Effectiveness of Anti-Inflammatory Agents to Prevent Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Background: Preventing postoperative atrial fibrillation (POAF) as one of the most significant complications of cardiovascular surgeries remains a major clinical challenge. We conducted a systematic review with network meta-analysis of randomized controlled trials, to identify the most effective and safe anti-inflammatory drugs to prevent new-onset POAF. Methods: MEDLINE, Embase, Web of Science, and Cochrane Library were searched without language or publication-date restriction on August 8, 2022 (updated on August 8, 2023). We assessed the risk of bias of included trials using the Cochrane risk-of-bias 2.0 tool. We conducted a frequentist random-effects network meta-analysis in R, and we assessed the certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Results: A total of 85 trials reported the incidence of new-onset POAF, including 18,981 patients. Use of nonsteroidal anti-inflammatory drugs (relative risk [RR] 0.37 [95% confidence interval [CI] 0.23-0.59]) and statins (RR 0.56 [95% CI 0.45-0.7]) potentially reduced the risk of POAF compared with placebo (both with a moderate certainty level). Use of fish oil in combination with vitamins C and E (RR 0.30 [95% CI 0.13-0.68]) may reduce the risk of POAF, compared with placebo (low level of certainty). Use of colchicine (RR 0.62 [95% CI 0.45- 0.85]), corticosteroids (RR 0.70 [95% CI 0.59-0.82]), and N-acetylcysteine (RR 0.69 [95% CI 0.49- 0.98]) may reduce the risk of POAF (all with a low level of certainty). None of the interventions had a significant effect on mortality rate or risk of serious adverse effects. Conclusions: Use of nonsteroidal anti-inflammatory drugs and statins probably are effective in preventing new-onset POAF, with a moderate level of certainty, compared to placebo.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.008 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".