• Gerhard Schneider, Denis Jordan, Gerhard Schwarz, Petra Bischoff, Cornelis J Kalkman, Hermann Kuppe, Ingrid Rundshagen, Adem Omerovic, Matthias Kreuzer, Gudrun Stockmanns, Eberhard F Kochs, and European Multicenter EEGAEP Anesthesia Monitoring Study Group and Research Group Knowledge-based Signal Processing.
• 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).
• Anesthesiology. 2014 Apr 1; 120 (4): 819-28.

BackgroundFor 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."MethodsTwo 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.ResultsThe 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).ConclusionA 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.

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