Depth of anesthesia

Depth of anesthesia monitoring in Norway-A web-based survey

BACKGROUND

The bispectral index (BIS) monitor is the most frequently used electroencephalogram (EEG)-based depth of anesthesia (DoA) technology in Norwegian hospitals. However, there is limited knowledge regarding the extent and clinical impact of its use and how anesthesiologists and nurse anesthetists use the information provided by the DoA monitors in their clinical practice.

METHODS

This cross-sectional survey on the use of DoA monitors in Norway used a web-based questionnaire distributed to anesthesia personnel in all hospitals in Norway. Participation was voluntary and anonymized, and the web form could not track IP sources or respondents' locations.

RESULTS

Three hundred and ninety-one nurse anesthetists (n = 324) and anesthesiologists (n = 67) responded. Among the EEG-based DoA monitoring tools, BIS was most often used to observe and assess patients' DoA (98%). Raw EEG waveform analysis (10%), EEG-spectrogram (9%), and suppression rate (10%) were seldom used. Twenty-seven percent of the anesthesia personnel were able to recognize a burst suppression pattern on EEG and its significance. Fifty-eight percent of the respondents considered clinical observations more reliable than BIS. Almost all respondents reported adjusting anesthetic dosage based on the BIS index values (80%). However, the anesthetic dose was more often increased (90%) because of high BIS index values than lowered (55%) because of low BIS index values.

CONCLUSION

Despite our respondents' extensive use of DoA monitoring, the anesthesia personnel in our survey did not use all the information and the potential to guide the titration of anesthetics the DoA monitors provide. Thus, anesthesia personnel could generally benefit from increased knowledge of how EEG-based DoA monitoring can be used to assess and determine individual patients' need for anesthetic medication.

Esketamine accelerates emergence from isoflurane general anaesthesia by activating the paraventricular thalamus glutamatergic neurones in mice

BACKGROUND

Delayed emergence from general anaesthesia poses a significant perioperative safety hazard. Subanaesthetic doses of ketamine not only deepen anaesthesia but also accelerate recovery from isoflurane anaesthesia; however, the mechanisms underlying this phenomenon remain elusive. Esketamine exhibits a more potent receptor affinity and fewer adverse effects than ketamine and exhibits shorter recovery times after brief periods of anaesthesia. As the paraventricular thalamus (PVT) plays a pivotal role in regulating wakefulness, we studied its role in the emergence process during combined esketamine and isoflurane anaesthesia.

METHODS

The righting reflex and cortical electroencephalography were used as measures of consciousness in mice during isoflurane anaesthesia with coadministration of esketamine. The expression of c-Fos was used to determine neuronal activity changes in PVT neurones after esketamine administration. The effect of esketamine combined with isoflurane anaesthesia on PVT glutamatergic (PVTGlu) neuronal activity was monitored by fibre photometry, and chemogenetic technology was used to manipulate PVTGlu neuronal activity.

RESULTS

A low dose of esketamine (5 mg kg-1) accelerated emergence from isoflurane general anaesthesia (474 [30] s vs 544 [39] s, P=0.001). Esketamine (5 mg kg-1) increased PVT c-Fos expression (508 [198] vs 258 [87], P=0.009) and enhanced the population activity of PVTGlu neurones (0.03 [1.7]% vs 6.9 [3.4]%, P=0.002) during isoflurane anaesthesia (1.9 [5.7]% vs -5.1 [5.3]%, P=0.016) and emergence (6.1 [6.2]% vs -1.1 [5.0]%, P=0.022). Chemogenetic suppression of PVTGlu neurones abolished the arousal-promoting effects of esketamine (459 [33] s vs 596 [33] s, P<0.001).

CONCLUSIONS

Our results suggest that esketamine promotes recovery from isoflurane anaesthesia by activating PVTGlu neurones. This mechanism could explain the rapid arousability exhibited upon treatment with a low dose of esketamine.

How anesthesiology can deal with innovation and new technologies?

Innovation and new technologies have always impacted significantly the anesthesiology practice all along the perioperative course, as it is recognized as one of the most transformative medical specialties specifically regarding patient's safety. Beside a number of major changes in procedures, equipment, training, and organization that aggregated to establish a strong safety culture with effective practices, anesthesiology is also a stakeholder in disruptive innovation. The present review is not exhaustive and aims to provide an overview on how innovation could change and improve anesthesiology practices through some examples as telemedicine (TM), machine learning and artificial intelligence (AI). For example, postoperative complications can be accurately predicted by AI from automated real-time electronic health record data, matching physicians' predictive accuracy. Clinical workflow could be facilitated and accelerated with mobile devices and applications, assuming that these tools should remain at the service of patients and care providers. Care providers and patients connections have improved, thanks to these digital and innovative transformations, without replacing existing relationships between them. It also should give time back to physicians and nurses to better spend it in the perioperative care, and to provide "personalized" medicine keeping a high level of standard of care.

Detection of Motor Cerebral Activity After Median Nerve Stimulation During General Anesthesia (STIM-MOTANA): Protocol for a Prospective Interventional Study

BACKGROUND

Accidental awareness during general anesthesia (AAGA) is defined as an unexpected awareness of the patient during general anesthesia. This phenomenon occurs in 1%-2% of high-risk practice patients and can cause physical suffering and psychological after-effects, called posttraumatic stress disorder. In fact, no monitoring techniques are satisfactory enough to effectively prevent AAGA; therefore, new alternatives are needed. Because the first reflex for a patient during an AAGA is to move, but cannot do so because of the neuromuscular blockers, we believe that it is possible to design a brain-computer interface (BCI) based on the detection of movement intention to warn the anesthetist. To do this, we propose to describe and detect the changes in terms of motor cortex oscillations during general anesthesia with propofol, while a median nerve stimulation is performed. We believe that our results could enable the design of a BCI based on median nerve stimulation, which could prevent AAGA.

OBJECTIVE

To our knowledge, no published studies have investigated the detection of electroencephalographic (EEG) patterns in relation to peripheral nerve stimulation over the sensorimotor cortex during general anesthesia. The main objective of this study is to describe the changes in terms of event-related desynchronization and event-related synchronization modulations, in the EEG signal over the motor cortex during general anesthesia with propofol while a median nerve stimulation is performed.

METHODS

STIM-MOTANA is an interventional and prospective study conducted with patients scheduled for surgery under general anesthesia, involving EEG measurements and median nerve stimulation at two different times: (1) when the patient is awake before surgery (2) and under general anesthesia. A total of 30 patients will receive surgery under complete intravenous anesthesia with a target-controlled infusion pump of propofol.

RESULTS

The changes in event-related desynchronization and event-related synchronization during median nerve stimulation according to the various propofol concentrations for 30 patients will be analyzed. In addition, we will apply 4 different offline machine learning algorithms to detect the median nerve stimulation at the cerebral level. Recruitment began in December 2022. Data collection is expected to conclude in June 2024.

CONCLUSIONS

STIM-MOTANA will be the first protocol to investigate median nerve stimulation cerebral motor effect during general anesthesia for the detection of intraoperative awareness. Based on strong practical and theoretical scientific reasoning from our previous studies, our innovative median nerve stimulation-based BCI would provide a way to detect intraoperative awareness during general anesthesia.

TRIAL REGISTRATION

Clinicaltrials.gov NCT05272202; https://clinicaltrials.gov/ct2/show/NCT05272202.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/43870.

Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network

The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models' performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman's rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels.

Perception and practices of depth of anesthesia monitoring and intraoperative awareness event rate among Jordanian anesthesiologists: a cross-sectional study

BACKGROUND

Intraoperative awareness is the second most common complication of surgeries, and it negatively affects patients and healthcare professionals. Based on the limited previous studies, there is a wide variation in the incidence of intraoperative awareness and in the practices and attitudes toward depth of anesthesia (DoA) monitoring among healthcare systems and anesthesiologists. This study aimed to evaluate the Jordanian anesthesiologists' practice and attitudes toward DoA monitoring and estimate the event rate of intraoperative awareness among the participating anesthesiologists.

METHODS

A descriptive cross-sectional survey of Jordanian anesthesiologists working in public, private, and university hospitals was utilized using a questionnaire developed based on previous studies. Practice and attitude in using DoA monitors were evaluated. Anesthesiologists were asked to best estimate the number of anesthesia procedures and frequency of intraoperative awareness events in the year before. Percentages and 95% Confidence Intervals (95%CI) were reported and compared between groups using chi-square tests.

RESULTS

A total of 107 anesthesiologists responded and completed the survey. About one-third of the respondents (34.6%; 95% CI 26.1-44.2) had never used a DoA monitor and only 6.5% (95% CI 3.1-13.2) reported using it as a "daily practice". The use of a DoA monitor was associated with experience and type of health sector. However, 81.3% (95% CI 66.5-83.5) believed that currently available DoA monitors are effective for DoA monitoring and only 4.7% (95%CI 1.9-10.8) reported it as being "invalid". Most respondents reported that the main purpose of using a DoA monitor was to prevent awareness (86.0%; 95%CI 77.9-91.4), guide the delivery of anesthetics (63.6%; 95%CI 53.9-72.2), and reduce recovery time (57%; 95%CI 47.4-66.1). The event rate of intraoperative awareness was estimated at 0.4% among participating anesthesiologists. Most Jordanian hospitals lacked policy intending to prevent intraoperative awareness.

CONCLUSIONS

Most anesthesiologists believed in the role of DoA monitors in preventing intraoperative awareness, however, their attitudes and knowledge are inadequate, and few use DoA monitors in routine practices. In Jordan, large efforts are needed to regulate the use of DoA monitoring and reduce the incidence of intraoperative awareness.

Association Between Burst-Suppression Latency and Burst-Suppression Ratio Under Isoflurane or Adjuvant Drugs With Isoflurane Anesthesia in Mice

The same doses of anesthesia may yield varying depths of anesthesia in different patients. Clinical studies have revealed a possible causal relationship between deep anesthesia and negative short- and long-term patient outcomes. However, a reliable index and method of the clinical monitoring of deep anesthesia and detecting latency remain lacking. As burst-suppression is a characteristic phenomenon of deep anesthesia, the present study investigated the relationship between burst-suppression latency (BSL) and the subsequent burst-suppression ratio (BSR) to find an improved detection for the onset of intraoperative deep anesthesia. The mice were divided young, adult and old group treated with 1.0% or 1.5% isoflurane anesthesia alone for 2 h. In addition, the adult mice were pretreated with intraperitoneal injection of ketamine, dexmedetomidine, midazolam or propofol before they were anesthetized by 1.0% isoflurane for 2 h. Continuous frontal, parietal and occipital electroencephalogram (EEG) were acquired during anesthesia. The time from the onset of anesthesia to the first occurrence of burst-suppression was defined as BSL, while BSR was calculated as percentage of burst-suppression time that was spent in suppression periods. Under 1.0% isoflurane anesthesia, we found a negative correlation between BSL and BSR for EEG recordings obtained from the parietal lobes of young mice, from the parietal and occipital lobes of adult mice, and the occipital lobes of old mice. Under 1.5% isoflurane anesthesia, only the BSL calculated from EEG data obtained from the occipital lobe was negatively correlated with BSR in all mice. Furthermore, in adult mice receiving 1.0% isoflurane anesthesia, the co-administration of ketamine and midazolam, but not dexmedetomidine and propofol, significantly decreased BSL and increased BSR. Together, these data suggest that BSL can detect burst-suppression and predict the subsequent BSR under isoflurane anesthesia used alone or in combination with anesthetics or adjuvant drugs. Furthermore, the consistent negative correlation between BSL and BSR calculated from occipital EEG recordings recommends it as the optimal position for monitoring burst-suppression.

Detecting Connected Consciousness During Propofol-Induced Anesthesia Using EEG Based Brain Decoding

Connected consciousness refers to the state when external stimuli can enter into the stream of our consciousness experience. Emerging evidence suggests that although patients may not respond behaviorally to external stimuli during anesthesia, they may be aware of their surroundings. In this work, we investigated whether EEG based brain decoding could be used for detecting connected consciousness in the absence of behavioral responses during propofol infusion. A total of 14 subjects participated in our experiment. Subjects were asked to discriminate two types of auditory stimuli with a finger press during an ultraslow propofol infusion. We trained an EEG based brain decoding model using data collected in the awakened state using the same auditory stimuli and tested the model on data collected during the propofol infusion. The model provided a correct classification rate (CCR) of [Formula: see text]% when subjects were able to respond to the stimuli during the propofol infusion. The CCR dropped to [Formula: see text]% when subjects ceased responding and further decreased to [Formula: see text]% when we increased the propofol concentration by another 0.2 [Formula: see text]g/ml. After terminating the propofol infusion, we observed that the CCR rebounded to [Formula: see text]% before the subjects regained consciousness. With the classification results, we provided evidence that loss of consciousness is a gradual process and may progress from full consciousness to connected consciousness and then to disconnected consciousness.

Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness

OBJECTIVE

This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth are also reported. The perspective of this work is to improve the detection of intraoperative awareness during general anesthesia.

METHODS

Various architectures of EEGNet were investigated to optimize MI detection. They have been compared to the state-of-the-art classifiers in Brain-Computer Interfaces (based on Riemannian geometry, linear discriminant analysis), and other deep learning architectures (deep convolution network, shallow convolutional network). EEG data were measured from 22 participants performing motor imagery with and without median nerve stimulation.

RESULTS

The proposed architecture of EEGNet reaches the best classification accuracy (83.2%) and false-positive rate (FPR 19.0%) for a setup with only six electrodes over the motor cortex and frontal lobe and for an extended 4-38 Hz EEG frequency range while the subject is being stimulated via a median nerve. Configurations with a larger number of electrodes result in higher accuracy (94.5%) and FPR (6.1%) for 128 electrodes (and respectively 88.0% and 12.9% for 13 electrodes).

CONCLUSION

The present work demonstrates that using an extended EEG frequency band and a modified EEGNet deep neural network increases the accuracy of MI detection when used with as few as 6 electrodes which include frontal channels.

SIGNIFICANCE

The proposed method contributes to the development of Brain-Computer Interface systems based on MI detection from EEG.

MOTANA: study protocol to investigate motor cerebral activity during a propofol sedation

Background

Accidental Accidental awareness during general anesthesia (AAGA) occurs in 1–2% of high-risk practice patients and is a cause of severe psychological trauma, termed post-traumatic stress disorder (PTSD). However, no monitoring techniques can accurately predict or detect AAGA. Since the first reflex for a patient during AAGA is to move, a passive brain-computer interface (BCI) based on the detection of an intention of movement would be conceivable to alert the anesthetist. However, the way in which propofol (i.e., an anesthetic commonly used for the general anesthesia induction) affects motor brain activity within the electroencephalographic (EEG) signal has been poorly investigated and is not clearly understood. For this reason, a detailed study of the motor activity behavior with a step-wise increasing dose of propofol is required and would provide a proof of concept for such an innovative BCI. The main goal of this study is to highlight the occurrence of movement attempt patterns, mainly changes in oscillations called event-related desynchronization (ERD) and event-related synchronization (ERS), in the EEG signal over the motor cortex, in healthy subjects, without and under propofol sedation, during four different motor tasks.

Methods

MOTANA is an interventional, prospective, exploratory, physiological, monocentric, and randomized study conducted in healthy volunteers under light anesthesia, involving EEG measurements before and after target-controlled infusion of propofol at three different effect-site concentrations (0 μg.ml ⁻¹, 0.5 μg.ml ⁻¹, and 1.0 μg.ml ⁻¹). In this exploratory study, 30 healthy volunteers will perform 50 trials for the four motor tasks (real movement, motor imagery, motor imagery with median nerve stimulation, and median nerve stimulation alone) in a randomized sequence. In each conditions and for each trial, we will observe changes in terms of ERD and ERS according to the three propofol concentrations. Pre- and post-injection comparisons of propofol will be performed by paired series tests.

Discussion

MOTANA is an exploratory study aimed at designing an innovative BCI based on EEG-motor brain activity that would detect an attempt to move by a patient under anesthesia. This would be of interest in the prevention of AAGA.

Trial registration

Agence Nationale de Sécurité du Médicament (EUDRACT 2017-004198-1), NCT03362775. Registered on 29 August 2018. https://clinicaltrials.gov/ct2/show/NCT03362775?term=03362775&rank=1

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3596-9) contains supplementary material, which is available to authorized users.

General anesthesia combined with epidural anesthesia maintaining appropriate anesthesia depth may protect excessive production of inflammatory cytokines and stress hormones in colon cancer patients during and after surgery

The purpose of this study was to investigate the influences of varied anesthetic methods and depths on inflammatory cytokines and stress hormone levels in radical operation among colon cancer patients during perioperative period.A total of 120 patients were collected in the study and randomly divided into 4 groups, A: general anesthesia + Narcotrend D1, B: general anesthesia + Narcotrend D2, C: general anesthesia + epidural anesthesia + Narcotrend D1, D: general anesthesia + epidural anesthesia + Narcotrend D2. The levels of tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-10, cortisol (Cor), adrenocorticotropic hormone (ACTH), and endothelin-1 (ET-1) were measured adopting commercial kits before anesthesia (T0), 4 hours after surgery (T1), 24 hours after surgery (T2), and 72 hours after surgery (T3).There was no significant difference in basic clinical characteristics among the groups. In comparison with group A, B and C, group D showed significantly lower levels of TNF-α, IL-6, IL-10, Cor, ACTH, and ET-1 at T1 and T2 (all, P < .05). Significantly higher levels of TNF-α, IL-6, IL-10, Cor, and ACTH were detected at T1 and T2 than those at T0 (all, P < .05), whereas, at T3, the levels of inflammatory cytokines and stress hormones were all decreased near to preoperation ones.General anesthesia combined with epidural anesthesia at Narcotrend D2 depth plays an important role in reducing immune and stress response in patients with colon cancer from surgery to 24 hours after surgery.

Median Nerve Stimulation Based BCI: A New Approach to Detect Intraoperative Awareness During General Anesthesia

Hundreds of millions of general anesthesia are performed each year on patients all over the world. Among these patients, 0.1–0.2% are victims of Accidental Awareness during General Anesthesia (AAGA), i.e., an unexpected awakening during a surgical procedure under general anesthesia. Although anesthesiologists try to closely monitor patients using various techniques to prevent this terrifying phenomenon, there is currently no efficient solution to accurately detect its occurrence. We propose the conception of an innovative passive brain-computer interface (BCI) based on an intention of movement to prevent AAGA. Indeed, patients typically try to move to alert the medical staff during an AAGA, only to discover that they are unable to. First, we examine the challenges of such a BCI, i.e., the lack of a trigger to facilitate when to look for an intention to move, as well as the necessity for a high classification accuracy. Then, we present a solution that incorporates Median Nerve Stimulation (MNS). We investigate the specific modulations that MNS causes in the motor cortex and confirm that they can be altered by an intention of movement. Finally, we perform experiments on 16 healthy participants to assess whether an MI-based BCI using MNS is able to generate high classification accuracies. Our results show that MNS may provide a foundation for an innovative BCI that would allow the detection of AAGA.

Joint consensus on abdominal robotic surgery and anesthesia from a task force of the SIAARTI and SIC

Minimally invasive surgical procedures have revolutionized the world of surgery in the past decades. While laparoscopy, the first minimally invasive surgical technique to be developed, is widely used and has been addressed by several guidelines and recommendations, the implementation of robotic-assisted surgery is still hindered by the lack of consensus documents that support healthcare professionals in the management of this novel surgical procedure. Here we summarize the available evidence and provide expert opinion aimed at improving the implementation and resolution of issues derived from robotic abdominal surgery procedures. A joint task force of Italian surgeons, anesthesiologists and clinical epidemiologists reviewed the available evidence on robotic abdominal surgery. Recommendations were graded according to the strength of evidence. Statements and recommendations are provided for general issues regarding robotic abdominal surgery, operating theatre organization, preoperative patient assessment and preparation, intraoperative management, and postoperative procedures and discharge. The consensus document provides evidence-based recommendations and expert statements aimed at improving the implementation and management of robotic abdominal surgery.

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

Insights from neuroenergetics into the interpretation of functional neuroimaging: an alternative empirical model for studying the brain's support of behavior

Functional neuroimaging measures quantitative changes in neurophysiological parameters coupled to neuronal activity during observable behavior. These results have usually been interpreted by assuming that mental causation of behavior arises from the simultaneous actions of distinct psychological mechanisms or modules. However, reproducible localization of these modules in the brain using functional magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging has been elusive other than for sensory systems. In this paper, we show that neuroenergetic studies using PET, calibrated functional magnetic resonance imaging (fMRI), (13)C magnetic resonance spectroscopy, and electrical recordings do not support the standard approach, which identifies the location of mental modules from changes in brain activity. Of importance in reaching this conclusion is that changes in neuronal activities underlying the fMRI signal are many times smaller than the high ubiquitous, baseline neuronal activity, or energy in resting, awake humans. Furthermore, the incremental signal depends on the baseline activity contradicting theoretical assumptions about linearity and insertion of mental modules. To avoid these problems, while making use of these valuable results, we propose that neuroimaging should be used to identify observable brain activities that are necessary for a person's observable behavior rather than being used to seek hypothesized mental processes.

A wavelet transform based method to determine depth of anesthesia to prevent awareness during general anesthesia

Awareness during general anesthesia for its serious psychological effects on patients and some juristically problems for anesthetists has been an important challenge during past decades. Monitoring depth of anesthesia is a fundamental solution to this problem. The induction of anesthesia alters frequency and mean of amplitudes of the electroencephalogram (EEG), and its phase couplings. We analyzed EEG changes for phase coupling between delta and alpha subbands using a new algorithm for depth of general anesthesia measurement based on complex wavelet transform (CWT) in patients anesthetized by Propofol. Entropy and histogram of modulated signals were calculated by taking bispectral index (BIS) values as reference. Entropies corresponding to different BIS intervals using Mann-Whitney U test showed that they had different continuous distributions. The results demonstrated that there is a phase coupling between 3 and 4 Hz in delta and 8-9 Hz in alpha subbands and these changes are shown better at the channel T₇ of EEG. Moreover, when BIS values increase, the entropy value of modulated signal also increases and vice versa. In addition, measuring phase coupling between delta and alpha subbands of EEG signals through continuous CWT analysis reveals the depth of anesthesia level. As a result, awareness during anesthesia can be prevented.

An alternative position for the BIS-Vista montage in frontal approach neurosurgical cases

BACKGROUND

Appropriate placement of the bispectral index (BIS)-vista montage for frontal approach neurosurgical procedures is a neuromonitoring challenge. The standard bifrontal application interferes with the operative field; yet to date, no other placements have demonstrated good agreement. The purpose of our study was to compare the standard BIS montage with an alternate BIS montage across the nasal dorsum for neuromonitoring.

MATERIALS AND METHODS

The authors performed a prospective study, enrolling patients and performing neuromonitoring using both the standard and the alternative montage on each patient. Data from the 2 placements were compared and analyzed using a Bland-Altman analysis, a Scatter plot analysis, and a matched-pair analysis.

RESULTS

Overall, 2567 minutes of data from each montage was collected on 28 subjects. Comparing the overall difference in score, the alternate BIS montage score was, on average, 2.0 (6.2) greater than the standard BIS montage score (P<0.0001). The Bland-Altman analysis revealed a difference in score of -2.0 (95% confidence interval, -14.1, 10.1), with 108/2567 (4.2%) of the values lying outside of the limit of agreement. The scatter plot analysis overall produced a trend line with the equation y=0.94x+0.82, with an R coefficient of 0.82.

CONCLUSIONS

We determined that the nasal montage produces values that have slightly more variability compared with that ideally desired, but the variability is not clinically significant. In cases where the standard BIS-vista montage would interfere with the operative field, an alternative positioning of the BIS montage across the nasal bridge and under the eye can be used.

Effect of sedation on pain perception

BACKGROUND

Sedation or anesthesia is used to facilitate many cases of an estimated 45 million diagnostic and therapeutic medical procedures in the United States. Preclinical studies have called attention to the possibility that sedative-hypnotic drugs can increase pain perception, but whether this observation holds true in humans and whether pain-modulating effects are agent-specific or characteristic of IV sedation in general remain unclear.

METHODS

To study this important clinical question, the authors recruited 86 healthy volunteers and randomly assigned them to receive one of three sedative drugs: midazolam, propofol, or dexmedetomidine. The authors asked participants to rate their pain in response to four experimental pain tasks (i.e., cold, heat, ischemic, or electrical pain) before and during moderate sedation.

RESULTS

Midazolam increased cold, heat, and electrical pain perception significantly (10-point pain rating scale change, 0.82 ± 0.29, mean ± SEM). Propofol reduced ischemic pain and dexmedetomidine reduced both cold and ischemic pain significantly (-1.58 ± 0.28, mean ± SEM). The authors observed a gender-by-race interaction for dexmedetomidine. In addition to these drug-specific effects, the authors observed gender effects on pain perception; female subjects rated identical experimental pain stimuli higher than male subjects. The authors also noted race-drug interaction effects for dexmedetomidine, with higher doses of drug needed to sedate Caucasians compared with African Americans.

CONCLUSIONS

The results of the authors' study call attention to the fact that IV sedatives may increase pain perception. The effect of sedation on pain perception is agent- and pain type-specific. Knowledge of these effects provides a rational basis for analgesia and sedation to facilitate medical procedures.

Glutamatergic function in the resting awake human brain is supported by uniformly high oxidative energy

Rodent (13)C magnetic resonance spectroscopy studies show that glutamatergic signaling requires high oxidative energy in the awake resting state and allowed calibration of functional magnetic resonance imaging (fMRI) signal in terms of energy relative to the resting energy. Here, we derived energy used for glutamatergic signaling in the awake resting human. We analyzed human data of electroencephalography (EEG), positron emission tomography (PET) maps of oxygen (CMR(O2)) and glucose (CMR(glc)) utilization, and calibrated fMRI from a variety of experimental conditions. CMR(glc) and EEG in the visual cortex were tightly coupled over several conditions, showing that the oxidative demand for signaling was four times greater than the demand for nonsignaling events in the awake state. Variations of CMR(O2) and CMR(glc) from gray-matter regions and networks were within ±10% of means, suggesting that most areas required similar energy for ubiquitously high resting activity. Human calibrated fMRI results suggest that changes of fMRI signal in cognitive studies contribute at most ±10% CMR(O2) changes from rest. The PET data of sleep, vegetative state, and anesthesia show metabolic reductions from rest, uniformly >20% across, indicating no region is selectively reduced when consciousness is lost. Future clinical investigations will benefit from using quantitative metabolic measures.

Cortical energy demands of signaling and nonsignaling components in brain are conserved across mammalian species and activity levels

The continuous need for ion gradient restoration across the cell membrane, a prerequisite for synaptic transmission and conduction, is believed to be a major factor for brain's high oxidative demand. However, do energy requirements of signaling and nonsignaling components of cortical neurons and astrocytes vary with activity levels and across species? We derived oxidative ATP demand associated with signaling (P(s)) and nonsignaling (P(ns)) components in the cerebral cortex using species-specific physiologic and anatomic data. In rat, we calculated glucose oxidation rates from layer-specific neuronal activity measured across different states, spanning from isoelectricity to awake and sensory stimulation. We then compared these calculated glucose oxidation rates with measured glucose metabolic data for the same states as reported by 2-deoxy-glucose autoradiography. Fixed values for P(s) and P(ns) were able to predict the entire range of states in the rat. We then calculated glucose oxidation rates from human EEG data acquired under various conditions using fixed P(s) and P(ns) values derived for the rat. These calculated metabolic data in human cerebral cortex compared well with glucose metabolism measured by PET. Independent of species, linear relationship was established between neuronal activity and neuronal oxidative demand beyond isoelectricity. Cortical signaling requirements dominated energy demand in the awake state, whereas nonsignaling requirements were ∼20% of awake value. These predictions are supported by (13)C magnetic resonance spectroscopy results. We conclude that mitochondrial energy support for signaling and nonsignaling components in cerebral cortex are conserved across activity levels in mammalian species.

Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface

During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.

Review article: closed-loop systems in anesthesia: is there a potential for closed-loop fluid management and hemodynamic optimization?

Closed-loop (automated) controllers are encountered in all aspects of modern life in applications ranging from air-conditioning to spaceflight. Although these systems are virtually ubiquitous, they are infrequently used in anesthesiology because of the complexity of physiologic systems and the difficulty in obtaining reliable and valid feedback data from the patient. Despite these challenges, closed-loop systems are being increasingly studied and improved for medical use. Two recent developments have made fluid administration a candidate for closed-loop control. First, the further description and development of dynamic predictors of fluid responsiveness provides a strong parameter for use as a control variable to guide fluid administration. Second, rapid advances in noninvasive monitoring of cardiac output and other hemodynamic variables make goal-directed therapy applicable for a wide range of patients in a variety of clinical care settings. In this article, we review the history of closed-loop controllers in clinical care, discuss the current understanding and limitations of the dynamic predictors of fluid responsiveness, and examine how these variables might be incorporated into a closed-loop fluid administration system.

Monitoring the depth of anaesthesia

One of the current challenges in medicine is monitoring the patients’ depth of general anaesthesia (DGA). Accurate assessment of the depth of anaesthesia contributes to tailoring drug administration to the individual patient, thus preventing awareness or excessive anaesthetic depth and improving patients’ outcomes. In the past decade, there has been a significant increase in the number of studies on the development, comparison and validation of commercial devices that estimate the DGA by analyzing electrical activity of the brain (i.e., evoked potentials or brain waves). In this paper we review the most frequently used sensors and mathematical methods for monitoring the DGA, their validation in clinical practice and discuss the central question of whether these approaches can, compared to other conventional methods, reduce the risk of patient awareness during surgical procedures.