Anticoagulation use in perioperative atrial fibrillation after noncardiac surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Perioperative atrial fibrillation is associated with an increased risk of stroke, myocardial infarction, and death after noncardiac surgery. Anticoagulation therapy is effective for stroke prevention in nonsurgical atrial fibrillation, but its efficacy and safety in perioperative atrial fibrillation are unknown. METHODS: We searched MEDLINE, EMBASE, and CENTRAL from database inception until January 2022. We included studies comparing anticoagulation versus no anticoagulation use in patients with perioperative atrial fibrillation after noncardiac surgery. Our study outcomes included stroke ± systemic embolism, bleeding, mortality, myocardial infarction, and venous thromboembolism. We pooled studies using fixed-effects models. We reported summary risk ratios (RRs) for studies reporting multivariable-adjusted results. RESULTS: Seven observational studies but no randomised trials were included. Of the 27,822 patients, 29.1% were prescribed therapeutic anticoagulation. Anticoagulation use was associated with a lower risk of stroke ± systemic embolism (RR 0.73; 95% CI, 0.62-0.85; I2 = 81%; 3 studies) but a higher risk of bleeding (RR 1.14; 95% CI, 1.04-1.25; 1 study). There was a lower risk of mortality associated with anticoagulation use (RR 0.45; 95% CI, 0.40-0.51; I2 = 80%; 2 studies). There was no difference in the risk of myocardial infarction (RR 2.19; 95% CI, 0.97-4.96; 1 study). The certainty of the evidence was very low across all outcomes. CONCLUSION: Anticoagulation is associated with a reduced risk of stroke and death but an increased risk of bleeding. The quality of the evidence is very poor. Randomised trials are needed to better determine the effects of anticoagulation use in this population.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.001 | 0.003 |
| 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; a candidate call from one teacher head, 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".