Author + information
- Received August 29, 2017
- Revision received November 27, 2017
- Accepted November 28, 2017
- Published online February 7, 2018.
- Tiffany M. Powell-Wiley, MD, MPHa,∗ (, )
- Julius Ngwa, PhDb,
- Selomie Kebede, MDb,
- Di Lu, MSc,
- Phillip J. Schulte, PhDd,
- Deepak L. Bhatt, MD, MPHe,
- Clyde Yancy, MDf,
- Gregg C. Fonarow, MDg and
- Michelle A. Albert, MD, MPHh
- aCardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- bDivision of Cardiovascular Medicine, Howard University Hospital and School of Medicine, Washington, DC
- cDuke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- dDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
- eHeart and Vascular Center, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- fCardiovascular Division, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- gRonald Reagan University of California at Los Angeles Medical Center, Los Angeles, California
- hCenter for the Study of Adversity and Cardiovascular Disease (NURTURE Center), Cardiology Division, University of California at San Francisco Medical Center, San Francisco, California
- ↵∗Address for correspondence:
Dr. Tiffany M. Powell-Wiley, Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Room 5E-3340, Bethesda, Maryland 20892.
Objectives This study sought to evaluate the influence of race/ethnicity on the relationship between body mass index (BMI) and mortality in heart failure with preserved ejection fraction (HFpEF) and HF with reduced EF (HFrEF) patients.
Background Prior studies demonstrated an “obesity paradox” among overweight and obese patients, where they have a better HF prognosis than normal weight patients. Less is known about the relationship between BMI and mortality among diverse patients with HF, particularly given disparities in obesity and HF prevalence.
Methods The authors used Get With The Guidelines–Heart Failure data to assess the relationship between BMI and in-hospital mortality by using logistic regression modeling. The authors assessed 30-day and 1-year rates of all-cause mortality following discharge by using Cox regression modeling.
Results A total of 39,647 patients with HF were included (32,434 [81.8%] white subjects; 3,809 [9.6%] black subjects; 1,928 [4.9%] Hispanic subjects; 544 [1.4%] Asian subjects; and 932 [2.3%] other subjects); 59.7% of subjects had HFpEF, and 30.7% were obese. More black and Hispanic patients had Class I or higher obesity (BMI ≥30 kg/m2) than whites, Asians, or other racial/ethnic groups (p < 0.0001). Among subjects with HFpEF, higher BMI was associated with lower 30-day mortality, up to 30 kg/m2 with a small risk increase above 30 kg/m2 (BMI: 30 vs. 18.5 kg/m2), hazard ratio (HR) of 0.63 (95% confidence interval [CI]: 0.54 to 0.73). A modest relationship was observed in HFrEF subjects (BMI: 30 vs. 18.5 kg/m2; HR: 0.73; 95% CI: 0.60 to 0.89), with no risk increase above 30 kg/m2. There were no significant interactions between BMI and race or ethnicity related to 30-day mortality (p > 0.05).
Conclusions This work is one of the first suggesting the obesity paradox for 30-day mortality exists at all BMI levels in HFrEF but not in patients with HFpEF. Higher BMI was associated with lower 30-day mortality across racial/ethnic groups in a manner inconsistent with the J-shaped relationship noted for coronary artery disease. The differential slope of obesity and mortality among HFpEF and patients with HFrEF potentially suggests differing mechanistic factors, requiring further exploration.
Both obesity and heart failure (HF) are unremitting in their rise. In the United States, 35% of Americans are obese (1) and HF affects 5.7 million individuals (2). Consequently, these conditions combined contribute to an estimated $246 billion in health care expenditure (3–5). Although evidence independently implicates being overweight (body mass index [BMI] ≥25 kg/m2) and obesity (BMI ≥30 kg/m2) with increased HF risk (1), most studies have suggested that increased BMI is associated with lower mortality in patients with HF (6,7). However, whether this relationship differs between patients with HF with preserved ejection fraction (HFpEF) and those with HF with reduced EF (HFrEF) and whether there are important differences among patients with HF of different racial/ethnic groups are less clear.
Considering the diverse racial composition of the United States, the association of race or ethnicity on any relationship between BMI and HF mortality becomes increasingly important. For example, African Americans have the highest rates of both overweight/obesity and HF compared to other racial/ethnic groups in the United States (8), as well as increased HF and HF hospitalization rates (9), factors likely significantly contributing to gaps in mortality and longevity by race or ethnicity. Additionally, as hospital readmissions are higher in blacks and Hispanics (10–13) and readmissions are closely linked to morbidity and mortality, it is imperative that the association between BMI and HF be characterized both in general and according to race/ethnicity to understand its effect on HF outcomes and to potentially inform therapeutic interventions. Using Get With The Guidelines Heart Failure (GWTG–HF) registry data for both HFpEF and patients with HFrEF, we sought, first, to assess the association between BMI and in-hospital mortality according to race/ethnicity; and, second, to determine associations between BMI and 30-day and 1-year all-cause mortality following discharge alive according to race/ethnicity.
The GWTG–HF is a registry and performance improvement initiative started in 2005 to enhance adherence to practice guidelines for hospitalized patients with HF. This voluntary American Heart Association program collects data for patient characteristics by using Web-based information systems. The program’s methods, design, and validity have been published previously (14–17). Hospitals participating in the registry submit clinical information regarding medical history, laboratory results, diagnostic test results, hospital care, and outcomes of patients hospitalized for HF by using an online, interactive case report form and patient management tool (Quintiles, Cambridge, Massachusetts). To be eligible for GWTG–HF, patients must be adults hospitalized for a HF episode as the primary cause of admission or demonstrate significant HF symptoms that developed during hospitalization with a primary discharge diagnosis of HF. Race/ethnicity data were collected for evaluating subgroup differences in outcomes. Patients were assigned to race/ethnicity group based on their self-reported race/ethnicity, using the following options defined by the case report form: American Indian or Alaska Native, Asian, black or African-American, Native Hawaiian or Pacific Islander, white or unable to be determined, or ethnicity, Hispanic: yes, no, or unable to be determined.
Data collection protocols
HF status, including HFpEF versus HFrEF diagnosis, was determined by point-of-care providers, based on American Heart Association guidelines. Patients with HFrEF had EF ≤40%, and patients with HFpEF had EF ≥40% (18). Patient height and weight were collected at time of admission, and BMI was imputed by the GWTG Patient Management Tool data collection form. Covariates were collected from historical records or during admission, depending on time of presentation. For example, HF clinical characteristics were recorded based on current hospital admission or when the condition was first recognized. Quintiles is the GWTG data collection (through their Patient Management Tool) and coordination center. Duke Clinical Research Institute (Duke University School of Medicine, Durham, North Carolina) serves as the data analysis center and has an agreement to analyze aggregate de-identified data for research purposes. Participating institutions were required to comply with local regulatory guidelines and the local institutional review board.
The starting population for this study consisted of 65,037 GWTG–HF patients linked to Centers for Medicare and Medicaid Services (Baltimore, Maryland) from 292 sites. The study period was January 2005 through December 2011. We sequentially excluded patients who were not enrolled in fee-for-service Medicare at discharge (n = 2,473), patients without race or ethnicity data (n = 1,768), missing BMI information (n = 14,661), missing EF (n = 5,416), and missing or undocumented transfer or discharge information or those who left against medical advice (n = 1,072) (Figure 1). A total of 39,647 patients with HFpEF or HFrEF and with BMI and race or ethnicity data were documented. For post-discharge outcomes, we excluded the study population patients who died in hospital or were discharged to hospice (n = 2,086).
The study objective was to assess the association between BMI and in-hospital mortality according to race/ethnicity and associations between BMI and 30-day or 1-year all-cause mortality following discharge alive according to race or ethnicity. The outcomes were in-hospital mortality and 30-day and 1-year all-cause mortality. The 30-day and 1-year mortality rates were evaluated from discharge date to 30 days and 1 year afterward.
The study population was divided by HFpEF and HFrEF, and analyses were performed separately for each cohort. For descriptive analyses, baseline patient and hospital characteristics were stratified among whites, blacks, Hispanic, Asians, and other race/ethnicity groups. For categorical variables, proportions were used, and differences were assessed by chi-square test. For continuous/ordinal variables, means and standard deviations were presented across race or ethnicity groups assessed by Kruskal-Wallis tests.
The relationships among BMI, race/ethnicity, and in-hospital mortality were evaluated using logistic regression. We tested the interaction between BMI and race/ethnicity to assess whether the relationship between BMI and odds of mortality were similar among racial/ethnic groups. A multivariate model adjusted for patient and hospital characteristics, including age, sex, medical history (anemia, ischemic history, cardiovascular accident/transient ischemic attack, diabetes, hyperlipidemia, hypertension, chronic obstructive pulmonary disease or asthma, peripheral vascular disease, renal insufficiency, smoking), admission vital signs and laboratory test results (systolic blood pressure, heart rate, and sodium and blood urea nitrogen concentrations), hospital region, and academic status and number of beds. Post-discharge outcomes of 30-day and 1-year mortality rates were similarly evaluated using Cox proportional hazards regression.
For missing adjustment variables, medical history variables were imputed as “no,” as data abstractors were likely to skip this section of the data collection form when none applied; multiple imputations with 25 imputations were used for other patient covariates. Hospital characteristics were not imputed. Adjustment covariates were assessed for linearity and proportional hazard assumptions as needed, and transformations were applied when appropriate. Restricted cubic spline transformations flexibly illustrate relationships between BMI and mortality. To interpret results numerically, we also fitted linear splines of BMI. A spline knot was chosen which balanced model fit by maximizing model likelihood and interpretation of results. In HFrEF, we reported results for BMI by 1 kg/m2 increase up to 25 kg/m2 and by 1 kg/m2 increase above 25 kg/m2 for in-hospital mortality. Among patients with HFpEF, the knot point for in-hospital mortality was 30 kg/m2. For 30-day and 1-year mortality rates, the BMI knot point was 30 kg/m2. Restricted cubic spline relationships were plotted for 30-day mortality.
All tests were 2-tailed, and a p value of <0.05 was considered statistically significant. All analyses were performed using SAS version 9.3 software (SAS Institute, Cary, North Carolina). The authors had full access to all study data and take responsibility for its integrity and data analysis.
Of the 39,647 patients in the study population, 23,653 (59.7%) had HFpEF, and 15,994 (40.3%) had HFrEF. Baseline patient and hospital characteristics for those with HFpEF are summarized in Table 1 (Online Table 1), whereas patient HFrEF data are summarized in Table 2 (Online Table 2). Figure 2 demonstrates the mean BMI distribution by race and HF status.
PATIENTS WITH HFpEF
Blacks with HFpEF were significantly younger than other racial/ethnic groups with HFpEF (mean age: 77 ± 8.3 years; p < 0.0001) (Table 1). Across all racial/ethnic groups, most patients with HFpEF were women. The mean BMI for the overall cohort was 28.6 ± 7.9 kg/m2; blacks with HFpEF had the highest BMI (mean BMI: 30.9 ± 9.3 kg/m2) and highest likelihood of Class III obesity (14.3%; p < 0.0001). Insulin-dependent diabetes was more common among Hispanics (26.2%) with HFpEF compared with other racial/ethnic groups (p < 0.0001). Additionally, hypertension, prior stroke, and renal insufficiency were more prevalent among blacks with HFpEF compared with other racial/ethnic groups (89.0%, 20.4%, and 26.2% respectively; p < 0.0001). Whites (55.7%) were more likely to have HFpEF caused by ischemia than Hispanics (54.7%), blacks (46.9%), Asians (50.3%), and others (54.7%) (p < 0.0001).
Patients With HFrEF
Among patients with HFrEF, blacks were younger (mean age: 76 ± 7.8 years) and more likely to be female (46.4%) compared with other racial/ethnic groups with HFrEF (Table 2). Mean BMI for the overall HFrEF population was 26.7 ± 6.6 kg/m2, and blacks with HFrEF had a higher mean BMI than other racial/ethnic groups (p < 0.0001); 3.7% of the overall HFrEF population had Class III obesity, and blacks (5.5%) had a higher likelihood of Class III obesity than whites (3.6%), Hispanics (3.1%), Asians (1.1%), and others (4.1%) (p < 0.0001). Hispanics (23.5%) had the highest rate of insulin-dependent diabetes (p < 0.0001), whereas Asians (34.3%) had the highest rate of non–insulin-dependent diabetes (p < 0.0001). Hispanics with HFrEF had the highest rates of hypertension (p < 0.0001), whereas blacks had the highest rates of prior cardiovascular accident (p = 0.0114). Asians with HFrEF (28.0%) had the highest rate of renal insufficiency compared to whites (19.6%), blacks (24.1%), Hispanics (19.8%), and others (20.4%) (p < 0.0001). As with patients with HFpEF, whites were more likely to have HRrEF caused by ischemia than the other racial/ethnic groups (p < 0.0001).
Patient mortality by BMI category
Patients with HFpEF
Among patients with HFpEF, higher BMI was associated with lower 30-day all-cause mortality up to 30 kg/m2, with a slight increase in risk above 30 kg/m2 (BMI: 30 kg/m2 vs. 18.5 kg/m2; hazard ratio [HR]: 0.63; 95% confidence interval [CI]: 0.54 to 0.73) (Figure 3). Additionally, hazard of 1-year, all-cause mortality was 4% lower for 1-unit BMI increase up to 30 kg/m2 (HR: 0.96; 95% CI: 0.95 to 0.96) (Table 3). There was no significant relationship between BMI and 1-year mortality among those with HFpEF above BMI of 30 kg/m2. With regard to in-hospital mortality, there was no significant relationship between BMI and in-hospital mortality among patients with HFpEF up to BMI of 30 kg/m2. For BMI above 30 kg/m2, each 1-unit increase in BMI was associated with a 2% greater odds of in-hospital death for patients with HFpEF (odds ratio [OR]: 1.02; 95% CI: 1.01 to 1.04) (Online Table 3).
Patients with HFrEF
For patients with HFrEF with BMI up to 30 kg/m2, 30-day all-cause mortality decreased with every BMI unit increase (BMI: 30 kg/m2 vs. 18.5 kg/m2; HR: 0.73; 95% CI: 0.60 to 0.89) (Figure 4). A smaller relationship between BMI and lower 30-day all-cause mortality was observed in patients with HFrEF, as shown in Figure 4. Thirty-day mortality rates were similar across obesity classes. Above BMI of 30 kg/m2, each 1-unit BMI increase was associated with a 1% higher hazard of 1-year all-cause mortality (HR: 1.01; 95% CI: 1.00 to 1.02) (Table 3). Among patients with HFrEF, each BMI unit increase up to 25 kg/m2 was associated with 5% lower odds of in-hospital death (OR: 0.95; 95% CI: 0.91 to 0.99). Above a BMI of 25 kg/m2, each 1-unit BMI increase was associated with 4% higher odds of in-hospital death (OR: 1.04; 95% CI: 1.03 to 1.05) (Online Table 3).
Interactions between BMI and race/ethnicity
No significant BMI by race/ethnicity interactions related to 30-day mortality were seen among HFpEF or patients with HFrEF (all p > 0.05) (Table 3). The relationship between BMI below 30 kg/m2 and hazard of 1-year all-cause mortality was significantly different among racial/ethnic groups with HFrEF. BMI by race or ethnicity interactions were not statistically significant for 1-year, all-cause mortality among patients with HFpEF and HFrEF with a BMI >30 kg/m2. There were no significant BMI by race/ethnicity interactions related to in-hospital mortality for patients with HFpEF or HFrEF (all p > 0.05) (Online Table 3).
Our findings demonstrate that in a nationally representative, racially and ethnically diverse cohort, high BMI was both common (30.7%) and associated with lower 30-day all-cause mortality for older (>65 years of age) patients with HFpEF and HFrEF. Although blacks and Hispanics had higher obesity rates than whites, Asians, and other racial/ethnic groups, there was no significant interaction between BMI and race in the relationship between HF and mortality. Unlike the relationship between BMI and mortality for coronary artery disease patients, the relationship between BMI and 30-day mortality was not J-shaped for HFpEF and patients with HFrEF, and 30-day mortality rates remained relatively constant in Class II and III obesity in patients with HFrEF. However, there was a small but statistically significant increase in 30-day mortality rates for patients with HFpEF with a BMI above 30 kg/m2. Differential slopes in the association between BMI below 30 kg/m2 and 30-day mortality among HFpEF and patients with HFrEF likely suggests differences in mechanistic factors that promote early mortality between the 2 HF phenotypes.
Our findings contribute to the research in BMI and mortality in patients with HF in several important ways. First, this is one of few studies that compares the relationship between BMI and 30-day all-cause mortality across racial/ethnic groups with HFpEF or HFrEF (19), as highlighted in Table 4. Prior GWTG–HF studies have been limited to evaluating in-hospital mortality among those with HFpEF and HFrEF in 1 racial/ethnic group (20) or have not distinguished outcomes between patients with HFpEF and HFrEF when comparing in-hospital or 30-day mortality across racial/ethnic groups (13,21). In GWTG–HF, Hispanics with HFpEF were less likely to have ischemic heart disease than Hispanics with HFrEF. Additionally, Hispanics with HFpEF but not HFrEF had lower in-hospital mortality than whites (21). Thomas et al. (20) demonstrated that blacks and Hispanics in the GWTG–HF registry had a lower likelihood of in-hospital death than white patients. One potential explanation for the differences was that HF with a nonischemic cause was more common among blacks and Hispanics. More recent data suggested that 30-day survival after index admission is greater among blacks than among whites, even after controlling for comorbidities, hospital characteristics, and socioeconomic status. Our work extends the findings from these prior GWTG–HF studies to demonstrate that BMI differences do not appear to explain differences in 30-day all-cause mortality across racial and ethnic groups. This conclusion is supported by a recent meta-analysis which demonstrated that greater BMI was associated with lower 1-year all-cause mortality in racially/ethnically diverse cohorts with HF from different parts of the world (e.g., Asia, North and South America, and Europe) (19).
Second, our findings enhance the body of knowledge about perplexing repercussions of increasing BMI on HF outcomes. Many prior studies have demonstrated the presence of an “obesity paradox” among overweight and Class I and II obese patients or that these patients have a better prognosis with HF than patients with a normal BMI (22–25). Our study is one of the first to suggest that the BMI paradox for 30-day mortality exists at all BMI levels, including Class II and Class III obesity, among patients with HFrEF; our findings appear consistent with a recent meta-analysis of 6 studies and >22,000 patients (26). Other cohorts in which the relationship between Class II or III obesity and HF mortality has been investigated have been limited in size (27), in the numbers of those with Class III obesity (28), or in diversity (29). Padwal et al. (29), for example, did not look across race/ethnic groups when evaluating the obesity paradox in patients with HF, whereas Ziaeian et al. (30) looked across race/ethnic groups without discussing the obesity paradox. Differences in the proportion of study participants with HFpEF compared with HFrEF in racial/ethnic composition of the populations and prevalent comorbidities among study participants may explain the differences between our findings and those of other studies looking at Class II and III obesity and mortality. In particular, further work is needed to characterize how Class III obesity impacts HF mortality because Class III obesity is strongly associated with adverse cardiac remodeling and the subsequent HF development (31).
Finally, this is one of the first studies to distinguish between HFpEF and HFrEF when evaluating the association between BMI and mortality among patients with HF. Unlike prior studies, which have looked only at HFrEF or HFpEF population (32,33), our findings suggest heterogeneity in mortality risk at lower BMI between the 2 HF phenotypes, with a greater 30-day mortality risk at higher BMI among patients with HFpEF. From a pathophysiologic standpoint, this may represent differences in comorbidities between those with HFrEF and HFpEF who survive hospitalization. For example, compared with patients with HFpEF, patients with HFrEF who survive a hospitalization may be more likely to have reductions in EF due to mechanisms unrelated to cardiovascular risk factors or cardiovascular disease and which may carry a better prognosis (27). Additionally, patients with HFpEF at a given BMI likely have higher blood pressure and tolerate higher doses of cardioprotective medications than those with HFrEF, leading to differences in mortality risk (34). Recent work suggests that patient characteristics, including age, left ventricular function, and HF chronicity, impact the prognostic association between BMI and all-cause mortality. Compared with our findings, BMI was more strongly associated with all-cause mortality for patients with HF with a left ventricular EF <50% than those with an EF ≥50% in this meta-analysis of international cohorts (19). Our study findings highlight the importance of differentiating between HFpEF and HFrEF when evaluating BMI and mortality in patients with HF.
The strengths of the current study include data from a nationally representative, multiethnic cohort with well-established protocols for data collection and analyses. The GWTG–HF data were also linked to high-quality, standardized data from the Centers for Medicare and Medicaid Services to determine 30-day mortality for study participants, further enhancing the reliability of the study data. However, limitations of the study must be acknowledged. There are significant differences in demographics, clinical characteristics, and treatments by BMI categories, and we cannot exclude residual measured and unmeasured confounding factors contributing to these findings. We were unable to compare mortality across measurements of body fat distribution or more accurate measurements of adiposity, such as waist circumference, in this cohort. Alternative measurements of body fat distribution instead of BMI may be of particular importance when attempting to differentiate cardiovascular risk relative to adiposity among racially and ethnically diverse populations (35). Patients’ weights for determining BMI were obtained during hospitalization for HF and thus may not represent the patients’ weight at a time they are well compensated. However, relatively few patients would be expected to change BMI categories on the basis of wet versus dry weight. Another potential limitation is that GWTG–HF program hospitals are voluntary participants and may be more motivated for quality improvement, which may lead to better patient outcomes than other hospitals around the country, limiting study generalizability. Additionally, the proportion of racial/ethnic minority patients seen in GWTG–HF hospitals may differ from the proportion in those hospitals not represented in the program. Hospitals outside of the GWTG–HF program may disproportionately care of racial/ethnic minority patients and may differ from GWTG–HF hospitals in quality of care provided. Finally, the method of recording race and ethnicity by patient self-designation as recorded by administrative staff or admitting providers is likely not as reliable as direct patient reporting.
Black and Hispanic patients in the GWTG–HF registry were more likely to be obese than whites, Asians, and other patients. Higher BMI was associated with lower 30-day all-cause mortality in each racial/ethnic group in a manner not consistent with the J-shaped obesity paradox noted in prior studies. Additionally, the differential slope of the BMI and 30-day all-cause mortality association below BMI of 30 kg/m2 in HFpEF and HFrEF and higher mortality risk above BMI of 30 kg/m2 in HFpEF possibly suggest differing mechanistic factors which require further exploration.
COMPETENCY IN MEDICAL KNOWLEDGE: Race and ethnicity do not appear to modify the relationship between BMI and mortality among patients with HF, and the obesity paradox appears to exist across BMI levels for patients with HF with reduced ejection fraction, but not with preserved ejection fraction.
TRANSLATIONAL OUTLOOK: Further research is needed to investigate mechanisms by which the obesity paradox exists across BMI levels for patients with reduced ejection fraction HF.
The authors acknowledge Samantha Thomas and Sophie Claudel for their assistance in manuscript preparation.
The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the National Institutes of Health, or the U.S. Department of Health and Human Services. The Get With The Guidelines–Heart Failure program is provided by the American Heart Association, is sponsored, in part, by Amgen Cardiovascular, and has been funded in the past through support from Medtronic, GlaxoSmithKline, Ortho-McNeil, and the American Heart Association Pharmaceutical Roundtable. Dr. Powell-Wiley is supported by the Division of Intramural Research of the National, Heart, Lung, and Blood Institute of the National Institutes of Health. Dr. Bhatt is an advisory board member of Cardax, Elsevier Practice Update Cardiology, Medscape Cardiology, and Regado Biosciences; a member of the board of directors of Boston VA Research Institute, and Society of Cardiovascular Patient Care; a chair of the American Heart Association Quality Oversight Committee; is on the data monitoring committees of 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), and WebMD (CME steering committees); has served on Clinical Cardiology (deputy editor), NCDR-ACTION Registry steering committee (chair), and VA CART Research and Publications Committee (chair); has received funding from Amarin, Amgen, AstraZeneca, Bristol-Myers Squibb, Eisai, Ethicon, Forest Laboratories, Ischemix, Medtronic, Pfizer, Roche, Sanofi, and The Medicines Company; receives royalties from Elsevier (editor, Cardiovascular Intervention: A Companion to Braunwald’s Heart Disease; is a site co-investigator for Biotronik, Boston Scientific, and St. Jude Medical; is a trustee for the American College of Cardiology; and has performed unfunded research for FlowCo, PLx Pharma, and Takeda. Dr. Fonarow has received research support from the Agency for Healthcare Research and Quality, and the National Institutes of Health; and is a consultant for Amgen, Janssen, Novartis, Medtronic, and St. Jude. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- body mass index
- Get With The Guidelines–Heart Failure
- heart failure
- heart failure with preserved ejection fraction
- heart failure with reduced ejection fraction
- Received August 29, 2017.
- Revision received November 27, 2017.
- Accepted November 28, 2017.
- ↵Centers for Disease Control and Prevention. Heart Failure Fact Sheet. Department of Health and Human Services, Centers for Disease Control and Prevention. 2016. Available at: https://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_heart_failure.htm. Accessed October 5, 2017.
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