Machine learning to predict venous thromboembolism after colorectal cancer surgery: a Chinese dynamic modelling study

BACKGROUND: Current prediction tools for venous thromboembolism (VTE) after colorectal cancer (CRC) surgery lack accuracy for individualized care. To address this critical gap, we developed machine learning (ML) models to dynamically predict postoperative VTE in Chinese CRC surgery patients. METHODS: We developed ML models using data from 1,836 CRC surgery patients across 46 Chinese centers in the CRC-VTE trial. The cohort was divided into a modeling set (n = 1,515) and an external test set (n = 321), with the modeling set further split into training and validation subsets (4:1 ratio). Preoperative and postoperative clinical features were selected using recursive feature elimination and Boruta. A total of 162 ML models were developed and evaluated across both preoperative and postoperative datasets, with model performance primarily assessed using the area under the receiver operating characteristic curve (AUROC). The final model underwent rigorous validation in an additional multicenter retrospective cohort and incorporated SHapley Additive exPlanations (SHAP) for risk calculation and decision interpretation. RESULTS: The CatBoost model achieved optimal performance, with AUROCs of 0.950 (preoperative data) and 0.971 (postoperative data) in the validation set, and 0.686 ± 0.037 (preoperative) and 0.715 ± 0.036 (postoperative) in the test set. Models incorporating postoperative data consistently outperformed preoperative-only models. Simplified models based on key predictors (7 preoperative and 9 postoperative features selected by SHAP values) maintained comparable performance, with AUROCs of 0.933 (preoperative data) and 0.949 (postoperative data) in the validation set, and 0.640 ± 0.030 (preoperative data) and 0.695 ± 0.030 (postoperative data) in the test set, respectively. CONCLUSIONS: Our study demonstrates the feasibility of a ML-based approach for predicting VTE following CRC surgery. The integration of ML with SHAP methodology provides a clinically actionable tool for individualized risk assessment and optimized VTE prevention strategies.