用高效快速EEG评估和预测发作:一个追溯的多中心比较有效性研究
Mariel Kalkach-Aparicio1, Safoora Fatima1, Atakan Selte1
1From the Department of Neurology (M.K.-A., R.M., A.F.S.), and Epilepsy Division of the Department of Neurology (S.F., A.S., G.A., P.V.K., J.L., S.H.), University of Wisconsin-Madison; Department of Neurology (S.F.), Southern Illinois University, Carbondale; Department of Neurology (A.S.), UCLA Harbor Medical Center, Torrance, CA; Epilepsy Division of Department of Neurology (I.S.S., K.G.), Massachusetts General Hospital, Boston; Comprehensive Epilepsy Center (J. Cormier, J. Cespedes, L.J.H.), Department of Neurology, Yale University, New Haven, CT; University of Connecticut School of Medicine (J. Cormier), Farmington; Epilepsy Division of Department of Neurology (K.G.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; UHS Wilson Square Neurology (G.A.), Johnson City, NY; Universidad Autonoma de Centro America (UACA) School of Medicine (J. Cespedes), Granadilla, Cipreses, Costa Rica; Neurology Department (A.A.E., N.K., M.D.), University of New Mexico, Albuquerque; University of South Dakota (A.A.E.), Sanford School of Medicine, Vermillion; Comprehensive Epilepsy Team (O.M.H.), Neurology Department, University of New Mexico, Albuquerque; Center for Neuroengineering and Therapeutics (J.L.), University of Pennsylvania, Philadelphia; Department of Neurology (B.W.), Massachusetts General Hospital; and Beth Israel Deaconess Medical Center (B.W.), Boston, MA.
使用2HELPS2B算法的快速响应脑电图 (rrEEG) 对于预测重症患者的发作而言,与传统的EEG (cEEG) 不逊色. 这一发现支持使用rrEEG来优化对发作检测的资源配置.
科学领域:
- 神经科学是一个神经科学.
- 关键护理医学 关键护理医学
- 医疗技术 医疗技术 医学技术
背景情况:
- 危急病患者经常经历发作,通常没有明显的临床症状,需要电脑电图 (EEG) 进行准确的诊断.
- 传统的EEG (cEEG) 是资源密集型的,无法满足不断增长的对发作检测的需求.
- 需要简短的EEG选方法来有效地分类cEEG资源.
研究的目的:
- 为了评估2HELPS2B风险预测得分的非劣势,该得分是根据1小时快速反应EEG (rrEEG) 计算的,与cEEG相比.
- 评估rrEEG作为在资源有限的重症监护机构中对发作风险分层的潜在解决方案.
主要方法:
- 一个多中心的追溯诊断准确性研究,涉及240名患者 (≥18岁) 从2018年1月到2022年6月.
- 2HELPS2B的得分是使用1小时的rrEEG记录来计算的,并与cEEG.
- 使用预先定义的非劣等差额的接收运营商特征曲线 (AUC) 下的面积来评估非劣等性.
主要成果:
- 在1小时rEEG的2HELPS2B得分显示,对于预测发作 (AUC0.85) 的cEEG,该得分不低于cEEG.
- 在rrEEG和匹配cEEG (AUC0.89) 之间没有观察到AUC的显著差异.
- 虚假阴性率和生存分析显示,rrEEG和cEEG之间的结果相似.
结论:
- 使用2HELPS2B算法进行1小时的rrEEG是cEEG用于预测发作风险的非劣质替代方案.
- 患有低风险评分 (2HELPS2B = 0) 的患者可能会避免长时间的脑电图监测.
- 这种方法可以使cEEG资源更有效地分配给风险较高的患者.
更多相关视频
10:23Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
Published on: June 23, 2023
09:57Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
Published on: September 20, 2024
