The Risk of Cardiovascular Events Associated With Disease-modifying Antirheumatic Drugs in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the comparative effects of biologic disease-modifying antirheumatic drugs (bDMARD) and tofacitinib against conventional synthetic DMARD (csDMARD) on incident cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA). METHODS: RA patients with ≥ 1 year of participation in the FORWARD study, from 1998 through 2017, were assessed for incident composite CVD events (myocardial infarction, stroke, heart failure, and CVD-related death validated from hospital/death records). DMARD were categorized into 7 mutually exclusive groups: (1) csDMARD-referent; (2) tumor necrosis factor-α inhibitor (TNFi); (3) abatacept (ABA); (4) rituximab; (5) tocilizumab; (6) anakinra; and (7) tofacitinib. Glucocorticoids (GC) were assessed using a weighted cumulative exposure model, which combines information about duration, intensity, and timing of exposure into a summary measure by using the weighted sum of past oral doses (prednisolone equivalent). Cox proportional hazard models were used to adjust for confounders. RESULTS: During median (IQR) 4.0 (1.7-8.0) years of follow-up, 1801 CVD events were identified in 18,754 RA patients. The adjusted model showed CVD risk reduction with TNFi (HR 0.81, 95% CI 0.71-0.93) and ABA (HR 0.50, 95% CI 0.30-0.83) compared to csDMARD. While higher GC exposure as weighted cumulative exposure was associated with increased CVD risk (HR 1.15, 95% CI 1.11-1.19), methotrexate (MTX) use was associated with CVD risk reduction [use vs nonuse HR 0.82, 95% CI 0.74-0.90, and high dose (> 15 mg/week) vs low dose (≤ 15 mg/week) HR 0.83, 95% CI 0.70-0.99]. CONCLUSION: ABA and TNFi were associated with decreased risk of CVD compared to csDMARD. Minimizing GC use and optimizing MTX dose may improve cardiovascular outcomes in patients with RA.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".