Comparative Cardiovascular Safety of Nonsteroidal Anti‐Inflammatory Drug Versus Colchicine Use When Initiating Urate‐Lowering Therapy Among Patients With Gout: Target Trial Emulations
Bibliographic record
Abstract
OBJECTIVE: Among patients with gout, nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used despite scarce safety data in this specific population. Therefore, we quantified the comparative cardiovascular safety of NSAIDs versus colchicine among patients with gout starting allopurinol. METHODS: We conducted a sequential, propensity score-matched, new-user comparative effectiveness study using the target trial emulation framework to compare the risk of major adverse cardiovascular events (MACE; composite of myocardial infarction [MI], stroke, or cardiovascular death) among patients with gout started on allopurinol who were prescribed NSAIDs or colchicine for gout flare prophylaxis. A sensitivity analysis employed inverse probability of treatment weighting (IPTW). Secondarily, we examined the risk of MACE with colchicine or NSAIDs versus no prophylaxis. RESULTS: Among 18,120 propensity score-matched adults with gout starting allopurinol with NSAIDs or colchicine (83.5% male, mean age 60.9 years), the incidence of MACE and cardiovascular death were higher among NSAID users compared to colchicine users, with rate differences of 38.8 (95% confidence interval [CI] 15.4-62.2) and 10.9 (95% CI 0.7-21.1) per 1,000 person-years, respectively, and hazard ratios (HRs) of 1.56 (95% CI 1.11-2.17) and 2.50 (95% CI 1.14-5.26), respectively. Results were similar when IPTW was applied. Compared to no prophylaxis, NSAID use was associated with a higher risk of MACE and MI, with HRs of 1.50 (95% CI 1.17-1.91) and 1.93 (95% CI 1.35-2.75), respectively. CONCLUSION: In these target trial emulations of patients with gout starting allopurinol, NSAID prophylaxis was associated with a higher risk of MACE than colchicine or no prophylaxis, suggesting the avoidance of NSAID for gout flare prophylaxis.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".