利用脑电图和标准测量相结合来监测麻醉深度。

Monitoring depth of anesthesia utilizing a combination of electroencephalographic and standard measures.

机构信息

From the Department of Anesthesiology I, Witten/Herdecke University, Helios Clinic Wuppertal, Wuppertal, Germany (G. Schneider); Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany (G. Schneider, D.J., A.O., M.K., and E.F.K.); Department of Anesthesiology and Intensive Care Medicine, Medical University of Graz, Graz, Austria (G. Schwarz); Department of Anesthesiology, Knappschaftskrankenhaus Bochum Langendreer, Klinikum der Ruhr Universität Bochum, Bochum, Germany, and Department of Anesthesiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (P.B.); Department of Anesthesiology F06-149, University Medical Center Utrecht, GA Utrecht, The Netherlands (C.J.K.); Department of Anesthesiology, German Heart Center Berlin, Berlin, Germany (H.K.); Department of Anesthesiology and Operative Intensive Care, University Hospital Charité, Humboldt-University Berlin, Berlin, Germany (I.R.); Institute of Information Logistics, Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Germany (Hans-Dieter Kochs, Ph.D.) (G. Stockmanns); and Institute of Pattern Recognition, Faculty of Electrical Engineering and Computer Science, University of Applied Sciences Hochschule Niederrhein, Krefeld, Germany (G. Stockmanns).

出版信息

Anesthesiology. 2014 Apr;120(4):819-28. doi: 10.1097/ALN.0000000000000151.

DOI:10.1097/ALN.0000000000000151

PMID:24694845

Abstract

BACKGROUND

For decades, monitoring depth of anesthesia was mainly based on unspecific effects of anesthetics, for example, blood pressure, heart rate, or drug concentrations. Today, electroencephalogram-based monitors promise a more specific assessment of the brain function. To date, most approaches were focused on a "head-to-head" comparison of either electroencephalogram- or standard parameter-based monitoring. In the current study, a multimodal indicator based on a combination of both electro encephalographic and standard anesthesia monitoring parameters is defined for quantification of "anesthesia depth."

METHODS

Two hundred sixty-three adult patients from six European centers undergoing surgery with general anesthesia were assigned to 1 of 10 anesthetic combinations according to standards of the enrolling hospital. The anesthesia multimodal index of consciousness was developed using a data-driven approach, which maps standard monitoring and electroencephalographic parameters into an output indicator that separates different levels of anesthesia from awake to electroencephalographic burst suppression. Obtained results were compared with either a combination of standard monitoring parameters or the electroencephalogram-based bispectral index.

RESULTS

The anesthesia multimodal index of consciousness showed prediction probability (P(K)) of 0.96 (95% CI, 0.95 to 0.97) to separate different levels of anesthesia (wakefulness to burst suppression), whereas the bispectral index had significantly lower PK of 0.80 (0.76 to 0.81) at corrected threshold P value of less than 0.05. At the transition between consciousness and unconsciousness, anesthesia multimodal index of consciousness yielded a PK of 0.88 (0.85 to 0.91).

CONCLUSION

A multimodal integration of both standard monitoring and electroencephalographic parameters may more precisely reflect the level of anesthesia compared with monitoring based on one of these aspects alone.

摘要

背景

几十年来，麻醉深度监测主要基于麻醉的非特异性效应，例如血压、心率或药物浓度。如今，基于脑电图的监测器有望更准确地评估大脑功能。迄今为止，大多数方法都集中在对脑电图或标准参数监测的“一对一”比较上。在当前的研究中，定义了一种基于脑电图和标准麻醉监测参数组合的多模态指标，用于量化“麻醉深度”。

方法

来自欧洲 6 个中心的 263 名接受全身麻醉手术的成年患者根据入组医院的标准被分配到 10 种麻醉组合中的 1 种。使用数据驱动的方法开发了意识的麻醉多模态指数，该方法将标准监测和脑电图参数映射到输出指标中，该指标将不同水平的麻醉从清醒状态到脑电图爆发抑制分离。获得的结果与标准监测参数的组合或基于脑电图的双频谱指数进行了比较。

结果

麻醉多模态意识指数（anesthesia multimodal index of consciousness）显示出区分不同麻醉水平（清醒至爆发抑制）的预测概率（P(K)）为 0.96（95%置信区间，0.95 至 0.97），而双频谱指数（bispectral index）的 P(K)显著较低，为 0.80（0.76 至 0.81），校正后的 P 值小于 0.05。在意识和无意识之间的过渡中，麻醉多模态意识指数的 P(K)为 0.88（0.85 至 0.91）。