Long-term trials of colchicine for secondary prevention of vascular events: a meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Colchicine has emerged as a safe and inexpensive anti-inflammatory medication to target the residual risk of cardiovascular events in the secondary prevention of coronary artery disease. Two recently published randomized controlled trials (RCTs) investigating colchicine in the post-stroke and post-myocardial infarction (MI) populations warrant a re-evaluation of colchicine. New evidence was synthesized in a systematic review and meta-analysis to determine the long-term efficacy and safety of colchicine for the secondary prevention of vascular disease. METHODS: Randomized controlled trials comparing the incidence of cardiovascular events between patients with clinically manifest vascular disease randomized to colchicine vs. placebo and ≥12-month follow-up were included. The primary efficacy endpoint is major adverse cardiovascular events (MACE) and includes cardiovascular mortality, MI, ischaemic stroke, and urgent coronary revascularization. The DerSimonian and Laird random effects model was used to calculate pooled effect estimates. RESULTS: Six RCTs, with a pooled sample size of 21 800 patients, were included (colchicine n = 10 871; placebo n = 10 929). Over a follow-up of 12-34 months, colchicine reduced the incidence of MACE compared with placebo [pooled hazard ratio .75, 95% confidence interval (CI) .56-.93]. The reduction in cardiovascular events among colchicine patients was driven by reductions in MIs, ischaemic strokes, and urgent coronary revascularizations (P < .05 for all). No differences were detected for safety outcomes (P > .05 for all), including non-cardiovascular deaths (risk ratio 1.08, 95% CI .76-1.54). CONCLUSIONS: This updated meta-analysis of RCTs demonstrated a substantial reduction in MACE, MI, ischaemic stroke, and recurrent coronary revascularization with colchicine compared with placebo. Therefore, the results support the use of colchicine to reduce recurrent cardiovascular events.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.055 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".