Open Reimplementation of the BIS Algorithms for Depth of Anesthesia

Developing an Electroencephalogram-based Model to Predict Awakening after Cardiac Arrest Using Partial Processing with the BIS Engine

BACKGROUND

Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. The authors sought to determine whether internal electroencephalogram (EEG) subparameters extracted by the BIS monitor (Medtronic, USA), a device commonly used to estimate depth of anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest.

METHODS

In this retrospective cohort study, a three-layer neural network was trained to predict recovery of consciousness to the point of command following versus not based on 48 h of continuous EEG recordings in 315 comatose patients admitted to a single U.S. academic medical center after cardiac arrest (derivation cohort, n = 181; validation cohort, n = 134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine ( i.e. , the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. The model was trained on hourly averaged measurements of these internal subparameters. This model's performance was compared to the modified Westhall qualitative EEG scoring framework.

RESULTS

Maximum prognostic accuracy in the derivation cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, the model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristics curve, 0.86; accuracy, 0.87; sensitivity, 0.83; specificity, 0.88; positive predictive value, 0.71; negative predictive value, 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest.

CONCLUSIONS

In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative accepted standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

The Effect of Electroencephalographic Trajectory During Anesthesia Emergence on the Indices Monitoring the Hypnotic Component

BACKGROUND

Postoperative neurocognitive disorders (PNDs) are frequent and serious complications that cause an enormous social and economic burden. A previous study demonstrated that certain electroencephalographic (EEG) patterns during emergence from general anesthesia are associated with a higher risk for PND. Compared to patients demonstrating the most favorable trajectory (Traj Ref: delta-dominant slow-wave anesthesia (ddSWA)→spindle-dominant SWA (sdSWA)→non-SWA (nSWA)→wake), patients presenting Traj Abrupt (ddSWA→wake) had 4-fold increased odds to develop PND and patients with Traj High (nSWA→wake) had 8-fold increased odds of developing PND. We hypothesized that commonly used neuromonitoring devices (state entropy [SE], quantium consciousness index [qCON], bispectral index [BIS], and Patient State Index [PSI]) can differentiate between the various trajectories.

METHODS

From the original database of the study by Hesse et al, we analyzed 59 EEGs from patients emerging from general anesthesia. They were selected according to their trajectory. We included 19 patients who had shown the most favorable trajectory (Traj Ref), 20 who had demonstrated Traj Abrupt, and 20 who had followed Traj High. To evaluate the performance of the neuromonitoring devices, we replayed the patients' EEGs to the monitors using an EEG player. We compared the index values for the 3 different trajectories (Traj Ref, Traj Abrupt, and Traj High) generated by the different monitoring devices, respectively. Additionally, we evaluated the correlation between the monitoring devices.

RESULTS

SE and PSI were able to resolve significant differences between Traj Ref and Traj Abrupt during a major part of emergence. Traj Ref showed an almost linear increase of index values, whereas Traj Abrupt led to an episode of low index values followed by a sudden increase. However, when comparing Traj Ref vs Traj High, qCON, PSI, and BIS were the indices showing significant differences, especially at the beginning of emergence. Patients representing Traj Ref patterns had significantly lower index values than those depicting Traj High. Due to the Traj High cases starting in nSWA, their indices were already high at the start of emergence.

CONCLUSIONS

Our analysis revealed that the course of the different indices reflects spectral EEG patterns during the emergence from general anesthesia. Considering certain emergence trajectories associated with a higher risk of developing PND, our approach might enable the anesthetist to identify patients particularly susceptible to PND by observing the course of index values before admission to the postanesthesia care unit.

EEG features associated with Alzheimer's disease and Frontotemporal dementia are not reflected by processed indices used in anesthesia monitoring

Patients with dementia face increased risks after general anesthesia. Improved perioperative electroencephalogram (EEG) monitoring techniques could aid in identifying vulnerable patients. However, current technology relies on processed indices to measure "depth-of-anesthesia". Analyzing OpenNeuro Dataset ds004504, we compared resting-state, eyes-closed EEG recordings of healthy controls (n = 27) with patients diagnosed with Alzheimer's disease (AD, n = 35) and Frontotemporal dementia (FTD, n = 23). We focused on prefrontal recordings. Analysis included spectral analysis, the "fitting-oscillations&-one-over-f"-algorithm for aperiodic and periodic signal features, as well as calculations of openibis, permutation entropy (PeEn), spectral entropy (SpEn), and spectral edge frequency (SEF). Spectral differences were pronounced, including a higher alpha/theta-ratio of controls (2.62 [95%CI: 1.54-3.62]) compared to both AD (0.55 [95%CI: 0.26-1.92], P < 0.001, AUC: 0.765 [0.642-0.888]) and FTD (0.83 [95%CI: 0.33-1.65], P = 0.007, AUC: 0.779 [0.652-0.907]). Oscillatory peak detection within the alpha frequency band was more robust in control (versus AD: P = 0.003, Cramér's V = 0.374; versus FTD: P = 0.003, Cramér's V = 0.414). Processed index parameters did not show a clear trend. FTD was associated with a higher prefrontal openibis (95.53 [95%CI: 93.43-97.39]) than control (91.98 [95%CI: 89.46-96.27], P = 0.033, AUC: 0.717 [0.572-0.862]) and an elevated SEF (23.68 [95%CI: 14.10-25.57] Hz) compared to AD (16.60 [95%CI: 14.22-22.22] Hz, P = 0.041, AUC: 0.676 [0.532-0.821]). AD and FTD are associated with EEG baseline abnormalities, and a standard prefrontal montage, as used intraoperatively, could present a promising technical screening approach for cognitive vulnerability. However, these EEG features are obscured by processed index parameters currently used in neuroanesthesia monitoring. OpenNeuro Dataset ds004504 "A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects" (doi: https://doi.org/10.18112/openneuro.ds004504.v1.0.7 ).

Cortical, subcortical, brainstem and autonomic responses to nociception under total intravenous anesthesia

BACKGROUND

Physiological responses to nociception are complex and involve intricate associations between the central, peripheral, and autonomic nervous systems. To optimize intraoperative analgesic titration, several monitoring devices have been developed, each targeting specific physiologic variables. However, existing devices primarily focus on isolated components of the nociceptive response, such as autonomic or cortical activity, without integrating these perspectives comprehensively. Our aim was to compare the performance of different nociception monitors in response to standardized tetanic stimulation and to investigate the correlation between these monitors' responses and varying concentrations of remifentanil.

METHODS

In this study, we evaluated and compared the responses of the Nociception Level index (NOL), Analgesia Nociception Index (ANI), Pupillary Reflex Dilation (PRD) and both raw and processed electroencephalogram (EEG) under varying concentrations of propofol and remifentanil. Standardized tetanic stimuli were administered to patients under general anesthesia with target-controlled infusion of propofol and remifentanil. EEG, PRD, NOL, ANI, heart rate (HR), Bispectral index (BIS), and CONOX monitor indices (qCON and qNOX) were concomitantly recorded.

RESULTS

ANI, BIS, HR, NOL, PRD, and qNOX significantly changed after noxious stimulation. In our dataset, PRD had the strongest correlation with varying remifentanil concentrations, while ANI, NOL, and qNOX did not show significant correlations with remifentanil concentrations. Following a noxious stimulus, the raw EEG in patients with low PRD exhibited a significant increase in power in the high EEG frequencies around 25 Hz and decreased power in frequencies corresponding to the alpha range (8-12 Hz) in the power spectral density.

CONCLUSIONS

PRD, HR, and BIS correlated with varying levels of remifentanil, with PRD exhibiting the strongest correlation. When CE remifentanil are low, noxious stimuli are more likely to dilate the pupil and be detected in the EEG. Considering the complexity of the nociceptive response, integrating multimodal neurophysiologic monitoring with pharmacological data may improve the anesthesiologist's ability to assess on the nociception-antinociception balance. However, further studies are needed to validate these findings and address the study's limitations.

Entropy of difference works similarly to permutation entropy for the assessment of anesthesia and sleep EEG despite the lower computational effort

EEG monitoring during anesthesia or for diagnosing sleep disorders is a common standard. Different approaches for measuring the important information of this biosignal are used. The most often and efficient one for entropic parameters is permutation entropy as it can distinguish the vigilance states in the different settings. Due to high calculation times, it has mostly been used for low orders, although it shows good results even for higher orders. Entropy of difference has a similar way of extracting information from the EEG as permutation entropy. Both parameters and different algorithms for encoding the associated patterns in the signal are described. The runtimes of both entropic measures are compared, not only for the needed encoding but also for calculating the value itself. The mutual information that both parameters extract is measured with the AUC for a linear discriminant analysis classifier. Entropy of difference shows a smaller calculation time than permutation entropy. The reduction is much larger for higher orders, some of them can even only be computed with the entropy of difference. The distinguishing of the vigilance states between both measures is similar as the AUC values for the classification do not differ significantly. As the runtimes for the entropy of difference are smaller than for the permutation entropy, even though the performance stays the same, we state the entropy of difference could be a useful method for analyzing EEG data. Higher orders of entropic features may also be investigated better and more easily.

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.

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.

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.

Effect of acute normovolemic hemodilution on anesthetic effect, plasma concentration, and recovery quality in elderly patients undergoing spinal surgery

OBJECTIVE

To explore the effect of acute normovolemic hemodilution (ANH) on the anesthetic effect, plasma concentration, and postoperative recovery quality in elderly patients undergoing spinal surgery.

METHODS

A total of 60 cases of elderly patients aged 65 to 75 years who underwent elective multilevel spinal surgery were assigned randomly into the ANH group (n = 30) and control group (n = 30). Hemodynamic and blood gas analysis indexes were observed and recorded before ANH (T1), after ANH (T2), immediately after postoperative autologous blood transfusion (T3), 10 min (T4), 20 min (T5), 30 min (T6), 40 min (T7), and 50 min (T8) after the transfusion, and at the end of the transfusion (i.e., 60 min; T9). At T3 ~ 9, bispectral index (BIS) and train-of-four (TOF) stimulation were recorded and the plasma propofol/cisatracurium concentration was determined. The extubation time and recovery quality were recorded.

RESULTS

The ANH group presented a lower MAP value and a higher SVV value at T2, and shorter extubation and orientation recovery time (P < 0.05) compared with the control group. BIS values at T8 and T9 were lower in the ANH group than those in the control group (P < 0.05). TOF values at T7 ~ 9 were lower in the ANH group than those in the control group (P < 0.05). There were no statistically significant differences in the postoperative plasma concentrations of propofol and cisatracurium between the groups (P > 0.05).

CONCLUSION

During orthopedic surgery, the plasma concentration of elderly patients is increased after autologous blood transfusion of ANH, and the depth of anesthesia and muscle relaxant effect are strengthened, thus leading to delayed recovery of respiratory function and extubation.

Changes in EEG frequency characteristics during sevoflurane general anesthesia: feature extraction by variational mode decomposition

Mode decomposition is a method for extracting the characteristic intrinsic mode function (IMF) from various multidimensional time-series signals. Variational mode decomposition (VMD) searches for IMFs by optimizing the bandwidth to a narrow band with the [Formula: see text] norm while preserving the online estimated central frequency. In this study, we applied VMD to the analysis of electroencephalogram (EEG) recorded during general anesthesia. Using a bispectral index monitor, EEGs were recorded from 10 adult surgical patients (the median age: 47.0, and the percentile range: 27.0-59.3 years) who were anesthetized with sevoflurane. We created an application named EEG Mode Decompositor, which decomposes the recorded EEG into IMFs and displays the Hilbert spectrogram. Over the 30-min recovery from general anesthesia, the median (25-75 percentile range) bispectral index increased from 47.1 (42.2-50.4) to 97.4 (96.5-97.6), and the central frequencies of IMF-1 showed a significant change from 0.4 (0.2-0.5) Hz to 0.2 (0.1-0.3) Hz. IMF-2, IMF-3, IMF-4, IMF-5, and IMF-6 increased significantly from 1.4 (1.2-1.6) Hz to 7.5 (1.5-9.3) Hz, 6.7 (4.1-7.6) Hz to 19.4 (6.9-20.0) Hz, 10.9 (8.8-11.4) Hz to 26.4 (24.2-27.2) Hz, 13.4 (11.3-16.6) Hz to 35.6 (34.9-36.1) Hz, and 12.4 (9.7-18.1) Hz to 43.2 (42.9-43.4) Hz, respectively. The characteristic frequency component changes in specific IMFs during emergence from general anesthesia were visually captured by IMFs derived using VMD. EEG analysis by VMD is useful for extracting distinct changes during general anesthesia.

Propofol for treatment resistant depression: A randomized controlled trial

Background

Anesthetic agents including ketamine and nitrous oxide have shown antidepressant properties when appropriately dosed. Our recent open-label trial of propofol, an intravenous anesthetic known to elicit transient positive mood effects, suggested that it may also produce robust and durable antidepressant effects when administered at a high dose that elicits an electroencephalographic (EEG) burst-suppression state. Here we report findings from a randomized controlled trial ( NCT03684447 ) that compared two doses of propofol. We hypothesized greater improvement with a high dose that evoked burst suppression versus a low dose that did not.

Methods

Participants with moderate-to-severe, treatment-resistant depression were randomized to a series of 6 treatments at low versus high dose (n=12 per group). Propofol infusions were guided by real-time processed frontal EEG to achieve predetermined pharmacodynamic criteria. The primary and secondary depression outcome measures were the 24-item Hamilton Depression Rating Scale (HDRS-24) and the Patient Health Questionnaire (PHQ-9), respectively. Secondary scales measured suicidal ideation, anxiety, functional impairment, and quality of life.

Results

Treatments were well tolerated and blinding procedures were effective. The mean [95%-CI] change in HDRS-24 score was -5.3 [-10.3, -0.2] for the low-dose group and -9.3 [-12.9, -5.6] for the high-dose group (17% versus 33% reduction). The between-group effect size (standardized mean difference) was -0.56 [-1.39, 0.28]. The group difference was not statistically significant (p=0.24, linear model). The mean change in PHQ-9 score was -2.0 [-3.9, -0.1] for the low dose and -4.8 [-7.7, -2.0] for the high dose. The between-group effect size was -0.73 [-1.59, 0.14] (p=0.09). Secondary outcomes favored the high dose (effect sizes magnitudes 0.1 - 0.9) but did not generally reach statistical significance (p>0.05).

Conclusions

The medium-sized effects observed between doses in this small, controlled, clinical trial suggest that propofol may have dose-dependent antidepressant effects. The findings also provide guidance for subsequent trials. A larger sample size and additional treatments in series are likely to enhance the ability to detect dose-dependent effects. Future work is warranted to investigate potential antidepressant mechanisms and dose optimization.

[Processed EEG for personalized dosing of anesthetics during general anesthesia]

Electroencephalogram (EEG)-guided anesthesia is indispensable in modern operating rooms and has become established as the standard form of monitoring. Many anesthesiologists rely on processed EEG indices in the hope of averting anesthesia-related complications, such as intraoperative awareness, postoperative delirium and other cognitive complications in their patients. This educational review aims to provide information on the five most prevalent monitors used to guide depth of sedation during general anesthesia. This article elucidates the principles underpinning the application of these monitors where known, which are generally based on power in various EEG frequency bands and on the burst suppression pattern. Convinced that EEG-guided anesthesia has the potential of benefitting many surgical patients, it is felt that many basic principles and shortcomings of processed EEG indices need to be better understood in the clinical practice. After discussing the different monitors and clinically relevant data from the literature, the article gives a short practical guidance on how to critically interpret processed EEG information and troubleshooting of confounded indices in the context of clinical situations.

Measures of Information Content during Anesthesia and Emergence in the Caenorhabditis elegans Nervous System

BACKGROUND

Suppression of behavioral and physical responses defines the anesthetized state. This is accompanied, in humans, by characteristic changes in electroencephalogram patterns. However, these measures reveal little about the neuron or circuit-level physiologic action of anesthetics nor how information is trafficked between neurons. This study assessed whether entropy-based metrics can differentiate between the awake and anesthetized state in Caenorhabditis elegans and characterize emergence from anesthesia at the level of interneuronal communication.

METHODS

Volumetric fluorescence imaging measured neuronal activity across a large portion of the C. elegans nervous system at cellular resolution during distinct states of isoflurane anesthesia, as well as during emergence from the anesthetized state. Using a generalized model of interneuronal communication, new entropy metrics were empirically derived that can distinguish the awake and anesthetized states.

RESULTS

This study derived three new entropy-based metrics that distinguish between stable awake and anesthetized states (isoflurane, n = 10) while possessing plausible physiologic interpretations. State decoupling is elevated in the anesthetized state (0%: 48.8 ± 3.50%; 4%: 66.9 ± 6.08%; 8%: 65.1 ± 5.16%; 0% vs. 4%, P < 0.001; 0% vs. 8%, P < 0.001), while internal predictability (0%: 46.0 ± 2.94%; 4%: 27.7 ± 5.13%; 8%: 30.5 ± 4.56%; 0% vs. 4%, P < 0.001; 0% vs. 8%, P < 0.001), and system consistency (0%: 2.64 ± 1.27%; 4%: 0.97 ± 1.38%; 8%: 1.14 ± 0.47%; 0% vs. 4%, P = 0.006; 0% vs. 8%, P = 0.015) are suppressed. These new metrics also resolve to baseline during gradual emergence of C. elegans from moderate levels of anesthesia to the awake state (n = 8). The results of this study show that early emergence from isoflurane anesthesia in C. elegans is characterized by the rapid resolution of an elevation in high frequency activity (n = 8, P = 0.032). The entropy-based metrics mutual information and transfer entropy, however, did not differentiate well between the awake and anesthetized states.

CONCLUSIONS

Novel empirically derived entropy metrics better distinguish the awake and anesthetized states compared to extant metrics and reveal meaningful differences in information transfer characteristics between states.

EDITOR’S PERSPECTIVE

A Real-Time Neurophysiologic Stress Test for the Aging Brain: Novel Perioperative and ICU Applications of EEG in Older Surgical Patients

As of 2022, individuals age 65 and older represent approximately 10% of the global population [1], and older adults make up more than one third of anesthesia and surgical cases in developed countries [2, 3]. With approximately > 234 million major surgical procedures performed annually worldwide [4], this suggests that > 70 million surgeries are performed on older adults across the globe each year. The most common postoperative complications seen in these older surgical patients are perioperative neurocognitive disorders including postoperative delirium, which are associated with an increased risk for mortality [5], greater economic burden [6, 7], and greater risk for developing long-term cognitive decline [8] such as Alzheimer's disease and/or related dementias (ADRD). Thus, anesthesia, surgery, and postoperative hospitalization have been viewed as a biological "stress test" for the aging brain, in which postoperative delirium indicates a failed stress test and consequent risk for later cognitive decline (see Fig. 3). Further, it has been hypothesized that interventions that prevent postoperative delirium might reduce the risk of long-term cognitive decline. Recent advances suggest that rather than waiting for the development of postoperative delirium to indicate whether a patient "passed" or "failed" this stress test, the status of the brain can be monitored in real-time via electroencephalography (EEG) in the perioperative period. Beyond the traditional intraoperative use of EEG monitoring for anesthetic titration, perioperative EEG may be a viable tool for identifying waveforms indicative of reduced brain integrity and potential risk for postoperative delirium and long-term cognitive decline. In principle, research incorporating routine perioperative EEG monitoring may provide insight into neuronal patterns of dysfunction associated with risk of postoperative delirium, long-term cognitive decline, or even specific types of aging-related neurodegenerative disease pathology. This research would accelerate our understanding of which waveforms or neuronal patterns necessitate diagnostic workup and intervention in the perioperative period, which could potentially reduce postoperative delirium and/or dementia risk. Thus, here we present recommendations for the use of perioperative EEG as a "predictor" of delirium and perioperative cognitive decline in older surgical patients.

On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology

Purpose of Review

The purpose of this review is to summarize the current research and critically examine artificial intelligence (AI) technologies and their applicability to the daily practice of anesthesiologists.

Recent Findings

Novel AI tools are developed using data from electronic health records, imaging, waveforms, clinical notes, and wearables. These tools can accurately predict the perioperative risk for adverse outcomes, the need for blood transfusion, and the risk of difficult intubation. Intraoperatively, AI models can assist with technical skill augmentation, patient monitoring, and management. Postoperatively, AI technology can aid in preventing complications and discharge planning. While further prospective validation is needed, these early applications demonstrate promise in every area of perioperative care.

Summary

The practice of anesthesiology is at a precipice fueled by technological innovation. The clinical AI implementation would enable personalized and safer patient care by offering actionable insights from the wealth of perioperative data.

EEG-Parameter-Guided Anesthesia for Prevention of Emergence Delirium in Children

Background: Emergence delirium (ED) usually occurs in children after surgery with an incidence of 10−80%. Though ED is mostly self-limited, its potential injuries cannot be ignored. Whether electroencephalography (EEG)-parameter-guided anesthesia could reduce the incidence of ED in pediatric surgery has not been fully discussed to date. Methods: Fifty-four boys aged 2−12 years undergoing elective hypospadias surgery under sevoflurane anesthesia were selected. In the EEG-parameter-guided group (E group), sevoflurane was used for anesthesia induction and was maintained by titrating the spectral edge frequency (SEF) to 10−15 and combining the monitoring of density spectral array (DSA) power spectra and raw EEG. While in the control group (C group), anesthesiologists were blinded to the SedLine screen (including SEF, DSA, and raw EEG) and adjusted the intraoperative drug usage according to their experience. Patients with a Pediatric Anesthesia Emergence Delirium (PAED) score > 10 were diagnosed with ED, while patients with a PAED score > 2 were diagnosed with emergence agitation (EA). Results: Finally, a total of 37 patients were included in this trial. The incidence of ED in the E group was lower than in the C group (5.6% vs. 36.8%; p = 0.04), while the incidence of EA was similar in the two groups (61% vs. 78.9%; p = 0.48). Intraoperative parameters including remifentanil dosage and the decrease in mean arterial pressure (MAP) were not different between the two groups (p > 0.05), but the mean end-tidal sevoflurane concentration (EtSevo) was lower in the E group than in the C group (p > 0.05). Moreover, during PACU stay, the extubation time and discharge time of the groups were similar, while the PAED scores within 5 min from extubation and the Face, Legs, Activity, Cry, and Consolability (FLACC) scores within 30 min from extubation were lower in the E group than in the C group. Conclusion: EEG-parameter-guided anesthesia management reduced the incidence of ED in children. Studies with larger sample sizes are needed to obtain more convincing results.