Tesh Ryan A, Zahoor Anika, Banks Jayme, Gallagher Kaileigh, Eckhardt Christine A, Sun Haoqi, Karakis Ioannis, Katyal Roohi, Williams Jonathan, Nayak Chetan, Herlopian Aline, Ng Marcus C, Greenblatt Adam S, Meyers Emma, Westmeijer Mike, Harrison Daniel S, Ganglberger Wolfgang, Fan Tracey, Struck Aaron F, Sheikh Irfan S, Nascimento Fábio A, Westover M Brandon
Department of Neurology, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, U.S.A.
Department of Neurology, Massachusetts General Hospital (MGH), Boston, Massachusetts, U.S.A.
J Clin Neurophysiol. 2025 Jul 2. doi: 10.1097/WNP.0000000000001185.
Visual EEG Confusion Assessment Method-Severity (VE-CAM-S) quantifies encephalopathy severity based on electroencephalography features. This study evaluated inter-rater reliability among experts using the VE-CAM-S scale.
Nine experts from six institutions independently reviewed 32 15-second electroencephalography samples in an online test, assessing 29 features (16 in the VE-CAM-S and 13 additional, or "VE-CAM-S+"). A consensus of three experts served as the gold standard. Performance was measured by the median Matthews correlation coefficient between expert and gold-standard VE-CAM-S+ scores, along with average sensitivity and specificity. Qualitative analysis identified common feature-recognition errors affecting scores.
Experts achieved a median Matthews correlation coefficient of 0.82 [95% CI: 0.74-0.99]. Specificity exceeded 90% for most features except background β (87%) and generalized delta (71%). Sensitivity was ≥65% except for burst suppression with epileptiform activity (61%), extreme delta brush (EDB; 61%), posterior dominant rhythm (50%), background α (59%) and β (42%). Common errors included missing subtle findings, confusing features, and misidentifying extreme delta brush.
This pilot study offers some initial support for the reliability of VE-CAM-S+ scoring. The largest errors occurred when experts missed or falsely identified features with higher weight in the VE-CAM-S. Encephalopathy grading through VE-CAM-S may be improved by breaking high-stakes features into smaller parts, creating a "cheat sheet" with scored examples, and designing teaching materials.
视觉脑电图混淆评估方法-严重程度（VE-CAM-S）基于脑电图特征对脑病严重程度进行量化。本研究使用VE-CAM-S量表评估了专家之间的评分者间信度。
来自六个机构的九位专家在在线测试中独立审查了32个15秒的脑电图样本，评估29个特征（VE-CAM-S中有16个，另外13个，即“VE-CAM-S+”）。三位专家的共识作为金标准。通过专家与金标准VE-CAM-S+评分之间的中位数马修斯相关系数以及平均灵敏度和特异性来衡量表现。定性分析确定了影响评分的常见特征识别错误。
专家们的中位数马修斯相关系数为0.82 [95%置信区间：0.74-0.99]。除背景β（87%）和广泛性δ波（71%）外，大多数特征的特异性超过90%。除了伴有癫痫样活动的爆发抑制（61%）、极慢δ刷（EDB；61%）、后头部优势节律（50%）、背景α波（59%）和β波（42%）外，灵敏度≥65%。常见错误包括遗漏细微发现、混淆特征以及错误识别极慢δ刷。
这项初步研究为VE-CAM-S+评分的可靠性提供了一些初步支持。当专家遗漏或错误识别VE-CAM-S中权重较高的特征时，出现的错误最大。通过将高风险特征分解为更小的部分、创建带有评分示例的“作弊表”以及设计教材，可能会改善通过VE-CAM-S进行的脑病分级。
