Cardiovascular Risk Factors and the Risk of Discontinuation of Advanced Therapies Due to Treatment Failure in Rheumatoid Arthritis: Results From the Ontario Best Practices Research Initiative
Bibliographic record
Abstract
OBJECTIVES: Our goal was to investigate whether cardiovascular disease (CVD) risk factors are associated with the retention of biologic disease-modifying antirheumatic drugs (bDMARDs) or targeted-synthetic DMARDs (tsDMARDs) in patients with rheumatoid arthritis (RA). METHODS: We included participants in the Ontario Best Practices Initiative RA registry who initiated their first bDMARD or tsDMARD. Participants were grouped by the number of baseline CVD risk factors (0, 1, or ≥2). The primary outcome was time-to-discontinuation of therapy for any reason. Secondary outcomes included discontinuation for primary failure, secondary failure, or due to adverse events. Competing risks hazards model, adjusted for clinically important confounders, estimated the association between CVD risk factors and treatment retention. RESULTS: The sample included 872 patients, of which 58% (n = 508) discontinued their b/tsDMARD after a median of 13 months from the time of initiation. The most common causes for treatment discontinuation were primary failure (n = 72), secondary failure (n = 126), or adverse events (n = 133). Patients with no CVD risk factors experienced significantly longer treatment survival compared to patients with 1 or ≥2 CVD risk factors. In multivariable-adjusted analysis, there was no association between all-cause discontinuation and CVD risk factors. However, there was a significant association between the presence of >1 CVD risk factor and treatment discontinuation, notably due to secondary treatment failure, but not due to adverse events. CONCLUSION: Multiple CVD risk factors increase the risk of treatment failure in RA, particularly for secondary treatment failure. To improve patient outcomes, future research should focus on developing strategies to identify early treatment nonresponse and investigate the potential modifiability of this association.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".