0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Investigation |

Epidemiology of Incident Heart Failure in a Contemporary Elderly Cohort:  The Health, Aging, and Body Composition Study FREE

Andreas Kalogeropoulos, MD; Vasiliki Georgiopoulou, MD; Stephen B. Kritchevsky, PhD; Bruce M. Psaty, MD, PhD; Nicholas L. Smith, PhD, MPH; Anne B. Newman, MD, MPH; Nicolas Rodondi, MD, MS; Suzanne Satterfield, MD, DrPH; Douglas C. Bauer, MD; Kirsten Bibbins-Domingo, PhD, MD; Andrew L. Smith, MD; Peter W. F. Wilson, MD; Ramachandran S. Vasan, MD, DM; Tamara B. Harris, MD, MS; Javed Butler, MD, MPH
[+] Author Affiliations

Author Affiliations: Departments of Medicine, Emory University, Atlanta, Georgia (Drs Kalogeropoulos, Georgiopoulou, A. L. Smith, Wilson, and Butler); and Wake Forest University, Winston-Salem, North Carolina (Dr Kritchevsky); Departments of Medicine (Dr Psaty) and Epidemiology (Dr N. L. Smith), University of Washington, and Seattle VA Epidemiologic Research and Information Center, VA Puget Sound Health Care System (Dr N. L. Smith), Seattle, Washington; Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania (Dr Newman); Department of Ambulatory Care and Community Medicine, University of Lausanne, Lausanne, Switzerland (Dr Rodondi); Department of Preventive Medicine, University of Memphis, Memphis, Tennessee (Dr Satterfield); Department of Medicine, University of California, San Francisco (Drs Bauer and Bibbins-Domingo); Department of Medicine, Boston University, Boston, Massachusetts (Dr Vasan); and Geriatrics Epidemiology Section, National Institute on Aging, National Institutes of Health, Bethesda, Maryland (Dr Harris).


Arch Intern Med. 2009;169(7):708-715. doi:10.1001/archinternmed.2009.40.
Text Size: A A A
Published online

Background  The race- and sex-specific epidemiology of incident heart failure (HF) among a contemporary elderly cohort are not well described.

Methods  We studied 2934 participants without HF enrolled in the Health, Aging, and Body Composition Study (mean [SD] age, 73.6 [2.9] years; 47.9% men; 58.6% white; and 41.4% black) and assessed the incidence of HF, population-attributable risk (PAR) of independent risk factors for HF, and outcomes of incident HF.

Results  During a median follow-up of 7.1 years, 258 participants (8.8%) developed HF (13.6 cases per 1000 person-years; 95% confidence interval, 12.1-15.4). Men and black participants were more likely to develop HF. No significant sex-based differences were observed in risk factors. Coronary heart disease (PAR, 23.9% for white participants and 29.5% for black participants) and uncontrolled blood pressure (PAR, 21.3% for white participants and 30.1% for black participants) carried the highest PAR in both races. Among black participants, 6 of 8 risk factors assessed (smoking, increased heart rate, coronary heart disease, left ventricular hypertrophy, uncontrolled blood pressure, and reduced glomerular filtration rate) had more than 5% higher PAR compared with that among white participants, leading to a higher overall proportion of HF attributable to modifiable risk factors in black participants vs white participants (67.8% vs 48.9%). Participants who developed HF had higher annual mortality (18.0% vs 2.7%). No racial difference in survival after HF was noted; however, rehospitalization rates were higher among black participants (62.1 vs 30.3 hospitalizations per 100 person-years, P < .001).

Conclusions  Incident HF is common in older persons; a large proportion of HF risk is attributed to modifiable risk factors. Racial differences in risk factors for HF and in hospitalization rates after HF need to be considered in prevention and treatment efforts.

Figures in this Article

Heart failure (HF) is primarily a disease of older persons, with an annual incidence of 10 cases per 1000 after age 65 years, which doubles every decade thereafter.1 Subjects older than 65 years represent more than 75% of prevalent HF cases in the United States.2 In a 2004 European study,3 participants older than 70 years accounted for 88% of new HF cases. Although some recent data suggest relative improvement in survival after development of HF,4,5 other evidence challenges this finding, especially in older persons.6 Nevertheless, the absolute survival rate for these patients remains poor, and the actual number of HF deaths has increased by 20.5% during the last decade, reflecting the increasing prevalence of HF and the aging of the population. In patients older than 67 years, the median survival is generally less than 3 years after hospitalization for HF.7,8 The annual hospitalization rate for these patients now exceeds 1 million in the United States, 80% of patients hospitalized with HF are older than 65 years, and readmission rates as high as 50% within 6 months of discharge have been reported.9

Risk factors and outcomes for HF in the general population have been well described.4,8,10,11 However, many of these data are old and were derived primarily from white subjects among young or middle-aged populations. The risk factor profile for cardiovascular diseases is changing with increasing prevalence of obesity, metabolic syndrome, and diabetes mellitus.12 Similarly, the therapeutic profile for risk factors such as hypertension or coronary heart disease (CHD) has evolved over time with increased use of angiotensin-converting enzyme inhibitors and statins. This may affect the development of HF and the outcomes thereafter. Recent evidence suggests substantial differences in disease development and progression among sex- and race-based subgroups.13,14 Understanding and quantifying these differences are imperative for planning appropriate preventive and therapeutic interventions. To our knowledge, the sex- and race-related risk factor profile and population-attributable risk (PAR) for HF risk factors in contemporary older persons are unknown. In this study, we sought to assess the epidemiology of incident HF among older persons enrolled in the Health, Aging, and Body Composition (Health ABC) Study.

STUDY POPULATION AND OUTCOMES

The Health ABC Study is a population-based study of 3075 community-dwelling men and women aged 70 to 79 years at enrollment. To be eligible, participants had to report no difficulty in walking one-quarter mile or climbing 10 stairs without resting. Participants were identified from a random sample of white Medicare beneficiaries and all age-eligible black community residents in designated zip code areas surrounding Pittsburgh, Pennsylvania, and Memphis, Tennessee. Exclusion criteria included difficulties with activities of daily living, obvious cognitive impairment, inability to communicate with the interviewer, intention of moving within 3 years, or participation in a trial involving a lifestyle intervention. All participants provided written informed consent, and the institutional review boards at both study sites approved the study protocol.

Baseline data were collected from April 1997 to June 1998. Cardiovascular disease status at baseline, including prevalent HF, was based on self-reported history, use of selected drugs, and International Classification of Diseases, Ninth Revision, Clinical Modification codes as reported by Medicare and Medicaid Services from 1995 through 1998. All participants were asked to report any hospitalizations, and every 6 months they were asked direct questions to elicit information about interim cardiovascular events. All admissions with an overnight stay were evaluated for cardiovascular events by reviewing the medical records at each site. All hospitalizations and deaths were reviewed by the Health ABC Study diagnosis and disease ascertainment committee, and underlying causes of death were determined by central adjudication.

Of 3075 subjects enrolled in the Health ABC Study, 95 had definite or possible HF at baseline, and 46 were excluded because of missing data on HF status. The final cohort analyzed for this study included 2934 participants.

STUDY DEFINITIONS
Incident HF

All first admissions with an overnight stay confirmed to be related to HF were classified as incident HF. Local adjudicators classified events as HF based on symptoms, signs, chest radiograph results, and echocardiographic findings based on criteria similar to those used in the Cardiovascular Health Study.15 The HF criteria required at least a HF diagnosis from a physician and treatment for HF (ie, a current prescription for a diuretic agent and either digitalis or a vasodilator); these criteria have been used in previous investigations.16 Because HF was not allowed as a cause of death, there were no deaths from incident HF.

Risk Factor Definitions

History of coronary revascularization, electrocardiographic evidence of myocardial infarction, or self-reported history of myocardial infarction or angina accompanied by antianginal medication use was considered definite evidence of CHD; self-reported history of CHD without medication use was considered probable CHD. Hypertension was classified as definite if there were both a history of hypertension or physician diagnosis and use of antihypertensive medications; history or medication use alone was classified as probable hypertension. Diabetes mellitus was considered present if the participant reported a history of diabetes or use of antidiabetic medications. Smoking was classified as current, past (if ≥100 lifetime cigarettes), or never. Cerebrovascular disease was based on a history of stroke, transient ischemic attack, or carotid intervention. Depression was defined as self-reported depression accompanied by medication use. Left ventricular hypertrophy (LVH) was determined from the baseline electrocardiogram using the following criteria17: R wave exceeding 12 mm in lead aVL; R wave exceeding 26 mm in lead V5 or V6; R wave exceeding 20 mm in any of leads I, II, III, and aVF; or R wave in lead V5 or V6 plus S wave in lead V1 exceeding 35 mm.

Risk Factors for Incident HF

Independent predictors of incident HF in the Health ABC Study were previously reported.18 The following 9 variables were independently associated with HF development: age, LVH, history of smoking and CHD, systolic blood pressure and heart rate, and serum levels of glucose, albumin, and creatinine; sex and race were considered, but neither was independently associated with incident HF. For the PAR calculation, continuous predictors were dichotomized using clinically relevant cutoff points. Systolic blood pressure was dichotomized as controlled vs uncontrolled at 140 mm Hg, fasting glucose level at 125 mg/dL (to convert glucose level to millimoles per liter, multiply by 0.0555), resting heart rate at 75 beats/min,19 and albumin level at 3.8 g/dL (to convert albumin level to grams per liter, multiply by 10).20 Creatinine level was converted to glomerular filtration rate (GFR) using the Modification of Diet in Renal Disease formula,21 and a cutoff of 60 mL/min/1.73 m2 was used to define impaired GFR. Smoking (current yes or no) and CHD status (definite yes or no) were collapsed into binary predictors. We classified independent risk factors into the following 2 groups: modifiable (CHD, LVH, smoking, glucose level, and systolic blood pressure) and potentially modifiable (heart rate, albumin level, and renal function). Age was included in regression models for multivariable PAR calculation, but the PAR of age was omitted from the tables because age cannot be modified.

STATISTICAL ANALYSIS

We compared continuous variables using the Mann-Whitney rank sum test and categorical variables using the Fisher exact test. Cumulative event rates were obtained using the Kaplan-Meier method and were compared using the log-rank statistic. Raw and adjusted hazard ratios (HRs) and the corresponding confidence intervals (CIs) were obtained using Cox proportional hazards regression models. Interaction terms were fitted where appropriate.

Because of the sampling approach of the Health ABC Study, the study sample is not representative of the race distribution of older persons in the United States. Therefore, we opted to calculate only race-specific PAR estimates and did not calculate pooled PARs. Sex-specific and sex-stratified (Mantel-Haenszel combined) rate ratio (RR) estimates for the risk factors of interest were calculated. Unadjusted (univariable) PARs were calculated using the following standard formula22: PARunadjusted = [Prevalence × (RR − 1)]/[Prevalence × (RR − 1) + 1]. However, this formula is only valid when no confounding exists.23 Therefore, we also calculated adjusted (multivariable) PARs using a Poisson regression model with incident HF as the outcome and the factors already described as predictors.24 Briefly, the predicted number of cases is calculated for the full model (Nfull), which equals the actual number of cases. Next, the effect of the risk factor of interest is “removed” by setting the value of the covariable to zero, and the predicted number of events is calculated (Nremoved). The adjusted PAR for the risk factor then becomes PARadjusted = 1 − (Nremoved/Nfull). Similarly, it is possible to calculate the total preventable fraction (ie, the proportion of cases that could be prevented if these risk factors were eliminated) by setting all covariables to zero; notably, the sum of the individual PARs does not add up to the total preventable fraction. Also, although maximum likelihood estimators have been shown to produce reliable point estimates and CIs of PAR in simulation studies,24 no formal method to compare PAR for a risk factor between groups has been developed to date.

Hospitalizations were analyzed as count data over time at risk; RRs and CIs were obtained by Poisson regression analysis. All analyses were performed using commercially available statistical software (STATA 10.0; StataCorp LP, College Station, Texas). Adjusted PARs were calculated using a STATA module (http://www.stata.com/stb/stb42/sbe21).

INCIDENT HF

The mean (SD) age of participants was 73.6 (2.9) years; 47.9% were men, and 58.6% were of white race. During a median follow-up of 7.1 years (25th-75th percentiles, 6.6-7.5 years), 258 participants (8.8%) developed HF (13.6 per 1000 person-years, 95% CI, 12.1-15.4). The baseline characteristics of participants are given in Table 1. Figure 1 shows the Kaplan-Meier estimates of incident HF in race-based and sex-based subgroups; men and black participants were more likely to develop HF than women and white participants. The rates were 16.3 per 1000 person-years in black participants vs 11.9 per 1000 person-years in white participants (age-adjusted HR, 1.41; 95% CI, 1.11-1.80; P = .006) and 15.8 per 1000 person-years in men vs 11.7 per 1000 persons-years in women (age-adjusted HR, 1.34; 95% CI, 1.05-1.71; P = .02). The sex × race interaction term was not statistically significant (P = .47).

Place holder to copy figure label and caption
Figure 1.

Incident heart failure by sex and race. In the Health, Aging, and Body Composition Study cohort, men and black participants were more likely to develop heart failure than women and white participants (stratified log-rank χ2 = 7.30, P =.007 for sex and χ2 = 7.85, P = .005 for race).

Graphic Jump Location
Table Graphic Jump LocationTable 1. Baseline Participant Characteristics
POPULATION-ATTRIBUTABLE RISKS

Table 2 and Table 3 give the prevalences and the unadjusted (univariable) and adjusted (multivariable) PARs for the independent risk factors. Smoking, LVH, decreased albumin levels, and increased heart rate, fasting glucose levels, and systolic blood pressure were more prevalent in black participants than in white participants (P < .001 for all except heart rate [P = .005] and albumin level [P = .01]). In contrast, decreased GFR was more prevalent among white participants (P = .001). Prevalence of CHD was comparable across race (P = .48). The preventable fractions owing to modifiable risk factors were 48.9% (95% CI, 35.1%-59.8%) in white participants vs 67.8% (95% CI, 55.1%-76.8%) in black participants. Uncontrolled systolic blood pressure and CHD had the highest PARs (>20%) in both race groups in unadjusted and adjusted analyses. Adjusted PARs for all independent risk factors except serum glucose level were higher in black participants than in white participants, with smoking, LVH, and GFR less than 60 mL/min/1.73 m2 being approximately 10% higher.

Table Graphic Jump LocationTable 2. Prevalence and Unadjusted Rate Ratios (RRs) and Population-Attributable Risk (PAR) of Modifiable Risk Factors for Incident Heart Failure
Table Graphic Jump LocationTable 3. Adjusted Rate Ratios (RRs) and Population-Attributable Risk (PAR) of Modifiable Risk Factors for Incident Heart Failurea
OUTCOMES AFTER INCIDENT HF

During a median of 2.1 years (25th-75th percentiles, 0.7-4.4 years) after initial hospitalization for HF, 121 of 258 participants (46.9%) with HF died, representing 18.0% (95% CI, 15.0%-21.5%) annual mortality. In addition, 312 all-cause readmissions were recorded (46.3 per 100 person-years); of these, 252 (80.8%) (37.4 per 100 person-years) were related to HF. In comparison, participants who remained HF-free throughout the study had an annual mortality of 2.72% (95% CI, 2.49%-2.97%) and a hospitalization rate of 21.9 per 100 person-years (RR, 2.11; 95% CI, 1.88-2.37; P < .001).

Table 4 summarizes the outcomes after incident HF in the race-based and sex-based subgroups. Overall survival did not differ between white participants and black participants (log-rank χ2 = 0.00, P = .98); however, there was a significant sex × race interaction (white male vs female HR, 0.70; 95% CI, 0.41-1.21; P = .20 and black male vs female HR, 2.10; 95% CI, 1.23-3.60; P = 007) (P = .02 for interaction) (Figure 2). Rates of all-cause rehospitalization were 2-fold higher in black participants compared with white participants (62.1 vs 30.3 per 100 person-years; RR, 2.05; 95% CI, 1.62-2.60; P < .001). This effect was mainly because of HF-related readmissions: black participants had 53.2 HF-related hospitalizations per 100 person-years compared with 21.3 for white participants (RR, 2.50; 95% CI, 1.90-3.30; P < .001). The differences in rehospitalization rates between race-based subgroups persisted after controlling for age and number of concomitant conditions (data not shown). No sex × race interaction was observed for all-cause and HF rehospitalization rates (P = .36 and P = .71, respectively, for the interaction terms).

Place holder to copy figure label and caption
Figure 2.

Mortality after incident heart failure by sex and race. Overall survival did not differ between white and black participants; however, there was a statistically significant sex × race interaction, with black men demonstrating higher mortality than white men compared with their female counterparts.

Graphic Jump Location
Table Graphic Jump LocationTable 4. Mortality and Rehospitalization Rates After Incident Heart Failurea

Data on ventricular function during hospitalization for HF were not prospectively collected in the Health ABC Study; therefore, the available data are based on medical record reviews. Data on left ventricular ejection fraction (LVEF), obtained from echocardiography or left ventriculography reports during the index HF hospitalization, were available in 197 of 258 participants (76.4%). The median LVEF was 40% (interquartile range, 26%-55%), without difference between white and black participants (median LVEF, 40% for both groups). However, LVEF was higher in female participants (median LVEF, 46% vs 35% in men; P = .01); no sex × race interaction was observed. Participants with LVEF greater than 40% (n = 97 [49.2%]) had subsequent age-adjusted mortality 14.9 per 100 person-years, all-cause hospitalization rate 51.3 per 100 person-years, and HF rehospitalization rate 38.6 per 100 person-years. In comparison, participants with LVEF of 40% or less (n = 100 [50.8%]) had age-adjusted mortality 23.2 per 100 person-years (HR, 1.46; 95% CI, 0.98-2.18;  = .06), all-cause hospitalization rate 53.5 per 100 person-years (RR, 1.04; 95% CI, 0.82-1.32;  = .74) and HF rehospitalization rate 46.3 per 100 person-years (RR, 1.20, 95% CI, 0.92-1.57, P = .18).

In the present study, we found a high rate of incident HF in older persons, poor prognosis for these participants once they developed HF, and race-related differences in risk factor profiles for incident HF. Considering the worsening risk factor profile for HF in the population (eg, diabetes and hypertension), aging of the population, and increasing HF prevalence, our study underscores the need for focused HF prevention efforts.

HF INCIDENCE

The rate of incident HF in our study was similar to a recent study composed of an elderly population25; however, these rates are lower compared with data from administrative databases.6,26 Varying rates for incident HF have also been reported in the Framingham Heart Study, in the Cardiovascular Health Study, and from Olmsted County, Minnesota.4,5,10 These variations are likely related to different age groups, racial mix, geographic variation, and other variables. Our study most likely underestimated the HF rate because the definition of incident HF required hospitalization and some patients with new-onset HF may not require hospitalization. Also, diagnosing HF is challenging, and investigations have primarily used signs and symptoms, medication profile review, and cardiac imaging–based assessment; some investigators have used medical record reviews, whereas others have used administrative databases. This becomes more complicated when assessing HF with preserved ejection fraction because its signs and symptoms are nonspecific, there is no standard medication profile, and imaging characteristics are complicated.

Irrespective of the precise quantitative assessment, several themes are similar across these studies, including a high incidence rate, worsening profile with aging, and stagnant or increasing incidence rates during the past several decades. These trends have tremendous implications as the population demographics are changing. With the aging of 78 million baby boomers, 1 in 5 Americans is expected to be older than 65 years by 2050.27 This trend is projected to significantly affect health care and health care economics because the use of formal and informal services strongly correlates with age. Without effective prevention efforts, the current HF epidemic can be expected to substantially worsen in the near future.

RISK FACTORS FOR HF

Although risk factors for HF have been described,10 those data have limitations because many are based on older studies primarily among younger white participants. Many investigations have assessed risk factors in isolation rather than assessing their multivariable independent association. The risk factor profile for cardiovascular diseases is changing with increasing obesity prevalence, and sex- and race-based differences in risk factors and outcomes for incident HF among older persons were not evaluated in some studies.1214 In the present study, most modifiable risk factors were significantly more prevalent among black participants (compared with their white counterparts) and constituted a major driving force for the higher incidence of HF among black participants. Last, although the literature on HF risk factors is rich, data on PAR are limited,28,29 and only the Cardiovascular Health Study10 has specifically addressed this issue.

In the Cardiovascular Health Study,10 the investigators assessed PAR for all significant associations with HF. Several of these may be collinear and may “dilute” the true effect of modifiable risk factors. For example, stroke, CHD, ankle-arm index, carotid intima medial thickness, and electrocardiographic evidence of ST-T wave changes were included in the analysis because of their statistical significance. However, they may be related to a common domain of vascular atherosclerosis as a risk factor. Indeed, the CHD-attributable risk was only 13%. Assessment of PAR requires binary categorization of continuous variables; such categorization in the Cardiovascular Health Study was not based on clinically used cutoffs (eg, C-reactive protein level, >7 mg/dL). Finally, sex- and race-based analyses were not performed, and we found significant differences in this regard.

Coronary heart disease and uncontrolled blood pressure were the leading causes of HF in sex- and race-based subgroups. We also observed that a substantial proportion of HF is attributed to metabolic and cardiorenal factors, including glucose level and renal abnormalities as described previously30,31 and low albumin levels. It is unclear whether a low albumin level signifies cachexia, inflammation, or comorbidity burden or whether hypoalbuminemia precipitates symptomatic HF due to fluid extravasation.32 Increased heart rate has been reported as an HF risk factor and may represent a surrogate of vasovagal imbalance or a physiologic response to worsening cardiac function.33 Although much work is needed to assess which of these risk factors can be modified and how much (if any) HF risk reduction may be achieved, it is still notable that most risk factors were modifiable or amenable to potential intervention.

Two observations regarding racial differences merit special comment. First, LVH seems to affect mainly blacks, which is consistent with the high prevalence of uncontrolled blood pressure among persons of black race. However, the risk associated with LVH in our study was additive to and independent of that associated with uncontrolled blood pressure; in fact, LVH was encountered in 8.6% of participants with systolic blood pressure of less than 140 mm Hg. Second, the higher incidence of HF observed among black participants in our study is accompanied by a higher prevalence of modifiable risk factors and a higher preventable fraction of incident HF. Except for increased fasting glucose level, PAR for all risk factors was higher in black participants, with 6 of 8 risk factors assessed (CHD, LVH, smoking, reduced GFR, increased heart rate, and uncontrolled blood pressure) having more than 5% higher PAR.

OUTCOMES AFTER HF DEVELOPMENT

Despite the selection bias resulting in lower comorbidity burden compared with that in the general population, mortality after hospitalization for HF in the Health ABC Study was high (annual rate, 18.0%), similar to that reported in recent community-based studies.5,8 In fact, investigations assessing temporal trends in mortality after HF development demonstrate none or only modest improvements during the past few decades.8 These results suggest that the benefit from recent advances in HF therapy observed in “real-life” patient populations is significantly less than that in the clinical trial setting, that recent advances may not have been translated yet into routine clinical practice, or that HF characteristics at the community level are different from those in clinical trials.

In concordance with a retrospective cohort study,34 we observed significantly higher all-cause and HF read mission rates among blacks, whereas mortality did not differ overall across race. However, there was a sex × race interaction, with black men demonstrating the highest mortality among subgroups of sex and race, although this finding should be interpreted with caution because of few events.

VALUE OF POPULATION-ATTRIBUTABLE RISK

The PAR for a risk factor represents the proportional reduction in disease risk that would be achieved by eliminating the risk factor from the population, assuming a causal relation. The relative importance of risk factors in the population as defined by PAR can help guide policy makers in planning public health interventions. However, the absolute PAR estimates are dependent on the definition of risk factors and on the presence or absence of other risk factors in the multivariable setting.

LIMITATIONS

Diagnosis of incident HF in our study was based on HF hospitalization; therefore, we likely underestimated the true incidence. Echocardiography was not performed at baseline. Therefore, participants with subclinical prevalent structural heart disease may have been included in the analysis. The Health ABC Study did not collect detailed data on valvular heart disease; however, it is unlikely that a large proportion of participants had significant subclinical valvular heart disease that would affect the results overall.29 Also, because ventricular function during hospitalization for HF was not prospectively assessed, we could not reliably assess the differential effect of risk factors on development of HF with preserved vs reduced LVEF. The available data on LVEF are based on medical record reviews and do not refer to a single modality. Therefore, we cannot be confident that the distribution of LVEF is representative of older persons hospitalized with new-onset HF. Finally, the causes of the observed differences in outcomes between groups cannot be ascertained in this study. These differences may represent sex, race, severity of illness, or therapy-related differences and need further study.

In this study, we observed a high incidence of HF among older persons. Race-based differences in risk factors were noted. Outcomes after development of HF remain poor. However, most risk factors for HF are modifiable or potentially amenable to interventions. Therefore, HF prevention efforts may succeed in reducing the community-based burden of HF. Considering the worsening risk factor profile and aging of the population, such efforts should be considered a public health priority.

Correspondence: Javed Butler, MD, MPH, Cardiology Division, Emory University Hospital, Emory University, 1365 Clifton Rd NE, Ste AT430, Atlanta, GA 30322 (javed.butler@emory.edu).

Accepted for Publication: October 19, 2008.

Author Contributions: Drs Kalogeropoulos and Butler had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Psaty, Harris, and Butler. Acquisition of data: Kritchevsky, Newman, Satterfield, and Harris. Analysis and interpretation of data: Kalogeropoulos, Georgiopoulou, Kritchevsky, N. L. Smith, Newman, Rodondi, Bauer, Bibbins-Domingo, A. L. Smith, Wilson, Vasan, Harris, and Butler. Drafting of the manuscript: Kalogeropoulos, Georgiopoulou, Newman, and Butler. Critical revision of the manuscript for important intellectual content: Kalogeropoulos, Kritchevsky, Psaty, N. L. Smith, Newman, Rodondi, Satterfield, Bauer, Bibbins-Domingo, A. L. Smith, Wilson, Vasan, Harris, and Butler. Statistical analysis: Kalogeropoulos, Rodondi, Wilson, Harris, and Butler. Obtained funding: Kritchevsky, Newman, and Harris. Administrative, technical, and material support: N. L. Smith, Newman, Satterfield, Harris, and Butler. Study supervision: Kritchevsky, A. L. Smith, Harris, and Butler.

Financial Disclosure: None reported.

Funding/Support: This study was supported in part by the Intramural Research Program of the National Institute on Aging and by grants N01-AG-6-2101, N01-AG-6-2103, and N01-AG-6-2106 from the National Institute on Aging, National Institutes of Health.

Rosamond  WFlegal  KFriday  G  et al. American Heart Association Statistics Committee and Stroke Statistics Subcommittee, Heart disease and stroke statistics: 2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee [published correction appears in Circulation. 2007;115(5):e172]. Circulation 2007;115 (5) e69- e171Accessed January 14, 2009
PubMed
Ammar  KAJacobsen  SJMahoney  DW  et al.  Prevalence and prognostic significance of heart failure stages: application of the American College of Cardiology/American Heart Association heart failure staging criteria in the community. Circulation 2007;115 (12) 1563- 1570
PubMed
Bleumink  GSKnetsch  AMSturkenboom  MC  et al.  Quantifying the heart failure epidemic: prevalence, incidence rate, lifetime risk and prognosis of heart failure: the Rotterdam Study. Eur Heart J 2004;25 (18) 1614- 1619
PubMed
Levy  DKenchaiah  SLarson  MG  et al.  Long-term trends in the incidence of and survival with heart failure. N Engl J Med 2002;347 (18) 1397- 1402
PubMed
Roger  VLWeston  SARedfield  MM  et al.  Trends in heart failure incidence and survival in a community-based population. JAMA 2004;292 (3) 344- 350
PubMed
Curtis  LHWhellan  DJHammill  BG  et al.  Incidence and prevalence of heart failure in elderly persons, 1994-2003. Arch Intern Med 2008;168 (4) 418- 424
PubMed
Croft  JBGiles  WHPollard  RAKeenan  NLCasper  MLAnda  RF Heart failure survival among older adults in the United States: a poor prognosis for an emerging epidemic in the Medicare population. Arch Intern Med 1999;159 (5) 505- 510
PubMed
Kosiborod  MLichtman  JHHeidenreich  PA  et al.  National trends in outcomes among elderly patients with heart failure. Am J Med 2006;119 (7) 616.e1- 616.e710.1016/j.amjmed.2005.11.019
Haldeman  GACroft  JBGiles  WHRashidee  A Hospitalization of patients with heart failure: National Hospital Discharge Survey, 1985 to 1995. Am Heart J 1999;137 (2) 352- 360
PubMed
Gottdiener  JSArnold  AMAurigemma  GP  et al.  Predictors of congestive heart failure in the elderly: the Cardiovascular Health Study. J Am Coll Cardiol 2000;35 (6) 1628- 1637
PubMed
Kenchaiah  SNarula  JVasan  RS Risk factors for heart failure. Med Clin North Am 2004;88 (5) 1145- 1172
PubMed
Mokdad  AHFord  ESBowman  BA  et al.  Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003;289 (1) 76- 79
PubMed
East  MAPeterson  EDShaw  LKGattis  WAO’Connor  CM Racial differences in the outcomes of patients with diastolic heart failure. Am Heart J 2004;148 (1) 151- 156
PubMed
O’Meara  EClayton  T McEntegart  MB  et al. CHARM Investigators, Sex differences in clinical characteristics and prognosis in a broad spectrum of patients with heart failure: results of the Candesartan in Heart failure: Assessment of Reduction in Mortality and Morbidity (CHARM) program. Circulation 2007;115 (24) 3111- 3120
PubMed
Fried  LPBorhani  NOEnright  P  et al.  The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991;1 (3) 263- 276
PubMed
Rodondi  NNewman  ABVittinghoff  E  et al.  Subclinical hypothyroidism and the risk of heart failure, other cardiovascular events, and death. Arch Intern Med 2005;165 (21) 2460- 2466
PubMed
Blackburn  H Classification of the electrocardiogram for population studies: Minnesota Code. J Electrocardiol 1969;2 (3) 305- 310
PubMed
Butler  JKalogeropoulos  APGeorgiopoulou  VV  et al. Health ABC Study, Incident heart failure prediction in the elderly: the Health ABC Heart Failure Score. Circ Heart Failure 2008;1 (2) 125- 133
Jouven  XEmpana  JPSchwartz  PJDesnos  MCourbon  DDucimetiere  P Heart-rate profile during exercise as a predictor of sudden death. N Engl J Med 2005;352 (19) 1951- 1958
PubMed
Visser  MKritchevsky  SBNewman  AB  et al.  Lower serum albumin concentration and change in muscle mass: the Health, Aging and Body Composition Study. Am J Clin Nutr 2005;82 (3) 531- 537
PubMed
Levey  ASBosch  JPLewis  JBGreene  TRogers  NRoth  DModification of Diet in Renal Disease Study Group, A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med 1999;130 (6) 461- 470
PubMed
Benichou  JGail  MH Estimates of absolute cause-specific risk in cohort studies. Biometrics 1990;46 (3) 813- 826
PubMed
Rockhill  BNewman  BWeinberg  C Use and misuse of population attributable fractions [published correction appears in Am J Public Health. 2008;98(12):2119]. Am J Public Health 1998;88 (1) 15- 19
PubMed
Greenland  SDrescher  K Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics 1993;49 (3) 865- 872
PubMed
Barker  WHMullooly  JPGetchell  W Changing incidence and survival for heart failure in a well-defined older population, 1970-1974 and 1990-1994. Circulation 2006;113 (6) 799- 805
PubMed
McCullough  PAPhilbin  EFSpertus  JAKaatz  SSandberg  KRWeaver  WDResource Utilization Among Congestive Heart Failure (REACH) Study, Confirmation of a heart failure epidemic: findings from the Resource Utilization Among Congestive Heart Failure (REACH) study. J Am Coll Cardiol 2002;39 (1) 60- 69
PubMed
The Booming Dynamics of Aging, 2005 White House Conference on Aging (WHCoA) Web site.  2005 White House Conference on Aging.http://www.whcoa.gov. Accessed August 8, 2007
Kannel  WB Incidence and epidemiology of heart failure. Heart Fail Rev 2000;5 (2) 167- 173
PubMed
He  JOgden  LGBazzano  LAVupputuri  SLoria  CWhelton  PK Risk factors for congestive heart failure in US men and women: NHANES I epidemiologic follow-up study. Arch Intern Med 2001;161 (7) 996- 1002
PubMed
Barzilay  JIKronmal  RAGottdiener  JS  et al.  The association of fasting glucose levels with congestive heart failure in diabetic adults ≥65 years: the Cardiovascular Health Study. J Am Coll Cardiol 2004;43 (12) 2236- 2241
PubMed
Bibbins-Domingo  KChertow  GMFried  LF  et al.  Renal function and heart failure risk in older black and white individuals: the Health, Aging, and Body Composition Study. Arch Intern Med 2006;166 (13) 1396- 1402
PubMed
Arquès  SAmbrosi  PGélisse  RLuccioni  RHabib  G Hypoalbuminemia in elderly patients with acute diastolic heart failure. J Am Coll Cardiol 2003;42 (4) 712- 716
PubMed
Kannel  WBBelanger  AJ Epidemiology of heart failure. Am Heart J 1991;121 (3, pt 1) 951- 957
PubMed
Alexander  MGrumbach  KRemy  LRowell  RMassie  BM Congestive heart failure hospitalizations and survival in California: patterns according to race/ethnicity. Am Heart J 1999;137 (5) 919- 927
PubMed

Figures

Place holder to copy figure label and caption
Figure 1.

Incident heart failure by sex and race. In the Health, Aging, and Body Composition Study cohort, men and black participants were more likely to develop heart failure than women and white participants (stratified log-rank χ2 = 7.30, P =.007 for sex and χ2 = 7.85, P = .005 for race).

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Mortality after incident heart failure by sex and race. Overall survival did not differ between white and black participants; however, there was a statistically significant sex × race interaction, with black men demonstrating higher mortality than white men compared with their female counterparts.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Baseline Participant Characteristics
Table Graphic Jump LocationTable 2. Prevalence and Unadjusted Rate Ratios (RRs) and Population-Attributable Risk (PAR) of Modifiable Risk Factors for Incident Heart Failure
Table Graphic Jump LocationTable 3. Adjusted Rate Ratios (RRs) and Population-Attributable Risk (PAR) of Modifiable Risk Factors for Incident Heart Failurea
Table Graphic Jump LocationTable 4. Mortality and Rehospitalization Rates After Incident Heart Failurea

References

Rosamond  WFlegal  KFriday  G  et al. American Heart Association Statistics Committee and Stroke Statistics Subcommittee, Heart disease and stroke statistics: 2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee [published correction appears in Circulation. 2007;115(5):e172]. Circulation 2007;115 (5) e69- e171Accessed January 14, 2009
PubMed
Ammar  KAJacobsen  SJMahoney  DW  et al.  Prevalence and prognostic significance of heart failure stages: application of the American College of Cardiology/American Heart Association heart failure staging criteria in the community. Circulation 2007;115 (12) 1563- 1570
PubMed
Bleumink  GSKnetsch  AMSturkenboom  MC  et al.  Quantifying the heart failure epidemic: prevalence, incidence rate, lifetime risk and prognosis of heart failure: the Rotterdam Study. Eur Heart J 2004;25 (18) 1614- 1619
PubMed
Levy  DKenchaiah  SLarson  MG  et al.  Long-term trends in the incidence of and survival with heart failure. N Engl J Med 2002;347 (18) 1397- 1402
PubMed
Roger  VLWeston  SARedfield  MM  et al.  Trends in heart failure incidence and survival in a community-based population. JAMA 2004;292 (3) 344- 350
PubMed
Curtis  LHWhellan  DJHammill  BG  et al.  Incidence and prevalence of heart failure in elderly persons, 1994-2003. Arch Intern Med 2008;168 (4) 418- 424
PubMed
Croft  JBGiles  WHPollard  RAKeenan  NLCasper  MLAnda  RF Heart failure survival among older adults in the United States: a poor prognosis for an emerging epidemic in the Medicare population. Arch Intern Med 1999;159 (5) 505- 510
PubMed
Kosiborod  MLichtman  JHHeidenreich  PA  et al.  National trends in outcomes among elderly patients with heart failure. Am J Med 2006;119 (7) 616.e1- 616.e710.1016/j.amjmed.2005.11.019
Haldeman  GACroft  JBGiles  WHRashidee  A Hospitalization of patients with heart failure: National Hospital Discharge Survey, 1985 to 1995. Am Heart J 1999;137 (2) 352- 360
PubMed
Gottdiener  JSArnold  AMAurigemma  GP  et al.  Predictors of congestive heart failure in the elderly: the Cardiovascular Health Study. J Am Coll Cardiol 2000;35 (6) 1628- 1637
PubMed
Kenchaiah  SNarula  JVasan  RS Risk factors for heart failure. Med Clin North Am 2004;88 (5) 1145- 1172
PubMed
Mokdad  AHFord  ESBowman  BA  et al.  Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003;289 (1) 76- 79
PubMed
East  MAPeterson  EDShaw  LKGattis  WAO’Connor  CM Racial differences in the outcomes of patients with diastolic heart failure. Am Heart J 2004;148 (1) 151- 156
PubMed
O’Meara  EClayton  T McEntegart  MB  et al. CHARM Investigators, Sex differences in clinical characteristics and prognosis in a broad spectrum of patients with heart failure: results of the Candesartan in Heart failure: Assessment of Reduction in Mortality and Morbidity (CHARM) program. Circulation 2007;115 (24) 3111- 3120
PubMed
Fried  LPBorhani  NOEnright  P  et al.  The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991;1 (3) 263- 276
PubMed
Rodondi  NNewman  ABVittinghoff  E  et al.  Subclinical hypothyroidism and the risk of heart failure, other cardiovascular events, and death. Arch Intern Med 2005;165 (21) 2460- 2466
PubMed
Blackburn  H Classification of the electrocardiogram for population studies: Minnesota Code. J Electrocardiol 1969;2 (3) 305- 310
PubMed
Butler  JKalogeropoulos  APGeorgiopoulou  VV  et al. Health ABC Study, Incident heart failure prediction in the elderly: the Health ABC Heart Failure Score. Circ Heart Failure 2008;1 (2) 125- 133
Jouven  XEmpana  JPSchwartz  PJDesnos  MCourbon  DDucimetiere  P Heart-rate profile during exercise as a predictor of sudden death. N Engl J Med 2005;352 (19) 1951- 1958
PubMed
Visser  MKritchevsky  SBNewman  AB  et al.  Lower serum albumin concentration and change in muscle mass: the Health, Aging and Body Composition Study. Am J Clin Nutr 2005;82 (3) 531- 537
PubMed
Levey  ASBosch  JPLewis  JBGreene  TRogers  NRoth  DModification of Diet in Renal Disease Study Group, A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med 1999;130 (6) 461- 470
PubMed
Benichou  JGail  MH Estimates of absolute cause-specific risk in cohort studies. Biometrics 1990;46 (3) 813- 826
PubMed
Rockhill  BNewman  BWeinberg  C Use and misuse of population attributable fractions [published correction appears in Am J Public Health. 2008;98(12):2119]. Am J Public Health 1998;88 (1) 15- 19
PubMed
Greenland  SDrescher  K Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics 1993;49 (3) 865- 872
PubMed
Barker  WHMullooly  JPGetchell  W Changing incidence and survival for heart failure in a well-defined older population, 1970-1974 and 1990-1994. Circulation 2006;113 (6) 799- 805
PubMed
McCullough  PAPhilbin  EFSpertus  JAKaatz  SSandberg  KRWeaver  WDResource Utilization Among Congestive Heart Failure (REACH) Study, Confirmation of a heart failure epidemic: findings from the Resource Utilization Among Congestive Heart Failure (REACH) study. J Am Coll Cardiol 2002;39 (1) 60- 69
PubMed
The Booming Dynamics of Aging, 2005 White House Conference on Aging (WHCoA) Web site.  2005 White House Conference on Aging.http://www.whcoa.gov. Accessed August 8, 2007
Kannel  WB Incidence and epidemiology of heart failure. Heart Fail Rev 2000;5 (2) 167- 173
PubMed
He  JOgden  LGBazzano  LAVupputuri  SLoria  CWhelton  PK Risk factors for congestive heart failure in US men and women: NHANES I epidemiologic follow-up study. Arch Intern Med 2001;161 (7) 996- 1002
PubMed
Barzilay  JIKronmal  RAGottdiener  JS  et al.  The association of fasting glucose levels with congestive heart failure in diabetic adults ≥65 years: the Cardiovascular Health Study. J Am Coll Cardiol 2004;43 (12) 2236- 2241
PubMed
Bibbins-Domingo  KChertow  GMFried  LF  et al.  Renal function and heart failure risk in older black and white individuals: the Health, Aging, and Body Composition Study. Arch Intern Med 2006;166 (13) 1396- 1402
PubMed
Arquès  SAmbrosi  PGélisse  RLuccioni  RHabib  G Hypoalbuminemia in elderly patients with acute diastolic heart failure. J Am Coll Cardiol 2003;42 (4) 712- 716
PubMed
Kannel  WBBelanger  AJ Epidemiology of heart failure. Am Heart J 1991;121 (3, pt 1) 951- 957
PubMed
Alexander  MGrumbach  KRemy  LRowell  RMassie  BM Congestive heart failure hospitalizations and survival in California: patterns according to race/ethnicity. Am Heart J 1999;137 (5) 919- 927
PubMed

Correspondence

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
Submit a Comment

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Collections
PubMed Articles