Yearly rates (smoothed using 3-year moving average) per 100 000 persons have been standardized by the direct method to the age distribution of the US population in 2010. HFpEF indicates heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.
Estimates are adjusted for age and sex (when applicable) and presented with 95% CIs. HFpEF indicates heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.
Gerber Y, Weston SA, Redfield MM, Chamberlain AM, Manemann SM, Jiang R, Killian JM, Roger VL. A Contemporary Appraisal of the Heart Failure Epidemic in Olmsted County, Minnesota, 2000 to 2010. JAMA Intern Med. 2015;175(6):996-1004. doi:10.1001/jamainternmed.2015.0924
Copyright 2015 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.
Heart failure (HF) is commonly referred to as an epidemic, posing major clinical and public health challenges. Yet, contemporary data on its magnitude and implications are scarce.
To evaluate recent trends in HF incidence and outcomes overall and by preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF).
Design, Setting, and Participants
Incidence rates of HF in Olmsted County, Minnesota (population, approximately 144 248), between January 1, 2000, and December 31, 2010, were assessed.
Main Outcomes and Measures
Patients identified with incident HF (n = 2762) (mean age, 76.4 years; 43.1% male) were followed up for all-cause and cause-specific hospitalizations (through December 2012) and death (through March 2014).
The age– and sex–adjusted incidence of HF declined substantially from 315.8 per100 000 in 2000 to 219.3 per 100 000 in 2010 (annual percentage change, −4.6), equating to a rate reduction of 37.5% (95% CI, −29.6% to −44.4%) over the last decade. The incidence declined for both HF types but was greater (interaction P = .08) for HFrEF (−45.1%; 95% CI, −33.0% to −55.0%) than for HFpEF (−27.9%; 95% CI, −12.9% to −40.3%). Mortality was high (24.4% for age 60 years and 54.4% for age 80 years at 5 years of follow-up), frequently ascribed to noncardiovascular causes (54.3%), and did not decline over time. The risk of cardiovascular death was lower for HFpEF than for HFrEF (multivariable-adjusted hazard ratio, 0.79; 95% CI, 0.67-0.93), whereas the risk of noncardiovascular death was similar (1.07; 95% CI, 0.89-1.29). Hospitalizations were common (mean, 1.34; 95% CI, 1.25-1.44 per person-year), particularly among men, and did not differ between HFpEF and HFrEF. Most hospitalizations (63.0%) were due to noncardiovascular causes. Hospitalization rates for cardiovascular causes did not change over time, whereas those for noncardiovascular causes increased.
Conclusions and Relevance
Over the last decade, the incidence of HF declined substantially, particularly for HFrEF, contrasting with no apparent change in mortality. Noncardiovascular conditions have an increasing role in hospitalizations and remain the most frequent cause of death. These results underscore the need to augment disease-centric management approaches with holistic strategies to reduce the population burden of HF.
Heart failure (HF) is a major clinical and public health problem owing to its high prevalence, mortality, hospitalization, and health care expenditures.1 Accordingly, it is commonly referred to as an epidemic.2- 4 A recent statement from the American Heart Association forecasted the prevalence and cost of care of HF to increase markedly in the United States over the next decades, reflecting the aging of the population and improving patient survival.5 However, contemporary data on key components of this epidemic are lacking. To this end, estimates of HF incidence and its temporal trends in the population are scarce and inconsistent. Data are frequently derived from hospital discharge records, self-reports, or administrative databases1,6- 13 and cannot accurately distinguish between incident and prevalent cases, have uncertain validity because of evolving coding practices,14- 17 or cannot fully capture the burden of the disease because of the shift of care toward outpatient settings.9,18 Moreover, because HF is a syndrome and not a disease, its diagnosis is challenging, standardized diagnostic criteria are inconsistently applied, and ejection fraction (EF) is not routinely measured, precluding the study of HF with preserved EF (HFpEF), a major component of the HF burden.19,20 Estimates based on validated cases are outdated21- 25 and do not reflect recent changes in the key determinants of HF, such as myocardial infarction and hypertension.26- 28 Consequently, it should come as no surprise that existing results on temporal changes in HF incidence are conflicting, with reports of increasing,23 plateau-like,22 decreasing,7,9,13 or mixed6,21 trends. Most important, there is no current report on trends in HF incidence according to EF. This point is critical because the determinants of these 2 conditions are likely different29,30 and might have evolved over time. Indeed, while decreasing mortality rates after HF were reported during the 1990s to early 2000s,7,9,13,22 the change in case mix with a growing proportion of HFpEF26,29 (for which there is no specific treatment31) might have attenuated this decline. The change in case mix might also affect hospitalization rates among patients with HF, particularly in light of the major role of comorbidity, which is known to be higher in HFpEF.32 To address these gaps in knowledge, this study was designed to assess contemporary trends in the incidence of HF (validated using diagnostic criteria and categorized as reduced ejection fraction [HFrEF] or HFpEF) and cause-specific hospitalization and mortality after its onset in a geographically defined population.
This study was conducted in Olmsted County, Minnesota, which had an approximate population of 144 248 according to the 2010 census, 85.7% of whom are of white race/ethnicity, and 13.6% of whom are 65 years or older. The Olmsted County population is largely middle class, with a higher median household income ($66 252 vs $53 054) and a lower percentage below the poverty line (8.0% vs 15.4%) than the US total population in 2010, and the estimated health insurance uninsured rate is 5.0%.33 Olmsted County constitutes a highly suitable setting for epidemiological research because of its relative isolation from other urban centers and because medical care is practically self-contained within the community, with the largest health care provider being the Mayo Clinic. Medical records from all sources of care for residents are extensively indexed and linked via the Rochester Epidemiology Project.34,35
The study was approved by the Mayo Clinic and Olmsted Medical Center institutional review boards, and patients were excluded from analysis if they declined to provide written Minnesota Research Authorization. The percentage of patients not providing research authorization was low overall (4.2%) and stable during the study period (trend P = .43).
A 2-stage design was implemented. Initially, a community surveillance study was conducted to estimate the incidence rates of HF between 2000 and 2010 in Olmsted County. Subsequently, patients with incident HF enrolled in the first stage were followed up for outcomes (mortality and hospitalizations) in a patient-level cohort study.
Residents diagnosed as having HF by International Classification of Diseases, Ninth Revision (ICD-9) code 428 between 2000 and 2010 were identified. These clinical codes were based on physician diagnoses during outpatient visits or at hospital discharge. From all patients with this code, a subset was randomly selected to undergo case validation and data abstraction (50% sample from 2000-2006 and 100% sample from 2007-2010). Abstractors reviewed records to validate HF using Framingham Study criteria. These criteria require the presence of at least 2 major criteria to confirm HF or 1 major criterion in addition to 2 minor criteria.36 This approach was applied previously, showing minimal missing data and excellent interobserver agreement.22 The ICD-9 codes 425 (cardiomyopathy), 429.3 (cardiomegaly), and 514 (pulmonary congestion) were also reviewed as sources of potential HF cases. For each code, a random sample of 20 patients was selected, and records were reviewed to validate HF using Framingham Study criteria. One case of validated HF was found in the cardiomyopathy and pulmonary congestion samples, and no cases were found in the cardiomegaly sample, confirming the appropriateness of using only ICD-9 code 428 to construct the HF cohort. Patients with validated HF before the study period were excluded, as were nonresidents of Olmsted County.22,37
Ejection fraction was measured using an approach that was previously described.38 Briefly, all echocardiography in Olmsted County during the study period was performed at the Mayo Clinic. No other health care providers offered these services. The assessment of EF is based on the echocardiographer’s combination of multiple methods (M-mode or 2-dimensional echocardiography using the Quinones formula from the parasternal views or by the quantitative 2-dimensional biplane volumetric Simpson method from 4-chamber and 2-chamber views) into an EF assessment quoted in the final impressions.39,40 The EF measurement that was closest to the HF diagnosis (applying a predefined maximum period of 90 days) was recorded for each participant. The cutoff of 50% was used to define HFpEF (≥50%) or HFrEF (<50%) according to the guidelines.41
For mortality, follow-up was performed through March 2014 using the medical record. In addition to death notes in clinical care, the Mayo Clinic registration office records obituaries and local death notices, and death data are obtained quarterly from the State of Minnesota Department of Vital and Health Statistics. Information on the date of death and its underlying cause was obtained, through which deaths were classified as cardiovascular (ICD-9 codes 390-459) and noncardiovascular.42
For hospitalizations, data on all-cause hospitalizations occurring after incident HF through December 2012 were obtained through the Rochester Epidemiology Project. The principal discharge diagnosis for each hospitalization was assessed using the primary ICD-9 code, which was assigned by clinical personnel after discharge and reflects the main reason for admission. The primary reason for hospitalization was divided into cardiovascular (ICD-9 codes 390-459) and noncardiovascular.
Baseline characteristics were abstracted from medical and administrative records. Cigarette smoking was classified as current, past, or none. Body mass index (calculated as weight in kilograms divided by height in meters squared) was obtained using the current weight and earliest available adult height measurement. Clinical definitions were used to assess whether patients had prior myocardial infarction, hypertension, or hyperlipidemia. Diabetes mellitus was defined according to the American Diabetes Association or by the use of diabetic medications. Overall comorbidity burden was assessed by the Charlson Comorbidity Index.
Sampling was accounted for in the analysis through weighting. Characteristics of patients with validated HF are presented as frequencies or means (SDs). Age-specific, sex-specific, and year-specific incidence rates of validated HF were calculated. The counts of validated cases (overall and by HFrEF or HFpEF) were used as the numerators, and the denominators were the Olmsted County population 20 years or older (as determined by census data for 2000 and 2010), with linear interpolation for the intercensus years.22 The rates were directly standardized to the age and sex distribution of the 2010 US total population. Poisson regression models were used to examine overall and category-specific mean annual percentage changes and temporal trends (using 2-way interaction terms) in HF incidence rates. Based on these models, the percentage changes during the entire period from 2000 to 2010 and corresponding 95% CIs were estimated. Age (as a continuous variable) and sex (when applicable) were adjusted for in the models.
Trends over time in the distribution of cardiovascular risk factors and HF characteristics were assessed with logistic regression or linear regression, as appropriate. Proportional hazards regression modeling was used to examine the associations of year of HF and other baseline characteristics with all-cause and cause-specific (ie, cardiovascular and noncardiovascular) mortality. Age-adjusted (using the age categorization of ≤65, 66-75, 76-85, and >85 years) and multivariable-adjusted hazard ratios for death are reported for each variable with respective 95% CIs. Age-specific 1-year and 5-year mortality rates were estimated from the proportional hazards regression models. For the latter purpose, age was modeled with linear and quadratic terms because of its nonlinear effect on mortality.
Overall and year-specific hospitalization rates within 2 years of follow-up (the last follow-up was December 2012) were estimated using negative binomial regression. Rates were estimated for all-cause and cause-specific (ie, cardiovascular and noncardiovascular) hospitalizations and are presented for patients 76.4 years old, the mean age of this cohort. Temporal trends in hospitalization rates were examined with year as a continuous variable after adjusting for age (as a continuous variable) and sex.
Data on EF were missing in 21.6% of the cases. A multiple imputation analysis was performed to impute missing EF values. Five data sets were created, with missing values replaced by imputed values based on a model that incorporated various demographic and clinical variables. The latter model included variables previously recognized as predictors of missing EF in HF43 and others identified in the present analysis. The results of these data sets were then combined using rules by Rubin.44
Analyses were performed with statistical software. Version 9.3 of SAS (SAS Institute Inc) was used.
In total, 2762 incident HF cases were estimated in the population between January 1, 2000, and December 31, 2010. The mean (SD) age of the cohort was 76.4 (13.4) years, and 43.1% were male. The proportion of individuals diagnosed as outpatients was 31.9%, and 52.5% were categorized as having HFpEF.
Over time, the proportion of cases with HFpEF increased from 47.8% in 2000 to 2003 to 56.9% in 2004 to 2007 and to 52.3% in 2008 to 2010 (P = .06). The proportion of men and the prevalence of hypertension at the time of HF increased in patients with HFrEF (Table 1). Among patients with HFpEF, the prevalence of hypertension, diabetes mellitus, and hyperlipidemia at the time of HF increased, as did the burden of comorbid conditions.
The age– and sex–adjusted incidence rates of HF declined substantially over time from 315.8 per 100 000 in 2000 to 219.3 per 100 000 in 2010. The overall mean annual percentage change was −4.6 (95% CI, −3.5 to −5.7), equating to a 37.5% (95% CI, −29.6% to −44.4%) decline over the last decade. This decline applied to both men and women and for HFrEF and HFpEF in absolute (Figure 1) and relative (Figure 2) terms. However, the magnitude of trends differed by sex and EF. Women (overall rate change, −43%) experienced a greater decline (interaction P = .06) than men (overall rate change, −29%), and the rates of HFrEF (−45%; 95% CI, −33% to −55%) decreased more sharply (interaction P = .08) than the rates of HFpEF (−28%; 95% CI, −13% to −40%) from 2000 to 2010. The heterogeneity by EF was largely limited to women, who exhibited a markedly larger decline in the incidence of HFrEF than HFpEF (−61% vs −27%, interaction P = .001) compared with men (−29% vs −27%, interaction P = .91) (Figure 2).
Among the incident HF cases, 2644 patients had follow-up data available for analysis. The outcomes after HF diagnosis were assessed.
For mortality, after a mean (SD) of 4.5 (3.5) years of follow-up, 1793 deaths were enumerated, which equated to mortality rates of 20.2% (95% CI, 18.7%-21.8%) at 1 year after diagnosis and 52.6% (95% CI, 50.6%-54.5%) at 5 years after diagnosis. Mortality rates increased with age: for 60-year-olds, the rates were 7.4% and 24.4% at 1 year and 5 years, respectively, and for 80-years-olds, the rates were 19.5% and 54.4% at 1 and 5 years, respectively (P < .001). Mortality was frequently (54.3%) ascribed to noncardiovascular causes. Among 1700 patients with cause of death available, the top 3 categories of noncardiovascular causes of death were respiratory (n = 241) (14.2%), neoplasm (n = 215) (12.6%), and mental or behavioral health (n = 121) (7.1%). Among patients with measured EF, the top 3 categories for the 594 patients with HFrEF and cause of death available were neoplasm (n = 76) (12.8%), respiratory (n = 57) (9.6%), and mental or behavioral health (n = 29) (4.9%). For the 668 patients with HFpEF and cause of death available, they were respiratory (n = 104) (15.6%), neoplasm (n = 83) (12.4%), and mental or behavioral health (n = 44) (6.6%). The hazard ratios for all-cause and cause-specific mortality associated with patient characteristics at the time of HF diagnosis are listed in Table 2. In addition to age, factors positively associated with all-cause death were diabetes mellitus, smoking, and increasing number of comorbidities. Body mass index, hyperlipidemia, HFpEF (borderline significance), and HF diagnosis at an outpatient visit showed an inverse association. In the cause-specific analysis, smoking and the Charlson Comorbidity Index were more strongly associated with noncardiovascular death than with cardiovascular death. Conversely, age and prior myocardial infarction were more strongly associated with cardiovascular death than with noncardiovascular death. An inverse association of HFpEF with cardiovascular death was found, with no apparent association with noncardiovascular death. Outpatient diagnosis of HF was inversely associated with cardiovascular and noncardiovascular death. No temporal trends in mortality were detected in all-cause or cause-specific analysis.
For hospitalizations, 4631 occurred during the first 2 years of follow-up. Hospitalizations were common (mean, 1.34; 95% CI, 1.25-1.44 per person-year), and most (63.0%) were due to noncardiovascular causes. The top 3 noncardiovascular causes of hospitalization were categorized as respiratory (n = 655) (14.1% of all hospitalizations); other symptoms, signs, and abnormal findings (including but not limited to alteration of consciousness, convulsions, and fever and other physiologic disturbances) (n = 437) (9.4%); and injury, poisoning, and other consequences of external causes (n = 351) (7.6%). For the 1699 hospitalizations among the 985 patients with HFrEF, the top 3 noncardiovascular causes of hospitalization were categorized as respiratory (n = 201) (11.8%); other symptoms, signs, and abnormal findings (n = 176) (10.4%); and infectious and parasitic diseases (n = 103) (6.1%). For the 2079 hospitalizations among the 1089 patients with HFpEF, the top 3 noncardiovascular causes of hospitalization were categorized as respiratory (n = 277) (13.3%); other symptoms, signs, and abnormal findings (n = 194) (9.3%); and injury, poisoning, and other consequences of external causes (n = 186) (8.9%). Total and cause-specific hospitalization rate estimates are listed in Table 3. A higher overall hospitalization rate was associated with male sex (particularly for noncardiovascular causes), while age showed little association (P = .15). Total hospitalization rates were similar regardless of EF, with some evidence of a higher rate of cardiovascular hospitalizations among those with HFrEF, offset by a higher rate of noncardiovascular hospitalizations among those with HFpEF. Hospitalization rates did not change significantly during the study period as a result of an increase in noncardiovascular hospitalizations, combined with a small, nonsignificant decrease in cardiovascular hospitalizations (particularly among HFrEF cases).
Several ancillary analyses were performed to assess the robustness of our results. To determine the effect on the results of using 50% as a cutoff for defining HFrEF, analyses were repeated using a cutoff of 40%. Similar trends were observed. In addition, a complete case analysis was performed in which individuals with missing EF were excluded. Similar results were obtained compared with the multiple imputation analysis. The HF-specific hospitalizations, defined as ICD-9 code 428, were analyzed as a separate outcome. Overall, the rates of HF hospitalizations over the study period remained constant (P = .54), with no change in HFrEF (P = .64) or HFpEF (P = .99).
Herein, we report major changes in the epidemiology of HF in the last decade, with a large decrease in incidence and a shift toward HFpEF, for which there is no specific treatment. Mortality did not change during the study period, nor did hospitalization rates. However, the cause of hospitalization transitioned toward noncardiovascular causes, likely reflecting the increasing comorbidity burden in this elderly population of patients.
Few studies have examined trends in the incidence of HF, and a systematic review found no evidence of any clear or consistent change in rates over time.45 Outside the United States, the results of some studies7,9,13 (but not the findings of another study46) suggested a recent decline in HF incidence in specific populations. In the United States, HF hospitalizations had increased from 1979 to 2004 among patients 65 years or older.12 More recently, a substantial decline in HF-related hospitalization rates was reported among fee-for-service Medicare beneficiaries in the United States.11 Hospitalizations do not reflect incidence. As previously reviewed,19,20 most of these data were derived from hospital discharge records or administrative databases. In these situations, standardized diagnostic criteria are not used, and case ascertainment is often affected by shifts in coding because of reimbursement incentives.14,15 The studies tend to be event based (not person based), with multiple hospitalizations counted per person.11,12 Furthermore, inpatient data (the sole information source in many reports) do not capture all cases of HF because care is increasingly delivered in the outpatient setting.9,18 In addition, published data were frequently based on a limited run-in (look-back) period to distinguish incident from prevalent HF.7,13 Using a run-in period can substantially overestimate the incidence rate if data covering a sufficient duration of time are unavailable.47 These inherent drawbacks underscore the importance of conducting population-based studies applying standardized case validation procedures in the framework of ongoing surveillance of all residents in a defined community. The few such studies available did not detect a decline in HF incidence in the past. In the Framingham Heart Study21 and in Olmsted County,22 the incidence of HF had been stable from the 1970s to the 1990s. The incidence increased only among the elderly in a study23 of Kaiser Permanente health plan members over that period.
Therefore, the present findings of a major decline in HF incidence over the last decade represent a large departure from previous reports, including from our group. Although decreased incidence over the last decade occurred in all demographic groups, a less pronounced decline was observed in men compared with women. Moreover, the present study provides one of the first longitudinal reports of trends in HF by type—information that was lacking in previous publications.19,20 We found a substantial decline over time in both HF types, yet the decline was greater for HFrEF. This finding in turn resulted in a change in the case mix, with a growing proportion of HFpEF, for which there is no specific treatment. Because it is often assumed that patients with HF and underlying coronary disease are more likely to present with reduced EF,48 the change in case mix may reflect the recent decrease in the incidence of myocardial infarction in the population,49,50 the increasing use of timely reperfusion in acute myocardial infarction, and the reduced risk of HF after myocardial infarction.26 While complex, the role of changes in cardiovascular risk factors in the genesis of HF is also important to consider. Although the prevalence of hypertension and diabetes mellitus has increased over time,1 so have the diagnostic criteria, which may have resulted in detection at earlier stages. The management of these conditions has improved, thereby leading to better outcomes, as recently shown in particular for diabetes mellitus.51
Heart failure survival improved substantially during the early 1990s and early to mid-2000s,6,7,9,13,22 likely reflecting increased use of evidence-based medications (eg, β-blockers, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers). As shown herein, survival after HF diagnosis seemingly leveled off thereafter, possibly reflecting the transition from HFrEF to HFpEF and the increasing comorbidity burden in HF. The increasing proportion of noncardiovascular causes of death (neoplasm, respiratory conditions) supports this hypothesis.52
Data on the cause of hospitalization among patients with HF suggest that cardiovascular hospitalizations may be less common than noncardiovascular hospitalizations.10,37 In our study, the latter were responsible for 63.0% of all HF hospitalizations. While hospitalization rates for cardiovascular causes did not change over time, the rates for noncardiovascular causes increased; while the range of noncardiovascular causes is extensive, the role of respiratory conditions and symptoms is noteworthy. This shift in the distribution of the cause of hospitalizations toward noncardiovascular causes is congruent with the major burden of comorbid conditions in HF and is critical to manage HF and interpret its outcomes. Indeed, current therapies (eg, medications, devices) are intrinsically disease centric and directed at reducing HF exacerbation. Therefore, HF-specific hospitalizations are a key indicator of the effectiveness of HF-specific treatments, but disease-specific interventions cannot be expected to reduce all hospitalizations appreciably among persons living with HF given the high prevalence of comorbidity in these patients. Our results support this hypothesis because cardiovascular hospitalizations declined over time among HFrEF cases; however, overall hospitalization rates did not decline, and noncardiovascular hospitalizations even increased. Within this context, it is important to distinguish hospitalizations due to HF11 from all hospitalizations experienced by patients living with HF. Our study captures all hospitalizations occurring among an incidence cohort of patients living with HF and allows partitioning of the cause of hospitalization. Therefore, we are able to report on a trend not previously documented.
Some limitations should be acknowledged in interpreting these data. These results emanate from a single community of predominantly of white race/ethnicity. As in any study, the racial/ethnic composition of the population may limit the generalizability to groups underrepresented in the population. However, the population of Olmsted County is representative of the state of Minnesota and the Upper Midwest region of the United States.53 Furthermore, age and sex–specific mortality rates are similar for Olmsted County, the state of Minnesota, and the entire United States, and broad disease trends in Olmsted County are commensurate with national trends, supporting the broad relevance of our data.53 Finally, the age of our patients is representative of the broad clinical experience of patients with HF, as shown, for example, in the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure registry.54
We cannot rule out an effect of the use of tests (B-type natriuretic peptide and others) in practice on temporal trends in HF incidence. However, the use of tests can operate in both directions, increasing incidence by diagnosing individuals as having HF that would have been otherwise classified as noncardiac dyspnea or ruling out HF and decreasing incidence.
The study has several notable strengths. The data are recent, reflecting the current burden of HF in a defined community, and are comprehensive, including inpatient and outpatient data. These factors are important because approximately one-third of the patients in our community cohort were diagnosed in the outpatient setting. Echocardiographic data allowed examination of the respective contributions of HFpEF and HFrEF to the burden of HF, which is important to note in understanding the HF syndrome.32
Our findings document a major change in the epidemiology of HF, which is consistent with the recent changes in the epidemiology of acute coronary syndromes.49,50 The changes in heart disease over the last decades have important implications for the planning of health care delivery and use in communities. Indeed, further reductions in mortality and hospitalizations among patients living with HF will require concerted efforts to address multimorbidity, augmenting disease-centric therapeutic guidelines with the deployment of holistic care models. While the rationale for such a strategy has been envisioned,55 the present data provide definite evidence to support a call for action in this regard.
We report major changes in the epidemiology of HF over the last decade, with a large decrease in incidence and a change in case mix toward HFpEF, for which there is no specific treatment. Mortality and hospitalization rates remained stable, while the cause of hospitalization changed, with an increase in noncardiovascular causes, likely reflecting the increasing comorbidity burden in these elderly patients. These findings have important implications to designing effective strategies to optimize the care of patients living with HF.
Accepted for Publication: January 12, 2015.
Corresponding Author: Véronique L. Roger, MD, MPH, Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (email@example.com).
Published Online: April 20, 2015. doi:10.1001/jamainternmed.2015.0924.
Author Contributions: Dr Roger had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Gerber, Weston, Roger.
Drafting of the manuscript: Gerber, Weston, Redfield, Chamberlain, Manemann, Roger.
Critical revision of the manuscript for important intellectual content: Gerber, Weston, Redfield, Chamberlain, Manemann, Roger.
Statistical analysis: Gerber, Weston, Jiang, Killian.
Obtained funding: Roger.
Administrative, technical, or material support: Roger.
Study supervision: Gerber, Roger.
Conflict of Interest Disclosures: None reported.
Funding/Support: This study was supported by grants R01 HL72435 and R01 HL120859 from the National Institutes of Health and was made possible by Rochester Epidemiology Project grant R01 AG034676 from the National Institute on Aging and Mayo Clinic Center for Translational Science Activities through grant UL1 TR000135 from the National Center for Advancing Translational Sciences, a component of the National Institutes of Health.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.