Cost-effectiveness of low-dose colchicine after myocardial infarction in the Colchicine Cardiovascular Outcomes Trial (COLCOT)
Bibliographic record
Abstract
AIMS: In the randomized, placebo-controlled Colchicine Cardiovascular Outcomes Trial (COLCOT) of 4745 patients enrolled within 30 days after myocardial infarction (MI), low-dose colchicine (0.5 mg once daily) reduced the incidence of the primary composite endpoint of cardiovascular death, resuscitated cardiac arrest, MI, stroke, or urgent hospitalization for angina leading to coronary revascularization. To assess the in-trial period and lifetime cost-effectiveness of low-dose colchicine therapy compared to placebo in post-MI patients on standard-of-care therapy. METHODS AND RESULTS: A multistate Markov model was developed incorporating the primary efficacy and safety results from COLCOT, as well as healthcare costs and utilities from the Canadian healthcare system perspective. All components of the primary outcome, non-cardiovascular deaths, and pneumonia were included as health states in the model as both primary and recurrent events. In the main analysis, a deterministic approach was used to estimate the incremental cost-effectiveness ratio (ICER) for the trial period (24 months) and lifetime (20 years). Over the in-trial period, the addition of colchicine to post-MI standard-of-care treatment decreased the mean overall per-patient costs by 47%, from $502 to $265 Canadian dollar (CAD), and increased the quality-adjusted life years (QALYs) from 1.30 to 1.34. The lifetime per-patient costs were further reduced (69%) and QALYs increased with colchicine therapy (from 8.82 to 11.68). As a result, both in-trial and lifetime ICERs indicated colchicine therapy was a dominant strategy. CONCLUSION: Cost-effectiveness analyses indicate that the addition of colchicine to standard-of-care therapy after MI is economically dominant and therefore generates cost savings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".