Anterior ischemic stroke: Analysis of the multivariable CT-based models for prediction of clinical outcome

Document Type

Journal Article

Publication Date

7-4-2023

Journal

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

Volume

32

Issue

8

DOI

10.1016/j.jstrokecerebrovasdis.2023.107242

Keywords

Angiography; Arterial collaterals; Cerebral veins; Cortical veins; Medullary veins; Stroke

Abstract

OBJECTIVE: To determine the predictive value of multiple CT-based measurements, individually and collectively, including arterial collateral filling (AC), tissue perfusion parameters, as well as cortical venous (CV) and medullary venous (MV) outflow, in patients with acute ischemic stroke (AIS). METHODS: We retrospectively reviewed a database of patients with AIS in the middle cerebral artery distribution, who underwent evaluation by multiphase CT-angiography and perfusion. AC pial filling was evaluated using a multiphase CTA imaging. The CV status was scored using the adopted PRECISE system based on contrast opacification of the main cortical veins. The MV status was defined by the degree of contrast opacification of medullary veins in one cerebral hemisphere as compared to the contralateral hemisphere. The perfusion parameters were calculated using FDA-approved automated software. A good clinical outcome was defined as a Modified Rankin Scale of 0-2 at 90 days. RESULTS: A total of 64 patients were included. Each of the CT-based measurements could predict clinical outcomes independently (P<0.05). AC pial filling and perfusion core based models did slightly better compared to each of the other models (AUC = 0.66). Among models with two variables, the perfusion core combined with MV status had the highest AUC=0.73 followed by a combination of MV status and AC (AUC=0.72). Multivariable modeling with all four variables resulted in the highest predictive value (AUC=0.77). CONCLUSION: The combination of arterial collateral flow, tissue perfusion, and venous outflow provides a more accurate prediction of clinical outcome in AIS than each variable alone. This additive effect of these techniques suggests that the information collected by each of these methods only partially overlaps.

Department

Radiology

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