Zanner Robert, Berger Sebastian, Schröder Natalie, Kreuzer Matthias, Schneider Gerhard
Department of Anesthesiology, HELIOS University Clinic Wuppertal, Witten/Herdecke University, Heusnerstr. 40, 42283, Wuppertal, Germany.
Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
J Clin Monit Comput. 2024 Feb;38(1):187-196. doi: 10.1007/s10877-023-01046-w. Epub 2023 Jul 12.
Electroencephalogram (EEG)-based monitoring during general anesthesia may help prevent harmful effects of high or low doses of general anesthetics. There is currently no convincing evidence in this regard for the proprietary algorithms of commercially available monitors. The purpose of this study was to investigate whether a more mechanism-based parameter of EEG analysis (symbolic transfer entropy, STE) can separate responsive from unresponsive patients better than a strictly probabilistic parameter (permutation entropy, PE) under clinical conditions. In this prospective single-center study, the EEG of 60 surgical ASA I-III patients was recorded perioperatively. During induction of and emergence from anesthesia, patients were asked to squeeze the investigators' hand every 15s. Time of loss of responsiveness (LoR) during induction and return of responsiveness (RoR) during emergence from anesthesia were registered. PE and STE were calculated at -15s and +30s of LoR and RoR and their ability to separate responsive from unresponsive patients was evaluated using accuracy statistics. 56 patients were included in the final analysis. STE and PE values decreased during anesthesia induction and increased during emergence. Intra-individual consistency was higher during induction than during emergence. Accuracy values during LoR and RoR were 0.71 (0.62-0.79) and 0.60 (0.51-0.69), respectively for STE and 0.74 (0.66-0.82) and 0.62 (0.53-0.71), respectively for PE. For the combination of LoR and RoR, values were 0.65 (0.59-0.71) for STE and 0.68 (0.62-0.74) for PE. The ability to differentiate between the clinical status of (un)responsiveness did not significantly differ between STE and PE at any time. Mechanism-based EEG analysis did not improve differentiation of responsive from unresponsive patients compared to the probabilistic PE.Trial registration: German Clinical Trials Register ID: DRKS00030562, November 4, 2022, retrospectively registered.
全身麻醉期间基于脑电图（EEG）的监测可能有助于预防高剂量或低剂量全身麻醉药的有害影响。目前，关于市售监测仪的专有算法，尚无令人信服的证据。本研究的目的是调查在临床条件下，一种基于机制的脑电图分析参数（符号转移熵，STE）是否比严格的概率参数（排列熵，PE）能更好地区分有反应和无反应的患者。在这项前瞻性单中心研究中，记录了60例美国麻醉医师协会（ASA）I-III级外科手术患者围手术期的脑电图。在麻醉诱导和苏醒期间，要求患者每15秒挤压一次研究人员的手。记录麻醉诱导期间的反应消失时间（LoR）和麻醉苏醒期间的反应恢复时间（RoR）。在LoR和RoR的-15秒和+30秒时计算PE和STE，并使用准确性统计评估它们区分有反应和无反应患者的能力。最终分析纳入了56例患者。STE和PE值在麻醉诱导期间降低，在苏醒期间升高。诱导期间的个体内一致性高于苏醒期间。LoR和RoR期间的准确性值，STE分别为0.71（0.62-0.79）和0.60（0.51-0.69），PE分别为0.74（0.66-0.82）和0.62（0.53-0.71）。对于LoR和RoR的组合，STE值为0.65（0.59-0.71），PE值为0.68（0.62-0.74）。在任何时候，STE和PE在区分（无）反应的临床状态方面的能力均无显著差异。与概率性的PE相比，基于机制的脑电图分析并未改善对有反应和无反应患者的区分。试验注册：德国临床试验注册编号：DRKS00030562，2022年11月4日，回顾性注册。
