Prediction of vedolizumab efficacy in ulcerative colitis: a nomogram incorporating pathological feature and serological marker

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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Publication Details

Journal
Clinical and Experimental Medicine
Published
2025-03-02
DOI
https://doi.org/10.1007/s10238-025-01601-6
Citations
1
Primary Topic
Inflammatory Bowel Disease
Type
article
Field-Weighted Citation Impact
1.82

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article

Prediction of vedolizumab efficacy in ulcerative colitis: a nomogram incorporating pathological feature and serological marker

Yong‐sheng Teng, Min Zou, Hong Guo, Wei Tan et al.
1 citations
Clinical and Experimental Medicine
Inflammatory Bowel Disease
1.82
article

Prediction of vedolizumab efficacy in ulcerative colitis: a nomogram incorporating pathological feature and serological marker

Yong‐sheng Teng, Min Zou, Hong Guo, Wei Tan, Tian Wang, Hui Yao, Yan Liu, Xuefeng Tang, Chaoqun Hu, Xiaomei Song
article en
1 citations

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.

Clinical and Experimental MedicineVol. 25(1)
Chongqing University (CN), First People's Hospital of Chongqing (CN), Chongqing Medical University (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 18%
Inflammatory Bowel Disease
1.82
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