Gradient Boosting Machine-Based Prognostic Model with SurvSHAP Analysis for Predicting Secondary Loss of Response to Infliximab Therapy in Crohn's Disease.

BACKGROUND: Infliximab (IFX), a cornerstone anti-TNF-α therapy for moderate-to-severe Crohn's disease (CD), is limited by secondary loss of response (SLOR), yet existing prognostic tools lack sufficient accuracy and interpretability. We developed and internally validated an interpretable machine learning (ML) framework for time-dependent SLOR prediction in IFX-treated CD patients. METHODS: We retrospectively enrolled 637 consecutive CD patients initiating IFX at a tertiary center. Multimodal predictors spanning demographic, pharmacokinetic (IFX trough concentrations and antibodies to infliximab [ATI] during induction and maintenance), hematological, biochemical, nutritional and coagulation domains were collected. Patients were randomly allocated to training (70%) and testing (30%) sets, stratified by SLOR status, and variables were screened via univariable and multivariable Cox regression. Nine ML algorithms were benchmarked at 12, 24 and 36 months using time-dependent AUC, concordance index, calibration and decision curve analysis. The top-performing model was further evaluated through risk-stratified Kaplan-Meier analysis and time-dependent SHapley Additive exPlanations (SurvSHAP). RESULTS: Seven independent determinants of SLOR were identified: maintenance-phase IFX trough concentration, disease duration, induction-phase ATI, platelet count, sex, concomitant immunosuppressant use and white blood cell count. The gradient boosting machine (GBM) demonstrated the strongest overall performance in the testing set, with time-dependent AUCs of 0.696, 0.825 and 0.854 at 12, 24 and 36 months, respectively, although discrimination at 12 months remained modest. GBM-based risk stratification yielded significantly divergent SLOR-free survival curves (HR = 6.462, 95% CI: 3.870-10.788, P < 0.001). SurvSHAP analysis ranked maintenance-phase IFX trough concentration as the predominant contributor with the widest effect range, followed by disease duration, induction-phase ATI and platelet count as adverse factors, while concomitant immunosuppressant use exhibited a protective signal. CONCLUSION: This internally validated ML framework showed improving discrimination over longer horizons, with maintenance-phase IFX trough concentration emerging as the dominant predictor, supporting proactive therapeutic drug monitoring pending external multicenter validation.