{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:24:44Z","timestamp":1792175084291,"version":"4.4.0"},"reference-count":29,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102022\u201000165404"],"award-info":[{"award-number":["RS\u20102022\u201000165404"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1A5A6000840"],"award-info":[{"award-number":["2022R1A5A6000840"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1I1A1A01073151"],"award-info":[{"award-number":["2020R1I1A1A01073151"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Cardiology"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Hypothesis<\/jats:title><jats:p>The recently introduced Bayesian quantile regression (BQR) machine\u2010learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel\u2010specific manner.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%\u201350% and 5.6%\u20137.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High\u2010density lipoprotein cholesterol showed a dynamic association along DS change in the per\u2010patient analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline\u2010grade CAD and its progression.<\/jats:p><\/jats:sec>","DOI":"10.1002\/clc.23964","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T10:43:30Z","timestamp":1674557010000},"page":"320-327","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Risk factors based vessel\u2010specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry"],"prefix":"10.1002","volume":"46","author":[{"given":"Hyung\u2010Bok","family":"Park","sequence":"first","affiliation":[{"name":"CONNECT\u2010AI Research Center, Yonsei University College of Medicine, Yonsei University Health System Seoul South Korea"},{"name":"Department of Cardiology Catholic Kwandong University International St. Mary's Hospital Incheon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-5474","authenticated-orcid":false,"given":"Jina","family":"Lee","sequence":"additional","affiliation":[{"name":"CONNECT\u2010AI Research Center, Yonsei University College of Medicine, Yonsei University Health System Seoul South Korea"},{"name":"Brain Korea 21 PLUS Project for Medical Science Yonsei University Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongtaek","family":"Hong","sequence":"additional","affiliation":[{"name":"CONNECT\u2010AI Research Center, Yonsei University College of Medicine, Yonsei University Health System Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"So","family":"Byungchang","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences Seoul National University Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wonse","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences Seoul National University Seoul South Korea"},{"name":"MetaEyes Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byoung K.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Cardiology Gangnam Severance Hospital, Yonsei University College of Medicine Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fay Y.","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Radiology New York\u2010Presbyterian Hospital and Weill 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