World journal of radiology · 2026
Predictive model for vedolizumab efficacy in moderate-to-severe ulcerative colitis based on computed tomography-derived body compositions and nutritional inflammatory markers.
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Zhang Xiao-Yan, Li Yu-Kun, Tian Zi-Bin, Guo Qing-Yu, Liu Jing-Nong, Liu Rui-Qing, Ren Ke-Yu
Аннотация
BACKGROUND: Vedolizumab (VDZ) is a key biologic for moderate-to-severe ulcerative colitis (UC), but therapeutic response varies widely among patients. The combined predictive value of computed tomography (CT)-derived body composition (e.g., intramuscular adipose tissue, skeletal muscle mass), inflammatory markers [C-reactive protein (CRP)], and anemia status for VDZ efficacy remains under-investigated.
AIM: To develop a predictive model for VDZ response in moderate-to-severe UC patients, based on CT-derived body composition and nutritional/inflammatory markers, and to clarify their clinical implications.
METHODS: A retrospective study was conducted on UC patients treated with VDZ at the Affiliated Hospital of Qingdao University. CT images were analyzed to quantify intramuscular adipose tissue and skeletal muscle mass. Clinical data including CRP levels and hemoglobin (HB) were collected. Multivariate logistic regression was used to identify independent predictors of treatment response, and a predictive model was constructed.
RESULTS: IMAT accumulation (odds ratio = 2.35, 95% confidence interval: 1.21-4.57, P = 0.012) and elevated CRP (odds ratio = 1.89, 95% confidence interval: 1.03-3.49, P = 0.041) were confirmed as independent predictors of poor VDZ response. Preserved skeletal muscle mass and normal HB levels were associated with better therapeutic outcomes. The combined predictive model demonstrated good discriminative ability (area under the curve = 0.78).
CONCLUSION: This study demonstrates that CT-derived body composition parameters (IMAT and skeletal muscle mass), combined with inflammatory markers (CRP) and HB levels, can effectively predict VDZ response in UC patients. The predictive model we developed offers a practical tool for identifying high-risk non-responders early, enabling clinicians to optimize individualized treatment strategies and improve clinical outcomes.
AIM: To develop a predictive model for VDZ response in moderate-to-severe UC patients, based on CT-derived body composition and nutritional/inflammatory markers, and to clarify their clinical implications.
METHODS: A retrospective study was conducted on UC patients treated with VDZ at the Affiliated Hospital of Qingdao University. CT images were analyzed to quantify intramuscular adipose tissue and skeletal muscle mass. Clinical data including CRP levels and hemoglobin (HB) were collected. Multivariate logistic regression was used to identify independent predictors of treatment response, and a predictive model was constructed.
RESULTS: IMAT accumulation (odds ratio = 2.35, 95% confidence interval: 1.21-4.57, P = 0.012) and elevated CRP (odds ratio = 1.89, 95% confidence interval: 1.03-3.49, P = 0.041) were confirmed as independent predictors of poor VDZ response. Preserved skeletal muscle mass and normal HB levels were associated with better therapeutic outcomes. The combined predictive model demonstrated good discriminative ability (area under the curve = 0.78).
CONCLUSION: This study demonstrates that CT-derived body composition parameters (IMAT and skeletal muscle mass), combined with inflammatory markers (CRP) and HB levels, can effectively predict VDZ response in UC patients. The predictive model we developed offers a practical tool for identifying high-risk non-responders early, enabling clinicians to optimize individualized treatment strategies and improve clinical outcomes.