E37. Does Body Mass Index Impact Long-Term Retention with Abatacept in Patients with Rheumatoid Arthritis who have Received at Least One Prior Biologic Agent? 2-Year Results from a Real-World, International, Prospective Study
Bibliographic record
Abstract
Background: In RA, reduced efficacy with anti-TNF therapy and dose escalation have been reported for obese patients compared with non-obese patients. Clinical trials have shown that BMI does not affect abatacept (ABA) efficacy or pharmacodynamics and real-world data show that short-term ABA retention, dosing and treatment outcomes are unaffected by BMI. We assessed the impact of BMI on the long-term retention of patients using i.v. ABA who had previously failed ≥1 biologic in clinical practice across Europe and Canada. Methods: ACTION was a 2-year, non-interventional, international, multicentre, cohort study that evaluated the retention and effectiveness of i.v. ABA in adults with moderate-to-severe RA. Patients who received ≥1 prior biologic and enrolled in countries with sufficient patient numbers to explore between-country effects were included in this analysis. Patients were stratified by their baseline BMI. Crude 2-year retention rate was estimated using the Kaplan-Meier method. The effect of BMI was analysed through a multivariate Cox proportional hazard model clustered for site effects with conditional imputation of missing data for covariates. Hazard ratios and corresponding 95% CI were adjusted for sociodemographic variables, disease characteristics, comorbidities at initiation and treatment characteristics. Patients were considered adherent to ABA if the ratio of the number of infusions received to the number expected was between 80% and 120%.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".