• Hironori Ishiguchi, Yang Chen, Bi Huang, Ying Gue, Elon Correa, Shunichi Homma, ThompsonJohn L PJLPColumbia University Medical Center, New York, USA., Min Qian, LipGregory Y HGYHLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.Danish Centre for Health Services Research, Department of Clinical Medicine, Aalb, and Azmil H Abdul-Rahim.
• Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
• Eur. J. Clin. Invest. 2025 Mar 1; 55 (3): e14360e14360.

BackgroundThe 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.MethodsWe 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.ResultsWe included 2213 patients with HFrEF but without AF (mean age 58 ± 11 years; 80% male). Of these, 74 (3.3%) had an ischaemic stroke in sinus rhythm during a mean follow-up of 3.3 ± 1.8 years. Out of the 29 patient-demographics variables, 12 were selected for the ML training. Almost all ML models demonstrated high AUC values, outperforming the CHA2DS2-VASc score (AUC: 0.643, 95% confidence interval [CI]: 0.512-0.767). The Support Vector Machine (SVM) and XGBoost models achieved the highest AUCs, with 0.874 (95% CI: 0.769-0.959) and 0.873 (95% CI: 0.783-0.953), respectively. The SVM and LightGBM consistently provided significant net clinical benefits. Key features consistently identified across these models were creatinine clearance (CrCl), blood urea nitrogen (BUN) and warfarin use.ConclusionsMachine-learning models can be useful in identifying incident ischaemic strokes in patients with HFrEF but without AF. CrCl, BUN and warfarin use were the key features.© 2024 The Author(s). European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation.

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