Departments of Neurology (B.V., F.E.), University of California, Irvine.
Department of Pediatric Rehabilitation Medicine, Spaulding Rehabilitation Hospital and Harvard Medical School, Boston, MA (J.W.).
Stroke. 2023 Feb;54(2):e25-e29. doi: 10.1161/STROKEAHA.122.040841. Epub 2023 Jan 23.
Clinical and neuroimaging measures incompletely explain behavioral deficits in the acute stroke setting. We hypothesized that electroencephalography (EEG)-based measures of neural function would significantly improve prediction of acute stroke deficits.
Patients with acute stroke (n=50) seen in the emergency department of a university hospital from 2017 to 2018 underwent standard evaluation followed by a 3-minute recording of EEG at rest using a wireless, 17-electrode, dry-lead system. Artifacts in EEG recordings were removed offline and then spectral power was calculated for each lead pair. A primary EEG metric was DTABR, which is calculated as a ratio of spectral power: [(DeltaTheta)/(AlphaBeta)]. Bivariate analyses and least absolute shrinkage and selection operator (LASSO) regression identified clinical and neuroimaging measures that best predicted initial National Institutes of Health Stroke Scale (NIHSS) score. Multivariable linear regression was then performed before versus after adding EEG findings to these measures, using initial NIHSS score as the dependent measure.
Age, diabetes status, and infarct volume were the best predictors of initial NIHSS score in bivariate analyses, confirmed using LASSO regression. Combined in a multivariate model, these 3 explained initial NIHSS score (adjusted r=0.47). Adding any of several different EEG measures to this clinical model significantly improved prediction; the greatest amount of additional variance was explained by adding contralesional DTABR (adjusted r=0.60, <0.001).
EEG measures of neural function significantly add to clinical and neuroimaging for explaining initial NIHSS score in the acute stroke emergency department setting. A dry-lead EEG system can be rapidly and easily implemented. EEG contains information that may be useful early after stroke.
临床和神经影像学测量并不能完全解释急性中风患者的行为缺陷。我们假设基于脑电图（EEG）的神经功能测量将显著提高对急性中风缺陷的预测能力。
2017 年至 2018 年，在一所大学医院的急诊室就诊的急性中风患者（n=50）接受了标准评估，随后使用无线、17 导联、干电极系统进行了 3 分钟的静息 EEG 记录。在线下去除 EEG 记录中的伪迹，然后计算每个导联对的频谱功率。主要 EEG 指标是 DTABR，它是通过计算频谱功率的比值得出的：[(DeltaTheta)/(AlphaBeta)]。双变量分析和最小绝对值收缩和选择算子（LASSO）回归确定了最能预测初始国立卫生研究院中风量表（NIHSS）评分的临床和神经影像学测量指标。然后，在将 EEG 结果添加到这些测量指标之前和之后，使用初始 NIHSS 评分作为因变量，进行多变量线性回归。
年龄、糖尿病状况和梗死体积是双变量分析中初始 NIHSS 评分的最佳预测因素，LASSO 回归也证实了这一点。在多变量模型中联合使用这 3 个因素可以解释初始 NIHSS 评分（调整后的 r=0.47）。将几种不同的 EEG 测量指标添加到该临床模型中显著提高了预测效果；添加对侧 DTABR 可解释的额外方差最大（调整后的 r=0.60，<0.001）。
神经功能的 EEG 测量指标在急性中风急诊室环境中显著增加了对初始 NIHSS 评分的临床和神经影像学解释。干电极 EEG 系统可以快速、轻松地实施。EEG 中包含的信息可能对中风后早期有用。
