Rhythm vs Rate Control Strategies for Perioperative Atrial Fibrillation After Noncardiac Surgery: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: For patients with perioperative atrial fibrillation (POAF) after noncardiac surgery, earlier conversion to sinus rhythm might improve outcomes. The efficacy of a rhythm vs rate control strategy for the acute management of POAF remains uncertain. Methods: We searched databases for randomized controlled trials (RCTs) and observational studies that included patients with POAF after noncardiac surgery and reported outcomes for patients acutely treated with a rhythm control strategy vs either a rate control or no treatment strategy. Studies were pooled using random effects models. Results: Of the observational studies, a rhythm control strategy was associated with higher conversion rates to sinus rhythm compared with a rate control or no treatment strategy (risk ratio [RR], 1.93; 95% confidence interval [CI], 1.25-2.97; 9 studies; N = 591). Compared with a rate control or no treatment strategy, a rhythm control strategy was not associated with differences in length of hospital stay (mean difference, -1.67 days; 95% CI, -7.10 to 3.76; 2 studies), length of intensive care stay (mean difference, -1.90 days; 95% CI, -7.62 to 3.82; 1 study), or all-cause mortality (RR, 1.12; 95% CI, 0.62-2.00; 5 studies). In an RCT that compared amiodarone vs magnesium, the RR was 0.56 for conversion to sinus rhythm (95% CI, 0.31-1.03; N = 34). Conclusions: A rhythm control strategy was associated with greater success rates for conversion to sinus rhythm compared with a rate control or no treatment strategy. However, the observational studies were of low quality and only 1 small RCT was identified, and few data were available for other outcomes.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.003 | 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".