Division of Emergency Medicine, the University of California Los Angeles, CA.
Division of Emergency Medicine, the University of California Los Angeles, CA.
Ann Emerg Med. 2024 Oct;84(4):422-427. doi: 10.1016/j.annemergmed.2024.04.026. Epub 2024 Jun 18.
Nonconvulsive status epilepticus is a commonly overlooked cause of altered mental status. This study assessed nonconvulsive status epilepticus prevalence in emergency department (ED) patients with acute neurologic presentations using limited electroencephalogram (EEG) coupled with artificial intelligence (AI)-enhanced seizure detection technology. We then compared the accuracy of the AI EEG interpretations to those performed by an epileptologist.
In a prospective observational cohort analysis, adult patients with unexplained mental status changes identified by emergency physicians received expedited placement of a limited EEG. Data collected encompassed patient demographics, clinical history, EEG interpretations by the AI algorithm and epileptologists, treatments, and disposition determinations.
There were 134 device applications on 132 patients (2 received the device twice) enrolled in the study, but 16 were missing data critical for identification or analysis and 9 did not meet the selection criteria. Of the 108 limited EEGs interpreted by an epileptologist, 69 were abnormal (diffuse slowing, highly epileptiform patterns, or spikes and sharps), 41 were normal, 5 were uninterpretable, and 3 captured episodes of seizure or status epilepticus. Limited EEG AI interpretation detected >90% seizure burden in 2 of 3 cases of seizure or status epilepticus as well as in 2 abnormal EEGs and 1 normal EEG, providing a sensitivity of 66.7% (95% confidence interval 9.4 to 99.2), a specificity of 97.0% (95% confidence interval 91.5 to 99.4), and a disease prevalence of 2.9%.
Limited AI-enhanced EEG can detect nonconvulsive status epilepticus in the ED; however, the technology tended to overestimate seizure burden in our cohort. This study found a lower nonconvulsive status epilepticus prevalence compared to prior literature reports.
非惊厥性癫痫持续状态是一种常被忽视的意识状态改变的原因。本研究使用有限的脑电图(EEG)结合人工智能(AI)增强的癫痫发作检测技术,评估了急诊科(ED)有急性神经表现的患者中,非惊厥性癫痫持续状态的发生率。然后,我们比较了 AI 脑电图解释的准确性与癫痫专家的解释。
在一项前瞻性观察队列分析中,由急诊医师识别的不明原因精神状态改变的成年患者接受了有限脑电图的快速放置。收集的数据包括患者的人口统计学数据、临床病史、AI 算法和癫痫专家的脑电图解释、治疗和处置决定。
该研究共纳入了 132 名患者(2 名患者接受了两次设备)的 134 次设备应用,但有 16 次的数据缺失对于识别或分析至关重要,9 次不符合选择标准。在由癫痫专家解释的 108 次有限脑电图中,69 次异常(弥漫性减慢、高度癫痫样模式或棘波和尖波),41 次正常,5 次无法解释,3 次捕捉到癫痫发作或癫痫持续状态的发作。在 3 例癫痫发作或癫痫持续状态中,有限的 AI 解释检测到 >90%的癫痫发作负荷,以及 2 例异常脑电图和 1 例正常脑电图,其敏感性为 66.7%(95%置信区间 9.4 至 99.2),特异性为 97.0%(95%置信区间 91.5 至 99.4),疾病流行率为 2.9%。
有限的 AI 增强脑电图可以在急诊科检测到非惊厥性癫痫持续状态;然而,该技术在我们的队列中往往高估了癫痫发作的负荷。与先前的文献报告相比,本研究发现非惊厥性癫痫持续状态的发生率较低。