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.
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