title: 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).
abstract: OBJECTIVE
  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.
authors.affiliation: Division of Cardiology, The Ohio State University, Columbus, Ohio, USA. William.Abraham@osumc.edu
authors.forename: William T
authors.initials: WT
authors.lastname: Abraham
authors.forename: Gregg C
authors.initials: GC
authors.lastname: Fonarow
authors.forename: Nancy M
authors.initials: NM
authors.lastname: Albert
authors.forename: Wendy Gattis
authors.initials: WG
authors.lastname: Stough
authors.forename: Mihai
authors.initials: M
authors.lastname: Gheorghiade
authors.forename: Barry H
authors.initials: BH
authors.lastname: Greenberg
authors.forename: Christopher M
authors.initials: CM
authors.lastname: O'Connor
authors.forename: Jie Lena
authors.initials: JL
authors.lastname: Sun
authors.forename: Clyde W
authors.initials: CW
authors.lastname: Yancy
authors.forename: James B
authors.initials: JB
authors.lastname: Young
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D001794:Blood Pressure; D003404:Creatinine / Q000097:blood; D005260:Female; D006333:Heart Failure / Q000401:mortality*; D017052:Hospital Mortality; D006760:Hospitalization; D006801:Humans; D008297:Male; D049451:Nomograms; D012044:Regression Analysis; D018570:Risk Assessment
chemical_list: D003404:Creatinine