Wusthoff Courtney J, Sullivan Joseph, Glass Hannah C, Shellhaas Renée A, Abend Nicholas S, Chang Taeun, Tsuchida Tammy N
Divisions of Child Neurology and Neonatal and Developmental Medicine, Stanford University, Palo Alto, California, U.S.A.
Departments of Neurology and Pediatrics, San Francisco Benioff Children's Hospital, University of California, San Francisco, California, U.S.A.
Epilepsia. 2017 Mar;58(3):429-435. doi: 10.1111/epi.13661. Epub 2017 Feb 6.
Research using neonatal electroencephalography (EEG) has been limited by a lack of a standardized classification system and interpretation terminology. In 2013, the American Clinical Neurophysiology Society (ACNS) published a guideline for standardized terminology and categorization in the description of continuous EEG in neonates. We sought to assess interrater agreement for this neonatal EEG categorization system as applied by a group of pediatric neurophysiologists.
A total of 60 neonatal EEG studies were collected from three institutions. All EEG segments were from term neonates with hypoxic-ischemic encephalopathy. Three pediatric neurophysiologists independently reviewed each record using the ACNS standardized scoring system. Unweighted kappa values were calculated for interrater agreement of categorical data across multiple observers.
Interrater agreement was very good for identification of seizures (κ = 0.93, p < 0.001), with perfect agreement in 95% of records (57 of 60). Interrater agreement was moderate for classifying records as normal or having any abnormality (κ = 0.49, p < 0.001), with perfect agreement in 78% of records (47 of 60). Interrater agreement was good in classifying EEG backgrounds on a 5-category scale (normal, excessively discontinuous, burst suppression, status epilepticus, or electrocerebral inactivity) (κ = 0.70, p < 0.001), with perfect agreement in 72% of records (43 of 60). Other specific background features had lower agreement, including voltage (κ = 0.41, p < 0.001), variability (κ = 0.35, p < 0.001), symmetry (κ = 0.18, p = 0.01), presence of abnormal sharp waves (κ < 0.20, p < 0.05), and presence of brief rhythmic discharges (κ < 0.20, p < 0.05).
We found good or very good interrater agreement applying the ACNS system for identification of seizures and classification of EEG background. Other specific EEG features showed limited interrater agreement. Of importance to both clinicians and researchers, our findings support using the ACNS system in identifying seizures and classifying backgrounds of neonatal EEG recordings, but also suggest limited reproducibility for certain other EEG features.
由于缺乏标准化的分类系统和解释术语，新生儿脑电图（EEG）研究受到了限制。2013年，美国临床神经生理学会（ACNS）发布了一项关于新生儿连续脑电图描述的标准化术语和分类指南。我们试图评估一组儿科神经生理学家应用该新生儿脑电图分类系统时的评分者间一致性。
从三个机构收集了总共60项新生儿脑电图研究。所有脑电图片段均来自患有缺氧缺血性脑病的足月儿。三位儿科神经生理学家使用ACNS标准化评分系统独立审查每份记录。计算多个观察者之间分类数据的评分者间一致性的非加权kappa值。
评分者间在癫痫识别方面的一致性非常好（κ = 0.93，p < 0.001），95%的记录（60份中的57份）完全一致。将记录分类为正常或有任何异常时，评分者间一致性为中等（κ = 0.49，p < 0.001），78%的记录（60份中的47份）完全一致。在将脑电图背景按5类量表（正常、过度不连续、爆发抑制、癫痫持续状态或脑电静息）分类时，评分者间一致性良好（κ = 0.70，p < 0.001），72%的记录（60份中的43份）完全一致。其他特定的背景特征一致性较低，包括电压（κ = 0.41，p < 0.001）、变异性（κ = 0.35，p < 0.001）、对称性（κ = 0.18，p = 0.01）、异常尖波的存在（κ < 0.20，p < 0.05）以及短暂节律性放电的存在（κ < 0.20，p < 0.05）。
我们发现应用ACNS系统在癫痫识别和脑电图背景分类方面评分者间一致性良好或非常好。其他特定的脑电图特征显示评分者间一致性有限。对临床医生和研究人员都很重要的是，我们的研究结果支持使用ACNS系统来识别癫痫和对新生儿脑电图记录的背景进行分类，但也表明某些其他脑电图特征的可重复性有限。
