Philippa J Karoly1,2, Dominique Eden3, Ewan S Nurse2,3, Mark J Cook1,2, Janelle Taylor3, Sonya Dumanis4, Mark P Richardson5, Benjamin H Brinkmann6, Dean R Freestone2,3. 1. Graeme Clark Institute and Department of Biomedical Engineering, University of Melbourne, Melbourne, Vic., Australia. 2. Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Vic., Australia. 3. Seer Medical, Melbourne, Vic., Australia. 4. Epilepsy Foundation, Landover, MA, USA. 5. Division of Neuroscience, Institute of Psychology Psychiatry and Neuroscience, King's College London, London, UK. 6. Department of Neurology, Mayo Foundation, Rochester, MN, USA.
Abstract
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. SIGNIFICANCE: 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.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. SIGNIFICANCE: 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.
Authors: Philippa J Karoly; Dean R Freestone; Dominique Eden; Rachel E Stirling; Lyra Li; Pedro F Vianna; Matias I Maturana; Wendyl J D'Souza; Mark J Cook; Mark P Richardson; Benjamin H Brinkmann; Ewan S Nurse Journal: Front Neurol Date: 2021-08-04 Impact factor: 4.003
Authors: James W Wheless; Daniel Friedman; Gregory L Krauss; Vikram R Rao; Michael R Sperling; Enrique Carrazana; Adrian L Rabinowicz Journal: Epilepsia Date: 2022-09 Impact factor: 6.740
Authors: Benjamin H Brinkmann; Philippa J Karoly; Ewan S Nurse; Sonya B Dumanis; Mona Nasseri; Pedro F Viana; Andreas Schulze-Bonhage; Dean R Freestone; Greg Worrell; Mark P Richardson; Mark J Cook Journal: Front Neurol Date: 2021-07-13 Impact factor: 4.003
Authors: Andrea Biondi; Viviana Santoro; Pedro F Viana; Petroula Laiou; Deb K Pal; Elisa Bruno; Mark P Richardson Journal: Epilepsia Date: 2022-03-27 Impact factor: 6.740