{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:18:20Z","timestamp":1787008700567,"version":"build-2736575974"},"reference-count":6,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005970","name":"German Foundation of Heart Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005970","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008497","name":"Boston Scientific Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008497","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Risk stratification and individual risk prediction play a key role in making treatment decisions in patients with complex coronary artery disease (CAD). The aim of this study was to assess whether machine learning (ML) algorithms can improve discriminative ability and identify unsuspected, but potentially important, factors in the prediction of long-term mortality following percutaneous coronary intervention or coronary artery bypass grafting in patients with complex CAD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>To predict long-term mortality, the ML algorisms were applied to the SYNTAXES database with 75 pre-procedural variables including demographic and clinical factors, blood sampling, imaging, and patient-reported outcomes. The discriminative ability and feature importance of the ML model was assessed in the derivation cohort of the SYNTAXES trial using a 10-fold cross-validation approach. The ML model showed an acceptable discrimination (area under the curve = 0.76) in cross-validation. C-reactive protein, patient-reported pre-procedural mental status, gamma-glutamyl transferase, and HbA1c were identified as important variables predicting 10-year mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The ML algorithms disclosed unsuspected, but potentially important prognostic factors of very long-term mortality among patients with CAD. A \u2018mega-analysis\u2019 based on large randomized or non-randomized data, the so-called \u2018big data\u2019, may be warranted to confirm these findings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Trial Registration<\/jats:title>\n                    <jats:p>SYNTAXES ClinicalTrials.gov reference: NCT03417050, SYNTAX ClinicalTrials.gov reference: NCT00114972<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztad014","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T06:22:08Z","timestamp":1677565328000},"page":"275-278","source":"Crossref","is-referenced-by-count":9,"title":["Can machine learning unravel unsuspected, clinically important factors predictive of long-term mortality in complex coronary artery disease? A call for \u2018big data\u2019"],"prefix":"10.1093","volume":"4","author":[{"given":"Kai","family":"Ninomiya","sequence":"first","affiliation":[{"name":"Department of Cardiology, National University of Ireland, Galway (NUIG) , University Road, Galway H91 TK33 , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigetaka","family":"Kageyama","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University of Ireland, Galway (NUIG) , University Road, Galway H91 TK33 , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8911-0278","authenticated-orcid":false,"given":"Scot","family":"Garg","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Royal Blackburn Hospital , Blackburn , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinichiro","family":"Masuda","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University of Ireland, Galway (NUIG) , University Road, Galway H91 TK33 , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nozomi","family":"Kotoku","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University of Ireland, Galway (NUIG) , University Road, Galway H91 TK33 , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pruthvi C","family":"Revaiah","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University of Ireland, Galway (NUIG) , University Road, Galway H91 TK33 , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neil","family":"O\u2019leary","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University of Ireland, Galway (NUIG) , University Road, Galway H91 TK33 , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshinobu","family":"Onuma","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University of Ireland, Galway (NUIG) , University Road, Galway H91 TK33 , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9636-1104","authenticated-orcid":false,"given":"Patrick W","family":"Serruys","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University of Ireland, Galway (NUIG) , University Road, Galway H91 TK33 , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the SYNTAX Extended Survival Investigators","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"2023060100332340400_ztad014-B1","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.1136\/bmj.k4245","article-title":"Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects","volume":"363","author":"Kent","year":"2018","journal-title":"BMJ"},{"key":"2023060100332340400_ztad014-B2","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1016\/S0140-6736(20)32114-0","article-title":"Redevelopment and validation of the SYNTAX score II to individualise decision making between percutaneous and surgical revascularisation in patients with complex coronary artery disease: secondary analysis of the multicentre randomised controlled SYNTAXES trial with external cohort validation","volume":"396","author":"Takahashi K, Serruys PW, Fuster V, Farkouh ME, Spertus JA, Cohen DJ","year":"2020","journal-title":"Lancet"},{"key":"2023060100332340400_ztad014-B3","doi-asserted-by":"crossref","first-page":"44","DOI":"10.4244\/EIJ-D-19-00543A","article-title":"The SYNTAX score on its way out or \u2026 towards artificial intelligence: part I","volume":"16","author":"Serruys PW, Chichareon P, Modolo R, Leaman DM, Reiber JHC, Emanuelsson H","year":"2020","journal-title":"EuroIntervention"},{"key":"2023060100332340400_ztad014-B4","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1016\/S0140-6736(19)31997-X","article-title":"Percutaneous coronary intervention versus coronary artery bypass grafting in patients with three-vessel or left main coronary artery disease: 10-year follow-up of the multicentre randomised controlled SYNTAX trial","volume":"394","author":"Thuijs DJFM, Kappetein AP, Serruys PW, Mohr FW, Morice MC, Mack 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