Frontiers in Neurology (Apr 2019)

Conveniently-Grasped Field Assessment Stroke Triage (CG-FAST): A Modified Scale to Detect Large Vessel Occlusion Stroke

  • Xiaoxian Gong,
  • Zhicai Chen,
  • Feina Shi,
  • Meixia Zhang,
  • Chao Xu,
  • Ruiting Zhang,
  • Min Lou,
  • Min Lou

DOI
https://doi.org/10.3389/fneur.2019.00390
Journal volume & issue
Vol. 10

Abstract

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Background and Purpose: Patients with large vessel occlusion stroke (LVOS) need to be rapidly identified and transferred to comprehensive stroke centers (CSC). However, previous pre-hospital strategy remains challenging. We aimed to develop a modified scale to better predict LVOS.Methods: We retrospectively reviewed our prospectively collected database for acute ischemic stroke (AIS) patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and had a detailed National Institutes of Health Stroke Scale (NIHSS) score at admission. Large vessel occlusion (LVO) was defined as the complete occlusion of large vessels, including the intracranial internal carotid artery (ICA), M1, and M2 segments of the middle cerebral artery (MCA), and basilar artery (BA). The Conveniently-Grasped Field Assessment Stroke Triage (CG-FAST) scale consisted of Level of Consciousness (LOC) questions, Gaze deviation, Facial palsy, Arm weakness, and Speech changes. Receiver Operating Characteristic (ROC) analysis was used to obtain the Area Under the Curve (AUC) of CG-FAST and previously established pre-hospital prediction scales.Results: Finally, 1,355 patients were included in the analysis. LVOS was detected in 664 (49.0%) patients. The sensitivity, specificity, positive predictive value, and negative predictive value of CG-FAST were 0.617, 0.810, 0.785, and 0.692 respectively, at the optimal cutoff (≥4). The AUC, Youden index and accuracy of the CG-FAST scale (0.758, 0.428, and 0.728) were all higher than other pre-hospital prediction scales.Conclusions: CG-FAST scale could be an effective and simple scale for accurate identification of LVOS among AIS patients.

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