Ishiguchi, H, Chen, Y, Huang, B, Gue, Y, Correa, E, Homma, S, Thompson, JLP, Qian, M, Lip, GYH and Abdul-Rahim, AH (2024) Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post-hoc analysis of the WARCEF trial. European Journal of Clinical Investigation. pp. 1-12. ISSN 0014-2972

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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). 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. Conclusions: Machine-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.

Item Type:	Article
Uncontrolled Keywords:	heart failure with reduced ejection fraction; machine learning; stroke; Cerebrovascular; Cardiovascular; Heart Disease; Stroke; Brain Disorders; Machine Learning and Artificial Intelligence; Cardiovascular; Stroke; 1103 Clinical Sciences; General Clinical Medicine
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	Computer Science and Mathematics Nursing and Advanced Practice
Publisher:	Wiley
SWORD Depositor:	A Symplectic
Date Deposited:	02 Dec 2024 11:14
Last Modified:	02 Dec 2024 11:15
DOI or ID number:	10.1111/eci.14360
URI:	https://researchonline.ljmu.ac.uk/id/eprint/24957

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