Abstract
Objective
To identify risk factors for the occurrence of all-cause hospitalizations among older persons following heart failure diagnosis, and to determine whether geriatric conditions would emerge as independent risk factors when evaluated in the context of other relevant clinical data.
Background
Efforts to reduce costs in heart failure have focused on hospital utilization, yet few studies have examined how geriatric conditions affect the long-term risk of hospitalization following heart failure diagnosis. With the aging of the heart failure population, geriatric conditions such as slow gait and muscle weakness are becoming increasingly common.
Methods
The study population included participants with incident heart failure from the Cardiovascular Health Study, a longitudinal study of community-living, older persons. Data were collected through annual examinations and medical record review. Anderson-Gill regression modeling was used to determine predictors of hospitalization after heart failure diagnosis.
Results
Of the 758 participants newly diagnosed with heart failure, the mean rate of hospitalization was 7.9 per 10 person-years (95% CI 7.4–8.4). Independent risk factors for occurrence of hospitalizations included depressed ejection fraction (HR 1.25, 95% CI 1.04–1.51), NYHA classes 3 or 4 (HR 1.32, 95% CI 1.11–1.57), diabetes mellitus (HR 1.36, 95% CI 1.13–1.64), chronic kidney disease (HR 1.32 95% CI 1.14–1.53), weak grip strength (HR 1.19, 95% CI 1.00–1.42), slow gait speed (HR 1.28 95% CI 1.06–1.55), and depression (HR 1.23, 95% CI 1.05–1.45).
Conclusions
Geriatric conditions are important, and potentially modifiable, risk factors for hospitalization in heart failure that should be routinely assessed at the time of heart failure diagnosis.
Keywords: Heart Failure, Hospitalizations, Geriatric conditions
INTRODUCTION
As the population has aged and survival with cardiovascular disease has increased, the number of older persons with heart failure has increased considerably over the past 20 years.1 Currently, 80% of patients with heart failure are 65 years or older, and nearly 25% are 80 years or older.2 Costs associated with heart failure exceed $35 billion annually and are largely driven by hospitalizations,3 yet relatively little is known about the long-term risk of hospital admission in older persons after heart failure diagnosis. Most studies have focused on short-term risk (i.e., 30 days to 1 year) of hospital readmission after an initial heart failure hospitalization. These short-term risk models do not fully characterize cumulative, lifetime hospital utilization after heart failure diagnosis, which is relevant from a public health perspective. Heart failure in older persons is often marked by recurrent episodes of clinical decompensation necessitating multiple hospitalizations. Further, while geriatric conditions such as slow gait, muscle weakness, and cognitive impairment are emerging as important predictors of outcomes among older persons with cardiovascular disease,4–6 information about these conditions is not available in most heart failure registries, and their prognostic relevance for hospitalization in older patients with heart failure remains unclear.
To address these gaps in knowledge, we evaluated data from a population-based sample of persons age 65 years or older with a follow-up period of up to 20 years after being diagnosed with heart failure. These data included a rich array of information, including clinical heart failure assessments, laboratory evaluations, comorbid diseases, and objective assessments of several geriatric conditions. Our goal was to identify risk factors for the occurrence of all-cause hospitalizations among older persons following a new diagnosis of heart failure, and to determine whether geriatric conditions would emerge as independent risk factors when evaluated in the context of other relevant clinical data. This prognostic information can be used to assist in clinical decision-making and identify potential targets for intervention after heart failure diagnosis in older persons.
METHODS
Study Population
The study population included participants of the Cardiovascular Health Study (CHS). The objective of CHS was to identify factors associated with the onset of cardiovascular disease in older persons; however potentially eligible participants with cardiovascular disease at baseline were not excluded. In 1989, 5201 men and women aged 65 years or older were recruited from four communities across the United States, with an additional 687 African Americans recruited in 1992 to enhance minority representation. Potential participants were identified from Medicare eligibility lists. Persons who were wheelchair-bound or were receiving cancer or hospice treatment were excluded. Complete details on the inclusion/exclusion criteria have been previously reported.7
Data Collection
Data about the development of heart failure and potential risk factors for hospitalization were collected every 12 months from 1989–1999 through in-person interviews and examinations, and hospitalizations were ascertained through 2009. According to CHS protocol, potential cases of incident heart failure were identified through two mechanisms: hospitalization for heart failure, representing 85% of the new heart failure cases included in these analyses, and self-report of a physician diagnosis of heart failure.8 CHS criteria for heart failure required that the participant have a diagnosis of heart failure from a physician, and be under medical treatment (e.g., diuretic, angiotensin converting enzyme inhibitor, or digitalis) for heart failure. The presence of cardiomegaly and pulmonary edema on chest x-ray, or evidence of left ventricular dysfunction by echocardiography or ventriculography were used to support the diagnosis of heart failure. All potential cases of heart failure were adjudicated by an expert panel who reviewed all pertinent data from medical records. Participants entered the analysis at the time of the CHS study visit immediately following their heart failure diagnosis. This study visit is hereafter referred to as “baseline.” Because our objective was to identify risk factors present at the time of heart failure diagnosis, and because of uncertainty about the duration of heart failure among prevalent cases, we excluded 275 participants who had heart failure at the time of CHS enrollment.
Study Variables
Potential Risk Factors
Demographics
Age was considered in 10-year categories. Sex, race (non-white versus white), marital status (currently married versus not currently married), and highest level of education (less than 12th grade versus 12th grade or higher) were also included in the analyses.
Heart Failure Status
Ejection fraction was classified as depressed (< 45%) or preserved (≥ 45%) based on clinical studies of left ventricular function (echocardiography, nuclear, or catheterization data) performed at the time of heart failure diagnosis hospitalization. New York Heart Association (NYHA) classification was ascertained through information obtained in participant interviews. Angiotensin converting enzyme inhibitor (ACE-i) and beta-blocker use were ascertained through participant self-report and medical record review.
Body Mass Index
Based on previous work demonstrating associations of body mass index (BMI) with heart failure outcomes,9 we selected BMI categories to represent low body weight (<18 kg/m2), normal body weight (18 – 24.9 kg/m2), overweight (25 – 29.9 kg/m2), and obese (>30 kg/m2), using data obtained from physical examinations.
Chronic Diseases
All diseases were assessed according to CHS protocol,10 and included: diabetes mellitus, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary artery disease (CAD), stroke, and anemia. Participants were considered diabetic if they: reported a diagnosis of diabetes mellitus; had fasting serum glucose greater than 126 mg/dl; had a serum glucose of at least 200 mg/dL 2 hours following the oral glucose tolerance test (OGTT); or reported use of diabetic medications. Chronic kidney disease was ascertained through laboratory evaluation with calculation of glomerular filtration rate (GFR), with a cut-point of < 60 ml/minute. COPD was ascertained through self report of a physician diagnosis. Discharge summaries, medication use, cardiac enzyme levels, electrocardiograms, and brain imaging were reviewed by the CHS Cardiovascular Events Committee to classify all potential cases of CAD and stroke. The presence of anemia was ascertained through laboratory evaluation. A cut point of hemoglobin less than 12 g/deciliter (dL) was used to indicate anemia in women, and a cut point of less than 13 g/dL was used in men.
Geriatric Conditions
These are conditions that occur in older adults and that are typically multifactorial in etiology, but not necessarily related to a specific disease.11 Four geriatric conditions were evaluated, including impairments in: muscle strength, gait speed, cognitive function, and psychological status. All conditions were classified as present or absent, as follows. Grip strength, an indicator of overall muscle strength12, was measured in the dominant hand using a hand-held dynamometer.13 Weak grip was defined as <28.5 kilograms in men and <18.5 kilograms in women.14 Gait speed was assessed by recording the time to walk 15 feet at usual pace, and slow gait was defined as less than 0.8 meters/second.15 Cognitive function was measured using the Modified Mini-Mental State Exam (3MS)16 and the Digital Symbol Substitution Test.17 The 3MS is an expanded version of the Folstein Mini-Mental State Exam, a widely used screening test for dementia.18 The Digit Symbol Substitution Test assesses several cognitive processes, including visual search, visuo-motor coordination, and cognitive flexibility. As in previous CHS work,19 cognitive impairment was defined as a score less than 80 on the 3MS or a score less than 19 (which represents 1.5 SD below the mean score for this group) on the Digital Symbol Substitution Test. Depressive symptoms were classified as present based on a score of at least 8 on the (short form) CES-D.20 The CES-D is a self-reported measure of depressive symptoms experienced during the previous week.
All-cause hospitalizations
At the annual contacts, participants were asked about major illnesses and hospital admissions. Medical records were obtained for all reported hospitalizations. Searches of Medicare Utilization files were also performed to ascertain hospitalizations that may have been missed. These procedures have been used in prior CHS work to ascertain hospital utilization.21 If the diagnosis of heart failure was made during a hospitalization, we did not include that event in the outcome, as we were interested in evaluating the risk of future hospitalizations after heart failure was diagnosed.
Statistical Analysis
We described characteristics of the study population at the time of heart failure diagnosis, and used Anderson-Gill regression modeling22 to evaluate the associations between the potential baseline risk factors and time to hospitalization. This technique allows all hospitalizations to be analyzed, in contrast to Cox modeling which considers only the first event. With the exception of age and sex which were retained in the final models, we selected factors according to a hierarchical screening process to create a parsimonious, multivariable model.23 First, we evaluated the bivariate association between each factor and the outcome. Only variables with a p-value ≤0.30 were considered further. Next, we sequentially evaluated the correlations among the remaining factors with the Kendall’s correlation coefficient. Those with a correlation coefficient > 0.3 could result in collinearity; thus we retained a single risk factor based on clinical judgment and the strength of association with the outcome. We then used a backward selection method to evaluate the impact of each of the remaining risk factors on the overall model fit through a series of Anderson-Gill models. To assess each factor’s contribution to the model fit, we used a chi-square distribution with degrees of freedom equaling the number of parameters for the added factor, based on the difference in the −2 Log Likelihood statistics between the models with and without the factor. After fitting a separate model for each factor, we added the factor with the largest difference in - 2LL to the overall model. We continued this process iteratively until no factor significantly increased the model fit based on the - 2LL criterion. Participants were censored at the time of death. To further understand the clinical impact of the independent risk factors, we calculated hospitalization rates (per 10 person years) with and without each of the factors.
Risk factor data that were missing from the baseline evaluation (i.e., CHS study visit immediately following heart failure diagnosis) were “carried forward” from the last available assessment, i.e., “last value carried forward.” For ejection fraction, NYHA, and heart failure medication (ACE-inhibitor and beta-blockers) use, we did not use the last value carried forward approach since values would be expected to change substantially at the time of heart failure diagnosis. For these variables, we retained missing values as distinct categories.
All statistical tests were 2-tailed, and P < .05 was considered to indicate statistical significance. All analyses were conducted using SAS software version 9.2 (SAS Institute, Cary, North Carolina).
RESULTS
Study Population
During the study period, 758 CHS participants were newly diagnosed with heart failure. As shown in Table 1, the mean age at the time of heart failure diagnosis was 79.7 years. Most participants were white, about a third had less than a 12th grade education and nearly a fifth were obese. The majority of the cohort had preserved ejection fraction, and 30.3% had NYHA Class 3 or 4. Angiotensin converting enzyme inhibitors were used in 40.7% of participants, and beta blockers in 15.7%. As expected in a heart failure cohort, coronary artery disease was the most common comorbid disease, followed by chronic kidney disease and diabetes mellitus. Geriatric conditions were common, with muscle weakness, slow gait, cognitive impairment, and depression all present in approximately 40% of the cohort. In comparison, at the time of enrollment into CHS, study participants (N=5888) had a mean age of 72 years, and 57.6% were female. Coronary artery disease was present in 19.4%, 22.8% were diabetic, and 4.2% had a history of stroke. Muscle weakness was present in 20% of participants, slow gait in 22.1%, cognitive impairment in 13.2%, and depression in 21.5%.
Table 1.
Baseline Patient Characteristics, N=758 (%)
| Demographics | |
| Age, years (mean, SD) | 79.7 (6.2) |
| Age group | |
| 65–74 | 170 (22.4) |
| 75–84 | 417 (55.0) |
| >= 85 | 171 (22.6) |
| Female sex | 383 (50.5) |
| Non-white race | 97 (12.8) |
| Education < high school | 270 (35.8) |
| Body mass index (kg/m2) | |
| <18 | 23 (3.0) |
| 18 – <25 | 329 (43.5) |
| 25 – <30 | 270 (35.7) |
| ≥ 30 | 135 (17.8) |
| Heart failure Status | |
| Ejection fraction < 45% | 203 (43.2) |
| New York Heart Association class | |
| I | 22 (3.2) |
| II | 462 (66.5) |
| III | 173 (24.9) |
| IV | 38 (5.4) |
| ACE-inhibitor | 257 (40.7) |
| Beta-blocker | 99 (15.7) |
| Medical history | |
| Coronary Artery Disease | 463 (61.1) |
| Chronic Kidney Disease | 280 (37.1) |
| Diabetes mellitus | 193 (25.5) |
| Stroke | 125 (16.5) |
| COPD | 112 (14.8) |
| Anemia | 111 (14.7) |
| Geriatric conditions | |
| Weak Grip strength | 317 (41.8) |
| Slow Gait speed | 317 (41.8) |
| Cognitive impairment | 264 (34.8) |
| Depression | 296 (39.1) |
Abbreviations used in this Table: ACE, angiotensin-converting enzyme; COPD, chronic obstructive pulmonary disease.
All data missing < 1% except ejection fraction (missing in 37.9% of patients), NYHA class (missing in 8.3%), ACE-inhibitor (missing in 16.6%) and Beta-blocker (missing in 16.6%).
Percentages calculated based on participants without missing data.
Hospitalizations after Heart Failure Diagnosis
A total of 2395 hospitalizations occurred during a median (IQR) follow-up of 3.4 (1.8–5.9) years. By the end of the follow-up period (2009), 75% of the participants had died. As shown in Figure 1, the number of hospitalizations per participant ranged from 0 (in 14% of the cohort) to 56, with the median (IQR) = 3.4 (1.8 – 5.9). The mean rate of hospitalization was 7.9 per 10 person-years (95% confidence interval [CI]: 7.4– 8.4). Among participants with preserved ejection fraction, the rate of hospitalizations was somewhat lower, 7.0 per 10 person-years (95% CI 6.6 – 7.4).
Figure 1. Number of hHospitalizations per person after heart failure diagnosis.
Median number of hospitalizations per participant during the follow-up period after heart failure diagnosis
Hospitalizations and Mortality over the Study Period
Shown in Table 2 are hospitalization and mortality rates over the 20 year study period. Participants whose heart failure was diagnosed between 1990–1994 generally had higher rates of hospitalization across the study period compared with participants diagnosed between 1995–1999 (P value < 0.001). The rate of hospitalizations (among all participants) was generally stable across the follow-up period with an increase in years 6–10 compared with years 1–5 and then a decrease in years 11–20. The mortality rates were slightly higher among participants diagnosed 1995–1999 than in 1990–1994, but these differences achieved statistical significance only for years 1–5. As expected, mortality rates increased in all participants over the study period (P < 0.001).
Table 2.
Hospitalization and Mortality Rates (95% CI) after Heart Failure Diagnosis per 10 Person-Years
| 1–5 Years After Diagnosis | 6–10 Years After Diagnosis | 11–20 Years After Diagnosis | |||||||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| Year of Diagnosis | N | Hospitalization | Mortality | N | Hospitalization | Mortality | N | Hospitalization | Mortality |
| 1990–1994 | 283 | 7.9 (7.4–8.4) | 1.3 (1.1–1.5) | 144 | 10.1 (9.3–11.0) | 1.7 (1.4– 2.1) | 60 | 7.7 (6.5–9.0) | 2.2 (1.7–3.0) |
| 1995–1999 | 475 | 7.4 (7.0–7.9) | 1.5 (1.4–1.8) | 141 | 7.5 (6.6–8.4) | 1.9 (1.5–2.4) | 25 | 6.0 (3.4–10.5) | 2.5 (1.0–6.0) |
| Total | 758 | 7.6 (7.3–8.0) | 1.4 (1.3–1.4) | 285 | 9.0 (8.4 – 9.7) | 1.8 (1.5 – 2.1) | 85 | 7.5 (6.4 – 8.8) | 2.3 (1.7–3.0) |
N refers to number of participants included at the beginning of each time period
Risk Factors for Hospitalization after Heart Failure Diagnosis
As shown in Table 3, several characteristics were associated with the occurrence of hospitalizations in bivariate analysis. Race, body mass index, ACE-inhibitor use, and coronary artery disease did not meet the bivariate P value criterion (P < 0.30) and were not considered further. No potential risk factor was excluded because of collinearity. In multivariable analysis (Table 4), depressed ejection fraction (HR 1.25, 95% CI 1.04–1.51), NYHA classes 3 or 4 (HR 1.32, 95% CI 1.11–1.57), diabetes mellitus (HR 1.36, 95% CI 1.13–1.64), chronic kidney disease (HR 1.32 95% CI 1.14–1.53), weak grip strength (HR 1.19, 95% CI 1.00–1.42), slow gait speed (HR 1.28 95% CI 1.06–1.55), and depression (HR 1.23, 95% CI 1.05–1.45) were independently associated with the occurrence of hospitalization (Table 3). Missing categories of ejection fraction, NYHA, ACE-inhibitor use, and beta blocker use were not significantly associated with hospitalization.
Table 3.
Bivariate Results for All-Cause Hospitalization
| Hazard Ratio (95% CI) | P Value | |
|---|---|---|
| Demographic factors | ||
| Age group, years | ||
| 65–74 | Reference Group | |
| 75–84 | 1.05 (0.86 – 1.28) | 0.666 |
| >= 85 | 1.33 (1.04 – 1.71) | 0.022 |
| Female sex | 0.98 (0.83 – 1.14) | 0.765 |
| Non-white race | 1.09 (0.86 – 1.39) | 0.475 |
| Education < 12th grade | 1.18 (1.00 – 1.40) | 0.048 |
| Body mass index (kg/m2) | ||
| <18 | 1.17 (0.76 – 1.81) | 0.470 |
| 18–25 | Reference Group | |
| 25–30 | 0.93 (0.78 – 1.10) | 0.393 |
| 30+ | 1.05 (0.83 – 1.34) | 0.675 |
| Heart failure Status | ||
| Ejection fraction < 45% | 1.32 (1.07 – 1.62) | 0.010 |
| NYHA III/IV | 1.37 (1.14 – 1.65) | <0.001 |
| Not taking ACE-inhibitor | 1.05 (0.87 – 1.27) | 0.616 |
| Not taking Beta-blocker | 1.23 (0.98 – 1.55) | 0.076 |
| Medical history | ||
| Coronary artery disease | 1.09 (0.92 – 1.29) | 0.317 |
| Chronic kidney disease | 1.31 (1.11 – 1.55) | 0.002 |
| Diabetes mellitus | 1.42 (1.17 – 1.74) | <0.001 |
| Stroke | 1.27 (1.03 – 1.56) | 0.024 |
| COPD | 1.16 (0.94 – 1.44) | 0.164 |
| Anemia | 1.16 (0.92 – 1.45) | 0.210 |
| Geriatric conditions | ||
| Weak grip strength | 1.21 (1.02 – 1.44) | 0.030 |
| Slow gait speed | 1.49 (1.24 – 1.80) | <0.001 |
| Cognitive impairment | 1.33 (1.14 – 1.56) | <0.001 |
| Depression | 1.33 (1.13 – 1.56) | <0.001 |
Table 4.
Multivariable Model for All-Cause Hospitalization
| Demographics | Hazard Ratio (95% CI) | P-value |
|---|---|---|
| Age group, years | ||
| 65–74 | Reference Group | |
| 75–84 | 0.96 (0.81 – 1.15) | 0.655 |
| >= 85 | 1.20 (0.94 – 1.53) | 0.139 |
| Female sex | 0.89 (0.77 – 1.03) | 0.124 |
| Education < 12th grade | 1.12 (0.96 – 1.31) | 0.146 |
| Heart failure Status | ||
| Ejection fraction < 45% | 1.25 (1.04 – 1.51) | 0.019 |
| NYHA III/IV | 1.32 (1.11 – 1.57) | 0.001 |
| Not taking Beta-blocker | 1.21 (0.99 – 1.46) | 0.059 |
| Medical history | ||
| Diabetes mellitus | 1.36 (1.13 – 1.64) | 0.001 |
| Chronic kidney disease | 1.32 (1.14 – 1.53) | <0.001 |
| Stroke | 1.15 (0.95 – 1.38) | 0.149 |
| Geriatric conditions | ||
| Weak grip strength | 1.19 (1.00 – 1.42) | 0.050 |
| Slow gait speed | 1.28 (1.06 – 1.55) | 0.010 |
| Depression | 1.23 (1.05– 1.43) | 0.010 |
Model is adjusted for year of heart failure diagnosis (1990–1999)
Hospitalization rates (per 10 person years) with and without each of the independent risk factors are shown in Figure 2. Presence of depression was associated with a 23% increase in hospitalization rates (i.e., participants without depression had 7.13 hospitalizations per 10 person years, compared with 9.26 in participants with depression). Similarly, slow gait was associated with a 30% increase in hospitalization rates, and weak grip with a 16% increase. Presence of diabetes mellitus was associated with a 29% increase, chronic kidney disease with a 22% increase, depressed ejection fraction with a 25% increase, and higher NYHA with a 27% increase.
Figure 2. Hospitalization rates per 10 person years with and without independent risk factors. (Bars represent 95% confidence intervals).
Rates per 10 person years, bar represent 95% confidence intervals
Results from analyses considering hospitalizations or death as a combined endpoint were similar to those considering hospitalizations alone. In multivariable analysis, depressed ejection fraction (HR 1.26, 95% CI 1.04–1.51), NYHA classes 3 or 4 (HR 1.32, 95% CI 1.12–1.57), diabetes mellitus (HR 1.36, 95% CI 1.13–1.63), chronic kidney disease (HR 1.33, 95% CI 1.15–1.53), weak grip (HR 1.19, 95% CI 1.00–1.42), slow gait (1.31, 95% CI 1.08–1.58), and depression (HR 1.22, 95% CI 1.05–1.43) were independently associated with the occurrence of hospitalization or mortality.
DISCUSSION
In this cohort of community-living older persons, three geriatric conditions, namely weak grip strength, slow gait speed, and depression, emerged as independent risk factors for hospitalizations after heart failure diagnosis, even when other relevant demographic, social, and clinical factors were considered. Other independent risk factors included depressed ejection fraction, NYHA classes 3 and 4, diabetes mellitus, and chronic kidney disease.
Why would these geriatric conditions predict hospital utilization among older persons with newly diagnosed heart failure? In the case of gait speed, walking places demands on multiple organ systems, including the cardiovascular, pulmonary, nervous, and musculoskeletal systems. Slow gait may reflect physiologic dysfunction in one or more of these systems. Grip strength is a reliable indicator of overall muscle strength,24 and therefore may similarly reflect overall physiologic reserve. Depressive symptoms may affect patients’ self care (including adherence with medications and follow-up appointments). Alternatively, depressive symptoms may result from poor health status.
Because muscle weakness, slow gait, and depressive symptoms are potentially modifiable, they should be routinely assessed in older persons with newly diagnosed heart failure. As with other geriatric conditions, however, these factors fall outside the traditional disease-oriented model of clinical medicine; 25 thus, they may be overlooked in the care of older persons with newly diagnosed heart failure, particularly when they are subtle.26–28 Modification of physical impairments and depressive symptoms is challenging, but may improve outcomes in older patients with cardiovascular disease. Exercise training improves gait speed, aerobic fitness, and quality of life in patients with heart failure, and may reduce hospitalizations and mortality. 29,30 Recent work demonstrates that both exercise and antidepressant treatment in depressed patients with coronary artery disease resulted in improvement in depressive symptoms and cardiovascular biomarkers.31 In addition to serving as targets for intervention, the presence of these geriatric conditions signals a high-risk group who may benefit from services such as nursing and pharmacy support. Whether assessment and management of geriatric conditions actually improves heart failure outcomes should be examined in future work.
Our study adds valuable information for understanding determinants of hospitalization utilization following heart failure diagnosis in older persons. The duration of follow-up allows a more complete assessment of hospitalization than is available in most heart failure studies. Previous work that has included a similar duration of follow-up in persons newly diagnosed with heart failure32 has not focused on an older population or included information about geriatric conditions, which emerged as important risk factors of hospitalization in these analyses. Detailed medical record review supplemented self-reported information for several chronic diseases, enhancing the validity of our data. There was no attrition for reasons other than death, further strengthening the validity and generalizability of our results. The generalizability of our results is also enhanced by the fact that the CHS data were collected from a representative sample of white and African-American community-living older persons from across the United States.
Of note, use of ACE inhibitors and beta-blockers was not significantly associated with hospitalizations. The high prevalence of heart failure with preserved ejection fraction in our study sample provides one explanation for this finding. Additionally, most hospitalizations in older patients with heart failure are due to non-heart failure causes,32 which these medications would not be expected to affect. This study also has several potential limitations, which should be considered when interpreting our results. Data were collected beginning in 1989; heart failure management has certainly changed since this time, and it is possible that the risk factors for hospitalization have changed. However, it is important to note that we ascertained the occurrence of hospitalization through 2009, thereby including up to 20 years of follow-up, which would not be possible with a later baseline date. We censored participants at the time of death. Our results may therefore have under-estimated the magnitude of risk associated with some factors (e.g., CAD) that were strong risk factors for both death and hospitalization, particularly since the observed mortality rate was high (75%).” Although we accounted for the presence of comorbid diseases, we were unable to assess whether the associations between geriatric conditions and hospitalizations were attenuated after adjustment for severity and duration of comorbid diseases. Finally, the average age of our study participants was 79.7 years, which is certainly older than particpants included in most heart failure registries. However, the age of our study participants is well-suited to our objective of examining the prognostic importance of geriatric conditions in older patients with heart failure.
Although heart failure primarily affects older persons, current heart failure guidelines have not incorporated routine assessment or management of geriatric conditions. Underscoring the relative inattention to geriatric conditions, even current quality indicators developed specifically for older patients, such as the Assessing Care of Vulnerable Elders or “ACOVE” measures for heart failure, do not include assessment of these conditions.33 Our results provide strong justification for developing strategies to routinely screen for and manage these conditions at the time of heart failure diagnosis. Through such interventions, it may be possible to reduce the burden of hospitalization among older persons newly diagnosed with heart failure, thereby improving their quality of life while reducing health care costs.
Acknowledgments
Role of the Funding Sources
The study was conducted at the Yale Claude D. Pepper Older Americans Independence Center (P30AG21342). Dr. Gill is the recipient of a Midcareer Investigator Award in Patient-Oriented Research (K24AG021507) from the National Institute on Aging. Dr. Chaudhry is the recipient of a Paul Beeson/K23 Career Development Award (K23AG030986) from the National Institute on Aging. The research reported in this article was supported by contracts HHSN268201200036C, N01-HC-85239, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, and grant HL080295 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG-023629, AG-15928, AG-20098, and AG-027058 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at http://www.chs-nhlbi.org/pi.htm. The manuscript was reviewed and approved by the Publications and Presentations committee of the Cardiovascular Health Study. The funding sources had no role in the design, conduct, or analysis of the study or in the decision to submit the manuscript for publication.
Footnotes
Relationship with industry: none
Conflicts of Interest: none
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