Multistate Model to Predict Heart Failure Hospitalizations and All-Cause Mortality in Outpatients With Heart Failure With Reduced Ejection Fraction: Model Derivation and External Validation. (August 2016) - Upshaw, Jenica N.; Konstam, Marvin A.; Klaveren, David van; Noubary, Farzad; Huggins, Gordon S.; Kent, David M.
Abstract : Background—: Outpatients with heart failure (HF) who are at high risk for HF hospitalization and death may benefit from early identification. We sought to develop and externally validate a model to predict both HF hospitalization and mortality that accounts for the semicompeting nature of the 2 outcomes and captures the risk associated with the transition from the stable outpatient state to the post-HF hospitalization state. Methods and Results—: A multistate model to predict HF hospitalization and all-cause mortality was derived using data (n=3834) from the HEAAL study (Heart Failure Endpoint evaluation of Angiotensin II Antagonist Losartan), a multinational randomized trial in symptomatic patients with reduced left ventricular ejection fraction. Twelve easily and reliably obtainable demographic and clinical predictors were prespecified for model inclusion. Model performance was assessed in the SCD-HeFT cohort (Sudden Cardiac Death in Heart Failure Trial; n=2521). At 1 year, the probability of being alive without HF hospitalization was 94% for a typical patient in the lowest risk quintile and 77% for a typical patient in the highest risk quintile and this variability in risk continued through 7 years of follow-up. The model c-index was 0.72 in the derivation cohort, 0.66 in the validation cohort, and 0.69 in the implantable cardiac defibrillator arm of the validation cohort. There was excellent calibration across quintiles of predicted risk. Conclusions—: Our findings illustrate the advantages of a multistate modeling approach, providing estimates of HF hospitalization and death in the same model, comparison of predictors for the different outcomes and demonstrating the different trajectories of patients based on baseline characteristics and intermediary events. Clinical Trial Registration—: URL:http://www.clinicaltrials.gov . Unique identifiers: NCT00000609 and NCT00090259. Abstract : Supplemental Digital Content is available in the text.