Evidence map›Paper›PMID 39344653›Full record

ArticleJournal of the American Heart Association2024

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

Seung-Jun Lee, Jaehyeong Cho, Jihye Shin, Sung-Jin Hong, Chul-Min Ahn, Jung-Sun Kim, Young-Guk Ko, Donghoon Choi, Myeong-Ki Hong, Seng Chan You and 1 more

Abstract read
In one paragraph

Article in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Seung-Jun LeeSeverance Cardiovascular Hospital Yonsei University, College of Medicine Seoul South Korea.ORCID 0000-0002-9201-4818
Jaehyeong ChoDepartment of Research Keimyung University Dongsan Medical Center Daegu South Korea.
Jihye ShinDepartment of Research Health Insurance Review & Assessment Service Wonju South Korea.ORCID 0000-0002-8157-8618
Sung-Jin HongDepartment of Research Keimyung University Dongsan Medical Center Daegu South Korea.ORCID 0000-0003-4893-039X
Chul-Min AhnSeverance Cardiovascular Hospital Yonsei University, College of Medicine Seoul South Korea.ORCID 0000-0002-7071-4370
Jung-Sun KimSeverance Cardiovascular Hospital Yonsei University, College of Medicine Seoul South Korea.ORCID 0000-0003-2263-3274
Young-Guk KoSeverance Cardiovascular Hospital Yonsei University, College of Medicine Seoul South Korea.ORCID 0000-0001-7748-5788
Donghoon ChoiSeverance Cardiovascular Hospital Yonsei University, College of Medicine Seoul South Korea.ORCID 0000-0002-2009-9760
Myeong-Ki HongSeverance Cardiovascular Hospital Yonsei University, College of Medicine Seoul South Korea.ORCID 0000-0002-2090-2031
Seng Chan YouDepartment of Biomedical Systems Informatics Yonsei University, College of Medicine Seoul South Korea.
Byeong-Keuk KimSeverance Cardiovascular Hospital Yonsei University, College of Medicine Seoul South Korea.ORCID 0000-0003-2493-066X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAfter 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

resultsThe 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.

conclusionsMachine 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.

Indexed as

Coronary Artery DiseaseDrug-Eluting StentsDual Anti-Platelet TherapyHemorrhageMachine LearningPercutaneous Coronary InterventionPlatelet Aggregation InhibitorsAgedDrug Administration ScheduleFemaleHumansMaleMiddle AgedRisk AssessmentRisk FactorsTime FactorsPlatelet Aggregation Inhibitorsdrug‐eluting stentsdual antiplatelet therapymachine learningpercutaneous coronary interventiontreatment effect heterogeneity

Identifiers

PMID39344653
PMCPMC11681487

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.