Factors related to morbidity and mortality in patients with chronic heart failure with systolic dysfunction: the HF-ACTION predictive risk score model.

BACKGROUND: We aimed to develop a multivariable statistical model for risk stratification in patients with chronic heart failure with systolic dysfunction, using patient data that are routinely collected and easily obtained at the time of initial presentation. METHODS AND RESULTS: In a cohort of 2331 patients enrolled in the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise TraiNing) study (New York Heart Association class II-IV, left ventricular ejection fraction ≤0.35, randomized to exercise training and usual care versus usual care alone, median follow-up of 2.5 years), we performed risk modeling using Cox proportional hazards models and analyzed the relationship between baseline clinical factors and the primary composite end point of death or all-cause hospitalization and the secondary end point of all-cause death alone. Prognostic relationships for continuous variables were examined using restricted cubic spline functions, and key predictors were identified using a backward variable selection process and bootstrapping methods. For ease of use in clinical practice, point-based risk scores were developed from the risk models. Exercise duration on the baseline cardiopulmonary exercise test was the most important predictor of both the primary end point and all-cause death. Additional important predictors for the primary end point risk model (in descending strength) were Kansas City Cardiomyopathy Questionnaire symptom stability score, higher serum urea nitrogen, and male sex (all P<0.0001). Important additional predictors for the mortality risk model were higher serum urea nitrogen, male sex, and lower body mass index (all P<0.0001). CONCLUSIONS: Risk models using simple, readily obtainable clinical characteristics can provide important prognostic information in ambulatory patients with chronic heart failure with systolic dysfunction. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00047437.

Duke Scholars

Author William Erle Kraus Medicine, Cardiology

Author Christopher Michael O'Connor Medicine, Clinical Pharmacology

Author Mona Fiuzat Medicine, Clinical Pharmacology

Author Kerry L. Lee Biostatistics & Bioinformatics, Division of Biostatistics