A Real-World Comparison of Clinical Effectiveness in Patients with Rheumatoid Arthritis Treated with Upadacitinib, Tumor Necrosis Factor Inhibitors, and Other Advanced Therapies After Switching from an Initial Tumor Necrosis Factor Inhibitor
Bibliographic record
Abstract
INTRODUCTION: This study compared the clinical effectiveness of switching from tumor necrosis factor inhibitor (TNFi) to upadacitinib (TNFi-UPA), another TNFi (TNFi-TNFi), or an advanced therapy with another mechanism of action (TNFi-other MOA) in patients with rheumatoid arthritis (RA). METHODS: Data were drawn from the Adelphi RA Disease Specific Programme™, a cross-sectional survey administered to rheumatologists and their consulting patients in Germany, France, Italy, Spain, the UK, Japan, Canada, and the USA from May 2021 to January 2022. Patients who switched treatment from an initial TNFi were stratified by subsequent therapy of interest: TNFi-UPA, TNFi-TNFi, or TNFi-other MOA. Physician-reported clinical outcomes including disease activity (with formal DAS28 scoring available for 29% of patients) categorized as remission, low/moderate/high disease activity, as well as pain were recorded at initiation of current treatment and ≥ 6 months from treatment switch. Fatigue and treatment adherence were measured ≥ 6 months from treatment switch. Inverse-probability-weighted regression adjustment compared outcomes by subsequent class of therapy: TNFi-UPA versus TNFi-TNFi, or TNFi-UPA versus TNFi-other MOA. RESULTS: Of 503 patients who switched from their first TNFi, 261 were in TNFi-UPA, 128 in TNFi-TNFi, and 114 in TNFi-other MOA groups. At the time of switch, most patients had moderate/high disease activity (TNFi-UPA: 73%; TNFi-TNFi: 52%; TNFi-other MOA: 60%). After adjustment for differences in characteristics at point of switch, patients in TNFi-UPA group (n = 261) were significantly more likely to achieve physician-reported remission (67.7% vs. 40.3%; p = 0.0015), no pain (55.7% vs. 25.4%; p = 0.0007), and complete adherence (60.0% vs. 34.2%; p = 0.0049) compared with patients in TNFi-TNFi group (n = 121). Similar findings were observed for TNFi-UPA versus TNFi-other MOA groups (n = 111). CONCLUSION: Patients who switched from TNFi to UPA had significantly better clinical outcomes of remission, no pain, and complete adherence than those who cycled TNFi or switched to another MOA.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".