Establishment of an In-hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on Machine Learning
Objectives:To evaluate and predict the risk for in-hospital mortality in elderly patients receiving cardiac valvular surgery preferably,we developed a new prediction model using machine learning.Methods:Clinical data including baseline characteristics,peri-operative data and primary endpoint of 7 163 elderly patients aged 65 years or older undergoing cardiac valvular surgery from January 2016 to December 2018 from 87 hospitals were collected from the Chinese Cardiac Surgery Registry(CCSR).Patients from January 2016 to June 2018 were assigened to the training cohort(n=5 774)and patients from July to December 2018 were assigened to the validation cohort(n=1 389).The primary endpoint was in-hospital mortality.Machine learning algorithms were used to analyze risk factors and develop prediction model.Results:Overall in-hospital mortality was 4.1%.Linear discriminant analysis(LDA),support vector classification(SVC)and logistic regression(LR)models in the training cohort all have high AUCs and low Brier scores,with good discrimination and calibration.In validation cohort,the AUC of LDA,SVC and LR were 0.744,0.744 and 0.746 respectively,which were significantly better than that of 0.642 using the European System for Cardiac Operative Risk Evaluation II(EuroSCORE II)model(P<0.05).Conclusions:The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high.LDA,SVC and LR can predict the risk for in-hospital mortality in elderly patients receiving cardiac valvular surgery with high accuracy.
万方数据
维普
