A decision tree model to predict successful endovascular recanalization of non-acute internal carotid artery occlusion
10.3760/cma.j.issn.1673-4165.2023.07.001
- VernacularTitle:非急性期颈内动脉闭塞血管内再通治疗后成功再通的决策树预测模型
- Author:
Shuxian HUO
1
;
Chao HOU
;
Xuan SHI
;
Qin YIN
;
Xianjun HUANG
;
Wen SUN
;
Guodong XIAO
;
Yong YANG
;
Hongbing CHEN
;
Min LI
;
Mingyang DU
;
Yunfei HAN
;
Xiaobing FAN
;
Xinfeng LIU
;
Ruidong YE
Author Information
1. 南京大学医学院附属金陵医院神经内科，南京 210002
- Keywords:
Carotid artery diseases;
Carotid artery, internal;
Chronic disease;
Endovascular procedures;
Treatment outcome;
Predictive value of tests;
Decision trees
- From:
International Journal of Cerebrovascular Diseases
2023;31(7):481-489
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate predictive factors for successful endovascular recanalization in patients with non-acute symptomatic internal carotid artery occlusion (SICAO), to develop a decision tree model using the Classification and Regression Tree (CART) algorithm, and to evaluate the predictive performance of the model.Methods:Patients with non-acute SICAO received endovascular therapy at 8 comprehensive stroke centers in China were included retrospectively. They were randomly assigned to a training set and a validation set. In the training set, the least absolute shrinkage and selection operator (LASSO) algorithm was used to screen important variables, and a decision tree prediction model was constructed based on CART algorithm. The model was evaluated using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow goodness-of-fit test and confusion matrix in the validation set.Results:A total of 511 patients with non-acute SICAO were included. They were randomly divided into a training set ( n=357) and a validation set ( n=154) in a 7:3 ratio. The successful recanalization rates after endovascular therapy were 58.8% and 58.4%, respectively. There was no statistically significant difference ( χ2=0.007, P=0.936). A CART decision tree model consisting of 5 variables, 5 layers and 9 classification rules was constructed using the six non-zero-coefficient variables selected by LASSO regression. The predictive factors for successful recanalization included fewer occluded segments, proximal tapered stump, ASITN/SIR collateral grading of 1-2, ischemic stroke, and a recent event to endovascular therapy time of 1-30 d. ROC analysis showed that the area under curve of the decision tree model in the training set was 0.810 (95% confidence interval 0.764-0.857), and the optimal cut-off value for predicting successful recanalization was 0.71. The area under curve in the validation set was 0.763 (95% confidence interval 0.687-0.839). The accuracy was 70.1%, precision was 81.4%, sensitivity was 63.3%, and specificity was 79.7%. The Hosmer-Lemeshow test in both groups showed P>0.05. Conclusion:Based on the type of ischemic event, the time from the latest event to endovascular therapy, proximal stump morphology, the number of occluded segments, and the ASITN/SIR collateral grading constructed the decision tree model can effectively predict successful recanalization after non-acute SICAO endovascular therapy.
