Author + information
- Received February 25, 2008
- Revision received April 14, 2008
- Accepted April 22, 2008
- Published online July 29, 2008.
- William T. Abraham, MD, FACP, FACC⁎,⁎ (, )
- Gregg C. Fonarow, MD, FACC†,
- Nancy M. Albert, PhD, RN‡,
- Wendy Gattis Stough, PharmD§,
- Mihai Gheorghiade, MD∥,
- Barry H. Greenberg, MD, FACC¶,
- Christopher M. O'Connor, MD, FACC#,
- Jie Lena Sun, MS⁎⁎,
- Clyde W. Yancy, MD, FACC††,
- James B. Young, MD, FACC‡‡,
- OPTIMIZE-HF Investigators and Coordinators
- ↵⁎Reprint requests and correspondence:
Dr. William T. Abraham, Director, Division of Cardiovascular Medicine, The Ohio State University Medical Center, Room 110P Davis Heart and Lung Research Institute, 473 West 12th Avenue, Columbus, Ohio 43210.
Objectives The aim of this study was to develop a clinical model predictive of in-hospital mortality in a broad hospitalized heart failure (HF) patient population.
Background Heart failure patients experience high rates of hospital stays and poor outcomes. Although predictors of mortality have been identified in HF clinical trials, hospitalized patients might differ greatly from trial populations, and such predictors might underestimate mortality in a real-world population.
Methods The OPTIMIZE-HF (Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure) is a registry/performance improvement program for patients hospitalized with HF in 259 U.S. hospitals. Forty-five potential predictor variables were used in a stepwise logistic regression model for in-hospital mortality. Continuous variables that did not meet linearity assumptions were transformed. All significant variables (p < 0.05) were entered into multivariate analysis. Generalized estimating equations were used to account for the correlation of data within the same hospital in the adjusted models.
Results Of 48,612 patients enrolled, mean age was 73.1 years, 52% were women, 74% were Caucasian, and 46% had ischemic etiology. Mean left ventricular ejection fraction was 0.39 ± 0.18. In-hospital mortality occurred in 1,834 (3.8%). Multivariable predictors of mortality included age, heart rate, systolic blood pressure (SBP), sodium, creatinine, HF as primary cause of hospitalization, and presence/absence of left ventricular systolic dysfunction. A scoring system was developed to predict mortality.
Conclusions Risk of in-hospital mortality for patients hospitalized with HF remains high and is increased in patients who are older and have low SBP or sodium levels and elevated heart rate or creatinine at admission. Application of this risk-prediction algorithm might help identify patients at high risk for in-hospital mortality who might benefit from aggressive monitoring and intervention. (Organized Program to Initiate Lifesaving Treatment In Hospitalized Patients With Heart Failure [OPTIMIZE-HF]; NCT00344513)
The OPTIMIZE-HF registry and this study were funded by GlaxoSmithKline, Philadelphia, Pennsylvania. GlaxoSmithKline funded the OPTIMIZE-HF registry under the guidance of the OPTIMIZE-HF Steering Committee and funded data collection and management by Outcome Sciences, Inc., analysis of registry data at Duke Clinical Research Institute (DCRI), and administrative and material support by Accel Health, New York. GlaxoSmithKline was involved in the design and conduct of the OPTIMIZE-HF registry and funded data collection and management through Outcome Sciences, Inc., and data management and statistical analyses through DCRI. The sponsor was not involved in the management, analysis, or interpretation of data or the preparation of the manuscript. GlaxoSmithKline did review the manuscript before submission. For full author disclosures, please see the end of this paper. Carl Pepine, MD, served as Guest Editor for this article.
- Received February 25, 2008.
- Revision received April 14, 2008.
- Accepted April 22, 2008.
- American College of Cardiology Foundation