Major Adverse Cardiovascular Events Associated With Postoperative Atrial Fibrillation After Noncardiac Surgery
Bibliographic record
Abstract
Background: Postoperative atrial fibrillation (POAF) is a frequent occurrence after noncardiac surgery. It remains unclear whether POAF is associated with an increased risk of major adverse events. We aimed to elucidate the risk of stroke, myocardial infarction, and death associated with POAF following noncardiac surgery by a meta-analysis of randomized controlled studies and observational studies. Methods: We searched electronic databases from inception up to August 1, 2019 for all studies that reported stroke or myocardial infarction in adult patients who developed POAF following noncardiac surgery. We used random-effects models to summarize the studies. Results: The final analyses included 28 studies enrolling 2 612 816 patients. At 1-month (10 studies), POAF was associated with an ≈3-fold increase in the risk of stroke (weighted mean 2.1% versus 0.7%; odds ratio [OR], 2.82 [95% CI, 2.15–3.70]; P <0.001). POAF was associated with ≈4-fold increase in the long-term risk of stroke with (weighted mean, 2.0% versus 0.6%; OR, 4.12 [95% CI, 3.32–5.11]; P ≤0.001) in 8 studies with ≥12-month follow-up. There was a significant overall increase in the risk of stroke and myocardial infarction associated with POAF (weighted mean, 2.5% versus 0.9%; OR, 3.44 [95% CI, 2.38–4.98]; P <0.001) and (weighted mean, 12.6% versus 2.7%; OR, 4.02 [95% CI, 3.08–5.24]; P <0.001), respectively. Furthermore, POAF was associated with a 3-fold increase in all-cause mortality at 30 days (weighted mean, 15.0% versus 5.4%; OR, 3.36 [95% CI, 2.13–5.31]; P <0.001). Conclusions: POAF was associated with markedly higher risk of stroke, myocardial infarction, and all-cause mortality following noncardiac surgery. Future studies are needed to evaluate the impact of optimal cardiovascular pharmacotherapies to prevent POAF and to decrease the risk of major adverse events in these high-risk patients.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".