Efficacy and safety of low-dose colchicine in patients with coronary disease: a systematic review and meta-analysis of randomized trials
Bibliographic record
Abstract
AIMS: Recent randomized trials demonstrated a benefit of low-dose colchicine added to guideline-based treatment in patients with recent myocardial infarction or chronic coronary disease. We performed a systematic review and meta-analysis to obtain best estimates of the effects of colchicine on major adverse cardiovascular events (MACE). METHODS AND RESULTS: We searched the literature for randomized clinical trials of long-term colchicine in patients with atherosclerosis published up to 1 September 2020. The primary efficacy endpoint was MACE, the composite of myocardial infarction, stroke, or cardiovascular death. We combined the results of five trials that included 11 816 patients. The primary endpoint occurred in 578 patients. Colchicine reduced the risk for the primary endpoint by 25% [relative risk (RR) 0.75, 95% confidence interval (CI) 0.61-0.92; P = 0.005], myocardial infarction by 22% (RR 0.78, 95% CI 0.64-0.94; P = 0.010), stroke by 46% (RR 0.54, 95% CI 0.34-0.86; P = 0.009), and coronary revascularization by 23% (RR 0.77, 95% CI 0.66-0.90; P < 0.001). We observed no difference in all-cause death (RR 1.08, 95% CI 0.71-1.62; P = 0.73), with a lower incidence of cardiovascular death (RR 0.82, 95% CI 0.55-1.23; P = 0.34) counterbalanced by a higher incidence of non-cardiovascular death (RR 1.38, 95% CI 0.99-1.92; P = 0.060). CONCLUSION: Our meta-analysis indicates that low-dose colchicine reduced the risk of MACE as well as that of myocardial infarction, stroke, and the need for coronary revascularization in a broad spectrum of patients with coronary disease. There was no difference in all-cause mortality and fewer cardiovascular deaths were counterbalanced by more non-cardiovascular deaths.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".