Temporal Trends in the Management Practices of Clinically Important Perioperative Atrial Fibrillation After Noncardiac Surgery
Bibliographic record
Abstract
Background: Clinically important perioperative atrial fibrillation (POAF) is a common cardiac complication after noncardiac surgery. Little is known about how patients with POAF are managed acutely and whether practices have changed over time. Methods: We conducted an observational substudy of patients who had POAF, were at elevated cardiovascular risk, and were enrolled in the PeriOperative Ischemic Evaluation (POISE)-1, 2 and 3 trials between 2002 and 2021. POAF was defined as new, clinically important atrial fibrillation occurring within 30 days after surgery. We assessed the use of rhythm-control and anticoagulation treatment in response to POAF, at hospital discharge and at 30 days after surgery. We assessed for temporal trends using multivariable logistic regression. Results: Of the 27,896 patients included, 545 (1.9%) developed clinically important POAF. Patients received rhythm-control treatment in 48.6% of cases. The level of use of rhythm-control treatment increased over the course of the trials (POISE-1 vs POISE-2 vs POISE-3; 40.9% vs 49.5% vs 59.1%). A later randomization date was associated independently with use of rhythm-control treatment (odds ratio, 1.05 per year; 95% confidence interval, 1.01-1.09). Anticoagulation treatment was prescribed in 21% of POAF cases. The level of anticoagulation treatement use was higher in POISE-3, compared to that in the 2 previous trials (POISE-1 vs POISE-2 vs POISE-3-16.4% vs 16.5% vs 33.6%). A later randomization date was associated independently with use of anticoagulation treatment (odds ratio, 1.06 per year; 95% confidence interval, 1.02-1.11). Conclusions: Despite the absence of randomized controlled trials, the level of use of rhythm-control and anticoagulation treatment for POAF is rising. High-quality trials are needed urgently to determine whether these interventions are safe and effective 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".