INFANT Research Centre, University College Cork, Cork, Ireland.
Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
Epilepsia. 2023 Feb;64(2):456-468. doi: 10.1111/epi.17468. Epub 2022 Dec 20.
To assess if early clinical and electroencephalography (EEG) features predict later seizure development in infants with hypoxic-ischemic encephalopathy (HIE).
Clinical and EEG parameters <12 h of birth from infants with HIE across eight European Neonatal Units were used to develop seizure-prediction models. Clinical parameters included intrapartum complications, fetal distress, gestational age, delivery mode, gender, birth weight, Apgar scores, assisted ventilation, cord pH, and blood gases. The earliest EEG hour provided a qualitative analysis (discontinuity, amplitude, asymmetry/asynchrony, sleep-wake cycle [SWC]) and a quantitative analysis (power, discontinuity, spectral distribution, inter-hemispheric connectivity) from full montage and two-channel amplitude-integrated EEG (aEEG). Subgroup analysis, only including infants without anti-seizure medication (ASM) prior to EEG was also performed. Machine-learning (ML) models (random forest and gradient boosting algorithms) were developed to predict infants who would later develop seizures and assessed using Matthews correlation coefficient (MCC) and area under the receiver-operating characteristic curve (AUC).
The study included 162 infants with HIE (53 had seizures). Low Apgar, need for ventilation, high lactate, low base excess, absent SWC, low EEG power, and increased EEG discontinuity were associated with seizures. The following predictive models were developed: clinical (MCC 0.368, AUC 0.681), qualitative EEG (MCC 0.467, AUC 0.729), quantitative EEG (MCC 0.473, AUC 0.730), clinical and qualitative EEG (MCC 0.470, AUC 0.721), and clinical and quantitative EEG (MCC 0.513, AUC 0.746). The clinical and qualitative-EEG model significantly outperformed the clinical model alone (MCC 0.470 vs 0.368, p-value .037). The clinical and quantitative-EEG model significantly outperformed the clinical model (MCC 0.513 vs 0.368, p-value .012). The clinical and quantitative-EEG model for infants without ASM (n = 131) had MCC 0.588, AUC 0.832. Performance for quantitative aEEG (n = 159) was MCC 0.381, AUC 0.696 and clinical and quantitative aEEG was MCC 0.384, AUC 0.720.
Early EEG background analysis combined with readily available clinical data helped predict infants who were at highest risk of seizures, hours before they occur. Automated quantitative-EEG analysis was as good as expert analysis for predicting seizures, supporting the use of automated assessment tools for early evaluation of HIE.
评估在患有缺氧缺血性脑病（HIE）的婴儿中，早期临床和脑电图（EEG）特征是否能预测后期癫痫发作的发生。
本研究使用来自欧洲 8 个新生儿病房的 HIE 婴儿出生后 12 小时内的临床和 EEG 参数，开发了癫痫预测模型。临床参数包括分娩过程中的并发症、胎儿窘迫、胎龄、分娩方式、性别、出生体重、阿普加评分、辅助通气、脐带 pH 值和血气值。最早的 EEG 小时提供了定性分析（不连续性、振幅、不对称/不同步、睡眠-觉醒周期[SWC]）和定量分析（功率、不连续性、频谱分布、半球间连通性），来自全导联和双通道振幅整合脑电图（aEEG）。还进行了仅包括 EEG 前无抗癫痫药物（ASM）的婴儿的亚组分析。使用马修斯相关系数（MCC）和接受者操作特征曲线下面积（AUC）评估了机器学习（ML）模型（随机森林和梯度提升算法）预测后期会发生癫痫的婴儿的能力。
本研究纳入了 162 名患有 HIE 的婴儿（53 名患有癫痫）。低 Apgar 评分、需要通气、高乳酸、低碱剩余、无 SWC、低 EEG 功率和增加的 EEG 不连续性与癫痫发作相关。开发了以下预测模型：临床（MCC 0.368，AUC 0.681）、定性 EEG（MCC 0.467，AUC 0.729）、定量 EEG（MCC 0.473，AUC 0.730）、临床和定性 EEG（MCC 0.470，AUC 0.721）和临床和定量 EEG（MCC 0.513，AUC 0.746）。临床和定性-EEG 模型明显优于单独的临床模型（MCC 0.470 比 0.368，p 值.037）。临床和定量-EEG 模型明显优于临床模型（MCC 0.513 比 0.368，p 值.012）。对于没有 ASM 的婴儿（n=131）的临床和定量-EEG 模型具有 MCC 0.588，AUC 0.832。定量 aEEG 的性能（n=159）为 MCC 0.381，AUC 0.696，临床和定量 aEEG 为 MCC 0.384，AUC 0.720。
早期 EEG 背景分析与易于获得的临床数据相结合，有助于在癫痫发作前数小时预测癫痫发作风险最高的婴儿。自动定量 EEG 分析与专家分析一样可用于预测癫痫发作，支持使用自动评估工具对 HIE 进行早期评估。
