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Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study

DC FieldValueLanguage
dc.contributor.author최의영-
dc.contributor.author장혁재-
dc.contributor.author이상은-
dc.contributor.author성지민-
dc.date.accessioned2020-09-28T12:03:24Z-
dc.date.available2020-09-28T12:03:24Z-
dc.date.issued2020-06-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/179327-
dc.description.abstractBackground: Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). We further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization. Methods: For prediction of obstructive CAD, the entire ICA arm of the study, comprising 719 patients was used. For revascularization, 1,028 patients were randomized to invasive coronary angiography (ICA) or coronary computed tomographic angiography (CCTA). Data was randomly split into 80% training 20% test sets for building and validation. Models used extreme gradient boosting (XGBoost). Results: Mean age was 60.6 ± 11.5 years and 64.3% were female. For the prediction of obstructive CAD, the AUC was significantly higher for ML at 0.779 (95% CI: 0.672-0.886) than for CAD2 (0.696 [95% CI: 0.594-0.798]) (P = 0.01). BMI, age, and angina severity were the most important variables. For revascularization, the model obtained an overall area under the receiver-operation curve (AUC) of 0.958 (95% CI = 0.933-0.983). Performance did not differ whether the imaging parameters used were from ICA (AUC 0.947, 95% CI = 0.903-0.990) or CCTA (AUC 0.941, 95% CI = 0.895-0.988) (P = 0.90). The ML model obtained sensitivity and specificity of 89.2% and 92.9%, respectively. Number of vessels with ≥70% stenosis, maximum segment stenosis severity (SSS) and body mass index (BMI) were the most important variables. Exclusion of imaging variables resulted in performance deterioration, with an AUC of 0.705 (95% CI 0.614-0.795) (P <0.0001). Conclusions: For obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores. In this ML model, imaging variables were most associated with revascularization. Imaging modality did not influence model performance. Removal of imaging variables reduced model performance.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherPublic Library of Science-
dc.relation.isPartOfPLOS ONE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAged-
dc.subject.MESHCoronary Angiography*-
dc.subject.MESHCoronary Artery Disease / diagnostic imaging*-
dc.subject.MESHCoronary Artery Disease / epidemiology-
dc.subject.MESHCoronary Artery Disease / pathology-
dc.subject.MESHCoronary Artery Disease / surgery-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMachine Learning*-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHModels, Statistical-
dc.subject.MESHMyocardial Revascularization / statistics & numerical data*-
dc.titleMachine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorLohendran Baskaran-
dc.contributor.googleauthorXiaohan Ying-
dc.contributor.googleauthorZhuoran Xu-
dc.contributor.googleauthorSubhi J Al'Aref-
dc.contributor.googleauthorBenjamin C Lee-
dc.contributor.googleauthorSang-Eun Lee-
dc.contributor.googleauthorIbrahim Danad-
dc.contributor.googleauthorHyung-Bok Park-
dc.contributor.googleauthorRavi Bathina-
dc.contributor.googleauthorAndrea Baggiano-
dc.contributor.googleauthorVirginia Beltrama-
dc.contributor.googleauthorRodrigo Cerci-
dc.contributor.googleauthorEui-Young Choi-
dc.contributor.googleauthorJung-Hyun Choi-
dc.contributor.googleauthorSo-Yeon Choi-
dc.contributor.googleauthorJason Cole-
dc.contributor.googleauthorJoon-Hyung Doh-
dc.contributor.googleauthorSang-Jin Ha-
dc.contributor.googleauthorAe-Young Her-
dc.contributor.googleauthorCezary Kepka-
dc.contributor.googleauthorJang-Young Kim-
dc.contributor.googleauthorJin-Won Kim-
dc.contributor.googleauthorSang-Wook Kim-
dc.contributor.googleauthorWoong Kim-
dc.contributor.googleauthorYao Lu-
dc.contributor.googleauthorAmit Kumar-
dc.contributor.googleauthorRan Heo-
dc.contributor.googleauthorJi Hyun Lee-
dc.contributor.googleauthorJi-Min Sung-
dc.contributor.googleauthorUma Valeti-
dc.contributor.googleauthorDaniele Andreini-
dc.contributor.googleauthorGianluca Pontone-
dc.contributor.googleauthorDonghee Han-
dc.contributor.googleauthorTodd C Villines-
dc.contributor.googleauthorFay Lin-
dc.contributor.googleauthorHyuk-Jae Chang-
dc.contributor.googleauthorJames K Min-
dc.contributor.googleauthorLeslee J Shaw-
dc.identifier.doi10.1371/journal.pone.0233791-
dc.contributor.localIdA04165-
dc.contributor.localIdA03490-
dc.contributor.localIdA03490-
dc.relation.journalcodeJ02540-
dc.identifier.eissn1932-6203-
dc.identifier.pmid32584909-
dc.contributor.alternativeNameChoi, Eui Young-
dc.contributor.affiliatedAuthor최의영-
dc.contributor.affiliatedAuthor장혁재-
dc.contributor.affiliatedAuthor장혁재-
dc.citation.volume15-
dc.citation.number6-
dc.citation.startPagee0233791-
dc.identifier.bibliographicCitationPLOS ONE, Vol.15(6) : e0233791, 2020-06-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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