Identifying Risk Factors for Poor Efficacy of Adalimumab Treatment in Patients With Crohn’s Disease: Insights From Machine Learning Models

Aim: Adalimumab (ADA) is an effective treatment for Crohn’s disease (CD); however, some patients still experience adverse reactions and nonresponse. This study aimed to explore the risk factors associated with ADA poor efficacy through machine learning algorithms, which provide promising guidance for the management of ADA in clinical practice. Methods: This single‐center investigation included 114 CD patients treated with ADA in the Department of Gastroenterology from January 2020 to January 2023. Risk factors associated with each poor efficacy event were explored using logistic regression and machine learning algorithms. Shapley additive explanations (SHAP) and partial dependence plot methods were used to analyze the risk factors of each event. Results: The results showed 8 of these patients experienced primary non‐response (PNR), 35 patients developed secondary loss of response (LOR), and 27.2% (31/114) of patients experienced at least one adverse events (AEs). After comparing the fit of the models established by 10 algorithms, the risk factors associated with PNR, LOR, and AEs were analyzed using the logistic regression algorithm, KNN algorithm, and Extra Tree algorithm, respectively. The most important variables related to the PNR, LOR, and AEs events were the history of corticosteroid use, baseline CDAI, and uric acid, respectively. Conclusions: This study confirmed the efficacy of ADA for clinical practice in the Chinese CD population, and that patients with a history of corticosteroid use, high levels of disease activity, and high inflammatory state before ADA treatment were associated with increased risks of poor efficacy.