Comparative Effectiveness of Upadacitinib and Tofacitinib in Ulcerative Colitis: A US Propensity-Matched Cohort Study
Bibliographic record
Abstract
INTRODUCTION: There are limited real-world data comparing the effectiveness of upadacitinib and tofacitinib in patients with ulcerative colitis (UC). METHODS: We conducted a retrospective cohort study using TriNetX, a multi-institutional database, to compare the effectiveness of upadacitinib and tofacitinib in patients with UC. The primary aim was to assess the risk of a composite outcome of hospitalization requiring intravenous steroids and/or colectomy within 6 and 12 months. One-to-one propensity score matching was performed for demographics, comorbid conditions, mean hemoglobin, C-reactive protein, albumin, and calprotectin, and prior UC medications including recent oral or intravenous steroid use between the cohorts. Risk was expressed as adjusted odds ratio (aOR) with 95% confidence intervals (CI). RESULTS: There were 526 patients in the upadacitinib cohort (mean age 40.4 ± 16.3, 44.8% female sex, 76.6% White race) and 1,149 patients in the tofacitinib cohort (mean age 42 ± 17.1, 41.9% female sex, 76% White race). After propensity score matching, there was no significant difference in the risk of the composite outcome of need for intravenous steroids and/or colectomy within 6 months (aOR 0.75, 95% CI 0.49-1.09). However, there was a lower risk of the composite outcome (aOR 0.63, 95% CI 0.44-0.89) in the upadacitinib cohort compared with the tofacitinib cohort within 12 months. There was no difference in the risk of intravenous steroid use (aOR 0.70, 95% CI 0.48-1.02) but lower risk of colectomy (aOR 0.46, 95% CI 0.27-0.79). In sensitivity analysis, there was also a lower risk of the composite outcome (aOR 0.64, 95% CI 0.44-0.94), including lower risk of intravenous steroid use (aOR 0.67, 95% CI 0.45-0.99) and colectomy (aOR 0.49, 95% CI 0.26-0.92) in the upadacitinib cohort compared with the tofacitinib cohort within 12 months. DISCUSSION: This study utilizing real-world data showed that upadacitinib was associated with improved disease-specific outcomes at 12 months compared with tofacitinib in patients with UC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".