Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery - Jiang, Haiye; Liu, Leping; Wang, Yongjun; Ji, Hongwen; Ma, Xianjun; Wu, Jingyi; Huang, Yuanshuai; Wang, Xinhua; Gui, Rong; Zhao, Qinyu; Chen, Bingyu (Frontiers in Cardiovascular Medicine, 2021)
Background: This study intended to use machine learning model to identify critical preoperative and intraoperative variables and predict the risk of several severe complications (myocardial infarction, stroke, renal failure, and hospital mortality) after cardiac valvular surgery. Study Design and Methods: A total of 1,488 patients undergoing cardiac valvular surgery in eight large tertiary hospitals in China were examined. Fifty-four perioperative variables were collected, including essential demographic characteristics, concomitant disease, preoperative laboratory indicators, operation type and intraoperative information. Machine learning models were developed and validated by using ten-fold cross-validation. In each fold, the Recursive Feature Elimination was used to select key variables. Ten machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC), accuracy (ACC), Youden index, sensitivity, specificity, F1-score, positive predictive value (PPV) and negative predictive value (NPV) were used to compare the prediction performance of different models. The SHapley Additive ex Planations package was applied to interpret the best machine learning model. Finally, a model was trained on the whole dataset with the merged key variables, and a web tool was created for clinicians to use. Results: In this study, fourteen vital variables were finally selected, including intraoperative total input, intraoperative blood loss, intraoperative colloid bolus, Classification of New York Heart Association (NYHA) heart function, preoperative hemoglobin (Hb), preoperative platelet (PLT), Age, preoperative fibrinogen (FIB), intraoperative minimum red blood cell volume (Hct), Body Mass Index (BMI), creatinine, preoperative Hct, intraoperative minimum Hb, and intraoperative autologous blood. The eXtreme Gradient Boosting algorithms (XGBOOST) algorithm model presented significantly better predictive performance (AUROC: 0.90) than other models (ACC: 81%, Youden index: 70%, sensitivity: 89%, specificity: 81%, F1-score: 0.26, PPV: 15% and NPV: 99%). Conclusion: A model for predicting several severe complications after cardiac valvular surgery was successfully developed using a machine learning algorithm based on fourteen perioperative variables, which could guide clinical physicians to take appropriate preventive measures and diminish the complications for high-risk patients.