Real-world Evidence Comparing Tofacitinib and Vedolizumab in Anti-TNF-experienced Patients With Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Antitumor necrosis factor (anti-TNF) inhibitors are first-line treatment among patients with ulcerative colitis (UC). With time, patients tend to lose response or become intolerant, necessitating switching to small cell biologics such as tofacitinib or vedolizumab. In this real-world study of a large, geographically diverse US population of TNF-experienced patients with UC, we evaluated the effectiveness and safety of newly initiating treatment with tofacitinib vs vedolizumab. METHODS: We conducted a cohort study using secondary data from a large US insurer (Anthem, Inc.). Our cohort included patients with UC newly initiating treatment with tofacitinib or vedolizumab. Patients were required to have evidence of treatment with anti-TNF inhibitors in the 6 months prior to cohort entry. The primary outcome was treatment persistence >52 weeks. Additionally, we evaluated the following secondary outcomes as additional measures of effectiveness and safety: (1) all-cause hospitalization; (2) total abdominal colectomy; (3) hospitalization for infection; (4) hospitalization for malignancy; (5) hospitalization for cardiac events; and (6) hospitalization for thromboembolic events. We used fine stratification by propensity scores to control for confounding by demographics, clinical factors, and treatment history at baseline. RESULTS: Our primary cohort included 168 new users of tofacitinib and 568 new users of vedolizumab. Tofacitinib was associated with lower treatment persistence (adjusted risked ratio, 0.77; 95% CI, 0.60 -0.99). Differences in secondary measures of effectiveness or safety between tofacitinib initiators vs vedolizumab initiators were not statistically significant (all-cause hospitalization, adjusted hazard ratio, 1.23; 95% CI, 0.83-1.84; total abdominal colectomy, adjusted HR, 1.79; 95% CI, 0.93-3.44;and hospitalization for any infection, adjusted HR, 1.94; 95% CI, 0.83-4.52). DISCUSSION: Ulcerative colitis patients with prior anti-TNF experience initiating tofacitinib demonstrated lower treatment persistence compared with those initiating vedolizumab. This finding is in contrast to other recent studies suggesting superior effectiveness of tofacitinib. Ultimately, head-to-head randomized, controlled trials that focus on directly measured end points may be needed to best inform clinical practice.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".