Establishment and internal validation of a model to predict the efficacy of Adalimumab in Crohn’s disease

Abstract

Abstract Background: Clinically, the ability to distinguish which Crohn’s disease patients can benefit from Adalimumab is limited. Aims: This study aimed to develop a model for predicting clinical remission probability for Crohn’s disease patients with Adalimumab at 12 weeks. The model assists clinicians in identifying which Crohn’s disease patients are likely to benefit from Adalimumab treatment before starting therapy, thus optimizing individualized treatment strategies. Methods: Demographic and clinical characteristics of Crohn’s disease patients were utilized to develop a model for clinical remission probability. LASSO regression was used to select predictive factors, and predictions were made using a logistic regression model. The model was internally validated using the bootstrap method (resampling 1000 times). Results: 68 patients with Crohn’s disease were enrolled in this study. Clinical remission was observed in 55.9% at 12 weeks. Three variables were selected through the least absolute shrinkage and selection operator regression method, including Adalimumab-positive cell count, disease duration, and neutrophil count of Crohn’s disease patients. A predictive model was constructed by multivariate logistic regression (Adalimumab-positive cell count (OR, 1.143; 95%CI, 1.056–1.261), disease duration (OR, 0.967; 95%CI, 0.937–0.986), and neutrophil count (×109/L) (OR, 1.274; 95%CI,1.014–1.734)). The predictive model yielded an area under the curve of 0.866 (95%CI, 0.776–0.956), and in the internal validation, the area under the curve was 0.870 (95%CI, 0.770–0.940). Conclusions: This model provides a convenient tool to assess the likelihood of patient remission prior to Adalimumab treatment, thereby supporting the development of personalized treatment plans.

Keywords

• Crohn’s disease
• Prediction
• Nomogram