Always Assess the Raw Electroencephalogram: Why Automated Burst Suppression Detection May Not Detect All Episodes.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Preoperative characterization of baseline EEG recordings for risk stratification of post-anesthesia care unit delirium.

STUDY OBJECTIVE: Delirium in the post-anesthesia care unit (PACU-D) presents a serious condition with a high medical and socioeconomic impact. In particular, PACU-D is among common postoperative complications of elderly patients. As PACU-D may be associated with postoperative delirium, early detection of at-risk patients and strategies to prevent PACU-D are important. We characterized EEG baseline signatures of patients who developed PACU-D following surgery and general anesthesia and patients who did not. DESIGN AND SETTING: We conducted a post-hoc analysis of preoperative EEG recordings between patients with and without PACU-D, as indicated by positive bCAM scores post general anesthesia and surgery. PATIENTS AND MEASUREMENTS: Preoperative baseline EEG recordings from 89 patients were recorded at controlled eyes-open (focused wakefulness) and eyes-closed (relaxed wakefulness) conditions. We computed power spectral densities, permutation entropy, spectral entropy and spectral edge frequency to see if these parameters can reflect potential baseline EEG differences between PACU-D (31.5%) and noPACU-D (68.5%) patients. Wilcoxon's Rank Sum Test as well as AUC values were used to determine statistical significance. MAIN RESULTS: Baseline EEG recordings showed significant differences between PACU-D and noPACU-D patients preoperatively. Compared to the noPACU-D group, PACU-D patients presented with lower power in higher frequencies during relaxed and focused wakefulness alike. These differences in power led to AUC values of 0.73 [0.59;0.85] (permutation entropy) and 0.72 [0.61;0.83] (spectral edge frequency) indicative of a "fair" performance to separate patients with and without PACU-D. CONCLUSIONS: The baseline EEG of relaxed wakefulness as well as focused wakefulness may be used to assess the risk of developing PACU-D following surgery under general anesthesia. Moreover, routinely used monitoring parameters capture these differences as well, potentially allowing an easy transfer to clinical settings. CLINICAL TRIAL NUMBER: NCT03775356.

Targeted Interventions to Increase Blood Pressure and Decrease Anaesthetic Concentrations Reduce Intraoperative Burst Suppression: A Randomised, Interventional Clinical Trial.

Background: It has been suggested that intraoperative electroencephalographic (EEG) burst suppression (BSupp) may be associated with post-operative neurocognitive disorders in the elderly, and EEG-guided anaesthesia may help to reduce BSupp. Despite of this suggestion, a standard treatment does not exist, as we have yet to fully understand the phenomenon and its underlying pathomechanism. This study was designed to address two underlying phenomena-cerebral hypoperfusion and individual anaesthetic overdose. Objectives: We aimed to demonstrate that targeted anaesthetic interventions-treating intraoperative hypotension and/or reducing the anaesthetic concentration-reduce BSupp. Methods: We randomly assigned patients to receive EEG-based interventions during anaesthesia or EEG-blinded standard anaesthesia. If BSupp was detected, defined as burst suppression ratio (BSR) > 0, the primary intervention aimed to adjust the mean arterial blood pressure to patient baseline (MAP intervention) followed by reduction of anaesthetic concentration (MAC intervention). Results: EEG-based intervention significantly reduced total cumulative BSR, BSR duration, and maximum BSR. MAP intervention caused a significant MAP increase at the end of a BSR > 0 episode compared to the control group. Coincidentally, the maximum BSR decreased significantly; in 55% of all MAP interventions, the BSR decreased to 0% without any further action. In the remaining events, additional MAC intervention was required. Conclusion: Our results show that targeted interventions (MAC/MAP) reduce total cumulative amount, duration, and maximum BSR > 0 in the elderly undergoing general anaesthesia. Haemodynamic intervention already interrupted or reduced BSupp, strengthening the current reflections that hypotension-induced cerebral hypoperfusion may be seen as potential pathomechanism of intraoperative BSupp. Clinical Trial Registration: NCT03775356 [ClinicalTrials.gov], DRKS00015839 [German Clinical Trials Register (Deutsches Register klinischer Studien, DRKS)].

Substance-Specific Differences in Human Electroencephalographic Burst Suppression Patterns.

Different anesthetic agents induce burst suppression in the electroencephalogram (EEG) at very deep levels of general anesthesia. EEG burst suppression has been identified to be a risk factor for postoperative delirium (POD). EEG based automated detection algorithms are used to detect burst suppression patterns during general anesthesia and a burst suppression ratio (BSR) is calculated. Unfortunately, applied algorithms do not give information as precisely as suggested, often resulting in an underestimation of the patients' burst suppression level. Additional knowledge of substance-specific burst suppression patterns could be of great importance to improve the ability of EEG based monitors to detect burst suppression. In a re-analysis of EEG recordings obtained from a previous study, we analyzed EEG data of 45 patients undergoing elective surgery under general anesthesia. The patients were anesthetized with sevoflurane, isoflurane or propofol (n = 15, for each group). After skin incision, the used agent was titrated to a level when burst suppression occurred. In a visual analysis of the EEG, blinded to the used anesthetic agent, we included the first distinct burst in our analysis. To avoid bias through changing EEG dynamics throughout the burst, we only focused on the first 2 s of the burst. These episodes were analyzed using the power spectral density (PSD) and normalized PSD, the absolute burst amplitude and absolute burst slope, as well as permutation entropy (PeEn). Our results show significant substance-specific differences in the architecture of the burst. Volatile-induced bursts showed higher burst amplitudes and higher burst power. Propofol-induced bursts had significantly higher relative power in the EEG alpha-range. Further, isoflurane-induced bursts had the steepest burst slopes. We can present the first systematic comparison of substance-specific burst characteristics during anesthesia. Previous observations, mostly derived from animal studies, pointing out the substance-specific differences in bursting behavior, concur with our findings. Our findings of substance-specific EEG characteristics can provide information to help improve automated burst suppression detection in monitoring devices. More specific detection of burst suppression may be helpful to reduce excessive EEG effects of anesthesia and therefore the incidence of adverse outcomes such as POD.