Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post-hoc analysis of the WARCEF trial.

ABSTRACT:

Background

The prediction of ischaemic stroke in patients with heart failure with reduced ejection fraction (HFrEF) but without atrial fibrillation (AF) remains challenging. Our aim was to evaluate the performance of machine learning (ML) in identifying the development of ischaemic stroke in this population.

Methods

We performed a post-hoc analysis of the WARCEF trial, only including patients without a history of AF. We evaluated the performance of 9 ML models for identifying incident stroke using metrics including area under the curve (AUC) and decision curve analysis. The importance of each feature used in the model was ranked by SAPley Additive exPlanations (SHAP) values.

Results

We included 2213 patients with HFrEF but without AF (mean age 58 ± 11 years; 80% male)

SUBMITTER: Ishiguchi H 

PROVIDER: S-EPMC11810539 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature