Aroor Sushanth, Singh Rajpreet, Goldstein Larry B
From the Department of Neurology, University of Kentucky, Lexington.
Stroke. 2017 Feb;48(2):479-481. doi: 10.1161/STROKEAHA.116.015169. Epub 2017 Jan 12.
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.
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.
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).
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.
FAST算法（面部、手臂、言语、时间）有助于识别急性中风患者。我们确定了未被FAST识别出的急性缺血性中风患者的比例，并评估了一种修订后的记忆法。
回顾了2014年1月至12月间入住肯塔基大学中风中心且出院时国际疾病分类第九版临床修订本中急性缺血性中风编码的所有患者的记录。排除那些分类错误、美国国立卫生研究院卒中量表数据缺失、昏迷或插管的患者。根据美国国立卫生研究院卒中量表数据提取患者的症状、人口统计学特征及检查结果。
在858份连续记录中，736份符合纳入标准；14.1%的患者在就诊时没有任何FAST症状。其中，42%有步态不稳或腿部无力，40%有视觉症状，70%有上述任一症状。加上这些症状后，未被识别出的中风患者比例降至4.4%（P<0.0001）。在一项敏感性分析中，如果除了有FAST症状史外，还考虑入院检查时的面部无力、手臂无力或言语障碍，漏诊比例降至9.9%（P=0.0010）。加上步态不稳/腿部无力或视觉症状史后，未被识别出的中风患者比例也有所降低（2.6%）（P<0.0001）。
在有潜在急性干预可能的缺血性中风患者中，14%未被FAST识别。纳入步态/腿部和视觉症状可减少中风漏诊。如果在前瞻性研究中得到验证，可能有必要修订公众教育项目。
