ArticleClinical and experimental medicine2025
Prediction of vedolizumab efficacy in ulcerative colitis: a nomogram incorporating pathological feature and serological marker.
Article in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Probiotics reduce the severity of DSS-induced colitis in aged mice by enhancing intestinal barrier function and modulating gut microbiota.GeroScience · 2026Article
- Current and novel biomarkers for predicting and assessing therapeutic response in inflammatory bowel disease: a systematic review.Therapeutic advances in gastroenterology · 2026Review
- Association of nutritional indices with early clinical remission in patients with moderate-to-severe ulcerative colitis treated with vedolizumab.Therapeutic advances in gastroenterology · 2026Article
- Predictive model for ulcerative colitis therapeutic response using clinical indicators and peripheral blood T-cell subsets.Frontiers in immunology · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Vedolizumab (VDZ) is a humanized, gut-selective biologic used in the treatment of ulcerative colitis (UC). However, data on predictive factors for treatment response are limited. This study aims to develop a nomogram to predict VDZ treatment responsiveness in UC. We retrospectively collected clinical data from patients with moderate-to-severe active UC who received VDZ induction therapy at Chongqing General Hospital from December 2020 to March 2024. Full-slide images of colon biopsies from UC patients prior to VDZ treatment were analyzed to quantify mean mucosal eosinophil density (MMED). Based on clinical response 14-week post-treatment, patients were categorized into responsive and non-responsive groups. In total, 84 UC patients were analyzed, with 58 responding to VDZ treatment and 26 not responding. Significant differences were observed in pathological indices, with MMED showing a statistically significant difference between the groups (p < 0.001). Serum biomarkers, including C-reactive protein (CRP), also showed a significant difference (P = 0.015), as did the CRP/albumin (CRP/ALB) ratio (P = 0.018). Additionally, UCEIS scores differed significantly between the groups (P = 0.025). Independent risk factors identified through multivariate logistic regression analysis were used to establish a predictive model, presented as a nomogram. The area under the curve (AUC) for the combined MMED and CRP predictive model was 0.867 (95% CI: 0.781-0.953, p < 0.001), indicating high accuracy in predicting VDZ efficacy. These data are easily accessible even in primary healthcare settings, allowing our predictive model to support improved treatment decisions for patients.
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