Pharmacological preventions and treatments for pericardial complications after open heart surgeries
Bibliographic record
Abstract
BACKGROUND: Pericardial complications following cardiac surgery are common and debilitating, significantly impacting patients' survival. We performed this network meta-analysis to identify the most effective and safest preventions and treatments for pericardial complications following cardiac surgery. METHODS: We systematically searched PubMed/MEDLINE, EMBASE and Cochrane CENTRAL from inception to 22 January 2024. Pairs of reviewers screened eligible studies. They included randomised controlled trials that enrolled adults undergoing major cardiac surgeries and reported postpericardiotomy syndrome, pericardial effusion and pericarditis as primary or secondary outcomes. We summarised the effects of interventions using relative risks and corresponding 95% CIs. We performed a frequentist random-effects network meta-analysis using the restricted maximum likelihood estimator. RESULTS: We included 39 trials that enrolled a total of 6419 participants. Our network meta-analysis demonstrates colchicine reduces the risk of postpericardiotomy syndrome (RR 0.53, 95% CI 0.38 to 0.73). Beta-blockers probably prevent atrial fibrillation with a large magnitude of effect (RR 0.4, 95% CI 0.20 to 0.81) and may prevent postoperative pericarditis (RR 0.66, 95% CI 0.45 to 0.97) compared with control. Fish oil (RR 0.28, 95% CI 0.09 to 0.90), non-steroidal anti-inflammatory drugs (RR 0.37, 95% CI 0.23 to 0.59) and colchicine (RR 0.37, 95% CI 0.23 to 0.59) may reduce the risk of postoperative atrial fibrillation. We found no evidence of a difference in the risk of pleural effusion, all-cause mortality, serious adverse events or postoperative ICU stay. CONCLUSIONS: The results of our study highly recommend colchicine use to reduce the risk of the postpericardiotomy syndrome and beta-blocker use to reduce postoperative atrial fibrillation. Additionally, our study suggests that further research is needed to investigate other interventions and to evaluate newly proposed interventions in large, high-quality trials, as the current evidence for some interventions is relatively weak.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".