Supplementary file 1_Machine learning-based risk prediction of postoperative deep vein thrombosis in Chinese patients undergoing gastrointestinal surgery.docx

BackgroundDeep vein thrombosis (DVT) is a common and potentially life-threatening complication after gastrointestinal surgery. Traditional risk assessment tools rely on static variables and may not effectively capture dynamic perioperative changes.
MethodsClinical data from 596 Chinese patients undergoing gastrointestinal surgery were retrospectively collected. Patients were randomly divided into training and validation sets (7:3 ratio). Five machine learning algorithms—logistic regression (LR), Extreme Gradient Boosting (XGBoost), multilayer perceptron (MLP), random forest (RF), and elastic net (ENet)—were applied to identify key predictive features and build risk prediction models. The optimal model was visualized using a nomogram and validated through calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).
ResultsAmong the five models, the RF model achieved the best predictive performance. Postoperative Day-7 D-dimer, Day-1 D-dimer, and Day-5 D-dimer were identified as the most important predictive features. The calibration curve and DCA further confirmed the nomogram's predictive accuracy and clinical utility.
ConclusionWe developed a novel machine learning–based model for predicting postoperative DVT in Chinese patients after gastrointestinal surgery. Integrating dynamic biomarkers and nonlinear modeling, the tool enhances early identification of high-risk individuals. Multicenter validation is warranted to further strengthen the model's applicability.