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Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry

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dc.contributor.author이병권-
dc.contributor.author장혁재-
dc.contributor.author한동희-
dc.date.accessioned2020-06-17T00:35:16Z-
dc.date.available2020-06-17T00:35:16Z-
dc.date.issued2020-03-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/176038-
dc.description.abstractBackground Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherWiley-Blackwell-
dc.relation.isPartOfJOURNAL OF THE AMERICAN HEART ASSOCIATION-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleMachine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorDonghee Han-
dc.contributor.googleauthorKranthi K Kolli-
dc.contributor.googleauthorSubhi J Al'Aref-
dc.contributor.googleauthorLohendran Baskaran-
dc.contributor.googleauthorAlexander R van Rosendael-
dc.contributor.googleauthorHeidi Gransar-
dc.contributor.googleauthorDaniele Andreini-
dc.contributor.googleauthorMatthew J Budoff-
dc.contributor.googleauthorFilippo Cademartiri-
dc.contributor.googleauthorKavitha Chinnaiyan-
dc.contributor.googleauthorJung Hyun Choi-
dc.contributor.googleauthorEdoardo Conte-
dc.contributor.googleauthorHugo Marques-
dc.contributor.googleauthorPedro de Araújo Gonçalves-
dc.contributor.googleauthorIlan Gottlieb-
dc.contributor.googleauthorMartin Hadamitzky-
dc.contributor.googleauthorJonathon A Leipsic-
dc.contributor.googleauthorErica Maffei-
dc.contributor.googleauthorGianluca Pontone-
dc.contributor.googleauthorGilbert L Raff-
dc.contributor.googleauthorSangshoon Shin-
dc.contributor.googleauthorYong-Jin Kim-
dc.contributor.googleauthorByoung Kwon Lee-
dc.contributor.googleauthorEun Ju Chun-
dc.contributor.googleauthorJi Min Sung-
dc.contributor.googleauthorSang-Eun Lee-
dc.contributor.googleauthorRenu Virmani-
dc.contributor.googleauthorHabib Samady-
dc.contributor.googleauthorPeter Stone-
dc.contributor.googleauthorJagat Narula-
dc.contributor.googleauthorDaniel S Berman-
dc.contributor.googleauthorJeroen J Bax-
dc.contributor.googleauthorLeslee J Shaw-
dc.contributor.googleauthorFay Y Lin-
dc.contributor.googleauthorJames K Min-
dc.contributor.googleauthorHyuk-Jae Chang-
dc.identifier.doi10.1161/JAHA.119.013958-
dc.contributor.localIdA02793-
dc.contributor.localIdA03490-
dc.contributor.localIdA04811-
dc.relation.journalcodeJ01774-
dc.identifier.eissn2047-9980-
dc.identifier.pmid32089046-
dc.subject.keywordcoronary artery disease-
dc.subject.keywordcoronary computed tomography angiography-
dc.subject.keywordmachine learning-
dc.subject.keywordplaque progression-
dc.subject.keywordrisk prediction-
dc.contributor.alternativeNameLee, Byoung Kwon-
dc.contributor.affiliatedAuthor이병권-
dc.contributor.affiliatedAuthor장혁재-
dc.contributor.affiliatedAuthor한동희-
dc.citation.volume9-
dc.citation.number5-
dc.citation.startPagee013958-
dc.identifier.bibliographicCitationJOURNAL OF THE AMERICAN HEART ASSOCIATION, Vol.9(5) : e013958, 2020-03-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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