Incremental Value of Traditional Chinese Medicine Features in Two-Stage Pre-Endoscopic Prediction

This observational study evaluates whether adding Traditional Chinese Medicine (TCM)-related clinical features improves the performance of machine-learning models that estimate disease risk before colonoscopy. We compare two modeling strategies that use the same sequential (two-stage) framework: Western-only models, based on demographic, clinical, and laboratory information commonly used in Western medicine. TCM-integrated models, which use the same types of Western information plus structured TCM features. Stage 1 aims to distinguish people more likely to have functional bowel problems from those more likely to have inflammatory or neoplastic colorectal disease. Stage 2 focuses on people in the higher-risk pathway and aims to distinguish ulcerative colitis from other inflammatory or neoplastic conditions. Models are developed in one dataset and tested in independent external data from other centers. We assess discrimination (including AUC), clinical usefulness with decision-curve analysis, and which TCM features contribute most in the integrated models. This study does not assign treatments. It analyzes existing clinical information to support future pre-endoscopic triage research. Results will not replace colonoscopy or clinician judgment.