Vedolizumab and Anti-Tumour Necrosis Factor α Real-World Outcomes in Biologic-Naïve Inflammatory Bowel Disease Patients: Results from the EVOLVE Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: This study aimed to compare real-world clinical effectiveness and safety of vedolizumab, an α4β7-integrin inhibitor, and anti-tumour necrosis factor-α [anti-TNFα] agents in biologic-naïve ulcerative colitis [UC] and Crohn's disease [CD] patients. METHODS: This was a 24-month retrospective medical chart study in adult UC and CD patients treated with vedolizumab or anti-TNFα in Canada, Greece and the USA. Inverse probability weighting was used to account for differences between groups. Primary outcomes were cumulative rates of clinical effectiveness [clinical response, clinical remission, mucosal healing] and incidence rates of serious adverse events [SAEs] and serious infections [SIs]. Secondary outcomes included cumulative rates of treatment persistence [patients who did not discontinue index treatment during follow-up] and dose escalation and incidence rates of disease exacerbations and disease-related surgeries. Adjusted analyses were performed using inverse probability weighting. RESULTS: A total of 1095 patients [604 UC, 491 CD] were included. By 24 months, rates of clinical effectiveness were similar between groups, but incidence rates of SAEs (hazard ratio [HR] = 0.42 [0.28-0.62]) and SIs (HR = 0.40 [0.19-0.85]) were significantly lower in vedolizumab vs anti-TNFα patients. Rates of treatment persistence [p < 0.01] by 24 months were higher in vedolizumab patients with UC. Incidence rates of disease exacerbations were lower in vedolizumab patients with UC (HR = 0.58 [0.45-0.76]). Other outcomes did not significantly differ between groups. CONCLUSION: In this real-world setting, first-line biologic therapy in biologic-naïve patients with UC and CD demonstrated that vedolizumab and anti-TNFα treatments were equally effective at controlling disease symptoms, but vedolizumab has a more favourable safety profile.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".