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

Author:

Tian Pengchao1ORCID,Liang Lin1ORCID,Zhao Xuemei1ORCID,Huang Boping1ORCID,Feng Jiayu1,Huang Liyan1ORCID,Huang Yan1,Zhai Mei1,Zhou Qiong1,Zhang Jian12ORCID,Zhang Yuhui1ORCID

Affiliation:

1. Heart Failure Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science, Peking Union Medical College Beijing China

2. Key Laboratory of Clinical Research for Cardiovascular Medications, National Health Committee Beijing China

Abstract

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 Gradient Boosting model had the best performance, with an accuracy of 85.4% and a precision of 70.3%. The area under the receiver‐operating characteristic curve was 0.916 (95% CI, 0.887–0.945). The Brier score was 0.12. Conclusions The MLBPMs could significantly improve outcome prediction in patients with heart failure with mildly reduced ejection fraction, which would further optimize the management of these patients.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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