Antiplatelets in patients with atrial fibrillation: a systematic review and meta-analysis of randomized clinical trials
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Dr. Benz reports a personal research grant from the German Heart Foundation (Deutsche Herzstiftung e.V.). Dr. Johansson reports personal unrestricted research grants from Swedish Heart-Lung Foundation (Hjärt-Lungfonden) and from Stockholm County Council (Region Stockholm). Dr. McIntyre holds a fellowship award from the Canadian Institutes for Health Research (CIHR). Dr. Shoamanesh reports funding support from the Marta and Owen Boris Foundation and the Heart and Stroke Foundation of Canada. Background/Introduction: There is an ongoing controversy surrounding the efficacy and safety of antiplatelet agents in patients with atrial fibrillation (AF). Purpose We aimed to systematically assess the effects of antiplatelets on stroke and other outcomes in patients with AF, both receiving oral anticoagulation or not. Methods We searched MEDLINE, Embase and CENTRAL up until September 2020 to identify randomized trials allocating patients with AF to aspirin or a P2Y12 inhibitor, versus control. Where applicable, we obtained unpublished data from study authors. Random-effects models were applied for meta-analysis. Results Based on 21,518 patients from 18 randomized trials, there was no reduction in stroke with antiplatelet therapy (risk ratio [RR] 0.89, 95% confidence interval [CI] 0.76-1.04). There was a significant qualitative interaction according to whether patients were receiving concomitant oral anticoagulation or not (p < 0.001). Without concomitant anticoagulation, antiplatelets reduced stroke (RR 0.77, 95% CI 0.69-0.86), while they appeared to increase stroke with it (RR 1.33, 95% CI 0.98-1.79). A similar pattern emerged for ischaemic stroke. Antiplatelets increased major bleeding (RR 1.54, 95% CI 1.35-1.77) and intracranial haemorrhage (RR 1.64, 95% CI 1.20-2.24), and reduced myocardial infarction (RR 0.79, 95% CI 0.65-0.94), consistently and irrespective of concomitant anticoagulation. Antiplatelets had a neutral effect on mortality (RR 1.02, 95% CI 0.89-1.17). Conclusions Antiplatelet therapy did not reduce stroke and increased major bleeding in patients with AF. Antiplatelets did not affect mortality. Subgroup analysis suggests a reduction in stroke with antiplatelets in patients without concomitant oral anticoagulation, and a corresponding signal for harm in those with it. Abstract Figure.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".