Comparative Effectiveness of Ustekinumab and Anti-TNF Agent as First-Line Biological Therapy in Luminal Crohn’s Disease: A Retrospective Study From 2 Referral Centers
Bibliographic record
Abstract
BACKGROUND: Real-life data on the efficacy of ustekinumab as first-line therapy for the treatment of luminal Crohn's disease (CD) compared with anti-tumor necrosis factor (anti-TNF) agents are lacking. We compared the clinical response rates at 3 months in 2 cohorts of biologic-naïve patients treated by ustekinumab and anti-TNF agents. METHODS: Biologic-naïve patients starting either ustekinumab or an anti-TNF agent for luminal CD between 2016 and 2019 in 2 tertiary centers were retrospectively included. The primary endpoint was clinical response at 3 months, defined as a Harvey-Bradshaw Index <4 or a 3-point drop in the score without steroids, need for CD-related surgery, or treatment discontinuation owing to failure or intolerance. Patients treated with ustekinumab were matched to patients receiving anti-TNF agents by a propensity score algorithm. RESULTS: We included 156 patients starting anti-TNF agents (95 adalimumab and 61 infliximab) and 50 ustekinumab. After matching, clinical response rates at 3 months were 64% and 86% in the ustekinumab and anti-TNF groups, respectively (P = .01). At 12 months, in multivariate analysis adjusted for disease duration, location, concomitant immunosuppressant and steroids, and symptoms, clinical remission was independently associated with the biological therapy received (odds ratio, 2.6 for anti-TNF agent vs ustekinumab; P = .02). With a median follow-up duration of 40 (interquartile range, 23-52) months, no difference was observed in terms of time to drug withdrawal (P = .29) or safety. CONCLUSIONS: This retrospective real-world data suggest that an anti-TNF agent as a first-line biological therapy is associated with higher rates of response at 3 months than ustekinumab in patients with CD.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".