Machine Learning for Mortality Prediction in Patients With Heart Failure With Mildly Reduced Ejection Fraction

作者：Pengchao Tian, Lin Liang, Xuemei Zhao, Boping Huang, Jiayu Feng, Liyan Huang, Yan Huang, Mei Zhai, Qiong Zhou, Jian Zhang, Yuhui Zhang · 发表于：Journal of the American Heart Association · 年份：2023 · DOI：10.1161/jaha.122.029124 · 被引用次数：17 · 研究领域：Heart Failure Treatment and Management、Cardiovascular Function and Risk Factors、Cardiac Valve Diseases and Treatments

Background Machine-learning-based prediction models (MLBPMs) have shown satisfactory performance in predicting clinical outcomes in patients with heart failure with reduced and preserved ejection fraction. However, their usefulness has yet to be fully elucidated in patients with heart failure with mildly reduced ejection fraction. This pilot study aims to evaluate the prediction performance of MLBPMs in a heart failure with mildly reduced ejection fraction cohort with long-term follow-up data. Methods and Results A total of 424 patients with heart failure with mildly reduced ejection fraction were enrolled in our study. The primary outcome was all-cause mortality. Two feature selection strategies were introduced for MLBPM development. The "All-in" (67 features) strategy was based on feature correlation, multicollinearity, and clinical significance. The other strategy was the CoxBoost algorithm with 10-fold cross-validation (17 features), which was based on the selection result of the "All-in" strategy. Six MLBPMs with 5-fold cross-validation based on the "All-in" and the CoxBoost algorithm with 10-fold cross-validation strategy were developed by the eXtreme Gradient Boosting, random forest, and support vector machine algorithms. The logistic regression model with 14 benchmark predictors was used as a reference model. During a median follow-up of 1008 (750, 1937) days, 121 patients met the primary outcome. Overall, MLBPMs outperformed the logistic model. The "All-in" eXtreme G...