Elsevier

American Heart Journal

Volume 200, June 2018, Pages 127-133
American Heart Journal

Clinical Investigations
A Hospital Level Analysis of 30-Day Readmission Performance for Heart Failure Patients and Long-Term Survival: Findings from Get With The Guidelines-Heart Failure

https://doi.org/10.1016/j.ahj.2017.11.018Get rights and content

Abstract

Background

Medicare utilizes 30-day risk-standardized readmission rates (RSRR) as a measure of hospital quality and applies penalties based on this measure. The objective of this study was to identify the relationship between hospital performance on 30-day RSRR in heart failure (HF) patients and long-term patient survival.

Methods

Data were collected from Get With The Guidelines (GWTG)-HF and linked with Medicare data. Based on hospital performance for 30-day RSRR, hospitals were grouped into performance quartiles: top 25% (N=11,181), 25-50% (N=10,367), 50-75% (N=8729), and bottom 25% (N=7180). The primary outcome was mortality at 3 years applying Cox proportional hazards regression adjusted for patient and hospital characteristics.

Results

The overall 30-day readmission rate was 19.8% and the 3-year mortality rates were 61.8%, 61.0%, 62.6%, and 59.9% for top 25%, 25-50%, 50-75%, and bottom 25% hospitals for 30-day RSRR performance, respectively. Compared to bottom 25% performing hospitals, adjusted hazard ratios (HR) for 3-year mortality were HR 0.96 (95% confidence interval [CI] 0.90-1.01), HR 0.89 (95% CI 0.84-0.94), HR 1.01 (95% CI 0.95-1.06) for the top 25%, 25-50% and 50-75% hospitals respectively. Median survival time was highest for the bottom 25% hospitals on the 30-day RSRR metric.

Conclusion

Hospital performance on 30-day readmissions in HF has no or little association with risk adjusted 3-year mortality or median survival. There is a compelling need to utilize more meaningful and patient-centered outcome measures for reporting and incentivizing quality care for HF.

Section snippets

Data source

The GWTG-HF database linked with CMS claims were the primary sources of the data. The design and objectives of the GWTG-HF registry have previously been described.11., 12., 13., 14. In brief, this registry includes more than 300 participating hospitals from each geographical region of the US. The registry includes teaching, non-teaching and community hospitals from both rural and urban areas, making it a comprehensive representation of the diversified US population of patients hospitalized with

Results

This study included 37,457 patients hospitalized with HF from 132 hospitals participating in GWTG-HF. The overall 30-day readmission rate was 19.8%. The quartiles for hospital performance on the 30-day RSRR measures were as follows: 11,181 patients were from hospitals which were in top 25% in their 30-day RSRR 14.9%-18.3%; 10,367 patients from 2nd quartile (25-50%) 30-day RSRR 18.3%-19.9%; 8729 patients in 3rd quartile (50-75%), 30-day RSSR 19.9%-21.3%; and 7189 patients in the bottom

Discussion

In this study of patients hospitalized with HF, we found no significant differences in risk-adjusted 3-year and median survival of HF patients admitted at the hospitals performing highest and lowest on 30-day RSRR. In unadjusted analyses, hospitals with the worst performance on the 30-day RSRR measure actually had HF patients with better median-survival duration and the lowest observed 3-year mortality when compared to the hospitals with best performance on the 30-day RSRR measure. There were

Conclusions

Hospital performance based on the CMS metric of 30-day RSRR in patients hospitalized for HF had no or little correlation with long-term survival of these patients. These finding raise concerns regarding additional limitations of the CMS HRRP and potential unintended consequences for Medicare beneficiaries. There is an important need to identify and utilize more meaningful and patient-centered outcome measures for reporting and incentivizing quality care for HF.

References (29)

  • J.H. Wasfy et al.

    Readmission Rates After Passage of the Hospital Readmissions Reduction Program: A Pre-Post Analysis

    Ann Intern Med

    (2016)
  • E.M. Bucholz et al.

    Life Expectancy after Myocardial Infarction, According to Hospital Performance

    N Engl J Med

    (2016)
  • Y. Hong et al.

    Overview of the American Heart Association “Get with the Guidelines” programs: coronary heart disease, stroke, and heart failure

    Crit Pathw Cardiol

    (2006)
  • R.D. Kociol et al.

    Are we targeting the right metric for heart failure? Comparison of hospital 30-day readmission rates and total episode of care inpatient days

    Am Heart J

    (2013)
  • Cited by (14)

    • Hospital-Level Medicaid Prevalence Is Associated with Increased Length of Stay after Asymptomatic Carotid Endarterectomy and Stenting Despite no Increase in Major Complications

      2021, Annals of Vascular Surgery
      Citation Excerpt :

      Hospitals treating a high proportion of Medicaid patients can have unique challenges with reducing LOS as Medicaid has been demonstrated in some studies to be an independent predictor for LOS in addition to perioperative complications.11–14 Patient resources, social support, overall health, postoperative care pathways, step-down unit availability and quality, and throughput of a hospital system can decrease hospital efficiency and affect LOS.11,15–17 However, previous analyses of hospital-level Medicaid prevalence have shown a high variability in the effects on outcomes and LOS across multiple surgical specialties.18–27

    • Early Unplanned Readmissions After Admission to Hospital With Heart Failure

      2019, American Journal of Cardiology
      Citation Excerpt :

      In terms of registries, the Get With The Guidelines-HF study only included a subset of hospitals in the United States which volunteer to take part and there is only linkage to Medicare inpatient data.12 For the readmissions study, there were only 130,146 patients eligible from 339 sites between 2005 and 2013 and 70% were excluded leaving 37,457 patients from 132 sites in the analysis.13 Our analysis represents complete national data which has advantages because of its size, representativeness and inclusion of all types of patients that present to hospital such as those with Medicaid, private and no health insurance.

    • Facilitators and barriers of heart failure care in Kerala, India: A qualitative analysis of health-care providers and administrators

      2019, Indian Heart Journal
      Citation Excerpt :

      These costs are related to direct medication expenses and recurrent hospitalizations, indirect costs associated with medicine-related travel and family care, and loss of economic productivity due to poor health. Economic analysis using data from the United States demonstrated 77% of lifetime costs of heart failure are accrued during hospitalizations, and the overall 30-day readmission rate among patients hospitalized for heart failure was almost 20%.27,28 A 2011 cross-sectional study of the microeconomic impact of cardiovascular disease hospitalization revealed a lack of health insurance was associated with nearly fourfold higher odds of catastrophic health spending in Trivandrum, India (odds ratio 3.93, 95% CI 2.23, 6.90).29

    View all citing articles on Scopus

    Javed Butler, MD, MPH served as guest editor for this article.

    Funding Sources: The American Heart Association provides the Get With The Guidelines Heart Failure program (GWTG-HF). GWTG-HF has been previously funded through support from Medtronic, GlaxoSmithKline, Ortho-McNeil, and the AHA Pharmaceutical Roundtable.

    Author Disclosures: Dr. Deepak L. Bhatt discloses the following relationships - Advisory Board: Cardax, Elsevier Practice Update Cardiology, Medscape Cardiology, Regado Biosciences; Board of Directors: Boston VA Research Institute, Society of Cardiovascular Patient Care; Chair: American Heart Association Quality Oversight Committee; Data Monitoring Committees: Cleveland Clinic, Duke Clinical Research Institute, Harvard Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine, Population Health Research Institute; Honoraria: American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org), Belvoir Publications (Editor in Chief, Harvard Heart Letter), Duke Clinical Research Institute (clinical trial steering committees), Harvard Clinical Research Institute (clinical trial steering committee), HMP Communications (Editor in Chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (Guest Editor; Associate Editor), Population Health Research Institute (clinical trial steering committee), Slack Publications (Chief Medical Editor, Cardiology Today’s Intervention), Society of Cardiovascular Patient Care (Secretary/Treasurer), WebMD (CME steering committees); Other: Clinical Cardiology (Deputy Editor), NCDR-ACTION Registry Steering Committee (Chair), VA CART Research and Publications Committee (Chair); Research Funding: Amarin, Amgen, AstraZeneca, Bristol-Myers Squibb, Chiesi, Eisai, Ethicon, Forest Laboratories, Ironwood, Ischemix, Lilly, Medtronic, Pfizer, Roche, Sanofi Aventis, The Medicines Company; Royalties: Elsevier (Editor, Cardiovascular Intervention: A Companion to Braunwald’s Heart Disease); Site Co-Investigator: Biotronik, Boston Scientific, St. Jude Medical (now Abbott); Trustee: American College of Cardiology; Unfunded Research: FlowCo, Merck, PLx Pharma, Takeda. Dr. Adam D. DeVore reports research support from the American Heart Association, Amgen, and Novartis and consulting with Novartis. Dr Gregg Fonarow reports research support from NIH, consulting with Abbott, Amgen, Novartis, and Medtronic, and serving as a GWTG Steering Committee member. All other authors have nothing to disclose.

    View full text