Frontotemporal EEG to guide sedation in COVID-19 related acute respiratory distress syndrome.

Frontotemporal EEG to guide sedation in COVID-19 related acute respiratory distress syndrome.
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额颞叶脑电图指导新冠肺炎相关急性呼吸窘迫综合征镇静治疗

DOI:
10.1016/j.clinph.2021.01.003
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发表时间:
2021-03
期刊:
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
影响因子:
--
通讯作者:
Claassen J
Claassen J
中科院分区:
其他
文献类型:
--
作者:
Michalak AJ;Mendiratta A;Eliseyev A;Ramnath B;Chung J;Rasnow J;Reid L;Salerno S;García PS;Agarwal S;Roh D;Park S;Bazil C;Claassen J

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研究有限额颞脑电图(EEG)是否可以指导接受神经肌肉阻滞剂的高传染性新型冠状病毒病2019(COVID-19)患者的镇静变化。癫痫病学家每天对11名连续患者的98天连续额颞叶EEG进行评估,以建议降低或维持镇静水平。我们评估了在6小时内增加镇静的必要性。使用机器学习算法将定量EEG的事后分析与镇静水平相关联。11名患者共接受了98天的镇静研究。EEG与57天(58%)的过度镇静和41天(42%)的充分镇静一致。59%(N = 58; 19例减少,39例保持镇静水平)的团队遵循了建议。在镇静减少后6小时内,7例(12%)需要增加镇静。EEG镇静水平的自动分类达到80%(±17%)的准确性。有限EEG的视觉检查有助于镇静深度指导。在二次分析中,我们的数据支持使用定量EEG分析可以自动进行该确定。我们的研究结果支持使用额颞叶EEG指导COVID-19患者的镇静。
To study if limited frontotemporal electroencephalogram (EEG) can guide sedation changes in highly infectious novel coronavirus disease 2019 (COVID-19) patients receiving neuromuscular blocking agent. 98 days of continuous frontotemporal EEG from 11 consecutive patients was evaluated daily by an epileptologist to recommend reduction or maintenance of the sedative level. We evaluated the need to increase sedation in the 6 h following this recommendation. Post-hoc analysis of the quantitative EEG was correlated with the level of sedation using a machine learning algorithm. Eleven patients were studied for a total of ninety-eight sedation days. EEG was consistent with excessive sedation on 57 (58%) and adequate sedation on 41 days (42%). Recommendations were followed by the team on 59% (N = 58; 19 to reduce and 39 to keep the sedation level). In the 6 h following reduction in sedation, increases of sedation were needed in 7 (12%). Automatized classification of EEG sedation levels reached 80% (±17%) accuracy. Visual inspection of a limited EEG helped sedation depth guidance. In a secondary analysis, our data supported that this determination may be automated using quantitative EEG analysis. Our results support the use of frontotemporal EEG for guiding sedation in patients with COVID-19.
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