Integrating inflammatory and coagulation biomarkers for surgical risk stratification and treatment benefit assessment in Crohn's disease.

Introduction: Inflammatory and coagulation abnormalities are closely linked to disease progression in Crohn's disease (CD). However, whether integrating these biomarkers can improve long-term surgical risk stratification and inform treatment decision-making remains unclear. Methods: A total of 1,060 patients with CD were enrolled. Candidate predictors, including inflammatory and coagulation biomarkers, were selected using LASSO-Cox regression and the Boruta algorithm. Surgery-free survival was assessed using Kaplan-Meier analysis. Eight machine learning models were developed and evaluated using five-fold cross-validation. Shapley additive explanations (SHAP) were used to interpret the best-performing model. Inverse probability weighting was applied to reduce confounding and selection bias and to assess the benefit of biologic therapy across risk strata. Results: Inflammatory and coagulation biomarkers, together with disease-related features, were major determinants of long-term surgical risk in CD. Among the eight models tested, the gradient boosting machine (GBM) achieved the best performance, with a C-index of 0.816 (95% confidence interval [CI], 0.789-0.843), significantly outperforming the Cox model (0.680, 95% CI, 0.643-0.717; Conclusion: Integrating inflammatory and coagulation biomarkers improves surgical risk stratification in Crohn's disease and may help identify patients most likely to benefit from biologic therapy.