Colchicine in Cardiovascular Disease: In-Depth Review
Bibliographic record
Abstract
Inflammation plays a prominent role in the development of atherosclerosis and other cardiovascular diseases, and anti-inflammatory agents may improve cardiovascular outcomes. For years, colchicine has been used as a safe and well-tolerated agent in diseases such as gout and familial Mediterranean fever. The widely available therapeutic has several anti-inflammatory effects, however, that have proven effective in a broad spectrum of cardiovascular diseases as well. It is considered standard-of-care therapy for pericarditis, and several clinical trials have evaluated its role in postoperative and postablation atrial fibrillation, postpericardiotomy syndrome, coronary artery disease, percutaneous coronary interventions, and cerebrovascular disease. We aim to summarize colchicine’s pharmacodynamics and the mechanism behind its anti-inflammatory effect, outline thus far accumulated evidence on treatment with colchicine in cardiovascular disease, and present ongoing randomized clinical trials. We also emphasize real-world clinical implications that should be considered on the basis of the merits and limitations of completed trials. Altogether, colchicine’s simplicity, low cost, and effectiveness may provide an important addition to other standard cardiovascular therapies. Ongoing studies will address complementary questions pertaining to the use of low-dose colchicine for the treatment of cardiovascular disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".