Construction of a prediction model for systemic inflammatory response syndrome in patients undergoing interventional surgery for type B aortic dissection based on logistic regression and decision tree algorithm
- VernacularTitle:基于逻辑回归和决策树算法构建B型主动脉夹层介入术后全身炎症反应综合征预测模型
- Author:
Pingzhen ZHANG
;
Guiqin WU
;
Yuanyuan FU
;
Chunyan LIU
1
;
Qiongyan DUAN
1
;
Xiaojing PAN
1
;
Zhouzhen CHEN
1
;
Xia CHEN
1
Author Information
1. Department of Cardiovascular Surgery, Peking University Shenzhen Hospital, Shenzhen, 518036, Guangdong, P. R. China
- Publication Type:Journal Article
- Keywords:
Logistic regression analysis;
decision tree algorithm;
type B aortic dissection;
interventional surgery;
systemic inflammatory response syndrome;
prediction model
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2026;33(07):1085-1092
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct and compare logistic regression and decision tree models for predicting systemic inflammatory response syndrome (SIRS) in patients with type B aortic dissection (TBAD) after interventional surgery. Methods A retrospective analysis was conducted on clinical data of TBAD patients at Peking University Shenzhen Hospital from 2020 to 2024. The patients were divided into a SIRS group and a non SIRS group based on whether SIRS occurred within 24 hours after surgery. Multivariate logistic regression was used to analyze the influencing factors of SIRS occurrence in TBAD intervention patients, and a decision tree model was constructed using SPSS Modeler to compare the predictive performance of the two models. Results A total of 742 patients with TBAD were included, including 579 males and 163 females, aged 27-97 (58.85±10.79) years. Within 24 hours after intervention, a total of 506 patients developed SIRS, with an incidence rate of 68.19%. Logistic regression analysis showed that the extensive involvement of the dissection, the surgical time≥2 hours, PET coated stents implanted, serum creatinine, white blood cell count, C-reactive protein, monocyte (MONO), neutrophil count levels elevated, estimated glomerular filtration rate and decreased albumin levels were independent risk factors for SIRS (P<0.05). The decision tree model selected a total of 10 explanatory variables and 6 layers with 37 nodes, among which MONO was the most important predictor. The area under the decision tree model curve was 0.829 [95%CI (0.800, 0.856)], which was better than the logistic regression model's 0.690 [95%CI (0.655, 0.723)], and the difference was statistically significant (P<0.001). Conclusion The incidence of SIRS after TBAD intervention is high, and the decision tree model has better predictive performance than logistic regression. It can identify high-risk patients with higher accuracy and provide a practical tool for early clinical intervention.
