Validation of Stroke Network of Wisconsin Scale at Aurora Health Care System

Document Type

Article

Publication Date

11-2018

Publisher

Zeenat Qureshi Stroke Institute

Source Publication

Journal of Vascular and Interventional Neurology (JVIN)

Source ISSN

1944-141x

Abstract

Background

The Stroke Network of Wisconsin (SNOW) scale, previously called the Pomona scale, was developed to predict large-vessel occlusions (LVOs) in patients with acute ischemic stroke (AIS). The original study showed a high accuracy of this scale. We sought to externally validate the SNOW scale in an independent cohort.

Methods

We retrospectively reviewed and calculated the SNOW scale, the Vision Aphasia and Neglect Scale (VAN), the Cincinnati Prehospital Stroke Severity (CPSS), the Los Angeles Motor Scale (LAMS), and the Prehospital Acute Stroke Severity Scale (PASS) for all patients who were presented within 24 hours after onset at AHCS (14 hospitals) between January 2015 and December 2016. The predictive performance of all scales and several National Institute of Health Stroke Scale cutoffs (≥6) were determined and compared. LVO was defined by total occlusions involving the intracranial internal carotid artery, middle cerebral artery (MCA; M1), or basilar arteries.

Results

Among 2183 AIS patients, 1381 had vascular imaging and were included in the analysis. LVO was detected in 169 (12%). A positive SNOW scale had comparable accuracy to predict LVO and showed a sensitivity of 0.80, specificity of 0.76, the positive predictive value (PPV) of 0.31, and negative predictive value of 0.96 for the detection of LVO versus CPSS ≥ 2 of 0.64, 0.87, 0.41, and 0.95. A positive SNOW scale had higher accuracy than VAN, LAMS, and PASS.

Conclusion

In our large stroke network cohort, the SNOW scale has promising sensitivity, specificity and accuracy to predict LVO. Future prospective studies in both prehospital and emergency room settings are warranted.

Comments

Published version. Journal of Vascular and Interventional Neurology, Vol. 10, No. 2 (2018): 69-73. Link. © 2018 JVIN. Used with permission.

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