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脑功能动态监测:神经重症监护单元的新目标。

Dynamic monitors of brain function: a new target in neurointensive care unit.

机构信息

Anaesthesiology and Intensive Care Unit, Treviso Hospital, Piazzale Ospedale 1, I-31100 Treviso, Italy.

出版信息

Crit Care. 2011 Jul 15;15(4):R170. doi: 10.1186/cc10315.

DOI:10.1186/cc10315
PMID:21762487
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3387611/
Abstract

INTRODUCTION

Somatosensory evoked potential (SEP) recordings and continuous electroencephalography (EEG) are important tools with which to predict Glasgow Outcome Scale (GOS) scores. Their combined use may potentially allow for early detection of neurological impairment and more effective treatment of clinical deterioration.

METHODS

We followed up 68 selected comatose patients between 2007 and 2009 who had been admitted to the Neurosurgical Intensive Care Unit of Treviso Hospital after being diagnosed with subarachnoid haemorrhage (51 cases) or intracerebral haemorrhage (17 cases). Quantitative brain function monitoring was carried out using a remote EEG-SEP recording system connected to a small amplification head box with 28 channels and a multimodal stimulator (NEMO; EBNeuro, Italy NeMus 2; EBNeuro S.p.A., Via P. Fanfani 97/A - 50127 Firenze, Italy). For statistical analysis, we fit a binary logistic regression model to estimate the effect of brain function monitoring on the probability of GOS scores equal to 1. We also designed a proportional odds model for GOS scores, depending on amplitude and changes in both SEPs and EEG as well as on the joint effect of other related variables. Both families of models, logistic regression analysis and proportional odds ratios, were fit by using a maximum likelihood test and the partial effect of each variable was assessed by using a likelihood ratio test.

RESULTS

Using the logistic regression model, we observed that progressive deterioration on the basis of EEG was associated with an increased risk of dying by almost 24% compared to patients whose condition did not worsen according to EEG. SEP decreases were also significant; for patients with worsening SEPs, the odds of dying increased to approximately 32%. In the proportional odds model, only modifications of Modified Glasgow Coma Scale scores and SEPs during hospitalisation statistically significantly predicted GOS scores. Patients whose SEPs worsened during the last time interval had an approximately 17 times greater probability of a poor GOS score compared to the other patients.

CONCLUSIONS

The combined use of SEPs and continuous EEG monitoring is a unique example of dynamic brain monitoring. The temporal variation of these two parameters evaluated by continuous monitoring can establish whether the treatments used for patients receiving neurocritical care are properly tailored to the neurological changes induced by the lesions responsible for secondary damage.

摘要

简介

体感诱发电位(SEP)记录和连续脑电图(EEG)是预测格拉斯哥预后量表(GOS)评分的重要工具。联合使用这两种方法可能有助于早期发现神经功能障碍,并更有效地治疗临床恶化。

方法

我们对 2007 年至 2009 年期间入住特雷维索医院神经外科重症监护病房的 68 例昏迷患者进行了随访,这些患者被诊断为蛛网膜下腔出血(51 例)或脑出血(17 例)。使用与小型放大头盒和多模态刺激器(NEMO;EBNeuro,意大利 NeMus 2;EBNeuro S.p.A.,意大利佛罗伦萨 Via P. Fanfani 97/A-50127)相连的远程 EEG-SEP 记录系统进行定量脑功能监测。为了进行统计分析,我们拟合了二元逻辑回归模型,以估计脑功能监测对 GOS 评分等于 1 的概率的影响。我们还根据 SEP 和 EEG 的幅度以及变化以及其他相关变量的联合效应,为 GOS 评分设计了比例优势模型。这两种模型家族,逻辑回归分析和比例优势比,都是通过最大似然检验拟合的,并且通过似然比检验评估了每个变量的部分效应。

结果

使用逻辑回归模型,我们观察到根据 EEG 观察到的进行性恶化与根据 EEG 病情未恶化的患者相比,死亡风险增加了近 24%。SEP 降低也具有显著意义;对于 SEP 恶化的患者,死亡的几率增加到大约 32%。在比例优势模型中,只有住院期间改良格拉斯哥昏迷量表评分和 SEP 的变化统计学上显著预测了 GOS 评分。在最后时间间隔内 SEP 恶化的患者,其 GOS 评分较差的概率大约是其他患者的 17 倍。

结论

SEP 和连续 EEG 监测的联合使用是动态脑监测的独特范例。通过连续监测评估这两个参数的时间变化,可以确定为接受神经危重症护理的患者使用的治疗方法是否适当地针对负责继发损伤的病变引起的神经变化进行了调整。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/b55adfe93ed6/cc10315-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/b82f4f659a98/cc10315-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/32c03d621b64/cc10315-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/08476afe643e/cc10315-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/b3e3d4ee4c13/cc10315-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/b55adfe93ed6/cc10315-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/b82f4f659a98/cc10315-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/32c03d621b64/cc10315-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/08476afe643e/cc10315-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/b3e3d4ee4c13/cc10315-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/3387611/b55adfe93ed6/cc10315-5.jpg

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