Department of Neonatology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
Brain Modelling Group, QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD, 4006, Australia.
Pediatr Res. 2024 Jan;95(1):193-199. doi: 10.1038/s41390-023-02753-5. Epub 2023 Jul 27.
Automated computational measures of EEG have the potential for large-scale application. We hypothesised that a predefined measure of early EEG-burst shape (increased burst sharpness) could predict neurodevelopmental impairment (NDI) and mental developmental index (MDI) at 2 years of age over-and-above that of brain ultrasound.
We carried out a secondary analysis of data from extremely preterm infants collected for an RCT (SafeBoosC-II). Two hours of single-channel cross-brain EEG was used to analyse burst sharpness with an automated algorithm. The co-primary outcomes were moderate-or-severe NDI and MDI. Complete data were available from 58 infants. A predefined statistical analysis was adjusted for GA, sex and no, mild-moderate, and severe brain injury as detected by cranial ultrasound.
Nine infants had moderate-or-severe NDI and the mean MDI was 87 ± 17.3 SD. The typical burst sharpness was low (negative values) and varied relatively little (mean -0.81 ± 0.11 SD), but the odds ratio for NDI was increased by 3.8 (p = 0.008) and the MDI was reduced by -3.2 points (p = 0.14) per 0.1 burst sharpness units increase (+1 SD) in the adjusted analysis.
This study confirms the association between EEG-burst measures in preterm infants and neurodevelopment in childhood. Importantly, this was by a priori defined analysis.
A fully automated, computational measure of EEG in the first week of life was predictive of neurodevelopmental impairment at 2 years of age. This confirms many previous studies using expert reading of EEG. Only single-channel EEG data were used, adding to the applicability. EEG was recorded by several different devices thus this measure appears to be robust to differences in electrodes, amplifiers and filters. The likelihood ratio of a positive EEG test, however, was only about 2, suggesting little immediate clinical value.
自动计算脑电图（EEG）的指标具有大规模应用的潜力。我们假设，预先定义的 EEG 爆发形状（增加爆发锐度）的指标可以预测神经发育障碍（NDI）和精神发育指数（MDI），其预测能力超过脑超声。
我们对一项随机对照试验（SafeBoosC-II）中收集的极早产儿数据进行了二次分析。使用自动算法分析 2 小时单通道跨脑 EEG 的爆发锐度。主要结局是中重度 NDI 和 MDI。58 例婴儿的完整数据可用。预先设定的统计分析调整了胎龄、性别以及无、轻度-中度和重度颅脑超声检查的脑损伤。
9 例婴儿有中重度 NDI，平均 MDI 为 87±17.3 标准差。典型的爆发锐度较低（负值），变化相对较小（平均值为-0.81±0.11 标准差），但在调整分析中，NDI 的比值比增加了 3.8（p=0.008），MDI 降低了-3.2 点（p=0.14），每个爆发锐度单位增加（+1 标准差）。
本研究证实了早产儿 EEG 爆发指标与儿童期神经发育之间的关联。重要的是，这是通过事先定义的分析得出的。
出生后第一周全自动、计算性 EEG 指标可预测 2 岁时的神经发育障碍。这证实了许多以前使用 EEG 专家阅读的研究。仅使用单通道 EEG 数据，增加了适用性。因此，该测量方法似乎对电极、放大器和滤波器的差异具有稳健性。然而，阳性 EEG 测试的似然比仅约为 2，表明其临床价值有限。
