BE-FAST (Balance, Eyes, Face, Arm, Speech, Time)

Abstract

Background and Purpose— The FAST algorithm (Face, Arm, Speech, Time) helps identify persons having an acute stroke. We determined the proportion of patients with acute ischemic stroke not captured by FAST and evaluated a revised mnemonic. Methods— Records of all patients admitted to the University of Kentucky Stroke Center between January and December 2014 with a discharge International Classification of Diseases, Ninth Revision, Clinical Modification code for acute ischemic stroke were reviewed. Those misclassified, having missing National Institutes of Health Stroke Scale data, or were comatose or intubated were excluded. Presenting symptoms, demographics, and examination findings based on the National Institutes of Health Stroke Scale data were abstracted. Results— Of 858 consecutive records identified, 736 met inclusion criteria; 14.1% did not have any FAST symptoms at presentation. Of these, 42% had gait imbalance or leg weakness, 40% visual symptoms, and 70% either symptom. With their addition, the proportion of stroke patients not identified was reduced to 4.4% ( P <0.0001). In a sensitivity analysis, if face weakness, arm weakness, or speech impairment on admission examination were considered in addition to a history of FAST symptoms, the proportion missed was reduced to 9.9% ( P =0.0010). The proportion of stroke patients not identified was also reduced (2.6%) with the addition of a history of gait imbalance/leg weakness or visual symptoms ( P <0.0001). Conclusions— Of patients with ischemic stroke with deficits potentially amenable to acute intervention, 14% are not identified using FAST. The inclusion of gait/leg and visual symptoms leads to a reduction in missed strokes. If validated in a prospective study, a revision of public educational programs may be warranted.