Discontinuation of tofacitinib and TNF inhibitors in patients with rheumatoid arthritis: analysis of pooled data from two registries in Canada
Bibliographic record
Abstract
OBJECTIVES: The similarity in retention of tumour necrosis factor inhibitors (TNFi) and tofacitinib (TOFA) was previously reported separately by the Ontario Best Practices Research Initiative and the Quebec cohort Rhumadata. However, because of small sample sizes in each registry, we aimed to confirm the findings by repeating the analysis of discontinuation of TNFi compared with TOFA, using pooled data from both these registries. DESIGN: Retrospective cohort study. SETTING: Pooled data from two rheumatoid arthritis (RA) registries in Canada. PARTICIPANTS: Patients with RA starting TOFA or TNFi between June 2014 and December 2019 were included. A total of 1318 patients were included TNFi (n=825) or TOFA (n=493). OUTCOME MEASURES: Time to discontinuation was assessed using Kaplan-Meier survival and Cox proportional hazards regression analysis. Propensity score (PS) stratification (deciles) and PS weighting were used to estimate treatment effects. RESULTS: The mean disease duration in the TNFi group was shorter (8.9 years vs 13 years, p<0.001). Prior biological use (33.9% vs 66.9%, p<0.001) and clinical disease activity index (20.0 vs 22.1, p=0.02) were lower in the TNFi group.Discontinuation was reported in 309 (37.5%) and 181 (36.7%) TNFi and TOFA patients, respectively. After covariate adjustment using PS, there was no statistically significant difference between the two groups in discontinuation due to any reason HR=0.96 (95% CI 0.78 to 1.19, p=0.74)) as well as discontinuation due to ineffectiveness only HR=1.08 (95% CI 0.81 to 1.43, p=0.61)).TNFi users were less likely to discontinue due to adverse events (AEs) (adjusted HRs: 0.46, 95% CI 0.29 to 0.74; p=0.001). Results remained consistent for firstline users. CONCLUSIONS: In this pooled real-world data study, the discontinuation rates overall were similar. However, discontinuation due to AEs was higher in TOFA compared with TNFi users.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".