不同时间节点aEEG监测对HIE远期神经预后的评估价值

The evaluation value of aEEG monitoring at different time points in the long-term neurological prognosis of HIE

目的 研究不同时间节点振幅整合脑电图(aEEG)单一背景活动分类、aEEG背景模式演变对新生儿缺氧缺血性脑病(HIE)远期不良神经预后的预测价值。方法 通过前瞻性研究,选取该院新生儿重症监护室收治的46例足月HIE患儿作为研究对象,均采集生后2~72 h aEEG数据。将患儿分为2~12 h、>12~24 h、>24~48 h、>48~72 h等4个不同时间节点单一背景活动组,以及aEEG背景模式演变组。患儿出院后定期到神经专科随访。比较各组间阳性似然比(LR)及受试者工作特征(ROC)曲线在预测足月HIE远期神经结局中的准确度及临床价值。结果 根据15月龄时盖泽尔发育量表(GDS)测试结果,将46例研究对象分为预后好组32例、预后差组14例。2组患儿性别、胎龄、出生体重、生产方式比较,差异均无统计学意义(P>0.05)。不同时间节点aEEG单一背景活动分类、aEEG背景模式演变与远期神经结局存在统计学差异(P<0.05);其中背景模式演变组判断预后的准确度最高(LR=25.44);时间节点组中>24~48 h的准确度最高(LR=21.18)。ROC曲线显示,4个不同时间节点aEEG单一背景活动分类及aEEG背景模式演变预测HIE远期不良预后的面积分别为0.609[95%置信区间(95%CI)=0.455~0.762]、0.652(95%CI=0.508~0.796)、0.671(95%CI=0.525~0.816)、0.660(95%CI=0.512~0.807)、0.800(95%CI=0.677~0.922)。结论 aEEG背景模式演变在判断HIE远期神经结局的准确度及预测能力方面高于任意时间节点aEEG单一背景活动分类,持续监测aEEG对判断HIE预后有重要临床价值。

Objective To explore the predictive value of amplitude-integrated electro-encephalo graphy(aEEG) single background activity classification and aEEG background pattern evolution at different time points in the long-term adverse neurological prognosis of children with full-term hypoxic-ischemic encephalopathy(HIE).Methods Through a prospective study, 46 full-term HIE infants admitted to the neonatal intensive care unit of this hospital were selected as the research subjects, and aEEG data from 2-72 h after birth were collected.The children were divided into four groups with single background activity at different time points, including 2-12 h, >12-24 h, >24-48 h, >48-72 h, and aEEG background pattern evolution group.Regular neurological follow-up the patients after discharge.The accuracy and clinical value of positive likelihood ratio(LR) and receiver operating characteristic(ROC) curve in predicting the long-term neurological outcome of full-term HIE were compared between groups.Results The 46 subjects were divided into the good prognosis group(32 cases) and the poor prognosis group(14 cases) according to the results of the Geisel Developmental Scale(GDS) at the age of 15 months.There were no significant differences in gender, gestational age, birth weight, and mode of production between the two groups(P>0.05).There were statistically significant differences in the predictive accuracy of long-term neurological outcomes among aEEG single backgroundactivity classification at different time points, and aEEG background pattern evolution(P<0.05).Among them, the background pattern evolution group had the highest accuracy in judging prognosis(LR=25.44).The accuracy of 24-48 h in the time node groups was the highest(LR=21.18).The ROC curve showed that the areas of aEEG single background activity classification at different time points, and aEEG background pattern evolution to predict the long-term poor prognosis of HIE were 0.609(95%CI=0.455-0.762),0.652(95%CI=0.508-0.796),0.671(95%CI=0.525-0.816),0.660(95%CI=0.512-0.807),0.800(95%CI=0.677-0.922),respectively.Conclusion The accuracy and predictive power of the aEEG background pattern evolution were higher than that of the aEEG single background activity classification at any time node in judging the long-term neurological outcome of HIE.Continuous monitoring of aEEG has important clinical value in judging the prognosis of HIE.