Can a machine learning model accurately predict heart failure hospitalization and cardiovascular death in patients with HFpEF?
A machine learning model using 58 variables successfully predicted heart failure hospitalization and cardiovascular death in HFpEF patients with high accuracy (AUC 86.9%).
Background: Few studies have incorporated echocardiography and laboratory data to predict clinical outcomes in heart failure with preserved ejection fraction (HFpEF). Objectives: This study aimed to use machine learning to find predictors of heart failure (HF) hospitalization and cardiovascular (CV) death in HFpEF. Methods: From the Chang Gung Research Database in Taiwan, 6,092 HFpEF patients (2,898 derivation, 3,194 validation) identified between 2008 and 2017 were followed until 2019. A random survival forest model, using 58 variables, was developed to predict the composite outcome of HF hospitalization and CV death. Results: ) level of <6% or ≥8%. The random survival forest model demonstrated robust external generalizability with an 86.9% area under curve in validation. Conclusions: Machine learning identified 15 predictors of HF hospitalization and CV death in HFpEF patients, helping doctors identify high-risk individuals for tailored treatment.
Chang et al. (Tue,) studied this question.
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