Development and validation of a machine learning-based predictive model for clinical remission in Crohn’s disease patients receiving Adalimumab therapy - Pianpian Xia; Feihong Deng; Deliang Liu (PLoS ONE, 2025)
Crohn’s disease (CD), a chronic inflammatory bowel disease, is witnessing a rising global incidence. Adalimumab (ADA), a biological agent, is widely used in its treatment. However, patients exhibit significant individual variability in responses to ADA therapy. This study focuses on developing and validating a machine learning – based predictive model to assess the clinical remission of CD patients at 12 and 48 weeks post – ADA treatment, while identifying the key influencing factors. A single – center retrospective study was conducted, involving patients from the Second Xiangya Hospital of Central South University between 2017 and 2024. Comprehensive data on demographics, lifestyle, disease characteristics, and laboratory indicators were collected and preprocessed. The dataset was partitioned into an 80% training set and a 20% test set. Six machine learning models, including Random Forest and Gradient Boosting Machine, were employed to construct the prediction model. Model performance was evaluated using metrics such as accuracy, sensitivity, and specificity. The SHAP analysis was performed to elucidate the key factors. The results indicated that the XGBoost model outperformed other models across multiple evaluation metrics. Fecal calprotectin (Fc), a marker of intestinal inflammation, showed that lower levels were associated with a tendency towards mucosal healing. C - reactive protein (CRP), on the other hand, reflected systemic inflammation. Both biomarkers significantly influenced the prediction outcomes at different time points. The developed model serves as a valuable tool for clinical stratification and personalized treatment planning. Future research should expand sample diversity through multi – center collaboration and integrate multi – omics data, such as gut microbiome and metabolomics, to further enhance the model’s ability to capture the molecular mechanisms underlying the disease.