Comparative Effectiveness of Tofacitinib vs Upadacitinib for the Treatment of Acute Severe Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND & AIMS: Tofacitinib and upadacitinib are Janus kinase (JAK) inhibitors that are increasingly used for the treatment of acute severe ulcerative colitis (ASUC). However, comparative analyses of safety and effectiveness have not been performed for their use in this setting. METHODS: This multicenter, retrospective study enrolled hospitalized adult patients treated with tofacitinib or upadacitinib for ASUC between January 2019 and June 2024. The main outcomes were clinical response, clinical remission, and colectomy-free survival. Propensity-adjusted analyses using inverse probability treatment weighting, and double robust estimations were used to control for confounding factors. RESULTS: In total, 111 patients (60 tofacitinib, 51 upadacitinib) were enrolled across 23 international centers. JAK inhibitors were used to induce response in 86 patients (77%) and used to maintain response after adequate intravenous steroid response in 25 (23%). The median follow up was 31 weeks (interquartile range, 13-64 weeks). Between days 3 and 7 after treatment initiation, upadacitinib was associated with greater response rates (84% vs 54%; P = .02), but response/remission was comparable at day 98 (45%/36% vs 55%/48%) and day 182 (29/29% vs 39/34%). Sub-analyses for JAK inhibitor use as salvage therapy (tofacitinib [n = 35] vs upadacitinib [n = 31]) showed similar effectiveness outcomes across both groups. The probabilities of colectomy-free survival at days 98 and 182 for tofacitinib and upadacitinib were 79% and 75% and 80% and 78%, respectively, with no significant differences in the comparison of survival curves (P = .99). Treatment failure rates (where JAK inhibitors were used to maintain remission) were similar at days 98 and 182. The frequency of adverse events was comparable. CONCLUSIONS: Tofacitinib and upadacitinib appear to have similar effectiveness and safety profiles when used for the treatment of ASUC.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".