Efficacy and safety of colchicine in patients with coronary artery disease: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
AIMS: Inflammation plays a central role in the pathogenesis and clinical manifestations of atherosclerosis. Randomized controlled trials have investigated the potential benefit of colchicine in reducing cardiovascular (CV) events in patients with coronary artery disease (CAD) but produced conflicting results. The aim of this meta-analysis was to evaluate the efficacy and safety of colchicine in patients with CAD. METHODS: We systematically searched selected electronic databases from inception until 10 December 2020. Primary clinical endpoints were: major adverse cardiac events; all-cause mortality; CV mortality; recurrent myocardial infarction; stroke; hospitalization; and adverse medication effects. Secondary endpoints were short-term effect of colchicine on inflammatory markers. RESULTS: Twelve randomized controlled trials with a total of 13 073 patients with CAD (colchicine n = 6351 and placebo n = 6722) were included in the meta-analysis. At mean follow-up of 22.5 months, the colchicine group had lower risk of major adverse cardiac events (6.20 vs. 8.87%; P < .001), recurrent myocardial infarction (3.41 vs. 4.41%; P = .005), stroke (0.40 vs. 0.90%; P = .002) and hospitalization due to CV events (0.90 vs. 2.87%; P = .02) compared to the control group. The 2 patient groups had similar risk for all-cause mortality (2.08 vs. 1.88%; P = .82) and CV mortality (0.71 vs. 1.01%; P = .38). Colchicine significantly reduced high-sensitivity C-reactive protein (-4.25, P = .001) compared to controls but did not significantly affect interleukin (IL)-β1 and IL-18 levels. CONCLUSION: Colchicine reduced CV events and inflammatory markers, high-sensitivity C-reactive protein and IL-6, in patients with coronary disease compared to controls. Its impact on cardiovascular and all-cause mortality requires further investigation.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".