ArticleInternational journal of colorectal disease2020
Development of a clinical model to predict secondary non-response of infliximab treatment in Crohn's disease.
Article in International journal of colorectal disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Gradient Boosting Machine-Based Prognostic Model with SurvSHAP Analysis for Predicting Secondary Loss of Response to Infliximab Therapy in Crohn's Disease.Drug design, development and therapy · 2026Article
- Distinct B Cell Subsets Changes as Potential Biomarkers of Response to Biologic Therapy in Crohn's Disease.International journal of molecular sciences · 2025Article
- Novel Gene Signatures Predicting Primary Non-response to Infliximab in Ulcerative Colitis: Development and Validation Combining Random Forest With Artificial Neural Network.Frontiers in medicine · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSecondary non-response to infliximab (IFX) in patients with Crohn's disease (CD) is so common that prediction of non-response is required to adopt the optimal therapy for an individual patient.
methodsThe clinical model was constructed in a training cohort. Clinical features, serological parameters, and genetic biomarkers at week 14 IFX therapy were obtained as predictors. Secondary non-response was defined at week 54. Univariate analysis was first performed to eliminate irrelevant predictors with non-response. Then, a logistic regression model was developed by using predictors with P < 0.1 in a univariate analysis. Finally, the model was validated with reference to its discrimination, calibration, and decision curve analysis (DCA).
resultsIn a univariate analysis, erythrocyte sedimentation rate (ESR), the previous surgery, disease location, C-reactive protein (CRP), and TNFRSF1B (676 T>G) were found associated with secondary non-response to IFX. Logistic regression analysis with stepwise backward selection P < 0.05 then identified that ESR, the previous surgery, CRP, and TNFRSF1B (676 T>G) could serve as independent predictors, and a clinical model was developed based on the above predictors. Model 2 with TNFRSF1B (676 T>G) incorporated demonstrated more satisfactory discrimination (P = 0.029), better calibration (U P2 > 0.05), and higher clinical value in the validation cohort.
conclusionsThe study presents a model to predict non-response to IFX in CD, which incorporates previous surgery, ESR, CRP, and TNFRSF1B (676 T>G). This model can be used to help clinicians adjust the therapeutic strategy and CD patients avoid unnecessary exposure to IFX.
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