Which advanced treatment should be used following the failure of a first-line anti-TNF in patients with rheumatoid arthritis? 15 years of evidence from the Quebec registry RHUMADATA
Bibliographic record
Abstract
BACKGROUND: Since 2000, advanced therapies (AT) have revolutionized the treatment of moderate to severe RA. Randomized control trials as well as observational studies together with medication availability often determine second-line choices after the failure of first TNF inhibitors (TNFi). This led to the observation that specific sequences provide better long-term effectiveness. We investigated which alternative medication offers the best long-term sustainability following the first TNFi failure in RA. METHODS: Data were extracted from RHUMADATA from January2007. Patients were followed until treatment discontinuation, loss to follow-up or 25 November 2022. Kaplan-Meier and Cox regression models were used to compare discontinuation between groups. Missing data were imputed, and propensity scores were computed to reduce potential attribution bias. Complete, unadjusted and propensity score-adjusted imputed data analyses were produced. RESULTS: Six hundred eleven patients [320 treated with a TNFi and 291 treated with molecules having another mechanism of action (OMA)] were included. The mean age at diagnosis was 44.5 and 43.9 years, respectively. The median retention was 2.84 and 4.48 years for TNFi and OMAs groups. Using multivariable analysis, the discontinuation rate of the OMA group was significantly lower than TNFi (adjHR: 0.65; 95% CI: 0.44-0.94). This remained true for the PS-adjusted MI Cox models. In a stratified analysis, rituximab (adjHR: 0.39; 95% CI: 0.18-0.84) had better retention than TNFi after adjusting for patient characteristics. CONCLUSION: Switching to an OMA, especially rituximab, in patients with failure to a first TNFi appears to be the best strategy as a second line of therapy.
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".