg, Sweden
- nNational Heart and Lung Institute, Imperial College, London, United Kingdom
- oInova Healthcare System, Fairfax, Virginia
- pDepartment of Medicine, University of Washington, Seattle, Washington
- ↵∗Address for correspondence:
Dr. Kenneth C. Bilchick, UVA Health System, Cardiovascular Division, P.O. Box 800158, Charlottesville, Virginia 22908.
Background Recent clinical trials highlight the need for better models to identify patients at higher risk of sudden death.
Objectives The authors hypothesized that the Seattle Heart Failure Model (SHFM) for overall survival and the Seattle Proportional Risk Model (SPRM) for proportional risk of sudden death, including death from ventricular arrhythmias, would predict the survival benefit with an implantable cardioverter-defibrillator (ICD).
Methods Patients with primary prevention ICDs from the National Cardiovascular Data Registry (NCDR) were compared with control patients with heart failure (HF) without ICDs with respect to 5-year survival using multivariable Cox proportional hazards regression.
Results Among 98,846 patients with HF (87,914 with ICDs and 10,932 without ICDs), the SHFM was strongly associated with all-cause mortality (p < 0.0001). The ICD−SPRM interaction was significant (p < 0.0001), such that SPRM quintile 5 patients had approximately twice the reduction in mortality with the ICD versus SPRM quintile 1 patients (adjusted hazard ratios [HR]: 0.602; 95% confidence interval [CI]: 0.537 to 0.675 vs. 0.793; 95% CI: 0.736 to 0.855, respectively). Among patients with SHFM-predicted annual mortality ≤5.7%, those with a SPRM-predicted risk of sudden death below the median had no reduction in mortality with the ICD (adjusted ICD HR: 0.921; 95% CI: 0.787 to 1.08; p = 0.31), whereas those with SPRM above the median derived the greatest benefit (adjusted HR: 0.599; 95% CI: 0.530 to 0.677; p < 0.0001).
Conclusions The SHFM predicted all-cause mortality in a large cohort with and without ICDs, and the SPRM discriminated and calibrated the potential ICD benefit. Together, the models identified patients less likely to derive a survival benefit from primary prevention ICDs.
At least 5 million people in the United States have heart failure (HF) (1); >500,000 are diagnosed each year (2), and 2.5 million are hospitalized for this disease (3). Although randomized trials have shown an overall mortality benefit of prophylactic implantable cardioverter defibrillators (ICDs) in those with severe systolic HF (4–6), studies have questioned the effectiveness of ICDs in certain subgroups of patients (7–9). Moreover, most patients with ICDs implanted for primary prevention do not receive therapeutic shocks, and only 21% of patients in the Sudden Cardiac Death in Heart Failure Trial received appropriate shocks during the trial (4). The DANISH-ICD (Danish Study to Assess the Efficacy of ICDs in Patients with Non-Ischemic Systolic Heart Failure on Mortality) trial recently demonstrated that patients with nonischemic HF had an approximate 50% reduction in sudden death with ICD implantation, but this did not translate into an improvement in survival, possibly due to the low rate of sudden death during follow-up and the low proportion of sudden death relative to all-cause mortality (10). In patients with HF associated with a previous myocardial infarction in the MADIT-II (Multicenter Automatic Defibrillator Implantation Trial II), for which a risk model was validated (11), the ICD-associated reduction in mortality was almost entirely due to a reduction in arrhythmic death (12), highlighting the importance of identifying subgroups of patients with a greater proportional risk of arrhythmic death. Considering the health resources used for ICDs implanted for primary prevention and the potential increase in such implantations if all patients meeting guideline-based criteria receive ICDs, the public health impact of improving risk stratification and prognostication in ICD patients could be considerable.
The Seattle Heart Failure Model (SHFM) (9) and the Seattle Proportional Risk Model (SPRM) (13–15) are innovative models for prediction of mortality and sudden death. The SHFM is a well-validated scoring system (9) that predicts the risk of all-cause mortality, and the SPRM is designed to predict the mode of death (sudden vs. nonsudden) by identifying the proportional risk of sudden death, including death from ventricular arrhythmia (VA) (14). Application of these models in a large cohort of potential ICD patients could improve our ability to target ICDs to the right patients using precision medicine (15) and could demonstrate the external validity of these models in an important cohort of patients. We hypothesized that the SHFM and SPRM scores would identify HF patients who were more likely and less likely to derive survival benefit from an ICD intended for primary prevention of sudden cardiac death. Our analysis applied both of these models to a large real-world population of National Cardiovascular Data Registry (NCDR) patients and a large control group of patients with HF without ICDs for comparison.
The analysis was approved by the University of Virginia Human Subjects Institutional Review Board, Yale University’s Human Investigation Committee, and a Swedish multisite ethics committee. Data from the NCDR ICD Registry Version 1 was linked to Social Security Death Index records to determine long-term mortality up to 5 years following device implantation. SPRM and SHFM risk scores were determined for all patients in the ICD Registry. In addition, SPRM and SHFM risk scores were also determined for a control cohort of 10,932 subjects (left ventricular ejection fraction [LVEF] ≤35%) without ICDs from 3 HF registries and 3 clinical trials: the University of Washington Registry (16), the Italian Network on Congestive Heart Failure (17), the Swedish Heart Failure Registry (18), the Carvedilol or Metoprolol European Trial (19), the Valsartan Heart Failure Trial (20), and the Prospective Randomized Amlodipine Survival Evaluation 1 trial (21). These studies were chosen for the control group because they provided high-quality and generalizable data on patients who would otherwise be candidates for primary prevention ICDs but who did not receive them, mainly because these trials enrolled patients before the widespread use of ICDs. Survival data were collected for patients in these studies and registries throughout follow-up and used for the present analysis. We performed overall survival analysis by calculating the SHFM and SPRM scores in both the ICD Registry and control patients to assess the association of a primary prevention ICD with survival. We further used these scores to identify key subgroups that would potentially derive more and less survival benefit from ICD implantation.
The cohort included: 1) patients in the ICD Registry who underwent ICD implantation between 2006 and 2009 for primary prevention of sudden cardiac death, with follow-up through 2011, linkage to the Social Security Death Index for determination of dates of death, and complete essential data in the registry; and 2) the control cohort with HF but without ICDs (approximate enrollment 2000 to 2010), as described previously, with the same data fields as those available in the ICD Registry and similar follow-up for death events. The control patients were not part of the ICD Registry, and data were collected separately for these patients. Based on current guidelines for ICD implantation (22), patients in both the ICD and control cohorts were required to have LVEFs of ≤35%. The following exclusion criteria were also applied to both ICD and control patients (Figure 1): systolic blood pressure <80 or >220 mm Hg; New York Heart Association (NYHA) functional class IV; NYHA functional class I with nonischemic cardiomyopathy; advanced chronic kidney disease with creatinine >4.0 mg/dl or requiring dialysis; age younger than 21 years or older than 90 years; serum sodium <120 mEq/l, >155 mEq/l, or missing; and previous pacemaker implantation. ICD Registry patients were excluded if the ICD implantation was performed during an inpatient hospitalization, or if a cardiac resynchronization therapy defibrillator (CRT-D) was implanted, because CRT-D devices modify the HF substrate through biventricular pacing.
Linkage and determination of outcomes
Linkage between the ICD Registry and the Social Security Death Index was performed using deterministic methods by the NCDR Analytic Center to determine vital status during follow-up. In the control cohort, survival data were obtained directly from the associated registries and clinical trials. Of note, time to death was available for all patients. Although the cause of death was not available for this analysis, the magnitude of the ICD-associated improvement in survival relative to control patients was easily determined and used as the primary outcome measure to address the study hypotheses. The rationale for this approach is that the mechanism by which ICDs improve survival is through treatment of VA to prevent arrhythmic death (12).
Determination of SHFM and SPRM scores from ICD registry
Because some variables for the SHFM model were missing in the NCDR registry (weight, carvedilol use, and diuretic daily dose) and could not be used in the SHFM model, the following variables were used to create a slightly revised version of the SHFM with comparable statistical power that could be prospectively applied to both NCDR registry patients and control subjects: age; sex; NYHA functional class; ischemic etiology; LVEF; systolic blood pressure; sodium; creatinine; angiotensin-converting enzyme inhibitor use or angiotensin receptor blocker use; beta-blocker use; digoxin use; diuretic use; statin use; and the new variables of diabetes mellitus, lung disease, and QRS width. The SPRM in this cohort was calculated as previously described using age, sex, NYHA functional class, LVEF, systolic blood pressure, sodium, creatinine, digoxin use, diabetes mellitus, and the substitution of ischemic etiology of cardiomyopathy for body mass index (not available in this NCDR cohort), because both ischemic etiology of cardiomyopathy and body mass index had similar statistical power. Both models were derived in the control group and prospectively applied in the NCDR registry. For simplicity, we refer to these slightly revised models as SHFM and SPRM in the present analysis.
Analyses were performed for the primary statistical analysis using SAS version 9.4 (SAS Institute, Cary, North Carolina). Baseline continuous variables in the control and ICD cohorts were described using the mean ± SD if normality criteria were satisfied with the Shapiro-Wilk test or with the median and interquartile range, whereas categorical variables were described based on the associated frequencies and percentages. Differences between continuous variables between groups were assessed using Student's t tests if normality criteria were satisfied or the Wilcoxon rank sum test if not, whereas differences between categorical variables were assessed using chi-square tests.
A Cox proportional hazards (CPH) analysis was performed based on the combined cohort with and without ICDs with adjustment by the SHFM score, which has been previously shown to account for the contribution of baseline variables to overall mortality in the setting of HF (9). We used CPH regression to determine how overall survival varied depending on the SHFM score, and then we tested the interaction term of SHFM*ICD in SHFM-adjusted CPH models. We evaluated the extent to which increased survival associated with ICD implantation varied as a function of the SPRM score by testing an interaction term of SPRM*ICD in the adjusted model. We performed a multivariable CPH model to assess the effect of the ICD on survival based on the SPRM after adjustment for the SHFM score. We determined the association of the ICD with survival in key subgroups by grouping patients relative to the median SHFM value, and then further subdividing each SHFM group based on the SPRM median. Associations between ICD use and survival were also assessed in quintiles of SHFM and quintiles of SPRM. Multivariable logistic regression and receiver-operating characteristic analysis were also performed to determine the association of the SHFM with 3-year survival in the NCDR cohort.
The number of life years gained and years needed to treat (YNT) to save a life were determined as previously described (23). The additional years of life gained for each year of therapy were calculated as: (total survival with ICD − total survival without ICD)/total survival with ICD. The inverse of this value was defined as the YNT value. YNT was calculated based on the scenario that all patients would continue in their treatment assignment until death.
Characteristics of cohort
The final analysis (n = 98,946) was based on 87,914 NCDR patients with primary prevention non-CRT ICD implants between 2006 and 2009, and 10,932 patients with systolic HF, derived from registries and clinical trials, who were enrolled between approximately 2000 and 2010 (Figure 1). Characteristics of these 2 groups are described in Table 1, which includes the standardized differences for all parameters. We accounted for parameter differences between groups through adjustment in the Cox proportional hazards models.
Overall effect of the ICD on survival
Among the 98,946 patients in the cohort, patients who underwent ICD implantation had a 25% lower risk of death after adjustment for the SHFM (hazard ratio [HR]: 0.751; 95% confidence interval [CI]: 0.721 to 0.782) during follow-up over 5 years (mean 3.2 ± 1.4 years) compared with control patients. Kaplan-Meier curves for patients with and without ICDs are shown in Figures 2A to 2C.
SHFM score and overall mortality
An increase in the SHFM quintile was associated with increased mortality in both patients with and without ICDs (Figures 2B and 2C). Table 2 demonstrates the robust prediction of overall survival with the ICD based on SHFM quintiles. In multivariable logistic regression models, SHFM was essentially as good as all the covariates combined for prediction of 3-year mortality (area under the curve for SHFM and all other covariates: 0.730; p < 0.0001; area under the curve with SHFM only: 0.723; p < 0.0001). The ICD HRs are shown in specific subgroups of interest based on the model covariates in Figure 3. The findings in Figure 3 with respect to the subgroups based on ischemic versus nonischemic etiology of cardiomyopathy stratified by age groups of younger than 59 years of age, 59 to 67 years of age, and 68 years of age or older, showed a decreased benefit in patients with nonischemic cardiomyopathy and age 68 years or older, which was consistent with the results of the recent DANISH ICD trial (10). Although both women and men had statistically significant HRs that favored the ICD, the ICD was favored even more strongly in men than in women (HR: 0.73 vs. HR: 0.85, respectively; interaction p = 0.004).
SPRM and overall mortality
While the SHFM score was strongly associated with all-cause mortality even with the ICD, the SPRM score was strongly associated with the magnitude of benefit from the ICD, even after adjustment for the SHFM score. Figure 4 demonstrates how an increasing SPRM score was associated with the effect of the ICD on survival based on the fitted line of the SPRM ICD interaction in the CPH model. For example, patients in SPRM quintiles 1 and 2 had a 19% to 21% reduction in mortality with the ICD, whereas patients in SPRM quintiles 4 and 5 had a 38% to 40% adjusted reduction in mortality with the ICD (p < 0.0001 for improvement in survival with the ICD in these groups). In a multivariable model for survival, the covariates of ICD (p < 0.0001), SPRM (p = 0.015), SHFM (p < 0.0001), and the interaction terms SPRM*ICD (p < 0.0001) and SHFM*ICD (p = 0.0006) were all significant. The high level of significance for the SPRM*ICD interaction term (HR: 0.811; 95% CI: 0.751 to 0.875; chi-square statistic = 29.2) demonstrated that the benefit of ICD implantation clearly increased as the SPRM-estimated proportion of sudden death increased. Of note, the SPRM*ICD interaction term favored the ICD more strongly when the cohort was limited to patients with ischemic cardiomyopathy (HR: 0.776; 95% CI: 0.703 to 0.856) versus those with nonischemic cardiomyopathy (HR: 0.871; 95% CI: 0.770 to 0.985).
Combined assessment of overall survival and effect of ICD on survival with SHFM and SPRM
The combined use of the SHFM score and the SPRM score for comprehensive risk stratification in ICD candidates was of particular interest. To create 4 groups of equal size based on SHFM and SPRM (lower SHFM/lower SPRM, lower SHFM/higher SPRM, higher SHFM/lower SPRM, and higher SHFM, higher SPRM), we used threshold values based on the median SPRM-predicted proportional rate of arrhythmic death and the median SHFM-predicted 1-year mortality rate (5.7%). Characteristics of the patients in these 4 groups can be found in the Online Table 1. Kaplan-Meier survival curves for patients in these 4 groups with and without an ICD are shown in Figures 5A to 5D. The ICD had a clear benefit (adjusted ICD HR: 0.599; 95% CI: 0.530 to 0.677; p < 0.0001) in patients with both an SHFM-predicted 1-year mortality of ≤5.7% and a SPRM-predicted proportional risk of sudden death above the median, but not in patients with an SHFM-predicted 1-year mortality of ≤5.7% and a SPRM-predicted proportional risk of sudden death below the median (adjusted HR: 0.921; 95% CI: 0.787 to 1.08; p = 0.31). Among patients with an SHFM-predicted 1-year mortality rate >5.7%, patients with a higher SPRM-predicted proportional risk of sudden death above the median (>44.3%) had a greater adjusted improvement in survival over 5 years with the ICD (adjusted ICD HR: 0.683; 95% CI: 0.641 to 0.727; p < 0.0001) compared with patients who had a SPRM-predicted proportional risk of sudden death of ≤44.3% (adjusted ICD HR: 0.791; 95% CI: 0.742 to 0.843; p < 0.0001). HRs for the ICD patients versus control patients are shown in more granular fashion in Table 2 in a 5 × 5 matrix form for specific SPRM and SHFM quintiles. The SHFM-adjusted mortality rates at 1 and 5 years by quintile of SPRM score and quintile of SHFM score are shown in Figure 6.
As shown in Table 3 and in Figures 5E and 5F, patients with a lower predicted overall mortality (lower SHFM) and a lower SPRM-predicted proportional risk of sudden death (the group without a statistically significant improvement in survival during follow-up with ICDs) would be projected to have minimal improvement in life expectancy with the ICD (0.5 years) during their lifetime, such that these patients would need to be treated 22.5 years with an ICD to add 1 year of life. In contrast, patients with a lower predicted overall mortality (lower SHFM) and a higher SPRM-predicted proportional risk of sudden death (the group with the greatest survival improvement with the ICD) would be projected to have a life expectancy improvement of 3.3 years resulting from the ICD, and these patients would only need to be treated for 4.2 years to add 1 year of life.
We found that the SPRM and SHFM together provide a useful assessment of overall mortality and expected ICD effectiveness. The SHFM provides a powerful measure of overall survival, whereas the SPRM provides an effective assessment of how overall survival with the SHFM is modified by ICD implantation (Central Illustration). Overall, the ICD was associated with an approximately 25% improvement in adjusted survival in the cohort of approximately 100,000 patients, consistent with results from clinical trials. We also identified a subgroup consisting of one-quarter of the NCDR cohort who did not have a significant survival benefit with an ICD. These patients would be expected to have minimal expected improvement in life expectancy with an ICD, such that they would have to be treated for 22.5 years to add a year of life. In contrast, another group with a high predicted proportion of arrhythmic deaths and a good predicted overall survival rate, consisting of one-quarter of the NCDR cohort, had a 40% reduction in mortality with the ICD during follow-up. These patients would need to be treated for only 4.2 years to add a year of life. The remaining one-half of the NCDR cohort with increased HF severity had an intermediate survival benefit with an ICD.
There were several strengths of this study. These included the use of high-quality registry data, linkage to a reliable death index, the use of a high-quality and generalizable control group from multiple sources, and adjustment for many covariates. The use of a control group has been overlooked in some previous registry studies that evaluated ICD prognosis (24), but it did provide important insights into the magnitude of the effect of the ICD on survival in addition to the overall expected survival with the ICD in this study. Use of the contemporary control group in this case facilitated an analysis of the modulation of the ICD effect on survival based on the SPRM after adjustment for the overall expected survival based on HF parameters, as determined by the SHFM.
Both women and men derived a significant benefit from standard (non-CRT) ICDs in our study, but the more favorable HR for the ICD in men versus women is consistent with previous data from our group that showed that women had a lower proportion of sudden death and a higher proportion of pump failure death for any given SHFM score (25). A network clinical trial meta-analysis also showed that men had a greater survival benefit than women with ICD therapy for the primary prevention indication (26). In contrast, the higher proportion of pump failure death in women might explain why women had a greater benefit with CRT-D in the MADIT-CRT (Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy) trial (27).
This analysis provides much needed data to support the use of the SPRM and SHFM in the general population of patients with implanted ICDs for primary prevention of sudden cardiac death from VA, while providing the tools to improve and individualize the clinical care of patients with HF according to the principles of precision medicine in several ways. First, these results provide evidence from a very large registry of ICD implantations that the SHFM score provides an effective assessment of overall survival, which promises to be useful for discussions between providers and patients regarding prognosis. Second, these results provide evidence for the use of the novel SPRM to characterize the proportional risk of sudden death, which was shown to translate into the magnitude of survival improvement with the ICD in the present study. This promises to be useful in clinical decision making. Third, the combined use of the SPRM and SHFM identified a low-risk cohort that included as many as one-quarter of patients with ICDs in the NCDR registry who had an overall survival during 5 years of follow-up of 83% that was not modified by the ICD. This is particularly relevant with the finding from the DANISH-ICD trial that ICDs reduced sudden death in a cohort of patients with nonischemic HF, but not overall survival, because of the smaller proportion of patients with sudden death (10).
The combined use of the SPRM and SHFM in the present study could be particularly useful in identifying patients unlikely to need ICDs because they have a low proportional risk of sudden death. The potential impact on the health care system, if further confirmatory studies showed that up to one-quarter of all patients receiving ICDs would do well without them, would be enormous. Considering that current ICD indications are broad and include low-risk patients unlikely to have improved survival as a result of the ICD (22), we believe a randomized clinical trial of ICDs in low-risk patients characterized by low SHFM and SPRM risk is appropriate. Application of these models at the time of ICD generator replacement might be useful because many patients have ICDs replaced even if they have not received appropriate therapy and no longer meet criteria for primary prevention ICDs.
One limitation of our analysis was that it was not a randomized clinical trial and used patient data from data sets from previous clinical trials and registries as the control group. Although we accounted for differences between groups with statistical adjustment, there might be unmeasured factors that differed between the groups. This is an inherent limitation of analyses of large databases and registries; however, this limitation was offset to a significant degree by the opportunity afforded by study designs such as this to evaluate risk models and other interventions in real-world patients. This was particularly important, considering the differences in patient selection and treatment between clinical trials and registries. In addition, although the time during which patients in the control group were enrolled in registries and clinical studies was similar to that of the patients in the NCDR group, the patients in the ICD and control cohorts were not enrolled at the same time in the same way, which could have introduced bias. The reasons that control patients did not receive ICDs were likely related to differences among clinical practice settings and the lower prevalence of ICD use before 2005, when ICDs were not as widely used for primary prevention of sudden cardiac death, especially in patients with nonischemic HF. Even so, available medical therapy for HF was similar for patients in the control group, and analyses were adjusted for differences in the use of these medications, which are included in the SHFM. Of note, we did use slightly adapted versions of the SHFM and SPRM based on available data fields, but the models used had similar statistical performance compared with the original models, and the same re-derived models were used in both ICD and control groups.
Our analysis showed how the SHFM and SPRM could be used to predict both overall survival and ICD benefit in a large cohort of approximately 100,000 patients. In particular, the SHFM provided a highly effective measure of HF outcomes, whereas the SPRM provided a powerful measure of the proportional risk of sudden death. Considering the large amount of time and expense presently allocated to patients with ICDs for implantation, post-operative care, and subsequent generator changes, application of these models could more effectively allocate these health care resources and personalize device therapy for the benefit of patients and society.
COMPETENCY IN PATIENT CARE AND PROCEDURAL SKILLS: Patients with ischemic or nonischemic cardiomyopathy face competing risks of heart failure and ventricular arrhythmias, limiting the benefit of ICD therapy to those at higher risk of sudden death.
COMPETENCY IN INTERPERSONAL AND COMMUNICATION SKILLS: Shared decision-making about ICD therapy should address both overall survival and the proportional risk of death from ventricular arrhythmias.
TRANSLATIONAL OUTLOOK: Further studies are needed to assess the value of ICD therapy for patients with heart failure and a low proportional risk of death from ventricular arrhythmia.
The authors thank the staff of the NCDR Analytic Center at Yale University and the American College of Cardiology for their assistance in facilitating the present analysis.
The University of Washington CoMotion holds the copyrights for the SHFM and SPRM and has received licensing fees from Thoratec (St. Jude Medical), HeartWare (Medtronic), GE Healthcare, and Athena Health. Dr. Bilchick was supported by grant R03 HL135463 from the National Institutes of Health. Drs. Bilchick, Cheng, and Levy were supported by a research grant from the National Cardiovascular Data Registry. Dr. Curtis has equity in Medtronic. Dr. Dharmarajan is supported by grant K23AG048331 from the National Institute on Aging and the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program; is also supported by grant P30AG021342 from the Yale Claude D. Pepper Older Americans Independence Center; has received salary support under contract with the Centers for Medicare & Medicaid Services; and serves as a consultant and scientific advisory board member for Clover Health. Dr. Lund has received research grants from Boston Scientific, AstraZeneca, and Novartis; speaker honoraria from St. Jude Medical, AstraZeneca, Novartis, and Merck; and consulting honoraria from AstraZeneca, Novartis, Sanofi, Bayer, Vifor Pharma, Relypsa, Merck, and Heartware. Dr. Dahlstrom has received speaker and consulting honoraria from Novartis; and research grants from AstraZeneca. Dr. Maggioni has received honoraria for serving on study committees for Novartis, Bayer, and Cardiorentis. Dr. Swedberg has received research grants from Servier; and serves as a consultant for Amgen, AstraZeneca, Novartis, Pfizer, Servier, and Vifor Pharma. Dr. Levy is a Clinical Endpoint Committee Member for the CardioMems CHAMPION Post Approval Study (Abbott) and RELAX-ASIA (Novartis); is a Steering Committee Member for ADMIRE ICD (GE Healthcare); and has received research grants from Amgen, Resmed, and Novartis. Arthur J. Moss, MD, served as Guest Editor for this paper.
- Abbreviations and Acronyms
- area under the curve
- confidence interval
- Cox proportional hazards
- cardiac resynchronization therapy defibrillator
- heart failure
- hazard ratio
- implantable cardioverter-defibrillator
- left ventricular ejection fraction
- National Cardiovascular Data Registry
- New York Heart Association
- sudden cardiac arrest
- Seattle Heart Failure Model
- Seattle Proportional Risk Model
- ventricular arrhythmia
- years needed to treat
- Received November 23, 2016.
- Revision received February 27, 2017.
- Accepted March 17, 2017.
- 2017 American College of Cardiology Foundation
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