Predictive analysis of postoperative death in patients with Stanford B acute aortic dissection by XGBoost model
10.3760/cma.j.cn115396-20210118-00026
- VernacularTitle:XGBoost模型对Stanford B型急性主动脉夹层患者术后死亡的预测分析
- Author:
Siyuan ZHANG
1
;
Xingjian WU
;
Zhongren SHANG
;
Rongjia ZHU
;
Jianyu JIAO
;
Lianguang LEI
Author Information
1. 云南省文山州人民医院胸心血管外科 663000
- Keywords:
Acute aortic dissection;
Stanford B type;
XGBoost model;
Prediction;
Postoperative death
- From:
International Journal of Surgery
2021;48(9):626-634,F4
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the analysis of postoperative death in patients with Stanford B acute aortic dissection (AAD) by XGBoost model.Methods:A retrospective study was conducted on 226 patients with Stanford type B AAD diagnosed in Yunnan Wenshan People′s Hospital from February 2012 to June 2019, including 126 males and 100 females, with an average age of (61.24±4.25) years. According to the outcome of discharge, the patients were divided into survival group ( n=129) and death group ( n=97), in which those who automatically gave up treatment and left the hospital were regarded as the death group. If the patients were admitted to Yunnan Wenshan People′s Hospital for many times during the study period, only the clinical data diagnosed as Stanford B AAD for the first time were selected for the study. The clinical data and hematological indexes of the subjects were collected, and the XGBoost model was used to predict the rapid diagnosis of postoperative death in patients with Stanford B AAD, and compared with the traditional Logistic regression model. Results:In the XGBoost model, the influencing factors were ranked according to the degree of importance. The top 6 factors were hypertension, neutrophil-to-lymphocyte(NLR), C-reactive protein (CRP), white blood cell count(WBC), D-dimer and heart rate. Hypertension and NLR had the greatest influence on postoperative death in patients with Stanford B AAD. Using receiver operator charateristic curve to compare the prediction performance of the two models, it was found that the prediction efficiency of the XGBoost algorithm was significantly stronger than that of the Logistic regression model in the training set, while the two models were equivalent in the verification set. The prediction models constructed by the two methods eventually included independent variables such as hypertension, NLR, CRP, WBC, D-dimer, heart rate, systolic blood pressure, diastolic blood pressure, surgical treatment and so on.Conclusions:XGBoost model can be used to predict the postoperative death of patients with Stanford B AAD. Its diagnostic performance is better than Logistic regression model in training set and equivalent to the latter in verification set. Hypertension and NLR are the most important predictors of postoperative mortality in patients with Stanford B type AAD.
