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Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
Predicting endovascular recanalization success in symptomatic chronic carotid occlusion: A decision tree model based
Xiguang Fu1, Mengyuan Yuan2, Yong Zhang1
1Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background:
Endovascular recanalization for chronic internal carotid artery occlusion (CICAO) remains technically challenging, with variable success rates and a lack of reliable predictive tools for patient selection. We aim to analyze risk factors associated with failed recanalization in CICAO patients and develop a decision tree model to quantify individualized recanalization potential.
Methods:
We retrospectively analyzed 321 patients with symptomatic CICAO who underwent endovascular recanalization. Univariate and multivariate analyses were used to identify risk factors for recanalization failure. A decision tree model and logistic model were constructed. Models' performance was evaluated using AUC analysis and decision curve analysis (DCA).
Results:
The overall recanalization success rate was 61.7 %. Our study identified three independent risk factors for failed recanalization: occlusion length > 10 cm, type III stump morphology, and contralateral internal carotid artery stenosis. The decision-tree model demonstrated good performance (AUC 0.839 in training, 0.834 in validation) and provided clinical interpretability compared to logistic regression. DCA confirmed clinical utility across probability thresholds.
Conclusions:
We developed and validated a decision tree model that effectively predicts endovascular recanalization success in CICAO patients, which may serve as a valuable tool to support clinical decision-making for patients with CICAO.
