Abstract 4363276: Efficacy of Anti-inflammatory Therapies for Pericarditis: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
Background: One of the most troublesome complications of acute pericarditis is recurrence. Non-steroidal anti-inflammatory drugs (NSAIDs) and colchicine form the cornerstone treatment, with the addition of steroids in cases of incomplete response. Recent evidence also suggests a role for interleukin-1 (IL-1) antagonists in challenging cases of recurrence. However, with an increasing number of potential therapies, our study aims to examine the relative efficacy of these anti-inflammatory treatments for recurrent pericarditis risk reduction. Methods: We searched Medline, Embase, and the Cochrane Central Register of Controlled Trials in October 2023. We included randomized controlled trials (RCTs) and prospective cohort studies of patients with pericarditis treated with a drug with anti-inflammatory properties compared with those treated with a control. The main outcome evaluated was pericarditis recurrence. Results: A total of 6,831 studies were screened, of which 25 articles were included (14 RCTs and 11 cohort studies) for a total number of patients of 3,561. The mean age across studies was 54.82 years for adults and 9.04 years for children (females = 40.55%). The average follow-up duration was 20.03 months. Bayesian network meta-analysis was performed to calculate risk estimates using a random effects model (Figure 1). Goflikicept, a fusion protein that neutralizes IL-1, showed a significant reduction in recurrent pericarditis rates (OR: 0.00, 95% Credible Limits [Crl]: 0.00-0.08). Similarly, anakinra (OR: 0.01, 95% Crl: 0.00-0.13), rilonacept (OR: 0.02, 95% Crl: 0.00-0.22) and colchicine (OR: 0.35, 95% Crl: 0.19-0.64) were associated with a significant reduction in recurrent pericarditis events. High dose colchicine (OR: 0.70, 95% Crl: 0.13-4.12) was not superior to regular dose colchicine (1 mg vs 0.6 mg twice daily, respectively). Interestingly, there was not enough data to make a meaningful comparison between steroids and control. Conclusion: In our analysis, goflikicept, rilonacept, and anakinra were most effective at reducing recurrent pericarditis rates. However, the evidence is based on a small number of studies. Our analysis also corroborated evidence regarding the efficacy of colchicine in reducing pericarditis recurrence. For a comprehensive analysis of the benefits and harms of anti-inflammatory therapies, larger comparative studies or network meta-analyses of patient-level data are required.
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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 | 0.001 | 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".