Open Reimplementation of the BIS Algorithms for Depth of Anesthesia

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.