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CLINICAL RESEARCH: HEART FAILURE

Predictors of In-Hospital Mortality in Patients Hospitalized for Heart Failure

Insights From the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure (OPTIMIZE-HF)

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 on behalf of the OPTIMIZE-HF Investigators and Coordinators

^_* Division of Cardiology, The Ohio State University, Columbus, Ohio
Department of Medicine, UCLA Medical Center, Los Angeles, California
George M. and Linda H. Kaufman Center for Heart Failure, Cleveland Clinic Foundation, Cleveland, Ohio
Department of Medicine, Duke University Medical Center, Durham, North Carolina, and Department of Clinical Research, Campbell University School of Pharmacy, Research Triangle Park, North Carolina
^|| Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
^¶ Department of Medicine, UCSD Medical Center, University of California, San Diego, California
^# Division of Cardiology, Duke University Medical Center/Duke Clinical Research Institute, Durham, North Carolina
^_** Duke Clinical Research Institute, Durham, North Carolina
Baylor University Medical Center, Dallas, Texas
Department of Cardiovascular Medicine, Heart Failure Section, Cleveland Clinic Foundation, Cleveland, Ohio.

Manuscript received February 25, 2008; revised manuscript received April 14, 2008, accepted April 22, 2008.

^_* 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. (Email: William.Abraham{at}osumc.edu).

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)

Key Words: age • heart failure • mortality risk • risk prediction algorithm • serum creatinine • systolic blood pressure

Abbreviations and Acronyms   CART = classification and regression tree   HF = heart failure   LVEF = left ventricular ejection fraction   LVSD = left ventricular systolic dysfunction   SBP = systolic blood pressure   SCr = serum creatinine

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