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Predicting Individual Treatment Effects to Determine Duration of Dual Antiplatelet Therapy After Stent Implantation
DC Field | Value | Language |
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dc.contributor.author | 고영국 | - |
dc.contributor.author | 김병극 | - |
dc.contributor.author | 김중선 | - |
dc.contributor.author | 안철민 | - |
dc.contributor.author | 유승찬 | - |
dc.contributor.author | 이승준 | - |
dc.contributor.author | 최동훈 | - |
dc.contributor.author | 홍명기 | - |
dc.contributor.author | 홍성진 | - |
dc.date.accessioned | 2024-12-06T02:41:50Z | - |
dc.date.available | 2024-12-06T02:41:50Z | - |
dc.date.issued | 2024-10 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/200879 | - |
dc.description.abstract | Background: 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.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Wiley-Blackwell | - |
dc.relation.isPartOf | JOURNAL OF THE AMERICAN HEART ASSOCIATION | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Coronary Artery Disease* / diagnosis | - |
dc.subject.MESH | Coronary Artery Disease* / therapy | - |
dc.subject.MESH | Drug Administration Schedule | - |
dc.subject.MESH | Drug-Eluting Stents* | - |
dc.subject.MESH | Dual Anti-Platelet Therapy* / methods | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Hemorrhage* / chemically induced | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Machine Learning* | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Percutaneous Coronary Intervention* / adverse effects | - |
dc.subject.MESH | Percutaneous Coronary Intervention* / instrumentation | - |
dc.subject.MESH | Percutaneous Coronary Intervention* / methods | - |
dc.subject.MESH | Platelet Aggregation Inhibitors* / administration & dosage | - |
dc.subject.MESH | Platelet Aggregation Inhibitors* / adverse effects | - |
dc.subject.MESH | Risk Assessment | - |
dc.subject.MESH | Risk Factors | - |
dc.subject.MESH | Time Factors | - |
dc.subject.MESH | Treatment Outcome | - |
dc.title | Predicting Individual Treatment Effects to Determine Duration of Dual Antiplatelet Therapy After Stent Implantation | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Seung-Jun Lee | - |
dc.contributor.googleauthor | Jaehyeong Cho | - |
dc.contributor.googleauthor | Jihye Shin | - |
dc.contributor.googleauthor | Sung-Jin Hong | - |
dc.contributor.googleauthor | Chul-Min Ahn | - |
dc.contributor.googleauthor | Jung-Sun Kim | - |
dc.contributor.googleauthor | Young-Guk Ko | - |
dc.contributor.googleauthor | Donghoon Choi | - |
dc.contributor.googleauthor | Myeong-Ki Hong | - |
dc.contributor.googleauthor | Seng Chan You | - |
dc.contributor.googleauthor | Byeong-Keuk Kim | - |
dc.identifier.doi | 10.1161/jaha.124.034862 | - |
dc.contributor.localId | A00127 | - |
dc.contributor.localId | A00493 | - |
dc.contributor.localId | A00961 | - |
dc.contributor.localId | A02269 | - |
dc.contributor.localId | A02478 | - |
dc.contributor.localId | A02927 | - |
dc.contributor.localId | A04053 | - |
dc.contributor.localId | A04391 | - |
dc.contributor.localId | A04403 | - |
dc.relation.journalcode | J01774 | - |
dc.identifier.eissn | 2047-9980 | - |
dc.identifier.pmid | 39344653 | - |
dc.subject.keyword | drug‐eluting stents | - |
dc.subject.keyword | dual antiplatelet therapy | - |
dc.subject.keyword | machine learning | - |
dc.subject.keyword | percutaneous coronary intervention | - |
dc.subject.keyword | treatment effect heterogeneity | - |
dc.contributor.alternativeName | Ko, Young Guk | - |
dc.contributor.affiliatedAuthor | 고영국 | - |
dc.contributor.affiliatedAuthor | 김병극 | - |
dc.contributor.affiliatedAuthor | 김중선 | - |
dc.contributor.affiliatedAuthor | 안철민 | - |
dc.contributor.affiliatedAuthor | 유승찬 | - |
dc.contributor.affiliatedAuthor | 이승준 | - |
dc.contributor.affiliatedAuthor | 최동훈 | - |
dc.contributor.affiliatedAuthor | 홍명기 | - |
dc.contributor.affiliatedAuthor | 홍성진 | - |
dc.citation.volume | 13 | - |
dc.citation.number | 19 | - |
dc.citation.startPage | e034862 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE AMERICAN HEART ASSOCIATION, Vol.13(19) : e034862, 2024-10 | - |
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