{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T23:10:52Z","timestamp":1777331452139,"version":"3.51.4"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>Patients with atrial fibrillation (AF) have a higher risk of ischaemic stroke and death. While anticoagulants are effective at reducing these risks, they increase the risk of bleeding. Current clinical risk scores only perform modestly in predicting adverse outcomes, especially for the outcome of death. We aimed to test the multi-label gradient boosting decision tree (ML-GBDT) model in predicting risks for adverse outcomes in a prospective global AF registry.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>We studied patients from phase II\/III of the Global Registry on Long-Term Oral Anti-Thrombotic Treatment in Patients with Atrial Fibrillation registry between 2011 and 2020. The outcomes were all-cause death, ischaemic stroke, and major bleeding within 1 year following the AF. We trained the ML-GBDT model and compared its discrimination with the clinical scores in predicting patient outcomes. A total of 25 656 patients were included [mean age 70.3 years (SD 10.3); 44.8% female]. Within 1 year after AF, ischaemic stroke occurred in 215 (0.8%), major bleeding in 405 (1.6%), and death in 897 (3.5%) patients. Our model achieved an optimized area under the curve in predicting death (0.785, 95% CI: 0.757\u20130.813) compared with the Charlson Comorbidity Index (0.747, P = 0.007), ischaemic stroke (0.691, 0.626\u20130.756) compared with CHA2DS2-VASc (0.613, P = 0.028), and major bleeding (0.698, 0.651\u20130.745) as opposed to HAS-BLED (0.607, P = 0.002), with improvement in net reclassification index (10.0, 12.5, and 23.6%, respectively).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The ML-GBDT model outperformed clinical risk scores in predicting the risks in patients with AF. This approach could be used as a single multifaceted holistic tool to optimize patient risk assessment and mitigate adverse outcomes when managing AF.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztae010","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T15:47:27Z","timestamp":1708444047000},"page":"235-246","source":"Crossref","is-referenced-by-count":12,"title":["Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study"],"prefix":"10.1093","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7508-0565","authenticated-orcid":false,"given":"Juan","family":"Lu","sequence":"first","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital , Thomas Drive, Liverpool L14 3PE , UK"},{"name":"Medical School, The University of Western Australia , 35 Stirling Hwy, Crawley WA 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Australia , 35 Stirling Hwy, Crawley WA 6009 , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tom","family":"Briffa","sequence":"additional","affiliation":[{"name":"School of Population and Global Health, The University of Western Australia , Perth , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brendan","family":"McQuillan","sequence":"additional","affiliation":[{"name":"Medical School, The University of Western Australia , 35 Stirling Hwy, Crawley WA 6009 , Australia"},{"name":"Sir Charles Gairdner Hospital , Perth , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathon","family":"Stewart","sequence":"additional","affiliation":[{"name":"Medical School, The University of Western Australia , 35 Stirling Hwy, Crawley WA 6009 , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gemma","family":"Figtree","sequence":"additional","affiliation":[{"name":"Kolling Institute and Charles Perkins Centre, University of Sydney , Sydney , Australia"},{"name":"Department of Cardiology, Royal North Shore Hospital , Sydney , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Menno V","family":"Huisman","sequence":"additional","affiliation":[{"name":"Department of Thrombosis and Hemostasis Leiden University Medical Center , Leiden , The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0717-740X","authenticated-orcid":false,"given":"Girish","family":"Dwivedi","sequence":"additional","affiliation":[{"name":"Medical School, The University of Western Australia , 35 Stirling Hwy, Crawley WA 6009 , Australia"},{"name":"Harry Perkins Institute of Medical Research , 5 Robin Warren Dr, Murdoch WA 6150 , Australia"},{"name":"Department of Cardiology, Fiona Stanley Hospital , Perth , 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