Point-of-Care Electroencephalography in Acute Neurological Care: A Narrative Review.

IF 3.2 Q2 CLINICAL NEUROLOGY
Roberto Fratangelo, Francesco Lolli, Maenia Scarpino, Antonello Grippo
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引用次数: 0

Abstract

Point-of-care electroencephalography (POC-EEG) systems are rapid-access, reduced-montage devices designed to address the limitations of conventional EEG (conv-EEG), enabling faster neurophysiological assessment in acute settings. This review evaluates their clinical impact, diagnostic performance, and feasibility in non-convulsive status epilepticus (NCSE), traumatic brain injury (TBI), stroke, and delirium. A comprehensive search of Medline, Scopus, and Embase identified 69 studies assessing 15 devices. In suspected NCSE, POC-EEG facilitates rapid seizure detection and prompt diagnosis, making it particularly effective in time-sensitive and resource-limited settings. Its after-hours availability and telemedicine integration ensure continuous coverage. AI-assisted tools enhance interpretability and accessibility, enabling use by non-experts. Despite variability in accuracy, it supports triaging, improving management, treatment decisions and outcomes while reducing hospital stays, transfers, and costs. In TBI, POC-EEG-derived quantitative EEG (qEEG) indices reliably detect structural lesions, support triage, and minimize unnecessary CT scans. They also help assess concussion severity and predict recovery. For strokes, POC-EEG aids triage by detecting large vessel occlusions (LVOs) with high feasibility in hospital and prehospital settings. In delirium, spectral analysis and AI-assisted models enhance diagnostic accuracy, broadening its clinical applications. Although POC-EEG is a promising screening tool, challenges remain in diagnostic variability, technical limitations, and AI optimization, requiring further research.

急性神经系统护理中的即时脑电图:叙述性回顾。
即时脑电图(POC-EEG)系统是一种快速访问、减少蒙太奇的设备,旨在解决传统脑电图(conve -EEG)的局限性,在急性环境中能够更快地进行神经生理评估。本综述评估了它们在非惊厥性癫痫持续状态(NCSE)、外伤性脑损伤(TBI)、中风和谵妄中的临床影响、诊断性能和可行性。对Medline、Scopus和Embase的全面搜索确定了69项研究,评估了15种设备。在疑似NCSE病例中,pocc - eeg有助于快速检测癫痫发作并及时诊断,在时间敏感和资源有限的情况下特别有效。其下班后的可用性和远程医疗集成确保了持续的覆盖。人工智能辅助工具增强了可解释性和可访问性,使非专家也能使用。尽管准确性存在差异,但它支持分诊、改善管理、治疗决策和结果,同时减少住院时间、转院时间和成本。在TBI中,poc -EEG衍生的定量脑电图(qEEG)指数可靠地检测结构性病变,支持分诊,并最大限度地减少不必要的CT扫描。它们还有助于评估脑震荡的严重程度并预测康复情况。对于中风,pocc - eeg通过在医院和院前检测大血管闭塞(LVOs)来辅助分诊,具有很高的可行性。在谵妄中,光谱分析和人工智能辅助模型提高了诊断的准确性,拓宽了其临床应用。虽然POC-EEG是一种很有前途的筛查工具,但在诊断的可变性、技术限制和人工智能优化方面仍存在挑战,需要进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurology International
Neurology International CLINICAL NEUROLOGY-
CiteScore
3.70
自引率
3.30%
发文量
69
审稿时长
11 weeks
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