Fleischmann Antonia, Georgii Marie-Therese, Schuessler Jule, Schneider Gerhard, Pilge Stefanie, Kreuzer Matthias
From the Department of Anesthesiology and Intensive Care, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany.
Anesth Analg. 2023 Feb 1;136(2):346-354. doi: 10.1213/ANE.0000000000006098. Epub 2022 Jun 2.
Electroencephalogram (EEG)-based monitors of anesthesia are used to assess patients' level of sedation and hypnosis as well as to detect burst suppression during surgery. One of these monitors, the Entropy module, uses an algorithm to calculate the burst suppression ratio (BSR) that reflects the percentage of suppressed EEG. Automated burst suppression detection monitors may not reliably detect this EEG pattern. Hence, we evaluated the detection accuracy of BSR and investigated the EEG features leading to errors in the identification of burst suppression.
With our study, we were able to compare the performance of the BSR to the visual burst suppression detection in the raw EEG and obtain insights on the architecture of the unrecognized burst suppression phases.
We showed that the BSR did not detect burst suppression in 13 of 90 (14%) patients. Furthermore, the time comparison between the visually identified burst suppression duration and elevated BSR values strongly depended on the BSR value being used as a cutoff. A possible factor for unrecognized burst suppression by the BSR may be a significantly higher suppression amplitude ( P = .002). Six of the 13 patients with undetected burst suppression by BSR showed intraoperative state entropy values >80, indicating a risk of awareness while being in burst suppression.
Our results complement previous results regarding the underestimation of burst suppression by other automated detection modules and highlight the importance of not relying solely on the processed index, but to assess the native EEG during anesthesia.
基于脑电图（EEG）的麻醉监测仪用于评估患者的镇静和催眠水平，并在手术期间检测爆发抑制。其中一种监测仪，熵模块，使用一种算法来计算反映EEG抑制百分比的爆发抑制率（BSR）。自动爆发抑制检测监测仪可能无法可靠地检测到这种EEG模式。因此，我们评估了BSR的检测准确性，并研究了导致爆发抑制识别错误的EEG特征。
通过我们的研究，我们能够将BSR的性能与原始EEG中的视觉爆发抑制检测进行比较，并获得关于未识别的爆发抑制阶段结构的见解。
我们发现，在90名患者中的13名（14%）中，BSR未检测到爆发抑制。此外，视觉识别的爆发抑制持续时间与升高的BSR值之间的时间比较在很大程度上取决于用作临界值的BSR值。BSR未能识别爆发抑制的一个可能因素可能是抑制幅度明显更高（P = .002）。在13名BSR未检测到爆发抑制的患者中，有6名患者术中状态熵值>80，表明在爆发抑制期间存在知晓风险。
我们的结果补充了先前关于其他自动检测模块低估爆发抑制的结果，并强调了不单纯依赖处理后的指标，而是在麻醉期间评估原始EEG的重要性。
