Trends in utilization of advanced therapies in patients with inflammatory bowel diseases in the USA, 2021-2025
Bibliographic record
Abstract
BACKGROUND AND AIMS: Seven new advanced therapies, belonging to three new classes, have been approved for the treatment of inflammatory bowel diseases (IBD) since 2021. We examined trends in utilization of advanced therapies in the USA since 2021. METHODS: Using de-identified electronic health record data from 71 US health systems, we examined quarterly trends in utilization of different advanced therapies in patients with ulcerative colitis and Crohn's disease between 2021 and 2025, using interrupted time-series analysis utilizing Prais-Winsten regression. RESULTS: In 11 093 patients with ulcerative colitis treated with advanced therapies, we observed an increase in use of upadacitinib (12.1% in Q2/2025; P < .001, compared with first quarter of prescription), interleukin-23p19 (IL23p19) antagonists (5.5%; P = .03), and ustekinumab (9.5%; P = .04), decline in use of tumor necrosis factor (TNF) antagonists (34.5%; P = .003), and stable use of vedolizumab (33.6%; P = .17) and sphingosine-1 phosphate receptor modulators (1.6%; P = .28). In 15 951 patients with Crohn's disease treated with advanced therapies, we observed an increase in use of IL23p19 antagonists (15.0% in Q1/2025; P = .003) and upadacitinib (5.9%; P < .001) and decline in use of TNF antagonists (50.2%; P = .001) and ustekinumab (13.5%; P < .001). CONCLUSIONS: Contemporary trends in advanced therapy utilization in patients with IBD suggest an increased utilization of upadacitinib and anti-interleukins, accompanied by a decline in use of TNF antagonists in the USA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".