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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| 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".