Incidence of Major Adverse Cardiovascular Events in Patients With Rheumatoid Arthritis Treated With <scp>JAK</scp> Inhibitors Compared With Biologic Disease‐Modifying Antirheumatic Drugs: Data From an International Collaboration of Registries
Bibliographic record
Abstract
OBJECTIVE: Our objective was to assess the incidence of major adverse cardiovascular events (MACEs) in patients with rheumatoid arthritis (RA) treated with JAK inhibitors (JAKi), tumor necrosis factor inhibitors (TNFi), or biologic disease-modifying antirheumatic drugs with other modes of action (bDMARD-OMA) in a multicountry, real-world population. METHODS: Patients with RA from 15 registries in the JAK-pot collaboration were included. MACE incidence was analyzed using two approaches: a within-registry analysis aggregating country-specific estimates from registers with >25 incident MACEs through meta-analysis and an individual-level data combined analysis. We used adjusted linear mixed Poisson regression to obtain incidence rate ratios (IRRs) of MACEs between treatment groups, accounting for multiple treatment courses. RESULTS: The study included 73,008 treatment courses (16,417 JAKi, 35,373 TNFi, and 21,218 bDMARD-OMA) and 828 incident MACEs among 51,233 patients. Median follow-up time was 1.3 years, with most of the follow-up concentrated in the first two years of treatment. Incidence rates were 7.0, 7.6, and 11.8 per 1,000 person-years for JAKi, TNFi, and bDMARD-OMA, respectively. Compared to TNFi, JAKi (within-registry adjusted IRR 0.89, 95% confidence interval [CI] 0.63-1.25) had similar incidence rates of MACEs and bDMARD-OMA had higher rates (within-registry adjusted IRR 1.35, 95% CI 1.10-1.66). Combined analysis showed similar results. CONCLUSION: Observational data from the JAK-pot collaboration show no evidence of an increase in cardiovascular events during the first two years of use with JAKi compared to TNFi in the general RA population.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".