Drivers of mortality in patients with chronic coronary disease in the low-dose colchicine 2 trial
Bibliographic record
Abstract
BACKGROUND: Low-dose colchicine significantly reduces the risk of cardiovascular events in patients with chronic coronary disease. An increase of non-cardiovascular death raised concerns about its safety. This study reports cause-specific mortality and baseline predictors of mortality in the Low-Dose Colchicine 2 (LoDoCo2) trial. METHODS: Patients with chronic coronary disease were randomly allocated to colchicine 0.5 mg once daily or placebo on a background of optimal medical therapy. Cause-specific mortality data were analysed, stratified by treatment status. Multivariate analyses were performed to examine the predictors of mortality as well as cardiovascular and non-cardiovascular death. RESULTS: After a median 28.6 months follow-up, 133 out of 5522 participants (2.4%) died. Forty-five deaths were cardiovascular (colchicine versus placebo: 20 [0.7%] versus 25 [0.9%], HR, 0.80; 95% CI, 0.44-1.44), while eighty-eight deaths were non-cardiovascular (53 [1.9%] versus 35 [1.3%]; HR, 1.51; 95% CI, 0.99-2.31). Forty-eight deaths were due to cancer (26 [0.9%] versus 22 [0.8%]), thirteen end-stage pulmonary disease (9 [0.3%] versus 4 [0.1%]), eight infection (4 [0.1%] versus 4 [0.1%]), five dementia (4 [0.1%] versus 1 [0.0%]) and five related multiple organ failure (3 [0.1%] versus 2 [0.1%]). Multivariable analysis demonstrated age > 65 years was the only independent baseline characteristic associated with non-cardiovascular death (HR, 3.65; 95% CI, 2.06-6.47). CONCLUSIONS: During the LoDoCo2 trial, assignment to colchicine was not associated with an adverse effect on any specific causes of death. Most deaths were related to non-cardiovascular causes, underscoring the importance of comorbidities as drivers of all-cause mortality in patients with chronic coronary disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".