Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Abstract

Purpose The occurrence of myocardial injury, a grave complication post complete mesocolic excision (CME), profoundly impacts the immediate and long-term prognosis of patients. The aim of this inquiry was to conceive a machine learning model that can recognize preoperative, intraoperative and postoperative high-risk factors and predict the onset of myocardial injury following CME. Patients and Methods This study included 1198 colon cancer patients, 133 of whom experienced myocardial injury after surgery. Thirty-six distinct variables were gathered, encompassing patient demographics, medical history, preoperative examination characteristics, surgery type, and intraoperative details. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor algorithm (KNN), were employed to fabricate the model, and k-fold cross-validation, ROC curve, calibration curve, decision curve analysis (DCA), and external validation were employed to evaluate it. Results Out of the four predictive models employed, the XGBoost algorithm demonstrated the best performance. The ROC curve findings indicated that the XGBoost model exhibited remarkable predictive accuracy, with an area under the curve (AUC) value of 0.997 in the training set and 0.956 in the validation set. For internal validation, the k-fold cross-validation method was utilized, and the XGBoost model was shown to be steady. Furthermore, the calibration curves demonstrated the XGBoost model’s high predictive capability. The DCA curve revealed higher benefit rates for patients who underwent interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.74, which indicated that the XGBoost prediction model possessed good extrapolative capacity. Conclusion The myocardial injury prediction model for patients undergoing CME that was developed using the XGBoost machine learning algorithm in this study demonstrates both high predictive accuracy and clinical utility.

Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease: A machine learning-based study

“…Previous studies have confirmed the application value of ML algorithms in clinical diagnosis and prognosis[ 19 , 20 ]. Moreover, ML algorithms play a crucial role in constructing predictive models, especially in assisting clinical decision-makers to precisely identify high-risk patients and offer timely and accurate treatment, thereby enhancing patient prognosis[ 21 , 22 ].…”