Colchicine Use Is Associated with Decreased Prevalence of Myocardial Infarction in Patients with Gout
Bibliographic record
Abstract
OBJECTIVE: The ability of antiinflammatory strategies to alter cardiovascular risk has not been rigorously examined. Colchicine is an antiinflammatory agent that affects macrophages, neutrophils, and endothelial cells, all of which are implicated in the pathogenesis of cardiovascular disease. We examined whether colchicine use was associated with a reduced risk of myocardial infarction (MI) in patients with gout. METHODS: We conducted a retrospective, cross-sectional study of all patients with an International Classification of Diseases, 9th ed, code for gout in the electronic medical record (EMR) of the New York Harbor Healthcare System Veterans Affairs network and ≥ 1 hospital visit between August 2007 and August 2008. Hospital pharmacy data were used to identify patients who had filled at least 1 colchicine prescription versus those who had not. Demographics and CV comorbidities were collected by EMR review. The primary outcome was diagnosis of MI. Secondary outcomes included all-cause mortality and C-reactive protein (CRP) level. RESULTS: In total, 1288 gout patients were identified. Colchicine (n = 576) and no colchicine (n = 712) groups had similar baseline demographics and serum urate levels. Prevalence of MI was 1.2% in the colchicine versus 2.6% in the no-colchicine group (p = 0.03). Colchicine users also had fewer deaths and lower CRP levels, although these did not achieve statistical significance. Colchicine effects persisted when allopurinol users were excluded from the analysis. CONCLUSION: In this hypothesis-generating study, gout patients who took colchicine had a significantly lower prevalence of MI and exhibited trends toward reduced all-cause mortality and lower CRP level versus those who did not take colchicine.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".