Graeme Clark Institute and Department of Biomedical Engineering, University of Melbourne, Melbourne, Vic., Australia.
Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Vic., Australia.
Epilepsia. 2021 Feb;62(2):416-425. doi: 10.1111/epi.16809. Epub 2021 Jan 28.
Video-electroencephalography (vEEG) is an important component of epilepsy diagnosis and management. Nevertheless, inpatient vEEG monitoring fails to capture seizures in up to one third of patients. We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG.
We used a database of ambulatory vEEG studies to select a cohort with linked electronic seizure diaries of more than 20 reported seizures over at least 8 weeks. The total cohort included 48 participants. Diary seizure times were used to detect individuals' multiday seizure cycles and estimate times of high seizure risk. We compared whether estimated seizure risk was significantly different between conclusive and inconclusive vEEGs, and between vEEG with and without recorded epileptic activity. vEEGs were conducted prior to self-reported seizures; hence, the study aimed to provide a retrospective proof of concept that cycles of seizure risk were correlated with vEEG outcomes.
Estimated seizure risk was significantly higher for conclusive vEEGs and vEEGs with epileptic activity. Across all cycle strengths, the average time in high risk during vEEG was 29.1% compared with 14% for the conclusive/inconclusive groups and 32% compared to 18% for the epileptic activity/no epileptic activity groups. On average, 62.5% of the cohort showed increased time in high risk during their previous vEEG when epileptic activity was recorded (compared to 28% of the cohort where epileptic activity was not recorded). For conclusive vEEGs, 50% of the cohort had increased time in high risk, compared to 21.5% for inconclusive vEEGs.
Although retrospective, this study provides a proof of principle that scheduling monitoring times based on personalized seizure risk forecasts can improve the yield of vEEG. Forecasts can be developed at low cost from mobile seizure diaries. A simple scheduling tool to improve diagnostic outcomes may reduce cost and risks associated with delayed or missed diagnosis in epilepsy.
视频脑电图（vEEG）是癫痫诊断和管理的重要组成部分。然而，多达三分之一的患者在住院 vEEG 监测中未能捕捉到发作。我们假设个性化发作预测可用于优化 vEEG 的时间安排。
我们使用一个动态 vEEG 研究数据库，选择了一个有超过 20 次记录在案的电子发作日记且持续时间至少 8 周的队列。总队列包括 48 名参与者。日记发作时间用于检测个体的多日发作周期并估计高发作风险时间。我们比较了明确和不确定的 vEEG 之间以及有记录的癫痫活动和无记录的癫痫活动的 vEEG 之间，估计的发作风险是否有显著差异。vEEG 是在自我报告的发作之前进行的；因此，该研究旨在提供一个回顾性的概念验证，即发作风险的周期与 vEEG 结果相关。
明确的 vEEG 和有癫痫活动的 vEEG 的估计发作风险明显更高。在所有周期强度下，vEEG 期间的高风险平均时间为 29.1%，而明确/不确定组为 14%，有癫痫活动/无癫痫活动组为 32%/18%。平均而言，当记录到癫痫活动时，62.5%的队列在之前的 vEEG 中显示出高风险时间增加（而在没有记录到癫痫活动的队列中，这一比例为 28%）。对于明确的 vEEG，50%的队列显示高风险时间增加，而不确定的 vEEG 为 21.5%。
尽管是回顾性的，但这项研究提供了一个原理证明，即根据个性化发作风险预测来安排监测时间可以提高 vEEG 的收益。预测可以从移动发作日记以低成本开发。一种简单的调度工具可以改善诊断结果，从而降低癫痫延迟或漏诊相关的成本和风险。
