Colchicine for secondary prevention of ischaemic stroke and atherosclerotic events: a meta-analysis of randomised trials
Bibliographic record
Abstract
Background: Guidelines recommend low-dose colchicine for secondary prevention in cardiovascular disease, but uncertainty remains concerning its efficacy for stroke, efficacy in key subgroups and about uncommon but serious safety outcomes. Methods: In this trial-level meta-analysis, we searched bibliographic databases and trial registries form inception to May 16, 2024. We included randomised trials of colchicine for secondary prevention of ischaemic stroke and major adverse cardiovascular events (MACE: ischaemic stroke, myocardial infarction, coronary revascularisation, or cardiovascular death). Secondary outcomes were serious safety outcomes and mortality. A fixed-effect inverse-variance model was used to generate a pooled estimate of relative risk (RR) with 95% confidence intervals (CI). This study is registered with PROSPERO, CRD42024540320. Findings: Six trials involving 14,934 patients with prior stroke or coronary disease were included. In all patients, colchicine compared with placebo or no colchicine reduced the risk for ischaemic stroke by 27% (132 [1.8%] events versus 186 [2.5%] events, RR 0.73 [95% CI 0.58-0.90]) and MACE by 27% (505 [6.8%] events versus 693 [9.4%] events, with RR 0.73 [0.65-0.81]). Efficacy was consistent in key subgroups (females versus males, age below versus above 70, with versus without diabetes, statin versus non-statin users). Colchicine was not associated with an increase in serious safety outcomes: hospitalisation for pneumonia (109 [1.5%] versus 106 [1.5%], RR 0.99 [0.76-1.30]), cancer (247 [3.5%] versus 255 [3.6%], RR 0.97 [0.82-1.15]), and gastro-intestinal events (153 [2.1%] versus 135 [1.9%]), RR 1.15 [0.91-1.44]. There was no difference in all-cause death (201 [2.7%] versus 181 [2.4%], RR 1.09 [0.89-1.33]), cardiovascular death (70 [0.9%] versus 80 [1.1%], RR 0.89 [0.65-1.23]), or non-cardiovascular death (131 [1.8%] versus 101 [1.4%], RR 1.26 [0.98-1.64]). Interpretation: In patients with prior stroke or coronary disease, colchicine reduced ischaemic stroke and MACE, with consistent treatment effect in key subgroups, and did not increase serious safety events or death. Funding: There was no funding source for this study.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.056 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".