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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Arch Intern Med. Author manuscript; available in PMC 2009 December 8.
Published in final edited form as:
PMCID: PMC2629051
NIHMSID: NIHMS87171

Acute and Long-Term Outcomes of Heart Failure in Elderly Persons, 2001–2005

Abstract

Background

Treatment of chronic heart failure has improved over the past 2 decades, but little is known about whether the improvements are reflected in trends in acute and long-term mortality and hospital readmission.

Methods

In a retrospective cohort study of 2 540 838 elderly Medicare beneficiaries hospitalized with heart failure between 2001 and 2005, we examined acute and long-term all-cause mortality and hospital readmission and patient- and hospital-level predictors of those outcomes.

Results

Unadjusted in-hospital mortality declined from 5.1% to 4.2% during the study period (P < .001), but 30-day, 180-day, and 1-year all-cause mortality remained fairly constant at 11%, 26%, and 37%, respectively. Nearly 1 in 4 patients were readmitted within 30 days of the index hospitalization, and two thirds were readmitted within 1 year. Controlling for patient- and hospital-level covariates, the hazard of all-cause mortality at 1 year was slightly lower in 2005 than in 2001 (hazard ratio, 0.98; 95% confidence interval, 0.97–0.99). The hazard of readmission did not decline significantly from 2001 to 2005 (HR, 0.99; 95% CI, 0.98–1.00).

Conclusions

Acute and long-term all-cause mortality and hospital readmission remain high and have improved little over time. The need to identify optimal management strategies for these clinically complex patients is urgent.

Introduction

Treatment of chronic heart failure has evolved substantially over the past 2 decades. By the early 1990s, clinical trials demonstrated that the use of angiotensin-converting enzyme (ACE) inhibitors lowered hospitalization rates and conferred a survival benefit in patients with reduced left ventricular function.13 Similarly, the survival benefit of beta-blockers in patients with reduced left ventricular function was established by the late 1990s.46 Since then, numerous efforts have been undertaken to improve quality of care and outcomes for patients with heart failure, including national initiatives sponsored by the Centers for Medicare & Medicaid Services (CMS), the Joint Commission, the American Heart Association, and the American College of Cardiology. In general, these efforts have focused on optimal use of evidence-based pharmacotherapies, lifestyle modifications, and management of coexisting illnesses.

Although there is some evidence that these efforts have improved outcomes in subsets of patients,7,8 little is known about whether the improvements are reflected in aggregate trends in mortality and hospital readmission. An analysis of Medicare data from 1992 through 1999 showed no improvements in mortality and a slight increase in readmission rates,9 but the study predates important therapeutic advances and national quality-improvement efforts. Therefore, using a national sample of Medicare beneficiaries hospitalized with heart failure between 2001 and 2005, we examined trends in acute and long-term mortality and readmission among elderly persons hospitalized with heart failure and patient- and hospital-level predictors of those outcomes.

Methods

Patients

We obtained inpatient claims and the corresponding denominator files from CMS for all Medicare beneficiaries discharged between January 1, 2000, and December 31, 2005. The inpatient files include institutional claims submitted for facility costs covered under Medicare Part A and beneficiary, physician, and hospital identifiers, admission and discharge dates, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes. The denominator files include beneficiary identifiers, dates of birth, sex, race/ethnicity, dates of death, and information about program eligibility and enrollment. Race/ethnicity were reported by Medicare beneficiaries at the time of enrollment. In this analysis, we used the reported category “black” and combined all others as “nonblack.”10

We included all beneficiaries with a primary diagnosis of heart failure (ICD-9-CM codes 428.x, 428.xx, 402.x1, 404.x1, 404.x3) on a single inpatient claim between January 1, 2001, and December 31, 2005. For patients with multiple admissions, we considered the earliest admission in each calendar year to be the index admission. We only used claims filed during periods of fee-for-service eligibility, and we limited the analysis to persons 65 years and older who were living in the United States.

Outcomes

Time to death was defined as the number of days between the index discharge date and the date of death. We calculated time to the first all-cause hospitalization within 1 year after the index discharge date as the number of days between the index discharge date and the subsequent readmission date. Transfers to or from another hospital and admission for rehabilitation (Diagnosis Related Group [DRG] 462 or an ICD-9-CM admitting diagnosis code of V57.xx) did not count as readmissions. We calculated time to first cardiovascular readmission in a similar way, defining cardiovascular readmissions by DRGs 104-112, 115-118, 121-125, 127-144 and excluding transfers and admissions for rehabilitation.

Covariates

Patient characteristics included age, sex, race/ethnicity, procedure history, and comorbidities and risks at the time of the index hospitalization. Patient-level information used for risk adjustment was obtained from the inpatient index claim and from inpatient claims for the 365 days before the index hospitalization. We included the following cardiac procedures: coronary artery bypass graft surgery (CABG; ICD-9-CM code 36.1x), implantable cardioverter-defibrillator (ICD) implantation (37.94, 37.95, 37.96, 37.97, and 37.98), and percutaneous transluminal coronary angioplasty (PTCA; 36.01, 36.02, and 36.05). We consulted a previous study11 and used clinical judgment to identify comorbid conditions of interest, and we defined the comorbid conditions using Hierarchical Condition Categories (HCC).12 Specifically, we included acute myocardial infarction (HCC 81), unstable angina and other acute ischemic heart disease (HCC 82), chronic atherosclerosis, (HCC 83, 84), cardiorespiratory failure and shock (HCC 79), valvular and rheumatic heart disease (HCC 86), hypertension (HCC 89, 91) stroke (HCC 96), renal failure (HCC 131), chronic obstructive pulmonary disease (HCC 108), pneumonia (HCC 111-113), diabetes mellitus (HCC 15-20, 120), protein-calorie malnutrition (HCC 21), dementia (HCC 49,50), hemiplegia, paralysis, functional disability (HCC 100-102, 68, 69, 177,178), peripheral vascular disease (HCC 104, 105), metastatic cancer (HCC 7,8), trauma in the past year (HCC 154-156, 158-162), major psychiatric disorders (HCC 54-56), chronic liver disease (HCC 25-27), specified heart arrhythmias (HCC 92), and other heart rhythm and conduction disorder (HCC 93).

For hospital-level variables, we used the CMS 100% inpatient files to calculate average yearly volume of cardiovascular discharges (DRG 104-112, 115-118, 121-125, 127-145, 479, 514-518, 525-527, 535, 536, 547-558) and volume of heart failure discharges (DRG 127). We used American Hospital Association files to determine whether hospitals were members of the Council of Teaching Hospitals and whether they provided cardiac intensive care, open heart surgery, and heart transplant services.

Statistical Analysis

For baseline characteristics, we present categorical variables as frequencies and continuous variables as means with SDs. We used Kaplan-Meier methods to calculate unadjusted mortality rates (including in-hospital mortality and mortality at 30 days, 90 days, 180 days, and 1 year). To account for the competing risk of death, we used the cumulative incidence function to calculate unadjusted, all-cause, and cardiovascular readmission rates (including 30-day, 90-day, 180-day, and 1-year readmission rates) at the patient level. We examined the distribution of DRGs for all-cause readmissions to identify the underlying reasons for readmission.

We used Cox proportional hazards models with adjustment for hospital clustering to examine predictors of mortality and readmission among patients with heart failure. In multivariable analyses, we modeled 1-year mortality as a function of age (per 5 years), sex, previous cardiac procedures (CABG, ICD implantation, PTCA), comorbid conditions, year of index hospitalization, and hospital characteristics. In addition to these variables, the readmission models included a variable indicating whether length of stay for the index hospitalization was greater than 7 days.13 The readmission models also accounted for the competing risk of mortality. Because data from the American Hospital Association were not available for every hospital, we refit the models based on patient-level predictors only as a sensitivity analysis.

We used SAS version 9.1 (SAS Institute Inc, Cary, North Carolina) for all analyses. The study was approved by the institutional review board of the Duke University Health System.

Results

From 2001 through 2005, more than 2 million Medicare fee-for-service beneficiaries were hospitalized for heart failure. The mean age of hospitalized patients was 80 years, and nearly 60% were women (Table 1). Approximately 5% underwent PTCA in the year before discharge, and 3% underwent CABG; these rates remained steady over the 5-year period. In contrast, the percentage of patients who received an ICD within 365 days before discharge quadrupled (1.2% in 2001 vs 6.0% in 2005; P < .001). The burden of comorbid conditions was high and changed little over time. More than two thirds of the patients had chronic atherosclerosis, nearly 30% had renal failure, and 40% had diabetes mellitus. About 55% had a documented heart arrhythmia.

Table 1
Baseline Characteristics of Medicare Beneficiaries Hospitalized for Heart Failure, 2001–2005

Unadjusted in-hospital mortality declined from 5.1% in 2001 to 4.2% in 2005 (P < .001) (Table 2). During the same period, unadjusted mortality at 30 days, 180 days, and 1 year remained fairly constant at 11%, 26%, and 37%, respectively. Nearly 1 in 4 patients were readmitted to the hospital within 30 days of the index hospitalization, and slightly more than half had cardiovascular readmissions. On average, patients were readmitted within 96 (SD, 95.5) days of the index heart failure admission. From 2001 through 2005, all-cause and cardiovascular readmission rates were high and showed little change. Sixty-five percent of patients hospitalized with heart failure were readmitted within 1 year, and nearly 40% were readmitted at least twice (198 371/497 292 in 2004). The cardiovascular readmission rate at 1 year was more than 40%, and 18% of patients had multiple cardiovascular readmissions (88 940/497 292 in 2004).

Table 2
Unadjusted Mortality and Annual Cumulative Incidence of Readmission Among Medicare Beneficiaries Hospitalized With Heart Failure, 2001–2005

The distribution of DRGs associated with readmissions was generally consistent over the study period. Of patients readmitted after an index heart failure admission, about 27% were rehospitalized for heart failure (Table 3). Although other cardiovascular and respiratory diagnoses were common reasons for hospitalization, 3% of readmissions were for renal failure and 2.5% were for gastrointestinal hemorrhage with comorbidities and complications. Readmission for ICD implantation rose steadily from 0.3% in 2001 to 1.2% in 2005.

Table 3
Frequency of First Readmission for the 20 Most Common Diagnosis Related Groupsa

Table 4 shows the results of the univariate and multivariate models of 1-year outcomes. Controlling for all other variables, age and male sex increased the hazard of mortality at 1 year by 24% and 21%, respectively. In contrast, the hazard of mortality was 16% lower in black patients compared to other patients. The hazard of death was significantly lower for patients who underwent CABG, ICD implantation, or PTCA in the year before discharge from the index hospitalization. Comorbidities and risks documented during the index hospitalization and in the prior year were strongly and independently associated with mortality. Compared to patients admitted to hospitals with the highest volume of heart failure discharges, patients admitted to hospitals with the lowest volume of heart failure discharges had a slightly higher hazard of mortality at 1 year (hazard ratio [HR], 1.05; 95% confidence interval [CI], 1.03–1.07] for hospitals in the first quartile of heart failure volume). Results of the multivariate analysis suggest that mortality declined slightly over the study period. After controlling for patient- and hospital-level covariates, the hazard of mortality at 1 year was approximately 2% lower in 2005 than in 2001 (HR, 0.98; 95% CI, 0.97–0.99). The results of the model that included patient-level covariates only were highly consistent, and the parameter estimates for the year of index admission were identical to those shown in Table 4.

Table 4
Predictors of 1-Year Mortality and Readmission

An index hospitalization longer than 7 days was associated with a 14% increase in the hazard of all-cause readmission (HR, 1.14; 95% CI, 1.14–1.15) but was less strongly associated with cardiovascular readmission (HR, 1.03; 95% CI, 1.02–1.03). The hazard of cardiovascular readmission was 15% higher among black patients (HR, 1.15; 95% CI, 1.14–1.16). CABG and ICD implantation during the index hospitalization or in the year before admission were associated with a lower hazard of readmission, although PTCA was not. Again, comorbidities and risks were important independent predictors of readmission. Among patients admitted to a hospital in the lowest quartile of heart failure discharge volume, the hazard of all-cause readmission was slightly but significantly higher (HR, 1.02; 95% CI, 1.00–1.03). After adjustment for covariates, the hazard of all-cause readmission did not decline significantly over time; the hazard of cardiovascular readmission declined slightly from 2001 to 2005 (HR, 0.97; 95% CI, 0.96–0.98). The results from the models that included patient-level covariates only were highly consistent. The HRs and 95% CIs for year of index hospitalization were identical to those shown in Table 4.

Comment

Among Medicare beneficiaries hospitalized for heart failure between 2001 and 2005, acute and long-term outcomes were poor and did not improve appreciably over time. Within 30 days of hospitalization for heart failure, more then 1 in 10 Medicare beneficiaries died and more than 1 in 5 were readmitted to the hospital. Nearly half of the readmissions were for cardiovascular reasons. Given the paucity of therapeutic options with demonstrated benefit for acute outcomes, these results are not surprising. To date, no placebo-controlled trial in acute heart failure has shown a short-term survival benefit or decreased hospitalizations.14,15 Identifying therapeutic approaches that improve acute outcomes should remain a top priority.

Chronic outcomes were similarly poor. During 1 year of follow-up, more than 1 in 3 Medicare beneficiaries died and two thirds were readmitted. Nearly 40% of patients were admitted at least twice. At first glance, these findings may seem surprising, given the demonstrated survival benefit associated with ACE inhibitors and beta-blockers in clinical trials of patients with heart failure.16 Several factors likely explain the discrepancy. First, clinical trials often exclude elderly patients,16 and databases in which to examine the effectiveness of therapies for treating heart failure in the elderly are limited. There is evidence, however, that patients who may benefit the most from beta-blockers, ACE inhibitors, and angiotensin receptor blockers (ARBs) may be least likely to receive them. Compared to high-risk patients, Lee et al17 found that low-risk patients were more likely to receive these therapies after controlling for survival time and potential contraindications.

Second, the analysis population, which was 60% women and had a mean age of 80 years, may disproportionately represent patients with preserved systolic function,18 and the evidence base for these patients is limited.1921 Moreover, the use of evidence-based therapies in patients with systolic dysfunction is suboptimal. An analysis of the Medicare Current Beneficiary Survey suggests that the prevalence of ACE inhibitor or ARB use was only 50% among beneficiaries with congestive heart failure.22 Smith et al23 found that the prevalence of beta-blocker use after onset of congestive heart failure increased by 2.4 percentage points per year from 1989 through 2000, but the prevalence was only 29% in 2000. Even among patients with low ejection fraction, the prevalence of beta-blocker use was only 43%.

Other findings are also noteworthy. Cardiovascular and respiratory DRGs dominated readmissions, but renal failure and gastrointestinal hemorrhage were not uncommon. Moreover, only a quarter of readmissions were specifically for heart failure. Consistent with the high prevalence of comorbid conditions at baseline, the readmissions likely reflect the high burden of coexisting disease in patients with heart failure. As work by Setoguchi et al24 has shown, the number of heart failure hospitalizations is an important predictor of mortality. Strategies designed to reduce readmissions must reflect the clinical complexity of patients with heart failure. Second, as shown in an earlier analysis,25 we found that black Medicare patients were less likely than other patients to die in the year after the index admission but more likely to be hospitalized. The data do not allow us to explore possible explanatory factors, including medication adherence,26 symptom recognition,27 and socioeconomic status.28

Third, the volume of heart failure discharges at the hospital level was significantly related to mortality and readmission, but the magnitude of the effect was small. Birkmeyer et al29 found a strong and significant relationship between hospital volume and short-term mortality in several surgical cohorts. More recent evidence suggests that a strong volume–outcome relationship exists in inpatient care for patients with stroke.30 In some ways, the modest volume–outcome relationship we observed is unsurprising. With a high burden of coexisting illness, patients with heart failure are often cared for by multiple specialists in a heterogeneous hospital service, and coordination of such care can be challenging. Moreover, the modest relationship may reflect unmeasured clinical heterogeneity and substantial variation in processes of care.

Combined with an analysis of Medicare beneficiaries hospitalized for heart failure in the 1990s,9,31 our findings suggest that survival following an index hospitalization for heart failure has changed little in 13 years. Kosiborod et al9 reported 30-day mortality of 10% to 11% and 1-year mortality of 32% from 1992 through 1999. Similarly, in an analysis of data from the National Heart Failure Project, a quality-of-care initiative for Medicare beneficiaries hospitalized with heart failure in the late 1990s, Rathore et al32 found 1-year mortality of 36%.

Consistent with the 1-year readmission rates from the National Heart Failure Project,33 we found that two thirds of patients were readmitted within a year of the index hospitalization. It is noteworthy that the 30-day and 6-month readmission rates were markedly higher than those reported by Kosiborod et al.9 Specifically, Kosiborod et al9 found 30-day all-cause readmission rates ranging from 10.2% to 13.8%, whereas we found an all-cause readmission rate of about 23%. However, the rates reported by Kosiborod et al9 are similar to the cardiovascular readmission rates we report (about 13%). The source of this difference is unclear.

Our study has some limitations. First, we relied on ICD-9-CM diagnosis codes from Medicare claims data, not medical chart review, to identify index heart failure admissions.33 Previous studies suggest that a single inpatient diagnosis of heart failure (ICD-9-CM code 428.XX, 402.X1, 404.x1, or 404.X3) has greater than 95% specificity for the diagnosis of heart failure.3436 Second, data regarding left ventricular function are not available in claims, so we were unable to differentiate between systolic heart failure and diastolic heart failure. ICD-9-CM diagnosis codes specific to diastolic heart failure were introduced in 2003, but the codes have not yet been validated. Third, the results may not generalize to all patients with heart failure. The analysis population consisted of elderly patients with a high prevalence of comorbid illness, and important clinical data were not available (eg, blood pressure, blood urea nitrogen count, and serum creatinine at admission). However, because the analysis included 100% of Medicare fee-for-service beneficiaries who were admitted with a principal diagnosis of heart failure, the findings are representative of a large population of relevant patients. Fourth, information regarding adherence to evidence-based guidelines and performance measures are not available in these data, so we cannot ascertain the extent of important treatment gaps. Finally, claims data are not available during periods of managed care coverage, so we may have underestimated readmission rates to the extent that fee-for-service beneficiaries switched to managed care and were subsequently hospitalized.

In conclusion, in this longitudinal analysis of Medicare claims for 100% of inpatient, fee-for-service admissions from 2001 through 2005, we found that the prognosis for elderly patients with heart failure was poor and improved little over time. Medicare beneficiaries comprise a large majority of heart failure patients, so these findings are highly representative of the heart failure population in the United States. Heart failure is a leading cause of hospitalization of Medicare beneficiaries and will likely remain so with the aging of the Medicare population. The need to identify optimal management strategies for these clinically complex patients is urgent.

Acknowledgments

Financial Disclosures: Dr Curtis reported receiving research and salary support from Allergan Pharmaceuticals, GlaxoSmithKline, Lilly, Medtronic, Novartis, Ortho Biotech, OSI Eyetech, Pfizer, and Sanofi-Aventis. Dr Curtis has made available online a detailed listing of financial disclosures (http://www.dcri.duke.edu/research/coi.jsp). Dr. Schulman reports receiving research and/or salary support from Actelion, Allergan, Amgen, Arthritis Foundation, Astellas Pharma, Bristol-Myers Squibb, The Duke Endowment, Genentech, Inspire Pharmaceuticals, Johnson & Johnson, Kureha Corporation, LifeMasters Supported SelfCare, Medtronic, Merck, Nabi Biopharmaceuticals, National Patient Advocate Foundation, North Carolina Biotechnology Center, Novartis, OSI Eyetech, Pfizer, Roche, Sanofi-Aventis, Schering-Plough, Scios, Tengion, Theravance, Thomson Healthcare, Vertex Pharmaceuticals, Wyeth, and Yamanouchi USA Foundation; receiving personal income for consulting from Avalere Health, LifeMasters Supported SelfCare, McKinsey & Company, and the National Pharmaceutical Council; having equity in Alnylam Pharmaceuticals; having equity in and serving on the board of directors of Cancer Consultants, Inc; and having equity in and serving on the executive board of Faculty Connection, LLC. Dr Schulman has made available online a detailed listing of financial disclosures (http://www.dcri.duke.edu/research/coi.jsp). Dr Hernandez reported receiving research grants from Scios, Medtronic, GlaxoSmithKline, and Roche Diagnostics; and serving on the speaker’s bureau or receiving honoraria in the past 5 years from Novartis. Ms Greiner, Mr Hammill, and Dr Whellan did report any disclosures.

Funding/Support: This study was funded in part by grant 1R01AG026038-01A1 from the National Institute on Aging; grant 5U01HL66461-05 from the National Heart, Lung, and Blood Institute; grant U18HS10548 from the Agency for Healthcare Research and Quality; and a research agreement between Medtronic, Inc, and Duke University.

Footnotes

Author Contributions: Dr Curtis had full access to all of 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: Curtis, Greiner, Hernandez. Acquisition of data: Curtis, Greiner. Analysis and interpretation of data: Curtis, Greiner, Hammill, Kramer, Whellan, Schulman, Hernandez. Drafting of the manuscript: Curtis, Greiner. Critical revision of the manuscript for important intellectual content: Curtis, Greiner, Hammill, Kramer, Whellan, Schulman, Hernandez. Statistical expertise: Greiner, Hammill. Obtained funding: Curtis, Schulman, Hernandez. Administrative, technical, or material support: Greiner. Study supervision: Curtis, Hernandez.

Role of the Sponsor: The funding bodies had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Publisher's Disclaimer: This project was supported by grant number U18HS010548 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.

Additional Contributions: We thank Damon M. Seils, MA, of Duke University for editorial assistance and manuscript preparation. Mr Seils did not receive compensation for his assistance apart from his employment at the institution where the study was conducted.

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