Comparison of De-escalation of DAPT Intensity or Duration in East Asian and Western Patients with ACS Undergoing PCI: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background Guideline-recommended dual antiplatelet therapy (DAPT; aspirin plus prasugrel/ticagrelor) for 12 months in acute coronary syndrome (ACS) patients increases bleeding, with East Asians (EAs) exhibiting higher bleeding and lower ischemic risk, compared with non-East Asians (nEAs). We sought to compare DAPT “de-escalation” strategies in EA and nEA populations. Methods A systematic review and meta-analysis of randomized controlled trials assessing reduction of DAPT intensity or duration in ACS patients undergoing percutaneous coronary intervention, in EA and nEA, was performed using a random-effects model. Results Twenty-three trials assessed reduction of DAPT intensity (n = 12) or duration (n = 11). Overall, reduced DAPT intensity attenuated major bleeding (odds ratio [OR]: 0.78, 95% confidence interval [CI]: 0.65–0.94, p = 0.009), without impacting net adverse cardiovascular events (NACE) or major adverse cardiovascular events (MACE). In nEA, this increased MACE (OR: 1.20, 95% CI: 1.09–1.31, p < 0.0001) without impacting NACE or bleeding; while in EA, it reduced major bleeding (OR: 0.71, 95% CI: 0.53–0.95, p = 0.02) without affecting NACE or MACE. Overall, abbreviation of DAPT duration reduced NACE (OR: 0.90, 95% CI: 0.82–0.99, p = 0.03) due to major bleeding (OR: 0.69, 95% CI: 0.53–0.99, p = 0.006), without impacting MACE. In nEA, this strategy did not impact NACE, MACE, or major bleeding; while in EA, it reduced major bleeding (OR: 0.60, 95% CI: 0.4–0.91, p = 0.02) without impacting NACE or MACE. Conclusion In EA, reduction of DAPT intensity or duration can minimize bleeding, without safety concerns. In nEA, reduction of DAPT intensity may incur an ischemic penalty, while DAPT abbreviation has no overall benefit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".