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 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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".