A Failure of M-Entropy^™ to Correctly Detect Burst Suppression Leading to Sevoflurane Overdosage

Abstract

Electroencephalogram depth of anaesthesia monitors are increasingly being used, with the aim of reducing awareness during anaesthesia. Most literature concentrates on the ability of these monitors to predict when a patient is likely to be aware. This case report highlights the opposite problem, where the monitor (M-Entropy™) indicated an awake state but the patient was in fact deeply anaesthetised. If the anaesthetist is unable to interpret the raw electroencephalogram and understand the limitations of the monitor being used, excessive doses of anaesthetic may be given with potentially serious consequences.

Preoperative paracetamol infusion reduces sevoflurane consumption during thyroidectomy under general anesthesia with spectral entropy monitoring

“…Aime and colleagues [22] demonstrated that sevoflurane anesthesia using either BIS or an entropy monitor to titrate the administered dose of anesthetics, both monitoring methods equally reduced the administered dose of sevoflurane by 29%. Although the recommended surgical level of general anesthesia for entropy monitor lies between 40-60; we preferred in this study to be more conservative and chose a lower target range of SE between 40-50; to ensure adequate level of hypnosis and to minimize the risk of intraoperative awareness; as its occurrence is often the direct consequence of using insufficient doses of anesthetics or light-anesthetic techniques; taking into consideration some reports of failure of entropy monitor [23,24]. Also, we believe that the use of the processed EEG based monitors to guide the depth of anesthesia could be associated with intentional overreduction in the anesthetic depth on the basis of the displayed values of these monitors.…”

Comparison of Bispectral Index and Entropy values with electroencephalogram during surgical anaesthesia with sevoflurane

“…We hypothesise that the reason for this discrepancy is the occasional inability of the Entropy algorithm to recognize the burst suppression correctly. 24 However, it is noteworthy that in 14 episodes during burst suppression, BIS was higher than SE (Fig. 1).…”

Age-Related EEG Features of Bursting Activity During Anesthetic-Induced Burst Suppression

“…Artifacts (i.e., EKG or motion) for instance can contaminate the signal and can spuriously cause increased EEG activity, thus hindering algorithms that detect BSUPP by identifying suppression (Willingham and Avidan, 2017). This leads to an underestimation of BSUPP by the monitoring devices (Muhlhofer et al, 2017) and to a possible misclassification of the BSUPP EEG as an awake EEG (Hart et al, 2009;Willingham and Avidan, 2017). Other reported approaches to detect BSUPP based on signal variance (An et al, 2015), may prove superior to commercially used algorithms but they also focus on the suppression episode or the transition from suppression to burst.…”