Longterm Retention of Tumor Necrosis Factor-α Inhibitor Therapy in a Large Italian Cohort of Patients with Rheumatoid Arthritis from the GISEA Registry: An Appraisal of Predictors
Bibliographic record
Abstract
OBJECTIVE: To evaluate 4-year retention rates of tumor necrosis factor-α (TNF-α) inhibitors adalimumab, etanercept, and infliximab among patients with longstanding rheumatoid arthritis (RA), as derived from an Italian national registry. METHODS: The clinical records of 853 adult patients with RA in the GISEA (Gruppo Italiano Studio Early Arthritis) registry were prospectively analyzed to compare drug survival rates and the baseline factors that may predict adherence to therapy. RESULTS: In 2003 and 2004, 324 patients started treatment with adalimumab, 311 with etanercept, and 218 with infliximab. After 4 years, the global retention rate of anti-TNF-α therapy was 42%. Etanercept survival (51.4%) was significantly better than that of infliximab (37.6%) or adalimumab (36.4%; p < 0.0001). Accordingly, the mean duration of therapy was significantly longer for etanercept (3.1 ± 2 yrs) than for adalimumab (2.6 ± 2 yrs) or infliximab (2.7 ± 2 yrs; p < 0.05). The use of concomitant disease-modifying antirheumatic drugs, mainly methotrexate, and the presence of comorbidities significantly predicted drug continuation (p < 0.01), whereas a high Disease Activity Score did not. CONCLUSION: The 4-year global drug survival of adalimumab, etanercept, and infliximab was lower than 50%, with etanercept having the best retention rate. The main positive predictor of adherence to anti-TNF-α therapy was the concomitant use of methotrexate. Our study provides further evidence that the real-life treatment of patients with RA may be different from that of randomized clinical trials.
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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 | 0.005 |
| 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 | 0.001 | 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".