Association of Atrial Fibrillation and Oral Anticoagulant Use With Perioperative Outcomes After Major Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: We examined the association of atrial fibrillation (AF) and oral anticoagulant use with perioperative death and bleeding among patients undergoing major noncardiac surgery. METHODS AND RESULTS: A population-based study of patients aged 66 years and older who underwent elective (n=87 257) or urgent (n=35 930) noncardiac surgery in Ontario, Canada (April 2012 to March 2015) was performed. Outcomes were compared between AF groups using inverse probability of treatment weighting using the propensity score. Of 4612 urgent surgical patients with AF, treatments before surgery included warfarin (n=1619), a direct oral anticoagulant (DOAC) (n=729), and no anticoagulation (n=2264). After urgent surgery, the death rate within 30 days was significantly higher in patients with AF compared with patients with no AF (hazard ratio [HR], 1.28; 95% confidence interval [CI], 1.12-1.45). In contrast, among 4769 elective surgical patients with AF treated with warfarin (n=1453), a DOAC (n=1165), or no anticoagulation (n=2151), prior AF was not associated with higher mortality. Comparing patients with AF who were or were not anticoagulated, there was no difference in 30-day mortality after urgent (HR, 0.95; 95% CI, 0.79-1.14) or elective (HR, 0.65; 95% CI, 0.38-1.09) surgery. There was no difference in 30-day mortality between patients with AF treated with a DOAC or warfarin after urgent (HR, 0.91; 95% CI, 0.70-1.18) or elective (HR, 1.64; 95% CI, 0.77-3.53) surgery. Bleeding and thromboembolic rates did not differ significantly among patients with AF prescribed a DOAC or warfarin. CONCLUSIONS: Prior AF was associated with 30-day mortality among patients undergoing urgent surgery. In patients with AF, neither the preoperative use of oral anticoagulants, nor the type of agent (either a DOAC or warfarin) were associated with the rate of 30-day 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".