{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T23:31:58Z","timestamp":1787959918977,"version":"build-2784847793"},"reference-count":29,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Eur J Clin Investigation"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We performed a post\u2010hoc 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We included 2213 patients with HFrEF but without AF (mean age 58\u2009\u00b1\u200911\u2009years; 80% male). Of these, 74 (3.3%) had an ischaemic stroke in sinus rhythm during a mean follow\u2010up of 3.3\u2009\u00b1\u20091.8\u2009years. Out of the 29 patient\u2010demographics variables, 12 were selected for the ML training. Almost all ML models demonstrated high AUC values, outperforming the CHA<jats:sub>2<\/jats:sub>DS<jats:sub>2<\/jats:sub>\u2010VASc score (AUC: 0.643, 95% confidence interval [CI]: 0.512\u20130.767). The Support Vector Machine (SVM) and XGBoost models achieved the highest AUCs, with 0.874 (95% CI: 0.769\u20130.959) and 0.873 (95% CI: 0.783\u20130.953), 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Machine\u2010learning 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.<\/jats:p><\/jats:sec>","DOI":"10.1111\/eci.14360","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T10:45:24Z","timestamp":1731926724000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post\u2010hoc analysis of the <scp>WARCEF<\/scp> trial"],"prefix":"10.1111","volume":"55","author":[{"given":"Hironori","family":"Ishiguchi","sequence":"first","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool Liverpool John Moores University and Liverpool Heart &amp; Chest Hospital  Liverpool UK"},{"name":"Division of Cardiology, Department of Medicine and Clinical Science Yamaguchi University Graduate School of Medicine  Ube Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2808-6286","authenticated-orcid":false,"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool Liverpool John Moores University and Liverpool Heart &amp; Chest Hospital  Liverpool UK"},{"name":"Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences University of Liverpool  Liverpool UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bi","family":"Huang","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool Liverpool John Moores University and Liverpool Heart &amp; Chest Hospital  Liverpool UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9994-8915","authenticated-orcid":false,"given":"Ying","family":"Gue","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool Liverpool John Moores University and Liverpool Heart &amp; Chest Hospital  Liverpool UK"},{"name":"Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences University of Liverpool  Liverpool UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elon","family":"Correa","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool Liverpool John Moores University and Liverpool Heart &amp; Chest Hospital  Liverpool UK"},{"name":"School of Computer Science and Mathematics Liverpool John Moores University  Liverpool UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunichi","family":"Homma","sequence":"additional","affiliation":[{"name":"Columbia University Medical Center  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John L. 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