Efficacy and prognostic factors of treatment retention with intravenous abatacept for rheumatoid arthritis: 24-month results from an international, prospective, real-world study.
Bibliographic record
Abstract
OBJECTIVES: To evaluate retention of abatacept over 24 months in patients with rheumatoid arthritis (RA) in routine clinical practice across Europe and Canada. METHODS: ACTION (AbataCepT In rOutiNe clinical practice) was a prospective, observational, multicentre study of adult patients with moderate-to-severe RA who, at their physician's discretion, initiated treatment with intravenous abatacept. Enrolment occurred from May 2008 to December 2010, with up to 30 months of follow-up. The primary endpoint was the abatacept retention rate over 24 months. Crude abatacept retention rate was estimated using the Kaplan-Meier method. Prognostic factors of abatacept retention in patients with ≥1 prior biologic failure were derived from a Cox proportional hazards regression model, accounting for clustered data. RESULTS: A total of 1137 patients were enrolled (1573 patient-years on abatacept); most (89.2%) had experienced prior biologic failure. The overall crude abatacept retention rate at 24 months was 54.4% (95% confidence interval: 51.3, 57.4). Positivity for both rheumatoid factor and anti-cyclic citrullinated antibody, previous exposure to one or no anti-tumour necrosis factor agents, and cardiovascular comorbidity were prognostic of higher abatacept retention. Erythrocyte sedimentation rate ≥51 mm/hour and introduction of corticosteroid use at abatacept initiation were predictors of lower abatacept retention. Abatacept retention varied according to country. Abatacept was well tolerated without any unexpected safety signals. CONCLUSIONS: In a real-world setting, intravenous abatacept treatment retention was more than 50% at 24 months. The identification of prognostic factors of abatacept retention could support individualised biologic treatment strategies in patients with moderate-to-severe 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".