Department of Neurology, Warren Alpert Medical School of Brown University, Providence, Rhode Island, U.S.A.
Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A.
J Clin Neurophysiol. 2023 Sep 1;40(6):553-561. doi: 10.1097/WNP.0000000000000919. Epub 2022 Feb 3.
To assess variability in interpretation of electroencephalogram (EEG) background activity and qualitative grading of cerebral dysfunction based on EEG findings, including which EEG features are deemed most important in this determination.
A web-based survey (Qualtrics) was disseminated to electroencephalographers practicing in institutions participating in the Critical Care EEG Monitoring Research Consortium between May 2017 and August 2018. Respondents answered 12 questions pertaining to their training and EEG interpretation practices and graded 40 EEG segments (15-second epochs depicting patients' most stimulated state) using a 6-grade scale. Fleiss' Kappa statistic evaluated interrater agreement.
Of 110 respondents, 78.2% were attending electroencephalographers with a mean of 8.3 years of experience beyond training. Despite 83% supporting the need for a standardized approach to interpreting the degree of dysfunction on EEG, only 13.6% used a previously published or an institutional grading scale. The overall interrater agreement was fair ( k = 0.35). Having Critical Care EEG Monitoring Research Consortium nomenclature certification (40.9%) or EEG board certification (70%) did not improve interrater agreement ( k = 0.26). Predominant awake frequencies and posterior dominant rhythm were ranked as the most important variables in grading background dysfunction, followed by continuity and reactivity.
Despite the preference for a standardized grading scale for background EEG interpretation, the lack of interrater agreement on levels of dysfunction even among experienced academic electroencephalographers unveils a barrier to the widespread use of EEG as a clinical and research neuromonitoring tool. There was reasonable agreement on the features that are most important in this determination. A standardized approach to grading cerebral dysfunction, currently used by the authors, and based on this work, is proposed.
评估基于脑电图（EEG）发现的 EEG 背景活动解读和脑功能障碍定性分级的变异性，包括在这种判断中哪些 EEG 特征被认为最重要。
一项基于网络的调查（Qualtrics）于 2017 年 5 月至 2018 年 8 月期间分发给在参与危重患者脑电图监测研究联盟的机构中执业的脑电图医师。受访者回答了 12 个与其培训和脑电图解释实践相关的问题，并使用 6 级量表对 40 个 EEG 片段（描绘患者最刺激状态的 15 秒时段）进行分级。Fleiss Kappa 统计评估了评分者间的一致性。
在 110 名受访者中，78.2%是主治脑电图医师，他们在培训后平均有 8.3 年的经验。尽管 83%的人支持需要一种标准化的方法来解释 EEG 上的功能障碍程度，但只有 13.6%的人使用了以前发表的或机构的分级量表。总体评分者间的一致性是适度的（κ=0.35）。拥有危重患者脑电图监测研究联盟命名法认证（40.9%）或脑电图委员会认证（70%）并不能提高评分者间的一致性（κ=0.26）。主导觉醒频率和后主导节律被评为分级背景功能障碍最重要的变量，其次是连续性和反应性。
尽管人们更喜欢用于 EEG 背景解读的标准化分级量表，但即使在经验丰富的学术脑电图医师中，功能障碍程度的评分者间一致性也存在差异，这揭示了 EEG 作为临床和研究神经监测工具广泛应用的障碍。在这种判断中，最重要的特征有一定程度的共识。目前，作者采用了一种基于该工作的标准化分级方法来对脑功能障碍进行分级。
