{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:17:37Z","timestamp":1782962257920,"version":"3.54.5"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100000664","name":"Korea Health Technology R&D","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000664","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003625","name":"Ministry of Health and Welfare","doi-asserted-by":"publisher","award":["HI22C0154"],"award-info":[{"award-number":["HI22C0154"]}],"id":[{"id":"10.13039\/501100003625","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>The outcomes of mitral valve replacement\/repair (MVR) in severe degenerative mitral regurgitation (MR) patients depend on various risk factors. We aimed to develop a risk prediction model for post-MVR mortality in severe degenerative MR patients using machine learning.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>Consecutive severe degenerative MR patients undergoing MVR were analysed (n = 1521; 70% training\/30% test sets). A random survival forest (RSF) model was constructed, with 3-year post-MVR all-cause mortality as the outcome. Partial dependency plots were used to define the thresholds of each risk factor. A simple scoring system (MVR-score) was developed to stratify post-MVR mortality risk. At 3 years following MVR, 90 patients (5.9%) died in the entire cohort (59 and 31 deaths in the training and test sets). The most important predictors of mortality in order of importance were age, haemoglobin, valve replacement, glomerular filtration rate, left atrial dimension, and left ventricular (LV) end-systolic diameter. The final RSF model with these six variables demonstrated high predictive performance in the test set (3-year C-index 0.880, 95% confidence interval 0.834\u20130.925), with mortality risk increased strongly with left atrial dimension &amp;gt;55 mm, and LV end-systolic diameter &amp;gt;45 mm. MVR-score demonstrated effective risk stratification and had significantly higher predictability compared to the modified Mitral Regurgitation International Database score (3-year C-index 0.803 vs. 0.750, P = 0.034).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>A data-driven machine learning model provided accurate post-MVR mortality prediction in severe degenerative MR patients. The outcome following MVR in severe degenerative MR patients is governed by both clinical and echocardiographic factors.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ehjci\/jead077","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T16:06:16Z","timestamp":1682697976000},"page":"1156-1165","source":"Crossref","is-referenced-by-count":6,"title":["Data-driven mortality risk prediction of severe degenerative mitral regurgitation patients undergoing mitral valve surgery"],"prefix":"10.1093","volume":"24","author":[{"given":"Soongu","family":"Kwak","sequence":"first","affiliation":[{"name":"Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital , 101, Daehak-ro, Jongno-gu, Seoul 03080 , South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung-Ah","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Department of Internal Medicine, Asan Medical 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