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Updated: Jun 26, 2025
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
A Comprehensive Prediction Model for Futile Recanalization in AIS Patients Post-Endovascular Therapy: Integrating
Shuangfeng Huang1, Jiali Xu1,2, Haijuan Kang3
1Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Abstract:
Our study aimed to construct a predictive model for identifying instances of futile recanalization in patients with anterior circulation occlusion acute ischemic stroke (AIS) who achieved complete reperfusion following endovascular therapy. We included 173 AIS patients who attained complete reperfusion, as indicated by a Modified Thrombolysis in Cerebral Infarction (mTICI) scale score of 3. Our approach involved a thorough analysis of clinical factors, imaging biomarkers, and potential no-reflow biomarkers through both univariate and multivariate analyses to identify predictors of futile recanalization. The comprehensive model includes clinical factors such as age, presence of diabetes, admission NIHSS score, and the number of stent retriever passes; imaging biomarkers like poor collaterals; and potential no-reflow biomarkers, notably disrupted blood-brain barrier (OR 4.321, 95% CI 1.794-10.405; p = 0.001), neutrophil-to-lymphocyte ratio (NLR; OR 1.095, 95% CI 1.009-1.188; p = 0.030), and D-dimer (OR 1.134, 95% CI 1.017-1.266; p = 0.024). The model demonstrated high predictive accuracy, with a C-index of 0.901 (95% CI 0.855-0.947) and 0.911 (95% CI 0.863-0.954) in the original and bootstrapping validation samples, respectively. Notably, the comprehensive model showed significantly improved predictive performance over models that did not include no-reflow biomarkers, evidenced by an integrated discrimination improvement of 8.86% (95% CI 4.34%-13.39%; p < 0.001) and a categorized reclassification improvement of 18.38% (95% CI 3.53%-33.23%; p = 0.015). This model, which leverages the potential of no-reflow biomarkers, could be especially beneficial in healthcare settings with limited resources. It provides a valuable tool for predicting futile recanalization, thereby informing clinical decision-making. Future research could explore further refinements to this model and its application in diverse clinical settings.
