Author + information
- Received February 3, 2016
- Revision received February 24, 2016
- Accepted March 3, 2016
- Published online August 1, 2016.
- Ambarish Pandey, MDa,
- Harsh Golwala, MDb,
- Adam D. DeVore, MDc,
- Di Lu, MSc,
- George Madden, MDd,
- Deepak L. Bhatt, MD, MPHe,
- Phillip J. Schulte, PhDc,
- Paul A. Heidenreich, MD, MSf,
- Clyde W. Yancy, MDg,
- Adrian F. Hernandez, MD, MHSc and
- Gregg C. Fonarow, MDh,∗ ()
- aDivision of Cardiology, University of Texas Southwestern Medical Center, Dallas, Texas
- bDivision of Cardiology, University of Louisville School of Medicine, Louisville, Kentucky
- cDuke Clinical Research Institute, Durham, North Carolina
- dIntegris Southwest Medical Center, Oklahoma City, Oklahoma
- eBrigham and Women’s Hospital Heart & Vascular Center and Harvard Medical School, Boston, Massachusetts
- fDivision of Cardiology, Stanford University, Palo Alto, California
- gDivision of Cardiology, Northwestern University, Chicago, Illinois
- hRonald Reagan-UCLA Medical Center, Los Angeles, California
- ↵∗Reprint requests and correspondence:
Dr. Gregg C. Fonarow, Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan UCLA Medical Center, 10833 LeConte Avenue, Room 47-123 CHS, Los Angeles, California 90095-1679.
Objectives The purpose of this study was to determine the temporal trends in the adherence to heart failure (HF)–related process of care measures and clinical outcomes among patients with acute decompensated HF with reduced ejection fraction (HFrEF) and end-stage renal disease (ESRD).
Background Previous studies have demonstrated significant underuse of evidence-based HF therapies among patients with coexisting ESRD and HFrEF. However, it is unclear if the proportional use of evidence-based medical therapies and associated clinical outcomes among these patients has changed over time.
Methods Get With The Guidelines-HF study participants who were admitted for acute HFrEF between January 2005 and June 2014 were stratified into 3 groups on the basis of their admission renal function: normal renal function, renal insufficiency without dialysis, and dialysis. Temporal change in proportional adherence to the HF-related process of care measures and incidence of clinical outcomes (1-year mortality, HF hospitalization, and all-cause hospitalization) during the study period was evaluated across the 3 renal function groups.
Results The study included 111,846 patients with HFrEF from 390 participating centers, of whom 19% had renal insufficiency but who did not require dialysis, and 3% were on dialysis. There was a significant temporal increase in adherence to evidence-based medical therapies (angiotensin-converting enzyme inhibitor/angiotensin receptor blocker: p trend <0.0001, β-blockers: p trend = 0.0089; post-discharge follow-up referral: p trend <0.0001) and defect-free composite care (p trend <0.0001) among dialysis patients. An improvement in adherence to these measures was also observed among patients with normal renal function and patients with renal insufficiency without a need for dialysis. There was no significant change in cumulative incidence of clinical outcomes over time among the HF patients on dialysis.
Conclusions In a large contemporary cohort of HFrEF patients with ESRD, adherence to the HF process of care measures has improved significantly over the past 10 years. Unlike patients with normal renal function, there was no significant change in 1-year clinical outcomes over time among HF patients on dialysis.
Heart failure (HF) is a common clinical manifestation among patients with end-stage renal disease (ESRD) (1–4). Nearly 30% to 40% of patients on renal replacement therapy have prevalent HF, and an additional 7% develop this disease each year (2,3). Patients with coexisting HF and who require dialysis have a considerably worse prognosis, with an increased risk of mortality and HF hospitalizations compared with HF patients not on dialysis (2,4–6). Effective management of these patients with evidence-based HF therapies is needed to improve clinical outcomes (5,7,8). Despite the absence of large-scale randomized trials (RCTs) that evaluate the efficacy of evidence-based HF therapies in patients with ESRD, the guidelines from the National Kidney Foundation Kidney Disease Outcomes Initiative work group recommend angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB) and β-blockers for all ESRD patients with symptomatic HF who are receiving dialysis and who have HF with a reduced ejection fraction (HFrEF) (9). These recommendations are on the basis of the results from several small RCTs that demonstrated that ACEi/ARB and β-blockers are associated with significant improvements in clinical outcomes among patients with HF and ESRD (7,8,10,11). However, several previous studies have demonstrated a marked underuse of guideline-recommended HF therapies among patients with significant renal impairment (12,13).
The Get With The Guidelines-Heart Failure (GWTG-HF) program was developed by the American Heart Association (AHA) with the aim to improve quality of care among patients hospitalized with decompensated HF (14,15). Overall, centers participating in the GWTG-HF program have had improved processes of care for HF over time (16). However, the temporal trends in the process of care and clinical outcomes among high-risk HF patients on dialysis are not known. To examine this issue, we compared the temporal trends in adherence to process of care measures and clinical outcomes among dialysis patients hospitalized for acute decompensated HFrEF at GWTG-HF participating centers with HFrEF patients who had normal renal function or who had renal impairment that did not require dialysis.
We used data from the GWTG-HF registry, which is an observational, ongoing, national, quality improvement program initiated in 2005 to improve the adherence to the quality of care guidelines for patients hospitalized with HF. The details of the design and objectives of this AHA-sponsored program have been previously described (14,15). Briefly, at each participating hospital, trained personnel use the AHA GWTG-HF Internet-based patient management tool (Quintiles Real-World & Late Research, Cambridge, Massachusetts) to collect patient-level information on consecutive HF patients admitted to the hospital. HF patients were identified if they were hospitalized with a primary diagnosis of HF or developed HF symptoms during hospitalization when HF was not the primary cause of admission but was the primary discharge diagnosis. Data collected included patient demographics, socioeconomic status, medical history, medications, laboratory data, inpatient care, compliance with HF-related performance measures (including use and contraindications to evidence-based medical therapies), outcomes, and hospital characteristics. Hospital participation is voluntary and includes various institutions that represent teaching and nonteaching, rural and urban, large and small hospitals from all geographic areas of the United States. Because data are primarily collected for quality improvement purposes, all participating centers are granted waivers for informed consent under the common rule. Duke Clinical Research Institute serves as the data analysis center and has approval to analyze the aggregate de-identified data for research purposes.
Patient-level 1-year clinical outcomes were obtained for all of the GWTG-HF participants age older than 65 years with fee-for-service Medicare coverage by linking the patient data in the registry with a Medicare denominator and Part A inpatient claims files using admission and discharge dates, hospital, date of birth, and sex (17).
For the primary analysis, we included patients admitted to GWTG-HF participating centers with acute decompensated HFrEF from January 1, 2005 through June 30, 2014. We excluded patients without documented left ventricular ejection fraction, patients with left ventricular ejection fraction ≥40%, patients with significant missing medical history, patients who were transferred out, had missing discharge information, or left against medical advice, patients who died during the hospitalization, and patients discharged to hospice (Figure 1).
The clinical outcomes analysis was performed in the subgroup of registry patients with linked Medicare follow-up data. This included patients 65 years and older with Medicare fee-for-service coverage whose index HF hospitalization was identified in the Medicare Claims Data. The exclusion criteria for this analysis were similar to that for the primary study population. We further limited this cohort to patients who were discharged before December 31, 2012 to allow for the availability of 1-year outcomes data.
Exposure variable of interest
The primary exposure variable of interest in this study was renal function. Information on the presence of renal insufficiency with or without a need for dialysis was obtained from the admission medical history.
Process of care measures
This included documentation of the following HF achievement measures at discharge among eligible patients without any contraindications: prescription of ACEi/ARB, any β-blockers, evidence-based specific β-blockers, and the post-discharge follow-up referral. The post-discharge follow-up evaluated in this study refers to the official American College of Cardiology/AHA performance measure for a post-discharge follow-up appointment in a physician’s office and requires documentation of the date, time, and location of the follow-up visit. This follow-up does not include the dialysis center follow-up, unless it was specified that the patient would be seeing a physician during dialysis. Use of defect-free care, which was defined as successful accomplishment of all the eligible achievement measures for HF, was also assessed among the study participants. Other process of care measures evaluated in this study included HF quality measures, such as cardiac resynchronization therapy device or placement (CRT-D or CRT-P) or prescription at discharge and implantable cardioverter-defibrillator (ICD) placement or counseling with prescription at discharge among eligible patients without contraindications. Although data on discharge prescription of ACEi/ARB and any β-blockers were available from 2005 onward, data on CRT-D/CRT-P or prescription at discharge were available from April 2009 onward, and information about use of evidence-based specific β-blockers, post-discharge follow-up referral, defect-free composite measure, and ICD counseling or ICD placement/prescription at discharge were available from October 2011 onward.
Clinical outcomes of interest
The clinical outcomes of interest evaluated in this study were 1-year mortality, 1-year all-cause hospitalization, and 1-year HF hospitalization. The data on mortality were obtained from Centers for Medicaid and Medicare Services (CMS) enrollment files, and hospitalization data were obtained from Part A inpatient claims files.
The study population was stratified into 3 groups: patients with normal renal function versus patients with renal insufficiency without dialysis versus patients on dialysis. For descriptive analyses, medians and 25th and 75th percentiles were reported for continuous variables, and percentages were reported for categorical variables. Baseline patient and hospital characteristics were compared across the 3 renal function groups stratified by the time of enrollment (2005 to 2010 and 2011 to 2014) using the chi-square test for categorical variables and the Kruskal-Wallis test for continuous variables.
The temporal trends in adherence to the different HF process of care measures across the 3 groups were compared using the Cochran-Armitage trend test. Multivariable logistic regression models were constructed to assess the association between calendar time and each outcome according to the renal function among the study participants. Specifically, an interaction between time and renal function was tested, and we report the odds ratio per 1 year among renal function groups. Models were adjusted for patients’ age, sex, race, medical history, admission characteristics (blood pressure, heart rate, sodium, and blood urea nitrogen levels), hospital region, hospital type (teaching vs. nonteaching), and hospital size (number of beds). For missing adjustment variables, medical history variables were imputed to “no” because data abstractors were likely to skip that section when none of these variables applied. For other patient covariates, multiple imputations were used with 25 imputations (Online Appendix). Hospital characteristics were not imputed, and patients from those sites were excluded from the models (1%). Generalized estimating equations were used to account for clustering within hospitals.
Among patients with Medicare-linked follow-up data, unadjusted cumulative incidence of 1-year mortality, 1-year all-cause hospitalization, and 1-year HF hospitalization in the first (2005) and last year (2012) were compared across the 3 renal function groups. Multivariable Cox proportional hazards regression was performed to determine if there was a significant change in the risk of 1-year mortality, 1-year all-cause hospitalization, and 1-year HF-specific hospitalization over time. The linearity of the relationship between calendar year and different clinical outcomes was assessed, and appropriate transformations, such as linear spline transformations, were used as needed to achieve linearity. Proportional hazards assumptions were examined and determined to have been met. Separate models were constructed for each outcome, with adjustment for age, sex, ethnicity, medical history (anemia, ischemic history, cerebrovascular accident and/or transient ischemic attack, diabetes, hyperlipidemia, hypertension, chronic obstructive pulmonary disease or asthma, peripheral vascular disease, smoking), admission characteristics (systolic blood pressure at admission, heart rate, sodium at admission, blood urea nitrogen at admission), hospital characteristics (region, teaching/nonteaching), and hospital size (number of beds). SAS version 9.3 (SAS Institute, Cary, North Carolina) was used to perform all analyses. A 2-sided p value of 0.05 was considered significant. All confidence intervals (CIs) were calculated at the 95% level.
A total of 111,846 unique hospitalizations for HFrEF patients enrolled at 390 sites in the GWTG-HF program were included in the present study. Of these, 78% (n = 87,411) had normal renal function, 19% (n = 21,194) had renal insufficiency that did not require dialysis, and 3% (n = 3,241) were on dialysis at admission. The CMS-linked outcomes cohort included 33,369 unique HFrEF patients (normal renal function: 76%; renal insufficiency without dialysis: 21%; dialysis patients: 3%) from 329 sites who were enrolled from January 2005 through December 2012. Among the dialysis patients with CMS-linked data, 98.3% received hemodialysis only, 1.2% received peritoneal dialysis only, and 0.4% received both.
Baseline characteristics of the study population
Baseline characteristics of the overall study population stratified by renal function (normal vs. renal insufficiency that did not need dialysis vs. dialysis) and the time of enrollment (2005 to 2010 and 2011 to 2014) are shown in Table 1. HFrEF patients in the dialysis group were more commonly African American, had a greater proportion of Medicare/Medicaid insurance coverage, a greater prevalence of co-morbidities (e.g., diabetes, hypertension, anemia), and lower mean body mass index compared with HF patients with normal renal function and renal insufficiency that did not require dialysis. The median baseline and discharge creatinine values were highest in the dialysis group (5.4 and 5.1 mg/dl, respectively) followed by the renal insufficiency without dialysis group (2.1 mg/dl for both). The median creatinine was not elevated in the normal renal function group (1.2 mg/dl for both).
Temporal trends in use of HF achievement and quality measures
Among HFrEF patients on dialysis, we observed a significant temporal increase in the adherence to HF achievement measures, such as ACEi/ARB prescription at discharge (74.6% in 2005 to 90.5% in 2014; p trend <0.0001), any β-blocker prescription at discharge (88.1% in 2005 to 97.0% in 2014; p trend <0.0001), HF-specific β-blocker prescription at discharge (76.8% in 2011 to 86.8% in 2014; p trend = 0.0089), post-discharge follow-up referral (34.9% in 2011 to 65.9% in 2014; p trend <0.0001), and proportion of patients with defect-free composite care (34.0% in 2011 to 61.5% in 2014; p trend <0.0001) (Figure 2). Furthermore, adherence to HF quality measures of CRT placement or prescription at discharge (41.2% in 2011 to 48.9% in 2014; p trend = 0.0106) also increased significantly over time in this patient population, whereas ICD placement or counseling with prescription at discharge increased over time, with a clinically significant trend (36.8% in 2011 to 59.6% in 2014) without achieving statistical significance (p trend = 0.0782). The improvement in adherence to the process of care measures among dialysis patients was similar to that observed among normal renal function patients and in renal insufficiency patients who did not need dialysis (Figure 2). Table 2 compares the proportional adherence to HF-specific process of care measures across the renal function groups observed in the initial and the final year (2014) of the study period. Overall, the adherence to different process of care measures for HF was high across all 3 groups in the final study year. However, the proportional adherence to most process of care measures was significantly lower among dialysis patients compared with the patients with normal renal function and the patients with renal insufficiency who did not require dialysis (Table 2).
On multivariable logistic regression analyses, the increase in adherence to the HF process of care measures over time among dialysis patients was statistically significant for ACEi/ARB prescription at discharge (OR per 1 year increase: 1.13; 95% CI: 1.07 to 1.21), any β-blocker use (OR per 1 year increase: 1.12; 95% CI: 1.06 to 1.19), post-discharge follow-up referral (OR: 1.48; 95% CI: 1.25 to 1.77), proportion of patients with defect-free composite care (OR: 1.35; 95% CI: 1.16 to 1.56), and CRT placement or prescription at discharge (OR: 1.42; 95% CI: 1.15 to 1.74), but it was not statistically significant for evidence-based β-blocker use (OR: 1.22; 95% CI: 0.96 to 1.56) and ICD counseling or placement at discharge (OR: 1.12; 95% CI: 0.86 to 1.47) (Table 3). In contrast, among patients with normal renal function and in those with renal insufficiency who did not need dialysis, the increase in adherence to all HF achievement and quality measures over time was statistically significant on adjusted analysis (Table 3). Furthermore, we observed a significant interaction between renal function and time for any β-blocker prescription (p interaction = 0.0004) (Table 3) and CRT placement prescription (p interaction = 0.03) (Table 3) at discharge, such that the increase in any β-blocker prescription was lowest and the CRT placement and/or prescription per year was highest among dialysis patients compared with the other 2 renal function groups. The renal function and time interaction were not significantly different for any other measure.
1-year cumulative incidence of clinical outcomes in the CMS-linked cohort
Compared with HF patients with normal renal function, the 1-year cumulative incidence of mortality, all-cause hospitalization, and HF hospitalization was higher among HF patients on dialysis and HF patients with renal insufficiency who did not require dialysis. This trend was consistent in the first and the last year of the study period with available Medicare follow-up data (Table 4). On multivariable analyses, there was no significant change in the risk of 1-year mortality, 1-year all cause hospitalization, and 1-year HF hospitalization over time in the renal insufficiency without dialysis and the dialysis groups. In contrast, among participants with normal renal function, there was a modest increase in the risk of 1-year mortality and 1-year HF-specific readmission between 2005 and 2010 (3% per year for both), followed by a modest but statistically significant decline in HF hospitalization risk (4% per year) and a statistically nonsignificant decrease in mortality risk from 2011 to 2012 (Table 5).
We observed several important findings in this study. First, there was a significant improvement in the adherence to the HF-specific process of care measures, such as prescription of ACEi/ARB at discharge, β-blockers at discharge, and referral to follow-up appointment over time among dialysis patients admitted for acute decompensated HFrEF. This trend was similar to that observed among patients with normal renal function and those with renal insufficiency who did not need dialysis. Second, the contemporary adherence to most of the HF process of care measures among dialysis patients was significantly lower than that observed in patients with normal renal function or in patients with renal insufficiency who did not need dialysis. Finally, compared with HF patients with normal renal function, the cumulative incidence of adverse clinical outcomes was significantly higher among HF patients on dialysis and in those with renal insufficiency who did not require dialysis, with no significant change over time. To our knowledge, this was one of the largest and most contemporary evaluations of trends in the process of care and outcomes among patients with dialysis with acute HFrEF hospitalization.
Our study findings have important clinical implications. HF in patients with ESRD is associated with increased morbidity and mortality (1,2,4,5). Furthermore, previous studies have demonstrated a significant underuse of evidence-based medications among HF patients with impaired renal function, including patients with renal failure (12,13). Patel et al. (13) showed that less than two-thirds of renal failure patients enrolled in the GWTG-HF registry between 2005 and 2006 were discharged on evidence-based β-blockers or ACEi/ARB therapy. Other studies from smaller cohorts have also reported similar low adherence to such therapies among HF patients with chronic kidney disease or renal failure (12,18). Findings from the present study demonstrate a significant improvement in adherence to prescription of evidence-based HF therapies and other HF process of care measures among patients on dialysis. These findings are encouraging and highlight the significant improvement in quality of care among these high-risk HF patients over time. However, it is noteworthy that the adherence to process of care measures in the last year of the study period was significantly lower among HF patients on dialysis than in those with normal renal function or in those with renal insufficiency who did not require dialysis. This underscores the persistent gaps in care, and calls for sustained efforts towards quality improvement among these patients (19).
Several physician- and hospital-level factors may have contributed to this improvement in adherence to achievement and quality measures among HF patients with significant renal impairment. First, implementation of large-scale quality improvement programs, such as the GWTG-HF, has led to improved HF process of care over time (16). Second, introduction of a hospital readmission reduction program for HF by the CMS has also incentivized hospitals to develop interventions aimed at reducing HF re-hospitalizations by improving in-hospital and post-discharge follow-up care. Third, an increasing body of evidence that demonstrates the safety and efficacy of evidence-based HF therapies such as ACEi/ARB, and β-blockers in this patient population may have influenced the practice patterns among physicians over time (7,8). Finally, greater awareness of previous underuse of guideline-directed medical therapies in HFrEF patients receiving dialysis might also have contributed to an increase in the adherence to certain quality measures.
Although adherence to evidence-based HF medical therapies (ACEi/ARB and evidence-based β-blockers) among dialysis patients improved significantly over time and was close to approximately 90% by end of the study period, adherence to guideline-recommended ICD placement/prescription was much lower, with improvements over time that were not significant on adjusted analyses. ICD prescription/placement or counseling was documented in ≤60% dialysis patients with HF by end of the study period. Similarly, CRT-P/prescription rates were also low in the dialysis patients, with a modest improvement over time. This difference in adherence to evidence-based medical therapies versus device therapies among dialysis patients with HF could be related to several factors. There is a lack of definitive evidence to support the use of device therapy in this group of patients (20,21). Most randomized trials that evaluated the benefits of ICD and CRT therapy in HF excluded ESRD patients (22,23). As a result, the current American College of Cardiology/AHA/European Society of Cardiology guidelines do not address this issue adequately (24,25). Furthermore, dialysis patients are at increased risk for device-related infections, which may bias the physicians against using devices (26).
Despite temporal improvement in adherence to HF achievement and quality measures, we did not observe a clinically significant improvement in outcomes over time among HF patients with renal insufficiency or who required dialysis. This discordance is consistent with the findings from previous studies from the GWTG-HF registry that have demonstrated poor associations between process of care measures and clinical outcomes (16,27). Several factors should be considered while interpreting the outcomes results observed in this study. First, the clinical outcomes assessed in our study were not confined to only those patients who were eligible for evidence-based HF therapies. Second, other confounding factors, such as baseline disease severity, socioeconomic status, co-morbid conditions, dialysis frequency, and so on, might have a stronger influence on the risk of an adverse event than just adherence to process of care measures. Third, the process of care measures assessed in this registry was reflective of only a limited portion of care and did not capture the downstream adherence to evidence-based HF specific therapies. Finally, because of the high rates of adherence to evidence-based HF therapies at the start of the study period, the magnitude of relative increase in adherence over time might not be large enough to demonstrate a significant association with clinical outcomes.
First, participation in GWTG program is voluntary, and our study findings may not be generalizable to other hospitals with a different patient case-mix, resource availability, and care patterns from GWTG-HF participating centers. Second, data were collected by medical chart review. Thus, the quality and validity of the data depend on the accuracy and completeness of the clinical documentation. It is possible that differences in adherence to different quality and achievement measures observed in this study may be attributable in part to variability and inaccuracies in chart documentation. Third, only a small proportion of all GWTG-HF participants were on dialysis (∼3%). This may have limited the statistical power of some of the analysis, leading to nonsignificant trends in some of the process of care measures, particularly for use of device therapies. Moreover, our analyses did not adjust for multiple comparisons; the false positive rate may be inflated, and thus, our study findings are hypothesis-generating and should be validated in additional studies. Fourth, because the GWTG-HF registry captures only in-hospital data, we cannot determine the proportion of patients who were prescribed evidence-based therapies on outpatient follow-up. Fifth, residual measured and unmeasured confounding may have influenced some of these findings. Sixth, the outcomes data are only available in the CMS-linked cohort and may not be generalizable to the other patient groups. Finally, dialysis patients have significant competing risks for adverse outcomes (e.g., mortality), and the implications of changing patterns in process of care among dialysis patients with HF is not well understood.
In a large contemporary cohort of dialysis patients with HF, proportional use of HF-specific, evidence-based therapies and quality of care has improved significantly in the past 10 years, with no significant change in clinical outcomes over time. These trends were similar to that observed among HF patients with normal renal function and in patients with impaired renal function who did not need dialysis. Nevertheless, HFrEF patients who received dialysis were less likely to receive guideline-directed therapy compared with those patients with normal renal function and those with impaired renal function who did not receive dialysis. Future studies are needed to identify strategies that may help further improve use of evidence-based therapies and clinical outcomes among HF patients with ESRD.
COMPETENCY IN MEDICAL KNOWLEDGE 1: Systolic HF is common among patients with ESRD and is associated with a worse prognosis. The proportional use of HF-specific, evidence-based therapies and quality of care among dialysis patients has improved significantly in the past 10 years. However, HF patients on dialysis are less likely to receive guideline-directed therapy compared with patients with normal renal function and patients with impaired renal function who do not need dialysis.
COMPETENCY IN MEDICAL KNOWLEDGE 2: There were no significant improvements in clinical outcomes over time among HFrEF patients with renal insufficiency or who required dialysis.
TRANSLATIONAL OUTLOOK: Future studies are needed to identify strategies that may help improve further use of evidence-based therapies and clinical outcomes among HF patients with ESRD.
For an expanded Methods section, please see the online version of this article.
The American Heart Association provides the Get With The Guidelines Heart Failure program (GWTG-HF). GWTG-HF has been previously funded through support from Medtronic, GlaxoSmithKline, Ortho-McNeil, and the AHA Pharmaceutical Roundtable.
Dr. Devore has received research support from Amgen, the American Heart Association, and Novartis. Dr. Bhatt is a member of the Advisory Board for Cardax, Elsevier Practice Update Cardiology, Medscape Cardiology, and Regado Biosciences; is a member of the Board of Directors of the Boston VA Research Institute, Society of Cardiovascular Patient Care; is the Chair of the American Heart Association Quality Oversight Committee; is a member of the Data Monitoring Committees for Duke Clinical Research Institute, Harvard Clinical Research Institute, Mayo Clinic, and Population Health Research Institute; has received honoraria from the American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org), Belvoir Publications (Editor in Chief, Harvard Heart Letter), Duke Clinical Research Institute (clinical trial steering committees), Harvard Clinical Research Institute (clinical trial steering committee), HMP Communications (Editor in Chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (Guest Editor; Associate Editor), Population Health Research Institute (clinical trial steering committee), Slack Publications (Chief Medical Editor, Cardiology Today’s Intervention), Society of Cardiovascular Patient Care (Secretary/Treasurer), WebMD (CME steering committees); and other: Clinical Cardiology (Deputy Editor), NCDR-ACTION Registry Steering Committee (Vice-Chair), VA CART Research and Publications Committee (Chair); has received research funding from Amarin, AstraZeneca, Bristol-Myers Squibb, Eisai, Ethicon, Forest Laboratories, Ischemix, Medtronic, Pfizer, Roche, Sanofi Aventis, and The Medicines Company; has received royalties from Elsevier (Editor, Cardiovascular Intervention: A Companion to Braunwald’s Heart Disease); has been a site co-investigator for Biotronik, Boston Scientific, St. Jude Medical; has been a trustee for the American College of Cardiology; and has performed unfunded research for FlowCo, PLx Pharma, and Takeda. Dr. Hernandez has received research funding from Amgen, Janssen, Novartis, Portola, and Bristol-Myers Squibb; and has received consulting fees from Bristol-Myers Squibb, Gilead, Boston Scientific, Janssen, and Novartis. Dr. Fonarow has received research funding from the Agency for Healthcare Research and Quality and National Institutes of Health; and has received consulting fees from Amgen, Baxter, Bayer, Janssen, Novartis, and Medtronic. All other authors have reported that they have no no relationships relevant to the conents of this paper to disclose.
- Abbreviations and Acronyms
- angiotensin-converting enzyme inhibitor
- American Heart Association
- angiotensin receptor blocker
- Centers for Medicaid and Medicare Services
- cardiac resynchronization therapy device/placement
- end-stage renal disease
- Get With The Guidelines-Heart Failure
- heart failure
- heart failure with reduced ejection fraction
- implantable cardioverter-defibrillator
- randomized controlled trial
- Received February 3, 2016.
- Revision received February 24, 2016.
- Accepted March 3, 2016.
- American College of Cardiology Foundation
- Stack A.G.,
- Mohammed A.,
- Hanley A.,
- Mutwali A.,
- Nguyen H.
- Wang A.Y.,
- Wang M.,
- Lam C.W.,
- Chan I.H.,
- Lui S.F.,
- Sanderson J.E.
- Cice G.,
- Di Benedetto A.,
- D'Isa S.,
- et al.
- Cice G.,
- Ferrara L.,
- D'Andrea A.,
- et al.
- Cice G.,
- Ferrara L.,
- Di Benedetto A.,
- et al.
- Heidenreich P.A.,
- Hernandez A.F.,
- Yancy C.W.,
- Liang L.,
- Peterson E.D.,
- Fonarow G.C.
- Ezekowitz J.,
- McAlister F.A.,
- Humphries K.H.,
- et al.
- Cannizzaro L.A.,
- Piccini J.P.,
- Patel U.D.,
- Hernandez A.F.
- Dickstein K.,
- Cohen-Solal A.,
- Filippatos G.,
- et al.
- Hunt S.A.,
- Abraham W.T.,
- Chin M.H.,
- et al.