Abstract 14449: Long-Term Colchicine for Cardiovascular and Limb Risk Reduction in Peripheral Artery Disease
Bibliographic record
Abstract
Introduction: Evidence from randomized trials suggests colchicine may reduce major adverse cardiovascular events (MACE) in patients with coronary artery disease. Colchicine's effect in lower extremity peripheral artery disease (PAD) is not known. Methods: To make inferences about the real-world effectiveness of colchicine in PAD, we emulated a pragmatic target trial, leveraging variable prescription durations when adding colchicine to urate lowering therapy in patients with gout and PAD. Eligible patients received care at a multicenter academic health system between July 2007 and December 2019. Electronic health records were linked to Medicare claims data. Patients received either colchicine indefinitely (long-term) or for 3 months (short-term) and the observational analog of the per-protocol effect was estimated. Inverse probability weighted marginal structural models were used to adjust for potential confounding, immortal-time, and collider-stratification biases. The primary outcome was a 2-year composite of major adverse limb events (MALE), MACE, and all-cause mortality. Results: A total of 1,025 patients were enrolled; mean age was 77 years (SD 7), 32% were female, and 9% were non-white race. The risk of the primary composite outcome at 2 years was 31.2% (95% CI 26.9, 35.2) in the long-term colchicine group and 32.7% (29.6, 36.4) in the short-term group (Figure), with a risk difference of -1.6% (-6.3, 3.4) and risk ratio of 0.95 (0.82, 1.11). Beyond 2 years, risk estimates of secondary outcomes in each group diverged, but confidence intervals also widened (Figure). Conclusions: In a real-world sample of high-risk patients with PAD and gout, estimates of the effect of colchicine provided no conclusive evidence that colchicine decreased the risk of adverse cardiovascular or limb events and death. The cardiovascular and limb benefits of colchicine in older comorbid populations with PAD and advanced systematic atherosclerosis remain uncertain.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".