0 35

Cited 0 times in

A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA

DC FieldValueLanguage
dc.contributor.author이지현-
dc.contributor.author장혁재-
dc.contributor.author한동희-
dc.date.accessioned2021-01-19T08:00:01Z-
dc.date.available2021-01-19T08:00:01Z-
dc.date.issued2020-10-
dc.identifier.issn1936-878X-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/181415-
dc.description.abstractObjectives: This study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics. Background: Coronary computed tomography angiography (CTA) has been validated for patient-level prediction of ACS. However, the applicability of coronary CTA to CL assessment is not known. Methods: Utilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested case-control study of 468 patients with baseline coronary CTA, the study included ACS patients with invasive coronary angiography-adjudicated CLs that could be aligned to CL precursors on baseline coronary CTA. Separate blinded core laboratories adjudicated CLs and performed atherosclerotic plaque evaluation. Thereafter, the study used a boosted ensemble algorithm (XGBoost) to develop a predictive model of CLs. Data were randomly split into a training set (80%) and a test set (20%). The area under the receiver-operating characteristic curve of this model was compared with that of diameter stenosis (model 1), high-risk plaque features (model 2), and lesion-level features of CL precursors from the ICONIC study (model 3). Thereafter, the machine learning (ML) model was applied to 234 non-ACS patients with 864 lesions to determine model performance for CL exclusion. Results: CL precursors were identified by both coronary angiography and baseline coronary CTA in 124 of 234 (53.0%) patients, with a total of 582 lesions (containing 124 CLs) included in the analysis. The ML model demonstrated significantly higher area under the receiver-operating characteristic curve for discriminating CL precursors (0.774; 95% confidence interval [CI]: 0.758 to 0.790) compared with model 1 (0.599; 95% CI: 0.599 to 0.599; p < 0.01), model 2 (0.532; 95% CI: 0.501 to 0.563; p < 0.01), and model 3 (0.672; 95% CI: 0.662 to 0.682; p < 0.01). When applied to the non-ACS cohort, the ML model had a specificity of 89.3% for excluding CLs. Conclusions: In a high-risk cohort, a boosted ensemble algorithm can be used to predict CL from non-CL precursors on coronary CTA.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier-
dc.relation.isPartOfJACC-CARDIOVASCULAR IMAGING-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleA Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorSubhi J Al'Aref-
dc.contributor.googleauthorGurpreet Singh-
dc.contributor.googleauthorJeong W Choi-
dc.contributor.googleauthorZhuoran Xu-
dc.contributor.googleauthorGabriel Maliakal-
dc.contributor.googleauthorAlexander R van Rosendael-
dc.contributor.googleauthorBenjamin C Lee-
dc.contributor.googleauthorZahra Fatima-
dc.contributor.googleauthorDaniele Andreini-
dc.contributor.googleauthorJeroen J Bax-
dc.contributor.googleauthorFilippo Cademartiri-
dc.contributor.googleauthorKavitha Chinnaiyan-
dc.contributor.googleauthorBenjamin J W Chow-
dc.contributor.googleauthorEdoardo Conte-
dc.contributor.googleauthorRicardo C Cury-
dc.contributor.googleauthorGudruf Feuchtner-
dc.contributor.googleauthorMartin Hadamitzky-
dc.contributor.googleauthorYong-Jin Kim-
dc.contributor.googleauthorSang-Eun Lee-
dc.contributor.googleauthorJonathon A Leipsic-
dc.contributor.googleauthorErica Maffei-
dc.contributor.googleauthorHugo Marques-
dc.contributor.googleauthorFabian Plank-
dc.contributor.googleauthorGianluca Pontone-
dc.contributor.googleauthorGilbert L Raff-
dc.contributor.googleauthorTodd C Villines-
dc.contributor.googleauthorHarald G Weirich-
dc.contributor.googleauthorIksung Cho-
dc.contributor.googleauthorIbrahim Danad-
dc.contributor.googleauthorDonghee Han-
dc.contributor.googleauthorRan Heo-
dc.contributor.googleauthorJi Hyun Lee-
dc.contributor.googleauthorAsim Rizvi-
dc.contributor.googleauthorWijnand J Stuijfzand-
dc.contributor.googleauthorHeidi Gransar-
dc.contributor.googleauthorYao Lu-
dc.contributor.googleauthorJi Min Sung-
dc.contributor.googleauthorHyung-Bok Park-
dc.contributor.googleauthorDaniel S Berman-
dc.contributor.googleauthorMatthew J Budoff-
dc.contributor.googleauthorHabib Samady-
dc.contributor.googleauthoreter H Stone-
dc.contributor.googleauthorRenu Virmani-
dc.contributor.googleauthorJagat Narula-
dc.contributor.googleauthorHyuk-Jae Chang-
dc.contributor.googleauthorFay Y Lin-
dc.contributor.googleauthorLohendran Baskaran-
dc.contributor.googleauthorLeslee J Shaw-
dc.contributor.googleauthorJames K Min-
dc.identifier.doi10.1016/j.jcmg.2020.03.025-
dc.contributor.localIdA03215-
dc.contributor.localIdA03490-
dc.contributor.localIdA03490-
dc.contributor.localIdA04811-
dc.contributor.localIdA04811-
dc.relation.journalcodeJ01192-
dc.identifier.eissn1876-7591-
dc.identifier.pmid32682719-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1936878X2030423X-
dc.subject.keywordacute coronary syndrome-
dc.subject.keywordcoronary computed tomography angiography-
dc.subject.keyworddiameter stenosis-
dc.subject.keywordmachine learning-
dc.contributor.alternativeNameLee, Jee Hyun-
dc.contributor.affiliatedAuthor이지현-
dc.contributor.affiliatedAuthor장혁재-
dc.contributor.affiliatedAuthor장혁재-
dc.contributor.affiliatedAuthor한동희-
dc.contributor.affiliatedAuthor한동희-
dc.citation.volume13-
dc.citation.number10-
dc.citation.startPage2162-
dc.citation.endPage2173-
dc.identifier.bibliographicCitationJACC-CARDIOVASCULAR IMAGING, Vol.13(10) : 2162-2173, 2020-10-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.