Department of Neurology (F.E., N.Z., B.V., N.K., H.S., L.N., J.W., W.Y., S.C.C.), UC Irvine, CA.
Department of Emergency Medicine (B.C., W.H.), UC Irvine, CA.
Stroke. 2020 Nov;51(11):3361-3365. doi: 10.1161/STROKEAHA.120.030150. Epub 2020 Sep 18.
Clinical methods have incomplete diagnostic value for early diagnosis of acute stroke and large vessel occlusion (LVO). Electroencephalography is rapidly sensitive to brain ischemia. This study examined the diagnostic utility of electroencephalography for acute stroke/transient ischemic attack (TIA) and for LVO.
Patients (n=100) with suspected acute stroke in an emergency department underwent clinical exam then electroencephalography using a dry-electrode system. Four models classified patients, first as acute stroke/TIA or not, then as acute stroke with LVO or not: (1) clinical data, (2) electroencephalography data, (3) clinical+electroencephalography data using logistic regression, and (4) clinical+electroencephalography data using a deep learning neural network. Each model used a training set of 60 randomly selected patients, then was validated in an independent cohort of 40 new patients.
Of 100 patients, 63 had a stroke (43 ischemic/7 hemorrhagic) or TIA (13). For classifying patients as stroke/TIA or not, the clinical data model had area under the curve=62.3, whereas clinical+electroencephalography using deep learning neural network model had area under the curve=87.8. Results were comparable for classifying patients as stroke with LVO or not.
Adding electroencephalography data to clinical measures improves diagnosis of acute stroke/TIA and of acute stroke with LVO. Rapid acquisition of dry-lead electroencephalography is feasible in the emergency department and merits prehospital evaluation.
临床方法对急性中风和大血管闭塞(LVO)的早期诊断具有不完全的诊断价值。脑电图对脑缺血迅速敏感。本研究探讨了脑电图对急性中风/短暂性脑缺血发作(TIA)和 LVO 的诊断效用。
在急诊科怀疑患有急性中风的 100 名患者接受了临床检查,然后使用干电极系统进行脑电图检查。有四个模型对患者进行分类,首先是急性中风/TIA 或非急性中风/TIA,然后是急性中风伴 LVO 或非急性中风伴 LVO:(1)临床数据,(2)脑电图数据,(3)使用逻辑回归的临床+脑电图数据,以及(4)使用深度学习神经网络的临床+脑电图数据。每个模型使用 60 名随机选择患者的训练集,然后在 40 名新患者的独立队列中进行验证。
在 100 名患者中,有 63 名患者患有中风(43 名缺血性/7 名出血性)或 TIA(13 名)。对于将患者分为中风/TIA 或非中风/TIA 患者,临床数据模型的曲线下面积为 62.3,而使用深度学习神经网络的临床+脑电图模型的曲线下面积为 87.8。对于将患者分为伴有 LVO 的中风患者或非伴有 LVO 的中风患者,结果具有可比性。
将脑电图数据添加到临床测量中可以改善急性中风/TIA 和伴有 LVO 的急性中风的诊断。在急诊科快速采集干电极脑电图是可行的,值得进行院前评估。