Colchicine in Patients With Coronary Artery Disease: A Systematic Review and Meta‐Analysis of Randomized Trials
Bibliographic record
Abstract
Background Inflammation plays a pivotal role in coronary artery disease (CAD). The anti‐inflammatory drug colchicine seems to reduce ischemic events in patients with CAD. So far there is equipoise about its safety and impact on mortality. Methods and Results To evaluate the utility of colchicine in patients with acute and chronic CAD, we performed a systematic review and meta‐analysis. MEDLINE, EMBASE, Cochrane CENTRAL and conference abstracts were searched from January 1975 to October 2020. Randomized trials assessing colchicine compared with placebo/standard therapy in patients with CAD were included. Data were combined using random‐effects models. The reliability of the available data was tested using trial sequential analyses . Of 3108 citations, 13 randomized trials (n=13 125) were included. Colchicine versus placebo/standard therapy in patients with CAD reduced risk of myocardial infarction (odds ratio [OR] 0.64; 95% CI, 0.46–0.90; P =0.01; I 2 41%) and stroke/transient ischemic attack (OR 0.50; 95% CI, 0.31–0.81; P =0.005; I 2 0%). But treatment with colchicine compared with placebo/standard therapy had no influence on all‐cause and cardiovascular mortality (OR 0.96; 95% CI, 0.65–1.41; P =0.83; I 2 24%; and OR 0.82; 95% CI, 0.55–1.22; P =0.45; I 2 0%, respectively). Colchicine increased the risk for gastrointestinal side effects ( P <0.001). According to trial sequential analyses, there is only sufficient evidence for a myocardial infarction risk reduction with colchicine. Conclusions Among patients with CAD, colchicine reduces the risk of myocardial infarction and stroke, but has a higher rate of gastrointestinal upset with no influence on all‐cause mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".