基于aEEG定量参数等资料建立诺莫图模型预测新生儿缺氧缺血性脑病预后的价值  

Value of establishing a nomogram model based on aEEG quantitative parameters for predicting prognosis in neo-natal hypoxic-ischemic encephalopathy

作　　者：李深 胡清远 卞艳兰 李中原 LI Shen;HU Qing-yuan;BIAN Yan-lan;LI Zhong-yuan(Electroen-cephalogram Room,Zhoukou Hospital of Traditional Chinese Medicine,Zhoukou 466000,Henan,CHINA;Department of Cardiology,Zhumadian Central Hospital,Zhumadian 463000,Henan,CHINA)

机构地区：[1]周口市中医院脑电图室,河南周口466000 [2]驻马店市中心医院心血管内二科,河南驻马店463000

出　　处：《海南医学》2025年第8期1121-1127,共7页Hainan Medical Journal

基　　金：河南省周口市科技攻关计划项目(编号:周科〔2024〕24号)。

摘　　要：目的探讨新生儿缺氧缺血性脑病(HIE)预后的影响因素及振幅整合脑电图(aEEG)定量参数,建立诺莫图模型并评估其对患儿预后的预测价值。方法前瞻性选取2022年6月至2023年6月周口市中医院收治的202例HIE患儿纳入研究,根据患儿出生9个月时Gesell发育量表评分作为预后判定标准,分为良好组和不良组。比较两组患儿的临床资料、aEEG定量参数,采用Logistic回归方程分析HIE患儿预后不良的影响因素,并将其纳入构建诺莫图模型,采用R软件中校准曲线、受试者工作特征(ROC)曲线及决策曲线(DCA)分析诺莫图模型对患儿预后的预测效能。另选取2023年7月至2023年10月56例HIE患儿相关资料用于外部验证。结果202例HIE患儿随访期间共有4例失访,143例Gesell评分>75分纳入良好组,55例Gesell评分≤75分纳入不良组;不良组患儿的高迁移率族蛋白B1(HMGB1)水平为(15.56±4.51)ng/mL,明显高于良好组的(11.01±3.38)ng/mL,活动睡眠期及安静睡眠期上界、下界电压分别为(11.21±1.34)μV、(6.34±0.85)μV,明显低于良好组的(13.40±2.28)μV、(8.20±1.16)μV,差异均有统计学意义(P<0.05);Logistic回归方程分析结果显示,HMGB1、活动睡眠期及安静睡眠期上界、下界电压均是HIE患儿预后不良的影响因素(P<0.05);基于Logistic回归方程结果构建诺莫图模型,内、外部验证结果显示,该模型对HIE患儿预后不良具有较高预测价值,预测概率与实际概率相吻合,且当阈值为0.05~0.58、0.08~0.55时,临床净获益率最大。结论基于aEEG定量参数构建诺莫图模型对HIE患儿预后不良具有良好的预测效能,可作为临床早期预测HIE患儿预后情况的有效模型,并可协助临床制定个性化防治措施。Objective To explore the influencing factors of prognosis in neonatal hypoxic-ischemic encepha-lopathy(HIE)and the quantitative parameters of amplitude-integrated electroencephalography(aEEG),and to establish a nomogram model to evaluate its predictive value for prognosis.Methods A prospective study was conducted on 202 HIE infants admitted to Zhoukou Hospital of Traditional Chinese Medicine from June 2022 to June 2023.Based on the Gesell Developmental Scale scores at 9 months of age,the infants were divided into a favorable prognosis group and a poor prognosis group.The clinical data and aEEG quantitative parameters of the two groups were compared.Logistic re-gression analysis was used to identify the influencing factors of poor prognosis in HIE infants,and these factors were in-corporated into the nomogram model.The predictive performance of the nomogram model was evaluated using calibra-tion curves,receiver operating characteristic(ROC)curves,and decision curve analysis(DCA)in R software.Additional-ly,data from 56 HIE infants admitted from July to October 2023 were used for external validation.Results Among the 202 HIE infants,4 were lost to follow-up,143 with Gesell scores>75 were assigned to the favorable prognosis group,and 55 with Gesell scores≤75 were assigned to the poor prognosis group.The level of high-mobility group pro-tein B1(HMGB1)in the poor prognosis group was(15.56±4.51)ng/mL,significantly higher than(11.01±3.38)ng/mL in the favorable prognosis group.The upper and lower voltage boundaries during active sleep and quiet sleep in the poor prognosis group were(11.21±1.34)μV and(6.34±0.85)μV,respectively,significantly lower than(13.40±2.28)μV and(8.20±1.16)μV in the favorable prognosis group(P<0.05).Logistic regression analysis showed that HMGB1 lev-els and the upper and lower voltage boundaries during active and quiet sleep were significant influencing factors for poor prognosis in HIE infants(P<0.05).A nomogram model was constructed based on the logistic regression results.Internal and

关 键 词：新生儿缺氧缺血性脑病 振幅整合脑电图 定量参数 影响因素 诺莫图模型 预后 预测价值 

分 类 号：R722.1[医药卫生—儿科]