4.2 Article

Quantitative collateral assessment evaluated by cerebral blood volume measured by CT perfusion in patients with acute ischemic stroke

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DOI: 10.1016/j.jstrokecerebrovasdis.2021.105797

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Collateral; LVO; CT perfusion; CBV; Mechanical thrombectomy

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Quantitative assessment of collateral status using CBV ratio measured by CTP imaging is an independent predictor of functional and radiological outcomes in AIS patients, particularly in those with recanalization.
Objectives: Collateral status (CS) is considered a predictor of clinical outcome after reperfusion therapy (RT) in patients with acute ischemic stroke (AIS). We proposed a quantitative assessment of CS using cerebral blood volume (CBV) measured by computed tomography perfusion (CTP) imaging. Materials and Methods: This retrospective study was approved by the Institutional Review Board. Between February 2019 and September 2020, 60 patients with anterior circulation large-vessel occlusion who presented to our institution within 8 h after stroke onset were included. The ratio of the average CBV values in the affected middle cerebral artery (MCA) territories to the unaffected side was defined as the CBV ratio. CS was assessed by scores from previously reported qualitative scoring systems (Tan & regional leptomeningeal collateral (rLMC) scores). Results: The CBV ratio was an independent factor contributing to a good functional outcome (P<0.01) and was significantly correlated with the Tan score (r=0.73, P<0.01) and the rLMC score (r=0.77, P<0.01). Among the patients with recanalization, the CBV ratio was a useful parameter that predicted both a good functional outcome (area under the receiver operating characteristic curve (AUC-ROC), 0.76; 95% CI, 0.55-0.89) and a good radiological outcome (AUC-ROC, 0.90; 95% CI, 0.72-0.97), and it was an independent predictor for good radiological outcome (OR: 4.38; 95% CI:1.29-14.82; P<0.01) in multivariate models. Conclusions: The CBV ratio is a suitable parameter for evaluating CS quantitatively for patients with AIS that can predict patient response to recanalization.

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