P953 Establishment and Internal Validation of a Clinical Risk Score to Predict the Efficacy of Adalimumab in Crohn's Disease

Abstract

Abstract Background Adalimumab (ADA) plays a crucial role in treating Crohn's disease (CD), but not all patients benefit from it. This study aimed to analyze the factors affecting the treatment effectiveness of ADA for CD and then establish an efficacy prediction model with internal validation. Methods Retrospectively collected data from CD patients receiving ADA at Xijing Hospital from October 2020 to February 2023. Collect clinical data of patients and use FITC-labeled ADA to perform immunofluorescence staining on paraffin tissue sections of CD patients before ADA treatment. The data were randomly split into training and validation. Single-factor and multiple-factor logistic regression models were used to analyze the factors influencing the 12-week clinical remission of CD patients treated with ADA in the training, determining the optimal predictive model. A line chart prediction model was established and the model's discrimination and calibration were tested. Finally, the model was internally validated in the validation set. Results A total of 68 patients were included in the study. The clinical remission rate of CD patients treated with ADA at 12 weeks was 55.88% (38/68). The results showed that baseline endoscopic stenosis [OR=0.019, 95% confidence interval (95% CI) 0.019-0.993], disease course [OR=0.966, 95% CI 0.940-0.993], and the number of FITC-labeled ADA-positive cells [OR=1.109, 95% CI 1.025-1.201] were factors affecting the therapeutic effect of ADA in CD patients (p<0.05) which used to establish the efficacy prediction model. The AUC of the prediction model in the training and validation were 0.868 (95% CI 0.769-0.977) and 0.802 (95% CI 0.604-1) (p<0.05), respectively. The clinical decision curve analysis showed that the prediction model had significant clinical benefits in both the training and validation. Conclusion The prediction model can provide reference for clinicians to make treatment decisions.