Role of Nonsteroidal Antiinflammatory Drugs in the Association Between Osteoarthritis and Cardiovascular Diseases: A Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: To elucidate the role of nonsteroidal antiinflammatory drugs (NSAIDs) in the increased risk of cardiovascular disease (CVD) among osteoarthritis (OA) patients. METHODS: This longitudinal study was based on linked health administrative data from British Columbia, Canada. From a population-based cohort of 720,055 British Columbians, we selected 7,743 OA patients and 23,229 age- and sex-matched non-OA controls. We used multivariable Cox proportional hazards models to estimate the risk of developing incident CVD (primary outcome) as well as ischemic heart disease, congestive heart failure, and stroke (secondary outcomes). To estimate the mediating effect of NSAIDs, defined as current use of an NSAID according to linked PharmaNet data, in the OA-CVD relationship, we implemented a marginal structural model. RESULTS: OA patients had a higher risk of developing CVD than controls without OA. After adjusting for socioeconomic status, body mass index, hypertension, diabetes, hyperlipidemia, chronic obstructive pulmonary disease, and Romano comorbidity score, the adjusted hazard ratio (HR) was 1.23 (95% confidence interval [95% CI] 1.17-1.28). The adjusted HRs for congestive heart failure, ischemic heart disease, and stroke were 1.42 (95% CI 1.33-1.51), 1.17 (95% CI 1.10-1.26), and 1.14 (95% CI 1.07-1.22), respectively. Approximately 41% of the total effect of OA on increased CVD risk was mediated through NSAIDs. For the secondary outcomes, the proportion mediated through NSAIDs was 23%, 56%, and 64% for congestive heart failure, ischemic heart disease, and stroke, respectively. CONCLUSION: The findings of this first study to evaluate the mediating role of NSAIDs in the relationship between OA and CVD suggest that NSAID use contributes substantially to the OA-CVD association.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".