Suppr超能文献

利用自动脑电图背景趋势对低温治疗的脑病新生儿进行早期预后预测。

Early outcome-prediction with an automated EEG background trend in hypothermia-treated newborns with encephalopathy.

作者信息

Gonzalez-Tamez Karla, Montazeri Saeed, Ågren Johan, Vanhatalo Sampsa, Hellström-Westas Lena

机构信息

Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.

Departments of Clinical Neurophysiology and Physiology, University of Helsinki, Helsinki, Finland.

出版信息

Pediatr Res. 2025 Jun 16. doi: 10.1038/s41390-025-04193-9.

Abstract

BACKGROUND

Therapeutic hypothermia is an intervention that improves outcomes and alters early outcome-prediction in infants with moderate-severe hypoxic-ischemic encephalopathy (HIE). This study evaluated the early predictive accuracy of a fully automated continuous EEG background trend, Brain State of the Newborn (BSN) in a regional Swedish cohort of infants with presumed HIE.

METHOD

The BSN trend characterizes 1-min segments of EEG from zero (inactive) to 100 (continuous) and was generated from aEEG/EEG in 85 infants treated with hypothermia. BSN trajectories were compared in relation to clinical grading of encephalopathy and outcome. Receiver operating characteristics were computed for good (no/mild impairment) and poor outcomes (moderate/severe impairment or death).

RESULTS

During the first 48 h, BSN levels differed significantly between moderate and severe HIE (typical median BSN levels >80 and <40, respectively). The predictive accuracy of BSN was high already at 6 h (AUC 0.84) and at 12 h (AUC 0.91), with corresponding positive predictive values (PPV) > 0.92 for good outcome (cutoff BSN > 80) and PPV > 0.95 for poor outcome (cutoff BSN < 40).

CONCLUSION

BSN gives a continuous and objective measure of EEG background activity, which is highly predictive of good and poor outcomes already from the first 6-12 h in hypothermia-treated infants with moderate-severe HIE.

IMPACT

Brain State of the Newborn (BSN) is a deep learning-based EEG trend displaying electrocortical activity as numerical values. Here we establish that BSN trends over the first 48 h differ between infants with moderate versus severe hypoxic-ischemic encephalopathy (HIE) and is highly predictive of long-term outcome. This is the first study applying BSN trends in a cohort of exclusively hypothermia-treated infants, demonstrating its value for outcome-prediction already from the first 12 h after birth. The BSN provides a continuous bedside evaluation of brain function that complements the visual aEEG/EEG review and can assist bedside assessments in neonatal intensive care units.

摘要

背景

治疗性低温是一种可改善中度至重度缺氧缺血性脑病(HIE)婴儿的预后并改变早期预后预测的干预措施。本研究评估了一种全自动连续脑电图背景趋势——新生儿脑状态(BSN)在瑞典一个地区性疑似HIE婴儿队列中的早期预测准确性。

方法

BSN趋势将脑电图的1分钟片段特征化为从0(无活动)到100(连续),并由85例接受低温治疗的婴儿的振幅整合脑电图(aEEG)/脑电图生成。比较了BSN轨迹与脑病临床分级及预后的关系。计算了良好(无/轻度损伤)和不良预后(中度/重度损伤或死亡)的受试者工作特征曲线。

结果

在最初48小时内,中度和重度HIE之间的BSN水平存在显著差异(典型的中位数BSN水平分别>80和<40)。BSN在6小时(曲线下面积[AUC]0.84)和12小时(AUC 0.91)时的预测准确性就很高,良好预后(截断值BSN>80)的相应阳性预测值(PPV)>0.92,不良预后(截断值BSN<40)的PPV>0.9

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验