Abstract 14289: Anti-Inflammatory Therapies to Prevent Cardiovascular Events: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Introduction: Anti-inflammatory therapies have been increasingly investigated for the reduction of cardiovascular (CV) events. The goal of this study was to summarize and compare the relative effectiveness of anti-inflammatory medications for the reduction of CV events in a systematic review and network-meta-analysis. Methods: In our systematic review, we included studies from Medline, Embase, the Cochrane Central Register of Controlled Trials, as well as clinical trial registry websites, Europe PMC, and conference abstract hand-searching. Randomized controlled trials were selected for inclusion if they included at least one anti-inflammatory treatment and involved patients with CAD. Bayesian network meta-analyses was performed to calculate risk estimates using random effects analyses in patients with ACS as well as stable CAD for the reduction of MACE. Results: A total of 14,699 studies were screened; sixty-one articles, all randomized control trials, met eligibility criteria for inclusion. A total of 41,647 patients were included in the stable CAD network analysis and 29,388 patients were included in the ACS network analysis. In the ACS analysis, both non-steroidal anti-inflammatory (NSAID) use (OR: 0.30, 95% Credible Limits [CrI]: 0.11-0.74) and colchicine use were associated with a significant reduction in MACE compared to control (OR: 0.71, 95% Credible Limits [CrI]: 0.58-0.88). In the stable CAD analysis, both corticosteroids (OR: 0.44, 95% CrI: 0.26-0.73) and colchicine (OR: 0.65, 95% Credible Limits [CrI]: 0.54-0.77) were associated with a significant reduction in MACE compared to control. Conclusions: In patients with acute coronary syndromes, NSAIDs and colchicine were associated with a reduction in the risk of major adverse cardiac events. In patients with stable coronary artery disease, both colchicine and steroids were associated with a reduction in the risk of major adverse cardiac events. For a comprehensive analysis of benefits and harms of anti-inflammatory therapies, large comparative studies or network meta-regression analyses of patient-level data will be required. Systematic review registration number: PROSPERO registry—CRD4202230328
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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 | 0.044 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".