发作期内 EEG 安排提高癫痫监测的检出率：验证多日发作周期在优化视频-脑电图时间安排中的应用。

Pro-Ictal EEG Scheduling Improves the Yield of Epilepsy Monitoring: Validating the Use of Multiday Seizure Cycles to Optimize Video-EEG Timing.

机构信息

Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia.

Graeme Clark Institute, University of Melbourne, Melbourne, Australia.

出版信息

Ann Neurol. 2024 Dec;96(6):1148-1159. doi: 10.1002/ana.27078. Epub 2024 Oct 1.

DOI:10.1002/ana.27078

PMID:39351926

Abstract

OBJECTIVE

A significant challenge of video-electroencephalography (vEEG) in epilepsy diagnosis is timing monitoring sessions to capture epileptiform activity. In this study, we introduce and validate "pro-ictal EEG scheduling", a method to schedule vEEG monitoring to coincide with periods of increased seizure likelihood as a low-risk approach to enhance the diagnostic yield.

METHODS

A database of long-term ambulatory vEEG monitoring sessions (n = 5,038) of adults and children was examined. Data from linked electronic seizure diaries were extracted (minimum 10 self-reported events) to generate cycle-based estimates of seizure risk. In adults, vEEG monitoring sessions coinciding with periods of estimated high-risk were allocated to the high-risk group (n = 305) and compared to remaining studies (baseline: n = 3,586). Test of proportions and risk-ratios (RR) were applied to index differences in proportions and likelihood of capturing outcome measures (abnormal report, confirmed seizure, and diary event) during monitoring. The impact of clinical and demographic factors (age, sex, epilepsy-type, and medication) was also explored.

RESULTS

During vEEG monitoring, the high-risk group was significantly more likely to have an abnormal vEEG report (190/305:62% vs 1,790/3,586:50% [%change = 12%], RR = 1.25, 95% confidence interval [CI] = [1.137-1.370], p < 0.001), present with a confirmed seizure (56/305:18% vs 424/3,586:11% [%change = 7%], RR = 1.63, 95% CI = [1.265-2.101], p < 0.001) and report an event (153/305:50% vs 1,267/3,586:35% (%change = 15%), RR = 1.420, 95% CI = [1.259:1.602], p < 0.001). Similar effects were observed across clinical and demographic features.

INTERPRETATION

This study provides the first large-scale validation of pro-ictal EEG scheduling in improving the yield of vEEG. This innovative approach offers a pragmatic and low-risk strategy to enhance the diagnostic capabilities of vEEG monitoring, significantly impacting epilepsy management. ANN NEUROL 2024;96:1148-1159.

摘要

目的

视频脑电图（vEEG）在癫痫诊断中的一个重大挑战是定时监测以捕捉癫痫样活动。在这项研究中，我们引入并验证了“发作前脑电图安排”，这是一种安排 vEEG 监测以与癫痫发作可能性增加的时期相吻合的方法，作为一种低风险方法来提高诊断效果。

方法

检查了成人和儿童的长期动态 vEEG 监测（n=5038）数据库。从相关的电子癫痫日记中提取数据（至少有 10 次自我报告的事件），以生成基于周期的癫痫发作风险估计。在成人中，将与估计高风险期相吻合的 vEEG 监测时段分配给高风险组（n=305），并与其余研究（基线：n=3586）进行比较。应用比例检验和风险比（RR）来评估在监测期间捕获结局指标（异常报告、确认的癫痫发作和日记事件）的比例和可能性的差异。还探讨了临床和人口统计学因素（年龄、性别、癫痫类型和药物）的影响。

结果

在 vEEG 监测期间，高风险组发生异常 vEEG 报告的可能性明显更高（305 例中有 190 例：62%，3586 例中有 1790 例：50%[变化率=12%]，RR=1.25，95%置信区间[CI]为[1.137-1.370]，p<0.001），出现确诊癫痫发作的可能性更高（305 例中有 56 例：18%，3586 例中有 424 例：11%[变化率=7%]，RR=1.63，95%CI为[1.265-2.101]，p<0.001），且报告事件的可能性更高（305 例中有 153 例：50%，3586 例中有 1267 例：35%[变化率=15%]，RR=1.420，95%CI为[1.259-1.602]，p<0.001）。在各种临床和人口统计学特征中均观察到类似的效果。