Author + information
- Received October 15, 2012
- Revision received January 22, 2013
- Accepted January 24, 2013
- Published online June 1, 2013.
- Zubin J. Eapen, MD∗∗ (, )
- Li Liang, PhD∗,
- Gregg C. Fonarow, MD†,
- Paul A. Heidenreich, MD, MS‡,
- Lesley H. Curtis, PhD∗,
- Eric D. Peterson, MD, MPH∗ and
- Adrian F. Hernandez, MD, MHS∗
- ↵∗Reprint requests and correspondence:
Dr. Zubin J. Eapen, Duke Clinical Research Institute, PO Box 17969, Durham, North Carolina 27715.
Objectives The study sought to derive and validate risk-prediction tools from a large nationwide registry linked with Medicare claims data.
Background Few clinical models have been developed utilizing data elements readily available in electronic health records (EHRs) to facilitate “real-time” risk estimation.
Methods Heart failure (HF) patients ≥65 years of age hospitalized in the GWTG-HF (Get With The Guidelines-Heart Failure) program were linked with Medicare claims from January 2005 to December 2009. Multivariable models were developed for 30-day mortality after admission, 30-day rehospitalization after discharge, and 30-day mortality/rehospitalization after discharge. Candidate variables were selected based on availability in EHRs and prognostic value. The models were validated in a 30% random sample and separately in patients with reduced and preserved ejection fraction (EF).
Results Among 33,349 patients at 160 hospitals, 3,002 (9.1%) died within 30 days of admission, 7,020 (22.8%) were rehospitalized within 30 days of discharge, and 8,374 (27.2%) died or were rehospitalized within 30 days of discharge. Compared with patients classified as low risk, high-risk patients had significantly higher odds of death (odds ratio [OR]: 8.82, 95% confidence interval [CI]: 7.58 to 10.26), rehospitalization (OR: 1.99, 95% CI: 1.86 to 2.13), and death/rehospitalization (OR: 2.65, 95% CI: 2.44 to 2.89). The 30-day mortality model demonstrated good discrimination (c-index 0.75) while the rehospitalization and death/rehospitalization models demonstrated more modest discrimination (c-indices of 0.59 and 0.62), with similar performance in the validation cohort and for patients with preserved and reduced EF.
Conclusions These predictive models allow for risk stratification of 30-day outcomes for patients hospitalized with HF and may provide a validated, point-of-care tool for clinical decision making.
This work was supported by an award from the American Heart Association Pharmaceutical Roundtable and David and Stevie Spina. The GWTG-HF (Get With The Guidelines-Heart Failure) program is provided by the American Heart Association. The GWTG-HF program is supported in part by Medtronic, Ortho-McNeil, and the American Heart Association Pharmaceuticals Roundtable. The GWTG-HF was funded in the past by GlaxoSmithKline. Dr. Eapen has received funding from an American Heart Association Pharmaceutical Roundtable outcomes training grant (0875142N). Dr. Fonarow is a significant consultant to Novartis and Gembro Research; and a modest consultant to Medtronic. Dr. Curtis has received support from Johnson & Johnson and GlaxoSmithKline. Dr. Peterson was co-principal investigator of the Data Analytic Center for the AHA GWTG Program; and has received research support from Eli Lilly and Janssen Pharmaceuticals. Dr. Hernandez has received research support from Johnson & Johnson, Amylin, and Portola Pharmaceuticals; and was co-principal investigator of the Data Analytic Center for AHA GWTG Program. All other authors have reported that they have no relationships relevant to the contents of this paper to report. Wayne Levy, MD, served as Guest Editor for this article.
- Received October 15, 2012.
- Revision received January 22, 2013.
- Accepted January 24, 2013.
- American College of Cardiology Foundation