Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A.
Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.
J Clin Neurophysiol. 2024 May 1;41(4):305-311. doi: 10.1097/WNP.0000000000000995. Epub 2023 Mar 8.
Continuous EEG monitoring (CEEG) is increasingly used to identify electrographic seizures (ES) in critically ill children, but it is resource intense. We aimed to assess how patient stratification by known ES risk factors would impact CEEG utilization.
This was a prospective observational study of critically ill children with encephalopathy who underwent CEEG. We calculated the average CEEG duration required to identify a patient with ES for the full cohort and subgroups stratified by known ES risk factors.
ES occurred in 345 of 1,399 patients (25%). For the full cohort, an average of 90 hours of CEEG would be required to identify 90% of patients with ES. If subgroups of patients were stratified by age, clinically evident seizures before CEEG initiation, and early EEG risk factors, then 20 to 1,046 hours of CEEG would be required to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation and EEG risk factors present in the initial hour of CEEG required only 20 (<1 year) or 22 (≥1 year) hours of CEEG to identify a patient with ES. Conversely, patients with no clinically evident seizures before CEEG initiation and no EEG risk factors in the initial hour of CEEG required 405 (<1 year) or 1,046 (≥1 year) hours of CEEG to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation or EEG risk factors in the initial hour of CEEG required 29 to 120 hours of CEEG to identify a patient with ES.
Stratifying patients by clinical and EEG risk factors could identify high- and low-yield subgroups for CEEG by considering ES incidence, the duration of CEEG required to identify ES, and subgroup size. This approach may be critical for optimizing CEEG resource allocation.
连续脑电图监测(CEEG)越来越多地用于识别危重病儿童的电癫痫发作(ES),但这需要大量资源。我们旨在评估通过已知 ES 风险因素对患者进行分层如何影响 CEEG 的利用。
这是一项对患有脑病的危重病儿童进行 CEEG 的前瞻性观察研究。我们计算了识别 ES 患者所需的 CEEG 平均持续时间,该时间适用于整个队列和按已知 ES 风险因素分层的亚组。
在 1399 名患者中,345 名(25%)发生 ES。对于整个队列,平均需要 90 小时的 CEEG 才能识别 90%的 ES 患者。如果按年龄、CEEG 启动前临床明显的癫痫发作和早期 EEG 风险因素对患者进行分层,则需要 20 至 1046 小时的 CEEG 才能识别出 ES 患者。CEEG 启动前有临床明显的癫痫发作和 CEEG 初始小时内存在 EEG 风险因素的患者仅需 20(<1 岁)或 22(≥1 岁)小时的 CEEG 即可识别出 ES 患者。相反,CEEG 启动前无临床明显的癫痫发作且 CEEG 初始小时内无 EEG 风险因素的患者需要 405(<1 岁)或 1046(≥1 岁)小时的 CEEG 才能识别出 ES 患者。CEEG 启动前有临床明显的癫痫发作或 CEEG 初始小时内有 EEG 风险因素的患者需要 29 至 120 小时的 CEEG 即可识别出 ES 患者。
通过考虑 ES 发生率、识别 ES 所需的 CEEG 持续时间和亚组大小,按临床和 EEG 风险因素对患者进行分层,可以确定 CEEG 的高和低产亚组。这种方法对于优化 CEEG 资源分配可能至关重要。