Construction of an online interactive calculation tool and corresponding risk stratification system for the probability of postoperative anastomotic leak in esophageal atresia based on machine learning algorithms
Objective:To predict postoperative anastomotic leak in esophageal atresia using machine learning techniques,to identify the risk factors for postoperative anastomotic leak,to calculate corresponding cut-off values,to develop an interactive web-based tool,and to help healthcare professionals quickly calculate the specific risk probability of postoperative anastomotic leak.Methods:Clinical data were collected from 251 patients with type Ⅲ congenital esophageal atresia who underwent surgical treatment in our hospital from January 2009 to December 2021,including demographic fea-tures,surgical data,and postoperative data.Five machine learning algorithms,i.e.,support vector machine(SVM),random forest(RF),logistic regression(LR),XGBoost,and Gaussian naive Bayes(GNB),were used to construct a predictive model for anastomotic leak after esophageal atresia repair.The area under the ROC curve(AUC),F1 score,accuracy,sensitivity,and specificity were used to evaluate the validity of the model,the Hosmer-Lemeshow test and Brier score were used to evaluate the degree of calibration,and the decision curve analysis(DCA curve)was used to evaluate the de-gree of calibration and stability.Restricted cubic spline techniques were used to calculate the cut-off value of each risk factor,and then an interactive web-based calculation tool was developed to establish a risk stratification system for postoperative anastomotic leak,which was used to facilitate healthcare professionals in convenient application.Results:The univariate analysis,importance ranking,and LASSO regression were performed for candidate risk factors,and the results showed that the distance between the ends of the esophageal gap,presence or absence of complex congenital heart disease,preoperative protein level,and presence or absence of pulmo-nary infection were the risk factors for postoperative anastomotic leak.Among the five machine learning algorithms,the logistic regres-sion model exhibited the best performance in terms of AUC,DCA,and calibration curve,with an AUC of 0.828,an accuracy of 0.772,and an F1 score of 0.532 in the training set and an AUC of 0.799,an accuracy of 0.765,and an F1 score of 0.544 in the validation set,suggesting that the model had good discriminatory ability and degree of calibration in predicting postoperative anastomotic leak in type Ⅲ congenital esophageal atresia.Meanwhile,the restricted cubic spline analysis showed that the distance between the ends of the esophageal gap and preoperative protein level had a cut-off value of 2 cm and 33.9 g/L,respectively,and healthcare professionals could use the online interactive web-based tool to input the results of related risk factors and calculate the specific probability of post-operative anastomotic leak for a given patient.Conclusion:The logistic regression model can predict the risk factors for postoperative anastomotic leak in patients with type Ⅲ congenital esophageal atresia,and the online interactive web-based tool is designed to quickly calculate the probability of postoperative anastomotic leak,thereby providing convenience for healthcare professionals.
万方数据
维普
