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Updated: Jun 14, 2026

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia
Published on: August 18, 2015
A Simple and Pragmatic Equation for Rapid Outcome Prediction in Endovascular Thrombectomy With Limited Information
Ximing Nie1,2,3, Bizhong Che4, Xiaoxiao Qin1,2
1Department of Neurology Beijing Tiantan Hospital, Capital Medical University Beijing China.
Background:
To rapidly predict outcomes of candidate for endovascular thrombectomy in time-sensitive situations with limited clinical information, we propose a simple but balanced approach, integrating National Institutes of Health Stroke Scale (NIHSS) and Alberta Stroke Program Early CT Score (ASPECTS).
Methods:
This study utilized data from 2 independent registries to investigate the associations of NIHSS scores and ASPECTS with clinical outcomes of patients with stroke with large vessel occlusion in the anterior circulation who underwent endovascular thrombectomy and to evaluate the accuracy of a novel clinical-imaging equation in predicting these outcomes. The primary outcome was functional independence.
Results:
A total of 2128 patients were included. Of these, 1052 (49.4%) achieved functional independence. ASPECTS, NIHSS scores, and age were identified as key predictors across all basic parameters. Clinical-imaging equations (with and without age adjustment, defined as ASPECTS-0.5×NIHSS-age×0.2 and ASPECTS-NIHSS×0.5) were developed. These equations exhibited superior discriminative ability (C statistic, 0.71 [95% CI, 0.68-0.74], 0.68 [95% CI, 0.65-0.70]) compared with traditional methods in the validation cohort. The prediction probability of functional independence by quartiles of clinical-imaging equation with age adjustment (≤-15.5, -15.5 to -12, -15.5 to -12, >-9.5) was 32% (95% CI, 17%-47%), 51% (95% CI, 43%-59%), 63% (95% CI, 58%-69%), and 77% (95% CI, 65%-90%) in the validation cohort.
Conclusions:
By integrating ASPECTS and NIHSS scores, our clinical-imaging equations improved rapid prediction of endovascular thrombectomy outcomes in time-sensitive situations such as patient transfers from primary stroke centers or in mobile stroke units, where clinical information is limited.
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