Prediction Model Construction Of Cerebral Complications After Acute Type A Aortic Dissection Based On Machine Learning Method

Background:Acute type A aortic dissection is one of the most serious diseases with an increasing incidence in recent years.Due to the difficulty of operation,long operation time,involving extracorporeal circulation and so on,postoperative complications are easy to occur,involving a wide range of organs,especially in the cerebral complications.Objective:This study aims to use machine learning method to build the best prediction model of cerebral complications after ATAAD surgery,offer support for early identification of high-risk patients,provide explanations to the model based on SHAP,explore related risk factors,and provide reference for clinical work to a certain extent.Methods:In this study,patients with ATAAD received treatment in the Department of Cardiovascular Surgery of the First Hospital of Lanzhou University from January 2017 to September 2021 were retrospectively included,and the data were screened strictly according to inclusion and exclusion criteria.Firstly,the data were cleaned,coded and grouped,all the data were standardized.All the data were randomly divided into training set and test set by 7:3.In the training set,dummy variables were introduced for unordered multicategorical variables,and the best features were screened by least absolute shrinkage and selection operator regression.A SMOTEEN algorithm combining oversampling and undersampling was introduced to balance data set.Prediction models were constructed based on Logistic regression,K-nearest neighbor,Random forest,Gradient boosting machine,Support vector machine and Multi-layer perception.Further,parameters were tuned using K-folds cross validation or hyperparameter grid search,and the optimal parameters were determined by Accuracy and substituted into the models.Next,the performance of each model was verified in the test set.The receiver operating characteristic curves of all models were plotted.Brier score,Accuracy,Sensitivity,Specificity,F1 score and area under the curve were used to evaluate the comprehensive efficacy of the models.The model with the largest area under the curve was regarded as the best model.Hosmer-lemeshow test was further conducted for the best model,and decision curve analysis was drawn to clarify the net income of the model.Shapley additive explanations algorithm was used to explain the model and the results were visualized lastly.Results:(1)The best features set determined by Lasso regression included: postoperative ALT value,total operative time,intraoperative cryoprecipitate infusion amount,postoperative LDH value,conditions of brachiocephalic trunk artery,postoperative urea value,age and hypertension.(2)The best model was RF model with the maximum AUC value of 0.828[95%CI(0.585,0.902)],and F1 Score,sensitivity,specificity,Brier Score was0.667[95%CI(0.375,0.959)],0.833[95%CI(0.535,0.932)],0.892[95%CI(0.792,0.992)],0.139[95%CI(0.035,0.244)],respectively.The comprehensive performance of RF was great.In Hosmer-lemeshow test,P value was 0.183,suggesting that the RF model was well fitted.DCA suggested that high net income can be obtained through RF model within a larger threshold range.(3)The model was interpreted based on the SHAP algorithm,and the results suggested that the most influential feature in the RF model on the prediction results was the postoperative ALT value,and the next two were the total operative time and the intraoperative cryoprecipitate infusion amount,respectively.The postoperative ALT value and the total operative time contributed positively to the prediction results of the model,and the intraoperative cryoprecipitate infusion amount contributed reversely to the model.Conclusion:(1)Postoperative ALT value,total operative time,postoperative LDH value,involvement of brachiocephalic trunk artery,postoperative urea value,age and hypertension are the risk factors of cerebral complications after ATAAD.Enough attention should be paid to the factors at the top of the list.(2)Appropriate infusion of cryoprecipitate during operation can reduce the occurrence of cerebral complications after ATAAD,which could be further verified by larger sample data in the future.(3)In the data of our center,RF prediction model has better prediction ability and better comprehensive performance than Logistic regression,suggesting that machine learning algorithm has unique advantages in data mining and analysis.In this study,RF model can identify high-risk patients in time and provide support for early intervention and improving patient prognosis.