Koren Johannes P, Herta Johannes, Fürbass Franz, Pirker Susanne, Reiner-Deitemyer Veronika, Riederer Franz, Flechsenhar Julia, Hartmann Manfred, Kluge Tilmann, Baumgartner Christoph
Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria.
Department of Neurology, General Hospital Hietzing With Neurological Center Rosenhügel, Vienna, Austria.
Front Neurol. 2018 Jun 19;9:454. doi: 10.3389/fneur.2018.00454. eCollection 2018.
Ongoing or recurrent seizure activity without prominent motor features is a common burden in neurological critical care patients and people with epilepsy during ICU stays. Continuous EEG (CEEG) is the gold standard for detecting ongoing ictal EEG patterns and monitoring functional brain activity. However CEEG review is very demanding and time consuming. The purpose of the present multirater, EEG expert reviewer study, is to test and assess the clinical feasibility of an automatic EEG pattern detection method (Neurotrend). Four board certified EEG reviewers used Neurotrend to annotate 76 CEEG datasets à 6 h (in total 456 h of EEG) for rhythmic and periodic EEG patterns (RPP), unequivocal ictal EEG patterns and burst suppression. All reviewers had a predefined time limit of 5 min (± 2 min) per CEEG dataset and were compared to a predefined gold standard (conventional EEG review with unlimited time). Subanalysis of specific features of RPP was conducted as well. We used Gwet's AC and AC coefficients to calculate interrater agreement (IRA) and multirater agreement (MRA). Also, we determined individual performance measures for unequivocal ictal EEG patterns and burst suppression. Bonferroni-Holmes correction for multiple testing was applied to all statistical tests. Mean review time was 3.3 min (± 1.9 min) per CEEG dataset. We found substantial IRA for unequivocal ictal EEG patterns (0.61-0.79; mean sensitivity 86.8%; mean specificity 82.2%, < 0.001) and burst suppression (0.68-0.71; mean sensitivity 96.7%; mean specificity 76.9% < 0.001). Two reviewers showed substantial IRA for RPP (0.68-0.72), whereas the other two showed moderate agreement (0.45-0.54), compared to the gold standard ( < 0.001). MRA showed almost perfect agreement for burst suppression (0.86) and moderate agreement for RPP (0.54) and unequivocal ictal EEG patterns (0.57). We demonstrated the clinical feasibility of an automatic critical care EEG pattern detection method on two levels: (1) reasonable high agreement compared to the gold standard, (2) reasonable short review times compared to previously reported EEG review times with conventional EEG analysis.
在神经重症监护患者以及重症监护病房（ICU）住院期间的癫痫患者中，持续或反复出现的癫痫发作活动且无明显运动特征是一个常见问题。连续脑电图（CEEG）是检测持续发作期脑电图模式和监测大脑功能活动的金标准。然而，CEEG审查要求很高且耗时。本多评估者、脑电图专家审查研究的目的是测试和评估一种自动脑电图模式检测方法（Neurotrend）的临床可行性。四位具备脑电图专业认证的审查者使用Neurotrend对76个时长为6小时的CEEG数据集（总共456小时的脑电图）进行节律性和周期性脑电图模式（RPP）、明确的发作期脑电图模式以及爆发抑制的标注。每位审查者对每个CEEG数据集有一个预先设定的5分钟（±2分钟）时间限制，并与预先设定的金标准（无时间限制的传统脑电图审查）进行比较。还对RPP的特定特征进行了亚分析。我们使用格韦特（Gwet）的AC和AC系数来计算评估者间一致性（IRA）和多评估者一致性（MRA）。此外，我们确定了明确的发作期脑电图模式和爆发抑制的个体性能指标。对所有统计检验均应用了用于多重检验的邦费罗尼 - 霍姆斯（Bonferroni-Holmes）校正。每个CEEG数据集的平均审查时间为3.3分钟（±1.9分钟）。我们发现明确的发作期脑电图模式（0.61 - 0.79；平均敏感性86.8%；平均特异性82.2%，<0.001）和爆发抑制（0.68 - 0.71；平均敏感性96.7%；平均特异性76.9%，<0.001）具有较高的IRA。与金标准相比，两位审查者对RPP显示出较高的IRA（0.68 - 0.72），而另外两位审查者显示出中等一致性（0.45 - 0.54）（<0.001）。MRA显示爆发抑制几乎完全一致（0.86），RPP和明确的发作期脑电图模式中等一致（分别为0.54和0.57）。我们在两个层面证明了一种自动重症监护脑电图模式检测方法的临床可行性：（1）与金标准相比具有合理的高一致性；（2）与先前报道的传统脑电图分析的脑电图审查时间相比，审查时间合理较短。
