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振幅整合脑电图与传统视频脑电图在新生儿惊厥检测中的比较。

Amplitude-integrated electroencephalography compared with conventional video-electroencephalography for detection of neonatal seizures.

作者信息

Rakshasbhuvankar Abhijeet A, Nagarajan Lakshmi, Zhelev Zhivko, Rao Shripada C

机构信息

Department of Neonatology, King Edward Memorial Hospital for Women, Subiaco, Australia.

Medical School, University of Western Australia, Crawley, Australia.

出版信息

Cochrane Database Syst Rev. 2025 Aug 11;8(8):CD013546. doi: 10.1002/14651858.CD013546.pub2.

Abstract

BACKGROUND

Conventional video-electroencephalography (cEEG) is the reference standard for diagnosing and managing neonatal seizures. However, continuous bedside cEEG services are not available in most neonatal units. Hence, an alternative and relatively simple method called amplitude-integrated EEG (aEEG), which uses a limited number of scalp electrodes, has become popular. aEEG allows continuous bedside monitoring of the electrical activity of the brain in neonates.

OBJECTIVES

The primary objective of the review was to assess the accuracy of aEEG against the reference standard cEEG for the detection of 'neonates with seizures' and 'individual seizures'. Detection of 'neonates with seizures' refers to the ability of the test to correctly identify a 'neonate' as 'seizure positive' or 'seizure negative' based on the detection of at least one seizure episode in the entire aEEG recording. Detection of 'individual seizures' refers to the ability of the test to correctly identify 'individual' seizure episodes within the same neonate rather than just diagnosing the neonate as 'seizure positive' or 'seizure negative'.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, clinical trials registries, and grey literature (Open Grey, Trove, and American Doctoral Dissertations) to 26 July 2022. We did not apply any language or publication status restrictions or any other filters.

SELECTION CRITERIA

We included prospective and retrospective studies investigating the accuracy of aEEG (index test) against the reference standard cEEG for the detection of neonatal seizures. To be eligible for inclusion, the studies must have compared aEEG with simultaneously recorded cEEG. There was no restriction on the number of leads, use of raw EEG traces, or experience and training of an aEEG interpreter. cEEG should have been recorded using at least nine electrodes and interpreted by a qualified person experienced in the interpretation of neonatal cEEG.

DATA COLLECTION AND ANALYSIS

Working independently, two review authors collected data from the included studies in a prespecified form and assessed the quality of the included studies using the QUADAS-2 tool. For the outcome of 'neonates with seizures', we used a bivariate mixed-effects regression model to conduct a meta-analysis to derive pooled sensitivity, specificity, positive and negative likelihood ratios (LR), and their respective 95% confidence intervals (CI). We generated a summary receiver operating characteristic (SROC) curve to display the results of individual studies. We calculated post-test probabilities based on Bayes' theorem through Fagan nomograms. For the outcome of 'individual seizures', pooling of data was not possible because of the 'unit of analysis' issue. Instead, we performed a narrative synthesis. We assessed the certainty of the evidence using GRADE guidelines.

MAIN RESULTS

We included 16 studies (562 infants) in the systematic review, of which only two studies interpreted the aEEGs prospectively at the bedside. Out of 16 studies, three studies (97 infants) described the accuracy of aEEG only for detecting 'infants with seizures', three studies (72 infants) described only 'individual seizures', while 10 studies (393 infants) described the accuracy of aEEG for detecting both. Ten of 16 studies were conducted in term and late preterm infants. Half of the included studies did not use raw EEG traces. Fourteen studies reported outcomes based only on retrospective interpretation. Ten of 16 studies used four electrodes (making this the most common approach amongst the included studies), and 10 studies' aEEG recordings exceeded six hours. Only two included studies used a seizure detection algorithm. In 14 studies, a neonatal or neurology consultant performed aEEG interpretation, and most (in 10 of 14 studies) had prior experience in aEEG interpretation. Accuracy of aEEG to diagnose 'neonates with seizures'. The only two prospective studies (53 participants) which interpreted aEEGs 'live' at the bedside, reported sensitivities of zero and 0.57 and specificities of 0.82 and 0.92, respectively. Meta-analysis of 13 studies (490 neonates) found that aEEG had a pooled sensitivity of 0.71 (95% CI 0.57 to 0.83), specificity of 0.84 (95% CI 0.59 to 0.95), positive LR of 4.50 (95% CI 1.55 to 13.04), and negative LR of 0.34 (95% CI 0.22 to 0.53) for the detection of 'neonates with seizures'. However, when we analysed only studies with low risk of bias (3 studies), sensitivity (0.56, 95% CI 0.02 to 0.99) and specificity (0.78, 95% CI 0.60 to 0.90) were even lower. There was significant statistical heterogeneity, which could not be explained based on threshold effect and exploratory analyses of forest plots. We graded the certainty of the evidence as low, in view of the high or unclear risk of bias in many studies, imprecision, and significant heterogeneity. Accuracy of aEEG to detect 'individual seizures' in neonates. The reported sensitivities of aEEG for the detection of 'individual seizures' ranged from 0 to 0.86 (13 studies, 465 neonates). We rated the certainty of the evidence as low. The common causes of missed seizures on aEEG (i.e. false negative) as reported by studies were short duration of seizures, localisation of seizures away from aEEG leads, low voltage, and inexperienced interpreter. The false-positive rates were high when interpreted live at the bedside and if the interpreters were inexperienced. Artefacts resulting from muscle movement, patting, hiccups, and insufficient electrode attachment were other common causes of false-positive results.

AUTHORS' CONCLUSIONS: Low-certainty evidence suggests that aEEG has only moderate sensitivity and specificity for detecting 'neonates with seizures', and its ability to detect 'individual seizures' varies widely. These findings suggest that aEEG may not be sufficiently accurate for diagnosing neonatal seizures as it can under-diagnose or over-diagnose seizures. Studies with low risk of bias are needed to address the issue definitively.

摘要

背景

传统视频脑电图(cEEG)是诊断和管理新生儿惊厥的参考标准。然而,大多数新生儿病房无法提供持续的床边cEEG服务。因此,一种使用有限数量头皮电极的替代且相对简单的方法——振幅整合脑电图(aEEG),变得流行起来。aEEG可对新生儿大脑的电活动进行持续床边监测。

目的

本综述的主要目的是评估aEEG相对于参考标准cEEG在检测“有惊厥的新生儿”和“个体惊厥”方面的准确性。检测“有惊厥的新生儿”是指该检测基于在整个aEEG记录中检测到至少一次惊厥发作,将“新生儿”正确识别为“惊厥阳性”或“惊厥阴性”的能力。检测“个体惊厥”是指该检测在同一新生儿中正确识别“个体”惊厥发作的能力,而不仅仅是将新生儿诊断为“惊厥阳性”或“惊厥阴性”。

检索方法

我们检索了截至2022年7月26日的Cochrane系统评价数据库、医学期刊数据库(MEDLINE)、荷兰医学文摘数据库(Embase)、临床试验注册库以及灰色文献(开放灰色文献库、澳大利亚国家图书馆数字化数据库Trove和美国博士论文数据库)。我们未应用任何语言或出版状态限制或任何其他筛选条件。

选择标准

我们纳入了前瞻性和回顾性研究,这些研究调查了aEEG(索引测试)相对于参考标准cEEG在检测新生儿惊厥方面的准确性。为符合纳入条件,研究必须将aEEG与同时记录的cEEG进行比较。导联数量、原始脑电图痕迹的使用、aEEG解释者的经验和培训均无限制。cEEG应使用至少九个电极进行记录,并由具有新生儿cEEG解释经验的合格人员进行解释。

数据收集与分析

两名综述作者独立工作,以预先指定的表格形式从纳入研究中收集数据,并使用QUADAS - 2工具评估纳入研究的质量。对于“有惊厥的新生儿”这一结果,我们使用双变量混合效应回归模型进行荟萃分析,以得出合并敏感性、特异性、阳性和阴性似然比(LR)及其各自的95%置信区间(CI)。我们生成了汇总受试者工作特征(SROC)曲线以展示个体研究的结果。我们通过费根诺模图根据贝叶斯定理计算检验后概率。对于“个体惊厥”这一结果,由于“分析单位”问题,无法进行数据合并。相反,我们进行了叙述性综述。我们使用GRADE指南评估证据的确定性。

主要结果

我们在系统评价中纳入了16项研究(562名婴儿),其中只有两项研究在床边对aEEG进行前瞻性解释。在16项研究中,三项研究(97名婴儿)仅描述了aEEG检测“有惊厥的婴儿”的准确性,三项研究(72名婴儿)仅描述了“个体惊厥”,而10项研究(393名婴儿)描述了aEEG检测两者的准确性。16项研究中有十项是针对足月儿和晚期早产儿进行的。纳入研究中一半未使用原始脑电图痕迹。十四项研究仅基于回顾性解释报告结果。16项研究中有十项使用了四个电极(这是纳入研究中最常见的方法),并且10项研究的aEEG记录超过6小时。仅两项纳入研究使用了惊厥检测算法。在14项研究中,由新生儿或神经科顾问进行aEEG解释,并且大多数(14项研究中的10项)有aEEG解释的既往经验。aEEG诊断“有惊厥的新生儿”的准确性。仅有的两项在床边“实时”解释aEEG的前瞻性研究(53名参与者)报告的敏感性分别为零和0.57,特异性分别为0.82和0.92。对13项研究(490名新生儿)的荟萃分析发现,aEEG检测“有惊厥的新生儿”的合并敏感性为0.71(95%CI 0.57至0.83),特异性为0.84(95%CI 0.59至0.95),阳性似然比为4.50(95%CI 1.55至13.04),阴性似然比为0.34(95%CI 0.22至0.53)。然而,当我们仅分析偏倚风险低的研究(3项研究)时,敏感性(0.56,95%CI 0.02至0.99)和特异性(0.78,95%CI 0.60至0.90)甚至更低。存在显著的统计学异质性,基于阈值效应和森林图的探索性分析无法解释。鉴于许多研究中偏倚风险高或不明确、不精确以及显著的异质性,我们将证据的确定性评为低。aEEG检测新生儿“个体惊厥”的准确性。aEEG检测“个体惊厥”报告的敏感性范围为0至0.86(13项研究,465名新生儿)。我们将证据的确定性评为低。研究报告的aEEG漏诊惊厥(即假阴性)的常见原因是惊厥持续时间短、惊厥定位远离aEEG导联、电压低以及解释者经验不足。在床边实时解释以及解释者经验不足时,假阳性率较高。肌肉运动、轻拍、打嗝和电极附着不足导致的伪迹是假阳性结果的其他常见原因。

作者结论

低确定性证据表明,aEEG在检测“有惊厥的新生儿”方面仅具有中等敏感性和特异性,其检测“个体惊厥”的能力差异很大。这些发现表明,aEEG在诊断新生儿惊厥方面可能不够准确,因为它可能会漏诊或误诊惊厥。需要进行偏倚风险低的研究来明确解决这一问题。

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