Antithrombotic Therapy in Patients With Atrial Fibrillation and Coronary Artery Disease With Recent or Remote Events: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Ongoing debate remains regarding optimal antithrombotic therapy in patients with atrial fibrillation (AF) and coronary artery disease. Methods: We performed a systematic review and meta-analysis to synthesize randomized controlled trials (RCTs) comparing the following: (i) dual-pathway therapy (DPT; oral anticoagulant [OAC] plus antiplatelet) vs triple therapy (OAC and dual-antiplatelet therapy) after percutaneous coronary intervention (PCI) or acute coronary syndrome (ACS), and (iii) OAC monotherapy vs DPT at least 1 year after PCI or ACS. Following a 2-stage process, we identified systematic reviews published between 2019 and 2022 on these 2 clinical questions, and we updated the most comprehensive search for additional RCTs published up to October 2022. Outcomes of interest were major adverse cardiovascular events (MACE), death, stent thrombosis, and major bleeding. We estimated risk ratios (RRs) and 95% confidence intervals (CIs) using a random-effects model. Results: Based on 6 RCTs (n = 10,435), DPT reduced major bleeding (RR 0.62, 95% CI 0.52-0.73) and increased stent thrombosis (RR 1.55, 95% CI 1.02-2.36), vs triple therapy after PCI or medically-managed ACS, with no significant differences in MACE and death. In 2 RCTs (n = 2905), OAC monotherapy reduced major bleeding (RR 0.66, 95% CI 0.49-0.91) vs DPT in AF patients with remote PCI or ACS, with no significant differences in MACE or death. Conclusions: In patients with AF and coronary artery disease, using less-aggressive antithrombotic treatment (DPT after PCI or ACS, and OAC alone after remote PCI or ACS) reduced major bleeding, with an increase in stent thrombosis with recent PCI. These results support a minimalist yet personalized antithrombotic strategy for these patients.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".