Objective:To evaluate the risk factors of serious adverse events in acute Stanford type A aortic dissection(ATAAD)using a random forest model.Methods:A total of 179 ATAAD patients were collected in the Zhongnan Hospital of Wuhan University from January 2013 to December 2020,and they were divided into two groups according to the occurrence of death or emergency surgery for aortic rupture or impending rupture within 30 days after diagnosis,and a random forest model was used to establish the model.The included cases were calculated,the Boruta algorithm was used to visualize the characteristics of the random forest model,and finally,the research results were compared and verified with the traditional logistic regression model.Results:We established a high-efficiency diag-nostic model of Random Forest with sensitivity,specificity,and AUC of 0.783,1.000,and 0.891,respectively.Penn classification,ascending aorta diameter,systolic blood pressure,pericardial hem-orrhage,C-reactive protein(CRP)level,and peri-aortic hematoma were the important features of the model.The traditional logistic regression model showed that Penn classification,ascending aorta di-ameter,systolic blood pressure and CRP level,and peri-aortic hematoma were independent predictors of adverse events.However,the sensitivity,specificity,and AUC of the traditional model were 0.674,0.895,and 0.844,respectively,and its diagnostic power was lower than that of the random forest model.Conclusion:The random forest algorithm can establish an efficient model to predict seri-ous adverse events within 30 days after the diagnosis of ATAAD,which can provide a reference for personalized surgical treatment plans.
Acute Type A Aortic DissectionRandom ForestPrediction Model
NETL
NSTL
万方数据
