A random forest model predicts responses to infliximab in Crohn’s disease based on clinical and serological parameters

Abstract

Background Infliximab (IFX) has revolutionised the treatment for Crohn’s disease (CD) recently, while a part of patients show no response to it at the end of the induction period. We developed a random forest-based prediction tool to predict the response to IFX in CD patients. Methods This observational study retrospectively enrolled the patients diagnosed with active CD and received IFX treatment at the Gastroenterology Department in Xiangya Hospital of Central South University between January 2017 and December 2019. The baseline data were recorded in the beginning and were used as predictor variables to construct models to forecast the outcome of the response to IFX. Results Our cohort identified a total of 174 patients finally with a response rate of 29.3% (51/174). The area under the receiver operating characteristic curve (AUC) for the model, based on the random forest was 0.90 (95%CI: 0.82–0.98), compared to the logistic regression model with AUC of 0.68 (95%CI: 0.52–0.85). The optimal cut-off value of the random forest model was 0.34 with the specificity of 0.94, the sensitivity of 0.81 and the accuracy of 0.85. We demonstrated a strong association of IFX response with the levels of complement C3 (C3), high density lipoprotein, serum albumin, Controlling Nutritional Status (CONUT) score and visceral fat area/subcutaneous fat area ratio (VSR). Conclusion A novel random forest model using the clinical and serological parameters of baseline data was established to identify CD patients with baseline inflammation to achieve IFX response. This model could be valuable for physicians, patients and insurers, which allows individualised therapy.