Explainable random forest-SHAP framework for Montreal phenotype-stratified prediction of one-year complications in Crohn's disease.

Background: Early identification of phenotype-stratified, short-term complication risk remains a critical unmet need in Crohn's disease (CD). Existing models focus on single outcomes and lack interpretability, limiting clinical use. We developed a clinically interpretable machine-learning framework to enable Montreal phenotype-stratified prediction of three one-year complication categories: bowel resection (Task 1), perianal complications (Task 2), and abdominal complications (Task 3). Methods: Data from 370 patients across two clinical centers were pooled for model development and evaluated using stratified 10-fold cross-validation. Within each fold, data preprocessing, LASSO feature selection, SMOTE-Tomek resampling, model optimization, calibration, and threshold determination were restricted to the training data. Seven machine-learning algorithms were compared in terms of discrimination, calibration, with decision-curve analysis used to explore potential net benefit. SHapley Additive exPlanations (SHAP) were used for model interpretation, and nomograms, heatmaps, and a web-based calculator were generated as exploratory visualizations of model-derived risk estimates rather than as clinical decision-support tools. Phenotype-stratified and non-linear associations between CDAI and complication risk were further analyzed within the Montreal framework. Results: Within one year after discharge, bowel resection, perianal complications, and abdominal complications occurred in 70 (18.9%), 142 (38.4%), and 84 (22.7%) patients, respectively. Random Forest showed the most consistent overall performance, achieving an AUROC of 0.86 (95% CI, 0.82-0.90) for bowel resection, 0.71 (95% CI, 0.64-0.78) for perianal complications, and 0.73 (95% CI, 0.69-0.78) for abdominal complications. SHAP identified distinct predictors across outcomes: disease behavior, nutritional support therapy, and disease duration for bowel resection; prior perianal complications, extraintestinal manifestations, and CDAI for perianal complications; and prior abdominal complications, nutritional support therapy, and Montreal L3 phenotype for abdominal complications. Receipt of NST was interpreted as a clinical marker of nutritional depletion and greater disease burden rather than as a causal risk factor. Montreal-based stratification revealed marked heterogeneity, with isolated small-bowel disease associated with higher risks of bowel resection and abdominal complications, and isolated colonic disease with the highest burden of perianal complications. Conclusion: The interpretable RF-SHAP framework showed potential for Montreal phenotype-stratified prediction of one-year complication risk in CD and inform future evaluation of risk stratification and phenotype-informed management.