Long-Term Survival of Subcutaneous Biosimilar Tumor Necrosis Factor Inhibitors Compared to Originators: Results From a Multicenter Prospective Registry
Bibliographic record
Abstract
OBJECTIVE: To analyze the long-term survival of subcutaneous biosimilar tumor necrosis factor inhibitors compared to the originator molecules in patients with rheumatic diseases, as well as the factors associated with drug discontinuation. METHODS: Retrospective analysis of BIOBADASER, the Spanish multicenter prospective registry of patients with rheumatic disease receiving biologic and targeted disease-modifying antirheumatic drugs. Patients who started etanercept (ETN) or adalimumab (ADA) from January 2016 to October 2023 were included. The survival probabilities of biosimilars and originators were compared using Kaplan-Meier estimating curves. To identify factors associated with differences in the retention rates, hazard ratios (HR) were estimated using Cox regression models for all and specific causes (inefficacy or adverse events [AEs]) of discontinuation. RESULTS: A total of 4162 patients received 4723 treatment courses (2991 courses of ADA and 1732 courses of ETN), of which 722 (15.29%) were with originator molecules and 4001 (84.71%) were with biosimilars. The originators were more frequently discontinued than biosimilars (53.32% vs 33.37%, respectively). The main reason for discontinuation was inefficacy (60.35% of the treatments). The risk of overall discontinuation was lower for biosimilars (adjusted HR 0.84, 95% CI 0.75-0.95). Female sex, obesity, and second or later treatment lines increased the risk of discontinuation, whereas disease duration and the use of concomitant methotrexate were associated with a greater survival. When assessing cause-specific reasons of discontinuation, excluding nonmedical switching, the results from the crude and adjusted analyses showed no significant differences in the retention rate between biosimilars and originators. CONCLUSION: No significant differences were found between treatments in long-term survival due to inefficacy or AEs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".