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Predicting Individual Treatment Effects to Determine Duration of Dual Antiplatelet Therapy After Stent Implantation

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dc.contributor.author고영국-
dc.contributor.author김병극-
dc.contributor.author김중선-
dc.contributor.author안철민-
dc.contributor.author유승찬-
dc.contributor.author이승준-
dc.contributor.author최동훈-
dc.contributor.author홍명기-
dc.contributor.author홍성진-
dc.date.accessioned2024-12-06T02:41:50Z-
dc.date.available2024-12-06T02:41:50Z-
dc.date.issued2024-10-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/200879-
dc.description.abstractBackground: After coronary stent implantation, prolonged dual antiplatelet therapy (DAPT) increases bleeding risk, requiring personalization of DAPT duration. The aim of this study was to develop and validate a machine learning model to predict optimal DAPT duration after contemporary drug-eluting stent implantation in patients with coronary artery disease. Methods and results: The One-Month DAPT, RESET (Real Safety and Efficacy of 3-Month Dual Antiplatelet Therapy Following Endeavor Zotarolimus-Eluting Stent Implantation), and IVUS-XPL (Impact of Intravascular Ultrasound Guidance on Outcomes of Xience Prime Stents in Long Lesion) trials provided a derivation cohort (n=6568). Using the X-learner approach, an individualized DAPT score was developed to determine the therapeutic benefit of abbreviated (1-6 months) versus standard (12-month) DAPT using various predictors. The primary outcome was major bleeding; the secondary outcomes included 1-year major adverse cardiac and cerebrovascular events and 1-year net adverse clinical events. The risk reduction with abbreviated DAPT (3 months) in the individualized DAPT-determined higher predicted benefit group was validated in the TICO (Ticagrelor Monotherapy After 3 Months in the Patients Treated With New Generation Sirolimus-Eluting Stent for Acute Coronary Syndrome) trial (n=3056), which enrolled patients with acute coronary syndrome treated with ticagrelor. The validation cohort comprised 1527 abbreviated and 1529 standard DAPT cases. Major bleeding occurred in 25 (1.7%) and 45 (3.0%) patients in the abbreviated and standard DAPT groups, respectively. The individualized DAPT score identified 2582 (84.5%) participants who would benefit from abbreviated DAPT, which was significantly associated with a lower major bleeding risk (absolute risk difference [ARD], 1.26 [95% CI, 0.15-2.36]) and net adverse clinical events (ARD, 1.59 [95% CI, 0.07-3.10]) but not major adverse cardiac and cerebrovascular events (ARD, 0.63 [95% CI, -0.34 to 1.61]), compared with standard DAPT in the higher predicted benefit group. Abbreviated DAPT had no significant difference in clinical outcomes of major bleeding (ARD, 1.49 [95% CI, -1.74 to 4.72]), net adverse clinical events (ARD, 2.57 [95% CI, -1.85 to 6.99]), or major adverse cardiac and cerebrovascular events (ARD, 1.54 [95% CI, -1.26 to 4.34]), compared with standard DAPT in the individualized DAPT-determined lower predicted benefit group. Conclusions: Machine learning using the X-learner approach identifies patients with acute coronary syndrome who may benefit from abbreviated DAPT after drug-eluting stent implantation, laying the groundwork for personalized antiplatelet therapy.-
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.subject.MESHAged-
dc.subject.MESHCoronary Artery Disease* / diagnosis-
dc.subject.MESHCoronary Artery Disease* / therapy-
dc.subject.MESHDrug Administration Schedule-
dc.subject.MESHDrug-Eluting Stents*-
dc.subject.MESHDual Anti-Platelet Therapy* / methods-
dc.subject.MESHFemale-
dc.subject.MESHHemorrhage* / chemically induced-
dc.subject.MESHHumans-
dc.subject.MESHMachine Learning*-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHPercutaneous Coronary Intervention* / adverse effects-
dc.subject.MESHPercutaneous Coronary Intervention* / instrumentation-
dc.subject.MESHPercutaneous Coronary Intervention* / methods-
dc.subject.MESHPlatelet Aggregation Inhibitors* / administration & dosage-
dc.subject.MESHPlatelet Aggregation Inhibitors* / adverse effects-
dc.subject.MESHRisk Assessment-
dc.subject.MESHRisk Factors-
dc.subject.MESHTime Factors-
dc.subject.MESHTreatment Outcome-
dc.titlePredicting Individual Treatment Effects to Determine Duration of Dual Antiplatelet Therapy After Stent Implantation-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorSeung-Jun Lee-
dc.contributor.googleauthorJaehyeong Cho-
dc.contributor.googleauthorJihye Shin-
dc.contributor.googleauthorSung-Jin Hong-
dc.contributor.googleauthorChul-Min Ahn-
dc.contributor.googleauthorJung-Sun Kim-
dc.contributor.googleauthorYoung-Guk Ko-
dc.contributor.googleauthorDonghoon Choi-
dc.contributor.googleauthorMyeong-Ki Hong-
dc.contributor.googleauthorSeng Chan You-
dc.contributor.googleauthorByeong-Keuk Kim-
dc.identifier.doi10.1161/jaha.124.034862-
dc.contributor.localIdA00127-
dc.contributor.localIdA00493-
dc.contributor.localIdA00961-
dc.contributor.localIdA02269-
dc.contributor.localIdA02478-
dc.contributor.localIdA02927-
dc.contributor.localIdA04053-
dc.contributor.localIdA04391-
dc.contributor.localIdA04403-
dc.relation.journalcodeJ01774-
dc.identifier.eissn2047-9980-
dc.identifier.pmid39344653-
dc.subject.keyworddrug‐eluting stents-
dc.subject.keyworddual antiplatelet therapy-
dc.subject.keywordmachine learning-
dc.subject.keywordpercutaneous coronary intervention-
dc.subject.keywordtreatment effect heterogeneity-
dc.contributor.alternativeNameKo, Young Guk-
dc.contributor.affiliatedAuthor고영국-
dc.contributor.affiliatedAuthor김병극-
dc.contributor.affiliatedAuthor김중선-
dc.contributor.affiliatedAuthor안철민-
dc.contributor.affiliatedAuthor유승찬-
dc.contributor.affiliatedAuthor이승준-
dc.contributor.affiliatedAuthor최동훈-
dc.contributor.affiliatedAuthor홍명기-
dc.contributor.affiliatedAuthor홍성진-
dc.citation.volume13-
dc.citation.number19-
dc.citation.startPagee034862-
dc.identifier.bibliographicCitationJOURNAL OF THE AMERICAN HEART ASSOCIATION, Vol.13(19) : e034862, 2024-10-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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