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NeuroTrend作为神经重症监护病房床边监测仪的适用性。

Applicability of NeuroTrend as a bedside monitor in the neuro ICU.

作者信息

Herta J, Koren J, Fürbass F, Zöchmeister A, Hartmann M, Hosmann A, Baumgartner C, Gruber A

机构信息

Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.

Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, 2nd Neurological Department, General Hospital Hietzing with Neurological Center Rosenhuegel, Vienna, Austria.

出版信息

Clin Neurophysiol. 2017 Jun;128(6):1000-1007. doi: 10.1016/j.clinph.2017.04.002. Epub 2017 Apr 11.

Abstract

OBJECTIVE

To assess whether ICU caregivers can correctly read and interpret continuous EEG (cEEG) data displayed with the computer algorithm NeuroTrend (NT) with the main attention on seizure detection and determination of sedation depth.

METHODS

120 screenshots of NT (480h of cEEG) were rated by 18 briefly trained nurses and biomedical analysts. Multirater agreements (MRA) as well as interrater agreements (IRA) compared to an expert opinion (EXO) were calculated for items such as pattern type, pattern location, interruption of recording, seizure suspicion, consistency of frequency, seizure tendency and level of sedation.

RESULTS

MRA as well as IRA were almost perfect (80-100%) for interruption of recording, spike-and-waves, rhythmic delta activity and burst suppression. A substantial agreement (60-80%) was found for electrographic seizure patterns, periodic discharges and seizure suspicion. Except for pattern localization (70.83-92.26%), items requiring a precondition and especially those who needed interpretation like consistency of frequency (47.47-79.15%) or level of sedation (41.10%) showed lower agreements.

CONCLUSIONS

The present study demonstrates that NT might be a useful bedside monitor in cases of subclinical seizures. Determination of correct sedation depth by ICU caregivers requires a more detailed training.

SIGNIFICANCE

Computer algorithms may reduce the workload of cEEG analysis in ICU patients.

摘要

目的

评估重症监护病房(ICU)护理人员能否正确读取和解读通过计算机算法NeuroTrend(NT)显示的连续脑电图(cEEG)数据,主要关注癫痫检测和镇静深度的判定。

方法

18名经过简短培训的护士和生物医学分析师对120张NT截图(480小时的cEEG)进行评分。计算了与专家意见(EXO)相比的多评估者一致性(MRA)以及评估者间一致性(IRA),涉及模式类型、模式位置、记录中断、癫痫疑似情况、频率一致性、癫痫倾向和镇静水平等项目。

结果

记录中断、尖波和慢波、节律性δ活动及爆发抑制的MRA和IRA几乎完美(80 - 100%)。对于脑电图癫痫模式、周期性放电和癫痫疑似情况,发现有实质性一致性(60 - 80%)。除模式定位(70.83 - 92.26%)外,需要前提条件的项目,尤其是那些需要解读的项目,如频率一致性(47.47 - 79.15%)或镇静水平(41.10%),一致性较低。

结论

本研究表明,NT在亚临床癫痫病例中可能是一种有用的床边监测工具。ICU护理人员确定正确的镇静深度需要更详细的培训。

意义

计算机算法可能会减轻ICU患者cEEG分析的工作量。

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