Longterm Efficacy and Safety of Abatacept in Patients with Rheumatoid Arthritis Treated in Routine Clinical Practice: Effect of Concomitant Methotrexate after 24 Weeks
Bibliographic record
Abstract
OBJECTIVE: Our study aimed to evaluate the longterm efficacy and safety of abatacept (ABA), and to explore factors that increase its longterm efficacy in patients with rheumatoid arthritis (RA) treated in routine clinical practice. METHODS: There were 231 participants with RA treated with ABA who were prospectively registered in a Japanese multicenter registry. They were followed up for at least 52 weeks. RESULTS: Mean age of the patients was 64.3 years, mean disease duration was 12.1 years, mean 28-joint Disease Activity Score (DAS28)-C-reactive protein was 4.49, and 48.5% of patients were concomitantly treated with methotrexate (MTX). Overall retention rate of ABA was 77.1% at 52 weeks; 14.8% of patients discontinued because of inadequate response and 3.5% because of adverse events. The proportion of patients achieving DAS28-defined low disease activity (LDA) significantly increased from baseline to 52 weeks (7.3% to 43.8%, p < 0.01); 40.9% of patients who did not achieve LDA at 24 weeks had more than 1 categorical improvement in DAS28-defined disease activity at 52 weeks. Multivariate logistic regression revealed concomitant MTX use to be an independent predictor of the categorical improvement in DAS28-defined disease activity from 24 to 52 weeks (adjusted OR 3.124, p = 0.010). CONCLUSION: In routine clinical practice, ABA demonstrated satisfactory clinical efficacy and safety in patients with established RA for 52 weeks. The clinical efficacy of ABA increased with time even after 24 weeks, and this was strongly influenced by concomitant MTX use. Our study provides valuable real-world findings on the longterm management of RA with ABA.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".