Development of Postanesthesia Care Unit Delirium Is Associated with Differences in Aperiodic and Periodic Alpha Parameters of the Electroencephalogram during Emergence from General Anesthesia: Results from a Prospective Observational Cohort Study.

BACKGROUND: Intraoperative alpha-band power in frontal electrodes may provide helpful information about the balance of hypnosis and analgesia and has been associated with reduced occurrence of delirium in the postanesthesia care unit. Recent studies suggest that narrow-band power computations from neural power spectra can benefit from separating periodic and aperiodic components of the electroencephalogram. This study investigates whether such techniques are more useful in separating patients with and without delirium in the postanesthesia care unit at the group level as opposed to conventional power spectra. METHODS: Intraoperative electroencephalography recordings of 32 patients who developed perioperative neurocognitive disorders and 137 patients who did not were considered in this post hoc secondary analysis. The power spectra were calculated using conventional methods and the "fitting oscillations and one over f" algorithm was applied to separate aperiodic and periodic components to see whether the electroencephalography signature is different between groups. RESULTS: At the group level, patients who did not develop perioperative neurocognitive disorders presented with significantly higher alpha-band power and a broadband increase in power, allowing a "fair" separation based on conventional power spectra. Within the first third of emergence, the difference in median absolute alpha-band power amounted to 8.53 decibels (area under the receiver operator characteristics curve, 0.74 [0.65; 0.82]), reaching its highest value. In relative terms, the best separation was achieved in the second third of emergence, with a difference in medians of 7.71% (area under the receiver operator characteristics curve, 0.70 [0.61; 0.79]). The area under the receiver operator characteristics curve values were generally lower toward the end of emergence with increasing arousal. CONCLUSIONS: Increased alpha-band power during emergence in patients who did not develop perioperative neurocognitive disorders can be traced back to an increase in oscillatory alpha activity and an overall increase in aperiodic broadband power. Although the differences between patients with and without perioperative neurocognitive disorders can be detected relying on traditional methods, the separation of the signal allows a more detailed analysis. This may enable clinicians to detect patients at risk for developing perioperative neurocognitive disorders in the postanesthesia care unit early in the emergence phase.

Permutation Entropy Does Not Track the Electroencephalogram-Related Manifestations of Paradoxical Excitation During Propofol-Induced Loss of Responsiveness: Results From a Prospective Observational Cohort Study.

BACKGROUND: During the anesthetic-induced loss of responsiveness (LOR), a "paradoxical excitation" with activation of ß-frequencies in the electroencephalogram (EEG) can be observed. Thus, spectral parameters-as widely used in commercial anesthesia monitoring devices-may mistakenly indicate that patients are awake when they are actually losing responsiveness. Nonlinear time-domain parameters such as permutation entropy (PeEn) may analyze additional EEG information and appropriately reflect the change in cognitive state during the transition. Determining which parameters correctly track the level of anesthesia is essential for designing monitoring algorithms but may also give valuable insight regarding the signal characteristics during state transitions. METHODS: EEG data from 60 patients who underwent general anesthesia were extracted and analyzed around LOR. We derived the following information from the power spectrum: (i) spectral band power, (ii) the spectral edge frequency as well as 2 parameters known to be incorporated in monitoring systems, (iii) beta ratio, and (iv) spectral entropy. We also calculated (v) PeEn as a time-domain parameter. We used Friedman's test and Bonferroni correction to track how the parameters change over time and the area under the receiver operating curve to separate the power spectra between time points. RESULTS: Within our patient collective, we observed a "paradoxical excitation" around the time of LOR as indicated by increasing beta-band power. Spectral edge frequency and spectral entropy values increased from 19.78 [10.25-34.18] Hz to 25.39 [22.46-30.27] Hz (P = .0122) and from 0.61 [0.54-0.75] to 0.77 [0.64-0.81] (P < .0001), respectively, before LOR, indicating a (paradoxically) higher level of high-frequency activity. PeEn and beta ratio values decrease from 0.78 [0.77-0.82] to 0.76 [0.73-0.81] (P < .0001) and from -0.74 [-1.14 to -0.09] to -2.58 [-2.83 to -1.77] (P < .0001), respectively, better reflecting the state transition into anesthesia. CONCLUSIONS: PeEn and beta ratio seem suitable parameters to monitor the state transition during anesthesia induction. The decreasing PeEn values suggest a reduction of signal complexity and information content, which may very well describe the clinical situation at LOR. The beta ratio mainly focuses on the loss of power in the gamma-band. PeEn, in particular, may present a single parameter capable of tracking the LOR transition without being affected by paradoxical excitation.

Substance specific EEG patterns in mice undergoing slow anesthesia induction.

The exact mechanisms and the neural circuits involved in anesthesia induced unconsciousness are still not fully understood. To elucidate them valid animal models are necessary. Since the most commonly used species in neuroscience are mice, we established a murine model for commonly used anesthetics/sedatives and evaluated the epidural electroencephalographic (EEG) patterns during slow anesthesia induction and emergence. Forty-four mice underwent surgery in which we inserted a central venous catheter and implanted nine intracranial electrodes above the prefrontal, motor, sensory, and visual cortex. After at least one week of recovery, mice were anesthetized either by inhalational sevoflurane or intravenous propofol, ketamine, or dexmedetomidine. We evaluated the loss and return of righting reflex (LORR/RORR) and recorded the electrocorticogram. For spectral analysis we focused on the prefrontal and visual cortex. In addition to analyzing the power spectral density at specific time points we evaluated the changes in the spectral power distribution longitudinally. The median time to LORR after start anesthesia ranged from 1080 [1st quartile: 960; 3rd quartile: 1080]s under sevoflurane anesthesia to 1541 [1455; 1890]s with ketamine. Around LORR sevoflurane as well as propofol induced a decrease in the theta/alpha band and an increase in the beta/gamma band. Dexmedetomidine infusion resulted in a shift towards lower frequencies with an increase in the delta range. Ketamine induced stronger activity in the higher frequencies. Our results showed substance-specific changes in EEG patterns during slow anesthesia induction. These patterns were partially identical to previous observations in humans, but also included significant differences, especially in the low frequencies. Our study emphasizes strengths and limitations of murine models in neuroscience and provides an important basis for future studies investigating complex neurophysiological mechanisms.

Substance-dependent EEG during recovery from anesthesia and optimization of monitoring.

The electroencephalographic (EEG) activity during anesthesia emergence contains information about the risk for a patient to experience postoperative delirium, but the EEG dynamics during emergence challenge monitoring approaches. Substance-specific emergence characteristics may additionally limit the reliability of commonly used processed EEG indices during emergence. This study aims to analyze the dynamics of different EEG indices during anesthesia emergence that was maintained with different anesthetic regimens. We used the EEG of 45 patients under general anesthesia from the emergence period. Fifteen patients per group received sevoflurane, isoflurane (+ sufentanil) or propofol (+ remifentanil) anesthesia. One channel EEG and the bispectral index (BIS A-1000) were recorded during the study. We replayed the EEG back to the Conox, Entropy Module, and the BIS Vista to evaluate and compare the index behavior. The volatile anesthetics induced significantly higher EEG frequencies, causing higher indices (AUC > 0.7) over most parts of emergence compared to propofol. The median duration of "awake" indices (i.e., > 80) before the return of responsiveness (RoR) was significantly longer for the volatile anesthetics (p < 0.001). The different indices correlated well under volatile anesthesia (rs > 0.6), with SE having the weakest correlation. For propofol, the correlation was lower (rs < 0.6). SE was significantly higher than BIS and, under propofol anesthesia, qCON. Systematic differences of EEG-based indices depend on the drugs and devices used. Thus, to avoid early awareness or anesthesia overdose using an EEG-based index during emergence, the anesthetic regimen, the monitor used, and the raw EEG trace should be considered for interpretation before making clinical decisions.

Separation of responsive and unresponsive patients under clinical conditions: comparison of symbolic transfer entropy and permutation entropy.

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.

An in-depth analysis of parameter settings and probability distributions of specific ordinal patterns in the Shannon permutation entropy during different states of consciousness in humans.

As electrical activity in the brain has complex and dynamic properties, the complexity measure permutation entropy (PeEn) has proven itself to reliably distinguish consciousness states recorded by the EEG. However, it has been shown that the focus on specific ordinal patterns instead of all of them produced similar results. Moreover, parameter settings influence the resulting PeEn value. We evaluated the impact of the embedding dimension m and the length of the EEG segment on the resulting PeEn. Moreover, we analysed the probability distributions of monotonous and non-occurring ordinal patterns in different parameter settings. We based our analyses on simulated data as well as on EEG recordings from volunteers, obtained during stable anaesthesia levels at defined, individualised concentrations. The results of the analysis on the simulated data show a dependence of PeEn on different influencing factors such as window length and embedding dimension. With the EEG data, we demonstrated that the probability P of monotonous patterns performs like PeEn in lower embedding dimension (m = 3, AUC = 0.88, [0.7, 1] in both), whereas the probability P of non-occurring patterns outperforms both methods in higher embedding dimensions (m = 5, PeEn: AUC = 0.91, [0.77, 1]; P(non-occurring patterns): AUC = 1, [1, 1]). We showed that the accuracy of PeEn in distinguishing consciousness states changes with different parameter settings. Furthermore, we demonstrated that for the purpose of separating wake from anaesthesia EEG solely pieces of information used for PeEn calculation, i.e., the probability of monotonous patterns or the number of non-occurring patterns may be equally functional.

Electroencephalographic monitoring of anesthesia during surgical procedures in mice using a modified clinical monitoring system.

Monitoring brain activity and associated physiology during the administration of general anesthesia (GA) in mice is pivotal to guarantee postanesthetic health. Clinically, electroencephalogram (EEG) monitoring is a well-established method to guide GA. There are no established methods available for monitoring EEG in mice (Mus musculus) during surgery. In this study, a minimally invasive rodent intraoperative EEG monitoring system was implemented using subdermal needle electrodes and a modified EEG-based commercial patient monitor. EEG recordings were acquired at three different isoflurane concentrations revealing that surgical concentrations of isoflurane anesthesia predominantly contained burst suppression patterns in mice. EEG suppression ratios and suppression durations showed strong positive correlations with the isoflurane concentrations. The electroencephalographic indices provided by the monitor did not support online monitoring of the anesthetic status. The online available suppression duration in the raw EEG signals during isoflurane anesthesia is a straight forward and reliable marker to assure safe, adequate and reproducible anesthesia protocols.

Continuity with caveats in anesthesia: state and response entropy of the EEG.

The growing use of neuromonitoring in general anesthesia provides detailed insights into the effects of anesthetics on the brain. Our study focuses on the processed EEG indices State Entropy (SE), Response Entropy (RE), and Burst Suppression Ratio (BSR) of the GE EntropyTM Module, which serve as surrogate measures for estimating the level of anesthesia. While retrospectively analyzing SE and RE index values from patient records, we encountered a technical anomaly with a conspicuous distribution of index values. In this single-center, retrospective study, we analyzed processed intraoperative electroencephalographic (EEG) data from 15,608 patients who underwent general anesthesia. We employed various data visualization techniques, including histograms and heat maps, and fitted custom non-Gaussian curves. Individual patients' anesthetic periods were evaluated in detail. To compare distributions, we utilized the Kolmogorov-Smirnov test and Kullback-Leibler divergence. The analysis also included the influence of the BSR on the distribution of SE and RE values. We identified distinct pillar indices for both SE and RE, i.e., index values with a higher probability of occurrence than others. These pillar index values were not age-dependent and followed a non-equidistant distribution pattern. This phenomenon occurs independently of the BSR distribution. SE and RE index values do not adhere to a continuous distribution, instead displaying prominent pillar indices with a consistent pattern of occurrence across all age groups. The specific features of the underlying algorithm responsible for this pattern remain elusive.

Electroencephalographic guided propofol-remifentanil TCI anesthesia with and without dexmedetomidine in a geriatric population: electroencephalographic signatures and clinical evaluation.

Elderly and multimorbid patients are at high risk for developing unfavorable postoperative neurocognitive outcomes; however, well-adjusted and EEG-guided anesthesia may help titrate anesthesia and improve postoperative outcomes. Over the last decade, dexmedetomidine has been increasingly used as an adjunct in the perioperative setting. Its synergistic effect with propofol decreases the dose of propofol needed to induce and maintain general anesthesia. In this pilot study, we evaluate two highly standardized anesthetic regimens for their potential to prevent burst suppression and postoperative neurocognitive dysfunction in a high-risk population. Prospective, randomized clinical trial with non-blinded intervention. Operating room and post anesthesia care unit at Hospital Base San José, Osorno/Universidad Austral, Valdivia, Chile. 23 patients with scheduled non-neurologic, non-cardiac surgeries with age > 69 years and a planned intervention time > 60 min. Patients were randomly assigned to receive either a propofol-remifentanil based anesthesia or an anesthetic regimen with dexmedetomidine-propofol-remifentanil. All patients underwent a slow titrated induction, followed by a target controlled infusion (TCI) of propofol and remifentanil (n = 10) or propofol, remifentanil and continuous dexmedetomidine infusion (n = 13). We compared the perioperative EEG signatures, drug-induced changes, and neurocognitive outcomes between two anesthetic regimens in geriatric patients. We conducted a pre- and postoperative Montreal Cognitive Assessment (MoCa) test and measured the level of alertness postoperatively using a sedation agitation scale to assess neurocognitive status. During slow induction, maintenance, and emergence, burst suppression was not observed in either group; however, EEG signatures differed significantly between the two groups. In general, EEG activity in the propofol group was dominated by faster rhythms than in the dexmedetomidine group. Time to responsiveness was not significantly different between the two groups (p = 0.352). Finally, no significant differences were found in postoperative cognitive outcomes evaluated by the MoCa test nor sedation agitation scale up to one hour after extubation. This pilot study demonstrates that the two proposed anesthetic regimens can be safely used to slowly induce anesthesia and avoid EEG burst suppression patterns. Despite the patients being elderly and at high risk, we did not observe postoperative neurocognitive deficits. The reduced alpha power in the dexmedetomidine-treated group was not associated with adverse neurocognitive outcomes.

Predictors of Low Risk for Delirium during Anesthesia Emergence.

BACKGROUND: Processed electroencephalography (EEG) is used to monitor the level of anesthesia, and it has shown the potential to predict the occurrence of delirium. While emergence trajectories of relative EEG band power identified post hoc show promising results in predicting a risk for a delirium, they are not easily transferable into an online predictive application. This article describes a low-resource and easily applicable method to differentiate between patients at high risk and low risk for delirium, with patients at low risk expected to show decreasing EEG power during emergence. METHODS: This study includes data from 169 patients (median age, 61 yr [49, 73]) who underwent surgery with general anesthesia maintained with propofol, sevoflurane, or desflurane. The data were derived from a previously published study. The investigators chose a single frontal channel, calculated the total and spectral band power from the EEG and calculated a linear regression model to observe the parameters' change during anesthesia emergence, described as slope. The slope of total power and single band power was correlated with the occurrence of delirium. RESULTS: Of 169 patients, 32 (19%) showed delirium. Patients whose total EEG power diminished the most during emergence were less likely to screen positive for delirium in the postanesthesia care unit. A positive slope in total power and band power evaluated by using a regression model was associated with a higher risk ratio (total, 2.83 [95% CI, 1.46 to 5.51]; alpha/beta band, 7.79 [95% CI, 2.24 to 27.09]) for delirium. Furthermore, a negative slope in multiple bands during emergence was specific for patients without delirium and allowed definition of a test for patients at low risk. CONCLUSIONS: This study developed an easily applicable exploratory method to analyze a single frontal EEG channel and to identify patterns specific for patients at low risk for delirium.

EEG-based sensory testing reveals altered nociceptive processing in elite endurance athletes.

Increased exercise loads, as observed in elite athletes, seem to modulate the subjective pain perception in healthy subjects. The combination of electroencephalography (EEG) and standardized noxious stimulation can contribute to an objective assessment of the somatosensory stimulus processing. We assessed the subjective pain ratings and the electroencephalogram (EEG)-based response after standardized noxious mechanical and thermal stimuli as well as during conditioned pain modulation (CPM) in 26 elite endurance athletes and compared them to 26 recreationally active controls. Elite endurance athletes had consistently stronger somatosensory responses in the EEG to both mechanical and thermal noxious stimuli than the control group. We observed no significant group differences in the subjective pain ratings, which may have been influenced by our statistics and choice of stimuli. The CPM testing revealed that our conditioning stimulus modulated the subjective pain perception only in the control group, whereas the EEG indicated a modulatory effect of the conditioning stimulus on the spectral response only in the athletes group. We conclude that a higher activation in the cortical regions that process nociceptive information may either be an indicator for central sensitization or an altered stimulus salience in the elite endurance athletes' group. Our findings from our CPM testing were limited by our methodology. Further longitudinal studies are needed to examine if exercise-induced changes in the somatosensory system might have a critical impact on the long-term health of athletes.

Electroencephalogram-based prediction and detection of responsiveness to noxious stimulation in critical care patients: a retrospective single-centre analysis.

BACKGROUND: Monitoring of pain and nociception in critical care patients unable to self-report pain remains a challenge, as clinical signs are neither sensitive nor specific. Available technical approaches are limited by various constraints. We investigated the electroencephalogram (EEG) for correlates that precede or coincide with behavioural nociceptive responses to noxious stimulation. METHODS: In this retrospective study, we analysed frontal EEG recordings of 64 critical care patients who were tracheally intubated and ventilated before, during, and after tracheal suctioning. We investigated EEG power bands for correlates preceding or coinciding with behavioural responses (Behavioural Pain Scale ≥7). We applied the Mann-Whitney U-test to calculate corresponding P-values. RESULTS: Strong behavioural responses were preceded by higher normalised power in the 2.5-5 Hz band (+17.1%; P<0.001) and lower normalised power in the 0.1-1.5 Hz band (-10.5%; P=0.029). After the intervention, strong behavioural responses were associated with higher normalised EEG power in the 2.5-5 Hz band (+16.6%; P=0.021) and lower normalised power in the 8-12 Hz band (-51.2%; P=0.037) CONCLUSIONS: We observed correlates in EEG band power that precede and coincide with behavioural responses to noxious stimulation. Based on previous findings, some of the power bands could be linked to processing of nociception, arousal, or sedation effects. The power bands more closely related to nociception and arousal could be used to improve monitoring of nociception and to optimise analgesic management in critical care patients. CLINICAL TRIAL REGISTRATION: DRKS00011206.

Five commercial 'depth of anaesthesia' monitors provide discordant clinical recommendations in response to identical emergence-like EEG signals.

BACKGROUND: 'Depth of anaesthesia' monitors claim to measure hypnotic depth during general anaesthesia from the EEG, and clinicians could reasonably expect agreement between monitors if presented with the same EEG signal. We took 52 EEG signals showing intraoperative patterns of diminished anaesthesia, similar to those that occur during emergence (after surgery) and subjected them to analysis by five commercially available monitors. METHODS: We compared five monitors (BIS, Entropy-SE, Narcotrend, qCON, and Sedline) to see if index values remained within, or moved out of, each monitors' recommended index range for general anaesthesia for at least 2 min during a period of supposed lighter anaesthesia, as observed by changes in the EEG spectrogram obtained in a previous study. RESULTS: Of the 52 cases, 27 (52%) had at least one monitor warning of potentially inadequate hypnosis (index above range) and 16 of the 52 cases (31%) had at least one monitor signifying excessive hypnotic depth (index below clinical range). Of the 52 cases, only 16 (31%) showed concordance between all five monitors. Nineteen cases (36%) had one monitor discordant compared with the remaining four, and 17 cases (33%) had two monitors in disagreement with the remaining three. CONCLUSIONS: Many clinical providers still rely on index values and manufacturer's recommended ranges for titration decision making. That two-thirds of cases showed discordant recommendations given identical EEG data, and that one-third signified excessive hypnotic depth where the EEG would suggest a lighter hypnotic state, emphasizes the importance of personalised EEG interpretation as an essential clinical skill.

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.

The Influence of Electromyographic on Electroencephalogram-Based Monitoring: Putting the Forearm on the Forehead.

BACKGROUND: Monitoring the electroencephalogram (EEG) during general anesthesia can help to safely navigate the patient through the procedure by avoiding too deep or light anesthetic levels. In daily clinical practice, the EEG is recorded from the forehead and available neuromonitoring systems translate the EEG information into an index inversely correlating with the anesthetic level. Electrode placement on the forehead can lead to an influence of electromyographic (EMG) activity on the recorded signal in patients without neuromuscular blockade (NMB). A separation of EEG and EMG in the clinical setting is difficult because both signals share an overlapping frequency range. Previous research showed that indices decreased when EMG was absent in awake volunteers with NMB. Here, we investigated to what extent the indices changed, when EEG recorded during surgery with NMB agents was superimposed with EMG. METHODS: We recorded EMG from the flexor muscles of the forearm of 18 healthy volunteers with a CONOX monitor during different activity settings, that is, during contraction using a grip strengthener and during active diversion (relaxed arm). Both the forehead and forearm muscles are striated muscles. The recorded EMG was normalized by z-scoring and added to the EEG in different amplification steps. The EEG was recorded during anesthesia with NMB. We replayed these combined EEG and EMG signals to different neuromonitoring systems, that is, bispectral index (BIS), CONOX with qCON and qNOX, and entropy module with state entropy (SE) and response entropy (RE). We used the Friedman test and a Tukey-Kramer post hoc correction for statistical analysis. RESULTS: The indices of all neuromonitoring systems significantly increased when the EEG was superimposed with the contraction EMG and with high EMG amplitudes, the monitors returned invalid values, representative of artifact contamination. When replaying the EEG being superimposed with "relaxed" EMG, the qCON and BIS showed significant increases, but not SE and RE. For SE and RE, we observed an increased number of invalid values. CONCLUSIONS: With our approach, we could show that EMG activity during contraction and resting state can influence the neuromonitoring systems. This knowledge may help to improve EEG-based patient monitoring in the future and help the anesthesiologist to use the neuromonitoring systems with more knowledge regarding their function.

Performance of the SEDLine Monitor: Age Dependency and Time Delay.

BACKGROUND: Devices monitoring the hypnotic component of general anesthesia can help to guide anesthetic management. The main purposes of these devices are the titration of anesthesia dose. While anesthesia at low doses can result in awareness with intraoperative memory formation, excessive administration of anesthetics may be associated with an increased risk of postoperative neurocognitive disorder. We have previously shown for various indices that they are significantly influenced by the patient's age and that the monitors have a significant time delay. Here, we evaluated the influence of patient's age and time delay on the patient state index (PSI) of the SEDLine monitor. METHODS: To analyze the influence of the patient's age, we replayed 2 minutes of electroencephalography (EEG) of 141 patients (19-88 years, ASA I-IV) undergoing general anesthesia maintained with desflurane, sevoflurane, or propofol to the SEDLine monitor. We extracted the PSI as well as the spectral edge frequency (SEF) and performed a linear regression analysis. For evaluation of the time delay, we replayed 5 minutes of EEG of stable episodes of adequate anesthesia (PSI between 25 and 50) or light sedation/wake (PSI >70) in different orders to the SEDLine to simulate sudden changes between the states. Time delays were defined as the required time span of the monitor to reach the stable target index. RESULTS: PSI and SEF increased significantly with the patient's age. These findings did not depend on the administered anesthetic. The evaluation of the correlation between SEF and PSI showed a strong correlation with Spearman's correlation coefficient of ρ = 0.86 (0.82; 0.89). The time delays depended on the type of transition. We found a median time delay of 54 (Min: 46; Max: 61) seconds for the important transition between adequate anesthesia and awake and 55 (Min: 50; Max: 67) seconds in the other direction. CONCLUSIONS: With our analyses, we show that the indices presented on the SEDLine display, the PSI and the SEF, increase with age for patients under general anesthesia. Additionally, a delay of the PSI to react to sudden neurophysiologic changes due to dose of the maintenance anesthetic is of a time course that is clinically significant. These factors should be considered when navigating anesthesia relying on only the proprietary index for the SEDLine monitor.

Targeted temperature management in cardiac surgery: a systematic review and meta-analysis on postoperative cognitive outcomes.

BACKGROUND: Postoperative cognitive decline occurs commonly after cardiac surgery. The available literature is inconclusive on the role of intraoperative causal or protective factors. METHODS: We systematically reviewed studies evaluating delayed neurocognitive recovery (DNR), postoperative neurocognitive disorder (NCD), stroke, and the mortality rates among patients undergoing hypothermic or normothermic cardiopulmonary bypass (CPB). We further performed a subgroup analysis for age, surgery type (coronary artery bypass grafting [CABG], valve surgery, or combined), and the mean arterial blood pressure (MAP) during CPB, and conducted a proportion meta-analysis after calculation of single proportions and confidence intervals (CIs). RESULTS: We included a total of 58 studies with 9609 patients in our analysis. Among these, 1906 of 4010 patients (47.5%) had DNR, and 2071 of 7160 (28.9%) had postoperative NCD. Ninety of 4625 patients (2.0%) had a stroke, and 174 of 7589 (2.3%) died. There was no statistically significant relationship between the considered variables and DNR, NCD, stroke, and mortality. In the subgroup analysis comparing hypothermic with normothermic CPB, we found higher NCD rates after combined surgery; for normothermic CPB cases only, the rates of DNR and NCD were lower after combined surgery compared with CABG surgery. A MAP >70 mm Hg compared with MAP=50-70 mm Hg during CPB was associated with a lower rate of DNR. CONCLUSIONS: Temperature, MAP during cardiopulmonary bypass age, and surgery type were not associated with neurocognitive disorders, stroke, and mortality in cardiac surgery. Normothermic cardiopulmonary bypass, particularly when performed with MAP >70 mm Hg, may reduce the risk of postoperative neurocognitive decline after cardiac surgery. PROSPERO REGISTRATION NUMBER: CRD42019140844.

Ketamine-Associated Intraoperative Electroencephalographic Signatures of Elderly Patients With and Without Preoperative Cognitive Impairment.

BACKGROUND: Ketamine is typically used by anesthesiologists as an adjunct for general anesthesia and as a nonopioid analgesic. It has been explored for prevention of postoperative delirium, although results have been contradictory. In this study, we investigated the association of ketamine with postoperative delirium and specific encephalographic signatures. Furthermore, we examined these associations in the context of baseline neurocognition as measured by a validated assessment. METHODS: We conducted a prospective observational study from January 2019 to December 2020. Ninety-eight patients aged ≥65 years and undergoing spine surgery scheduled for ≥3 hours were included in the study. All participants who completed the University of California San Francisco (UCSF) Brain Health Assessment preoperatively and postoperatively were assessed with the confusion assessment method for intensive care unit (CAM-ICU) and/or the Nursing Delirium Screening Scale (NuDESC). Patients had frontal electroencephalogram (EEG) recordings (SedLine Root, Masimo, Corp) quantitatively analyzed. We used 60 seconds of artifact-free EEG (without burst suppression) extracted from the middle of the maintenance period to calculate the normalized power spectral density (PSD). Comparisons were made between those who did or did not receive ketamine and according to results from neurocognitive assessments. RESULTS: Ninety-eight patients (of a total of 155, enrolled and consented) had EEG of sufficient quality for analysis (42 women). Overall, we found a significant increase in the EEG power in the moderate frequency range (10-20 Hz) in patients that received ketamine. When the patients were divided by their preoperative cognitive status, this result in the ketamine group only held true for the cognitively normal patients. Patients that were cognitively impaired at baseline did not demonstrate a significant change in EEG characteristics based on ketamine administration, but impaired patients that received ketamine had a significantly higher rate of postoperative delirium (52% ketamine versus 20% no ketamine) (odds ratio [OR], 4.36; confidence interval [CI], 1.02-18.22; P = .048). In patients determined to be preoperatively cognitively normal, the incidence of postoperative delirium was not significantly associated with ketamine administration (19% ketamine versus 17% no ketamine) (OR, 1.10; CI, 0.30-4.04; P = .5833). CONCLUSIONS: Ketamine-related changes in EEG are observed in a heterogeneous group of patients receiving spine surgery. This result was driven primarily by the effect of ketamine on cognitively normal patients and not observed in patients that were cognitively impaired at baseline. Furthermore, patients who were cognitively impaired at baseline and who had received ketamine were more likely to develop postoperative delirium, suggesting that cognitive vulnerability might be predicted by the lack of a neurophysiologic response to ketamine.

The First Derivative of the Electroencephalogram Facilitates Tracking of Electroencephalographic Alpha Band Activity During General Anesthesia.

BACKGROUND: Intraoperative neuromonitoring can help to navigate anesthesia. Pronounced alpha oscillations in the frontal electroencephalogram (EEG) appear to predict favorable perioperative neurocognitive outcomes and may also provide a measure of intraoperative antinociception. Monitoring the presence and strength of these alpha oscillations can be challenging, especially in elderly patients, because the EEG in these patients may be dominated by oscillations in other frequencies. Hence, the information regarding alpha oscillatory activity may be hidden and hard to visualize on a screen. Therefore, we developed an effective approach to improve the detection and presentation of alpha activity in the perioperative setting. METHODS: We analyzed EEG records of 180 patients with a median age of 60 years (range, 18-90 years) undergoing noncardiac, nonneurologic surgery under general anesthesia with propofol induction and sevoflurane maintenance. We calculated the power spectral density (PSD) for the unprocessed EEG as well as for the time-discrete first derivative of the EEG (diffPSD) from 10-second epochs. Based on these data, we estimated the power-law coefficient κ of the PSD and diffPSD, as the EEG coarsely follows a 1/fκ distribution when displayed in double logarithmic coordinates. In addition, we calculated the alpha (7.8-12.1 Hz) to delta (0.4-4.3 Hz) ratio from the PSD as well as diffPSD. RESULTS: The median κ was 0.899 [first and third quartile: 0.786, 0.986] for the unaltered PSD, and κ = -0.092 [-0.202, -0.013] for the diffPSD, corresponding to an almost horizontal PSD of the differentiated EEG. The alpha-to-delta ratio of the diffPSD was strongly increased (median ratio = -8.0 dB [-10.5, -4.7 dB] for the unaltered PSD versus 30.1 dB [26.1, 33.8 dB] for the diffPSD). A strong narrowband oscillatory alpha power component (>20% of total alpha power) was detected in 23% using PSD, but in 96% of the diffPSD. CONCLUSIONS: We demonstrated that the calculation of the diffPSD from the time-discrete derivative of the intraoperative frontal EEG is a straightforward approach to improve the detection of alpha activity by eliminating the broadband background noise. This improvement in alpha peak detection and visualization could facilitate the guidance of general anesthesia and improve patient outcome.

Sedative Properties of Dexmedetomidine Are Mediated Independently from Native Thalamic Hyperpolarization-Activated Cyclic Nucleotide-Gated Channel Function at Clinically Relevant Concentrations.

Dexmedetomidine is a selective α2-adrenoceptor agonist and appears to disinhibit endogenous sleep-promoting pathways, as well as to attenuate noradrenergic excitation. Recent evidence suggests that dexmedetomidine might also directly inhibit hyperpolarization-activated cyclic-nucleotide gated (HCN) channels. We analyzed the effects of dexmedetomidine on native HCN channel function in thalamocortical relay neurons of the ventrobasal complex of the thalamus from mice, performing whole-cell patch-clamp recordings. Over a clinically relevant range of concentrations (1-10 µM), the effects of dexmedetomidine were modest. At a concentration of 10 µM, dexmedetomidine significantly reduced maximal Ih amplitude (relative reduction: 0.86 [0.78-0.91], n = 10, and p = 0.021), yet changes to the half-maximal activation potential V1/2 occurred exclusively in the presence of the very high concentration of 100 µM (-4,7 [-7.5--4.0] mV, n = 10, and p = 0.009). Coincidentally, only the very high concentration of 100 µM induced a significant deceleration of the fast component of the HCN activation time course (τfast: +135.1 [+64.7-+151.3] ms, n = 10, and p = 0.002). With the exception of significantly increasing the membrane input resistance (starting at 10 µM), dexmedetomidine did not affect biophysical membrane properties and HCN channel-mediated parameters of neuronal excitability. Hence, the sedative qualities of dexmedetomidine and its effect on the thalamocortical network are not decisively shaped by direct inhibition of HCN channel function.