Pro-ictal EEG scheduling improves the yield of epilepsy monitoring: Validating the use of multiday seizure cycles to optimize video-EEG timing

Abstract

AbstractA significant challenge of video-electroencephalography (vEEG) in epilepsy diagnosis is timing monitoring sessions to capture epileptiform activity. Given the significant consequences of misdiagnosis or delayed diagnosis, new techniques to improve diagnostic yield of vEEG are needed. In this study, we introduce and validate “pro-ictal EEG scheduling”, a method to schedule vEEG monitoring to coincide with periods of heightened seizure probability as a low-risk approach to enhance the diagnostic yield. A database of long-term ambulatory vEEG monitoring sessions (n=5038) of adults and children was examined. Data from linked electronic seizure diaries were extracted (minimum 10 self-reported events over 12-months) to generate cycle-based estimates of seizure risk. VEEG monitoring sessions coinciding with periods of estimated high-risk were allocated to the high-risk group (adultsn=305, childrenn=82) and compared to remaining studies (baseline: adultsn=3586, childrenn=1065). Test of Proportions and Risk-Ratios (RR) were used 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 (sex, epilepsy-type, medication) was also explored. During vEEG monitoring, the high-risk group was 25% more likely to have an abnormal vEEG report (190/305:62.3% vs 1790/3586:49.9%, RR=1.25, 95% CI[1.137:1.370],p<0.001), 63% more likely to present with a confirmed seizure (56/305:18.4% vs 424/3586:11.3%, RR=1.63, 95% CI[1.265:2.101],p<0.001) and 42% more likely to report an event (153/305:50.2% vs 1267/3586:35.3%, RR=1.420, 95% CI[1.259:1.602],p<0.001). In children, the high-risk group was 93% more likely to have a confirmed seizure (21/82:25.6% vs 141/1065:13.2%, RR=1.93, 95% CI[1.297:2.885],p=0.002). Similar effects were observed across clinical and demographic features. 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.