Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction

OBJECTIVES This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial. BACKGROUND Although risk assessment models are available for patients with HF with reduced ejection fraction, few have assessed the risks of death and hospitalization in patients with HFpEF. METHODS The following 5 methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization for variable selection; random forest (RF); gradient descent boosting; and support vector machine, were used to train models for assessing risks of mortality and HF hospitalization through 3 years of follow-up and were validated using 5-fold cross-validation. Model discrimination and calibration were estimated using receiver-operating characteristic curves and Brier scores, respectively. The top prediction variables were assessed by using the best performing models, using the incremental improvement of each variable in 5-fold cross-validation. RESULTS The RF was the best performing model with a mean C-statistic of 0.72 (95% confidence interval [CI]: 0.69 to 0.75) for predicting mortality (Brier score: 0.17), and 0.76 (95% CI: 0.71 to 0.81) for HF hospitalization (Brier score: 0.19). Blood urea nitrogen levels, body mass index, and Kansas City Cardiornyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, whereas hemoglobin level, blood urea nitrogen, time since previous HF hospitalization, and KCCQ scores were the most significant predictors of HF hospitalization. CONCLUSIONS These models predict the risks of mortality and HF hospitalization in patients with HFpEF and emphasize the importance of health status data in determining prognosis. (C) 2020 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.