Association between osteoarthritis and increased risk of cardiovascular diseases : investigation of the role of NSAIDs as an underlying mechanism
Bibliographic record
Abstract
Osteoarthritis (OA) has been reported as an independent risk factor for cardiovascular diseases (CVD). There is no cure for OA and non-steroidal anti-inflammatory drugs (NSAIDs) are the mainstay of OA treatment. NSAIDs are known to be associated with various cardiovascular adverse effects, but their direct impact on CVD risk among OA patients is not well studied. There is a need to understand better the underlying mechanism of the increased risk of CVD among OA patients and to what extent NSAIDs play a role. This thesis conducted three studies using health administrative data (HAD) from British Columbia (BC), Canada and the Canadian Community Health Survey (CCHS) focusing on the role of NSAIDs in the OA-CVD association. The objective of study 1 was to quantify the role of NSAIDs in the increased risk of CVD among OA patients. This longitudinal study performed a mediation analysis using a marginal structural model and showed that a substantial proportion of total CVD risk among OA patients was attributable to NSAID use. The objective of study 2 was to evaluate the overall cardiovascular safety of various NSAIDs that are used in treating OA patients. This retrospective cohort study used time-dependent Cox regression analysis to estimate CVD risk associated with NSAID use overall and four unique groups of NSAIDs, i.e., coxibs, naproxen, ibuprofen and other conventional NSAIDs. This study showed that exposure to NSAIDs substantially increased CVD risk compared to unexposed person-time. It also showed that, relative to unexposed person-time coxibs and naproxen may increase CVD risk more than conventional NSAIDs including ibuprofen. The objective of study 3 was to identify a valid approach in imputing body mass index (BMI), an important confounding variable in the OA-CVD relationship for which information is usually not available in HAD. Multiple imputation was compared with proportion-based imputation (PBI) approach using plasmode simulated dataset created from CCHS data. This study showed that multiple imputation was superior to PBI in imputing BMI category using information from an external dataset. As a collective work, this thesis provides a better understanding of OA-CVD association that hopefully will improve clinical management of OA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".