Persistence of Tofacitinib in the Treatment of Rheumatoid Arthritis in Open‐Label, Long‐Term Extension Studies up to 9.5 Years
Bibliographic record
Abstract
OBJECTIVE: Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis (RA). This post hoc analysis evaluated tofacitinib persistence in patients with RA in long-term extension (LTE) studies up to 9.5 years. METHODS: Data were pooled from two LTE studies: ORAL Sequel (NCT00413699) and Study A3921041 (NCT00661661). Patients received tofacitinib 5 or 10 mg twice daily (BID), as monotherapy or with background conventional synthetic disease-modifying antirheumatic drugs. Kaplan-Meier estimates for tofacitinib drug survival and reasons for discontinuation were evaluated. Baseline factors were analyzed as predictors of persistence. RESULTS: In 4967 tofacitinib-treated patients entering LTE studies, mean (maximum) treatment duration was 3.5 (9.4) years. Median drug survival (95% confidence interval) was 4.9 (4.7, 5.1) years. Estimated 2- and 5-year drug survival rates were 75.5% and 49.4%, respectively. Median drug survival was similar between the tofacitinib 5 and 10 mg BID groups, and slightly higher for patients receiving tofacitinib monotherapy versus combination therapy. Overall, 50.7% of patients discontinued tofacitinib; of these, 47.2% were due to adverse events and 7.1% for lack/loss of efficacy. An increased risk of discontinuation was associated with baseline diabetes, hypertension, negative anticyclic citrullinated peptide (anti-CCP), negative rheumatoid factor (RF), and inadequate response to tumor necrosis factor inhibitors (TNFi-IR). CONCLUSION: Median drug survival of tofacitinib-treated patients participating in LTE studies was approximately 5 years and was similar for tofacitinib dosed at 5 and 10 mg BID. Reduced drug survival was associated with negative anti-CCP/RF status, TNFi-IR, and certain comorbidities. These data support tofacitinib use for long-term management of 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 | 0.048 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".