Jodie Naim-Feil PhD, Rachel E. Stirling PhD, Philippa J. Karoly PhD, Daniel Payne PhD, Nicholas Winterling MEng, Dominique Eden BEng(Hons), Mark J. Cook MBBS, MD, David B. Grayden PhD, Matias Maturana PhD, Dean R. Freestone PhD, Ewan S. Nurse PhD
{"title":"前直肠脑电图调度提高了癫痫监测的收益率：验证使用多日发作周期来优化视频脑电图时间。","authors":"Jodie Naim-Feil PhD, Rachel E. Stirling PhD, Philippa J. Karoly PhD, Daniel Payne PhD, Nicholas Winterling MEng, Dominique Eden BEng(Hons), Mark J. Cook MBBS, MD, David B. Grayden PhD, Matias Maturana PhD, Dean R. Freestone PhD, Ewan S. Nurse PhD","doi":"10.1002/ana.27078","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <h3> Objective</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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], <i>p</i> < 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], <i>p</i> < 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], <i>p</i> < 0.001). Similar effects were observed across clinical and demographic features.</p>\n </section>\n \n <section>\n \n <h3> Interpretation</h3>\n \n <p>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</p>\n </section>\n </div>","PeriodicalId":127,"journal":{"name":"Annals of Neurology","volume":"96 6","pages":"1148-1159"},"PeriodicalIF":8.1000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ana.27078","citationCount":"0","resultStr":"{\"title\":\"Pro-Ictal EEG Scheduling Improves the Yield of Epilepsy Monitoring: Validating the Use of Multiday Seizure Cycles to Optimize Video-EEG Timing\",\"authors\":\"Jodie Naim-Feil PhD, Rachel E. Stirling PhD, Philippa J. Karoly PhD, Daniel Payne PhD, Nicholas Winterling MEng, Dominique Eden BEng(Hons), Mark J. Cook MBBS, MD, David B. Grayden PhD, Matias Maturana PhD, Dean R. Freestone PhD, Ewan S. 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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. 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This innovative approach offers a pragmatic and low-risk strategy to enhance the diagnostic capabilities of vEEG monitoring, significantly impacting epilepsy management. 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Pro-Ictal EEG Scheduling Improves the Yield of Epilepsy Monitoring: Validating the Use of Multiday Seizure Cycles to Optimize Video-EEG Timing
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
期刊介绍：
Annals of Neurology publishes original articles with potential for high impact in understanding the pathogenesis, clinical and laboratory features, diagnosis, treatment, outcomes and science underlying diseases of the human nervous system. Articles should ideally be of broad interest to the academic neurological community rather than solely to subspecialists in a particular field. Studies involving experimental model system, including those in cell and organ cultures and animals, of direct translational relevance to the understanding of neurological disease are also encouraged.
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