Antiplatelet therapy in patients with atrial fibrillation: a systematic review and meta-analysis of randomized trials
Bibliographic record
Abstract
AIMS: The aim of this study was to systematically assess the effects of antiplatelets on clinical outcomes in patients with atrial fibrillation (AF), treated and not-treated with oral anticoagulation. METHODS AND RESULTS: We searched MEDLINE, Embase, and CENTRAL from inception until September 2020. From 5446 citations, we selected randomized trials allocating patients with AF to antiplatelet therapy vs. control. We applied random-effects models for meta-analysis and assessed potential effect modification with background anticoagulation use. Eighteen trials including 21 518 participants met our prespecified eligibility criteria. In 10 studies without background anticoagulation, antiplatelets reduced all-cause stroke [486/6165 (events/patients) vs. 621/6061; risk ratio (RR) 0.77, 95% confidence interval (CI) 0.69-0.86, I2 = 0%]. In eight studies with background anticoagulation, there was a signal for an increase in all-cause stroke with antiplatelets (97/4608 vs. 72/4684; RR 1.33, 95% CI 0.98-1.79, I2 = 0%, P-value for interaction <0.001). A similar pattern emerged for ischaemic stroke. Irrespective of background anticoagulation use, antiplatelets increased major bleeding (509/10 402 vs. 328/10 496; RR 1.54, 95% CI 1.35-1.77, I2 = 0%) and intracranial haemorrhage (107/10 221 vs. 65/10 232; RR 1.64, 95% CI 1.20-2.24, I2 = 0%), and reduced myocardial infarction (201/9679 vs. 260/9751; RR 0.79, 95% CI 0.65-0.94, I2 = 0%, all P-values for interaction ≥0.36). Antiplatelets did not affect mortality (1221/10 299 vs. 1211/10 287; RR 1.02, 95% CI 0.89-1.17, I2 = 29%, P-value for interaction = 0.23). CONCLUSIONS: In patients with AF not receiving oral anticoagulation, antiplatelet therapy modestly reduced stroke. There was a corresponding signal for harm when used on top of anticoagulation. Irrespective of background anticoagulation use, antiplatelet therapy significantly increased bleeding, moderately reduced myocardial infarction, and did not affect mortality.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".