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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.046 | 0.050 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".