Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy.

PURPOSE

This study aims to construct a machine learning model that can recognize preoperative, intraoperative, and postoperative high-risk indicators and predict the onset of venous thromboembolism (VTE) in patients.

PATIENTS AND METHODS

A total of 1239 patients diagnosed with gastric cancer were enrolled in this retrospective study, among whom 107 patients developed VTE after surgery. We collected 42 characteristic variables of gastric cancer patients from the database of Wuxi People's Hospital and Wuxi Second People's Hospital between 2010 and 2020, including patients' demographic characteristics, chronic medical history, laboratory test characteristics, surgical information, and patients' postoperative conditions. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN), were employed to develop predictive models. We also utilized Shapley additive explanation (SHAP) for model interpretation and evaluated the models using k-fold cross-validation, receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and external validation metrics.

RESULTS

The XGBoost algorithm demonstrated superior performance compared to the other three prediction models. The area under the curve (AUC) value for XGBoost was 0.989 in the training set and 0.912 in the validation set, indicating high prediction accuracy. Furthermore, the AUC value of the external validation set was 0.85, signifying good extrapolation of the XGBoost prediction model. The results of SHAP analysis revealed that several factors, including higher body mass index (BMI), history of adjuvant radiotherapy and chemotherapy, T-stage of the tumor, lymph node metastasis, central venous catheter use, high intraoperative bleeding, and long operative time, were significantly associated with postoperative VTE.

CONCLUSION

The machine learning algorithm XGBoost derived from this study enables the development of a predictive model for postoperative VTE in patients after radical gastrectomy, thereby assisting clinicians in making informed clinical decisions.