Prevention of awareness during anaesthesia

Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.

Organ donation after euthanasia starting with anesthesia at home is legal in The Netherlands, Belgium, Canada and Spain

We would like to respond to the article "Organ donation after euthanasia starting at home in a patient with multiple system atrophy Tajaâte et al., [2021] 22:120" on organ donation after euthanasia from home [ODEH]. Although we welcome the performance of ODEH, we would like to make some critical comments regarding the article, both in relation to factual inaccuracies and in terms of the vision expressed on this subject. In this letter we stress the protection of autonomy of vulnerable euthanasia patients, we contradict the assumption of illegality, we question if the anesthesia method utilized is optimal and correct a mistake in regard to an article to which is referred of ourselves.

Practice and challenges for organ donation after medical assistance in dying: A scoping review including the results of the first international roundtable in 2021

The procedure combining medical assistance in dying (MAiD) with donations after circulatory determination of death (DCDD) is known as organ donation after euthanasia (ODE). The first international roundtable on ODE was held during the 2021 WONCA family medicine conference as part of a scoping review. It aimed to document practice and related issues to advise patients, professionals, and policymakers, aiding the development of responsible guidelines and helping to navigate the issues. This was achieved through literature searches and national and international stakeholder meetings. Up to 2021, ODE was performed 286 times in Canada, the Netherlands, Spain, and Belgium, including eight cases of ODE from home (ODEH). MAiD was provided 17,217 times (2020) in the eight countries where ODE is permitted. As of 2021, 837 patients (up to 14% of recipients of DCDD donors) had received organs from ODE. ODE raises some important ethical concerns involving patient autonomy, the link between the request for MAiD and the request to donate organs and the increased burden placed on seriously ill MAiD patients.

Machine Learning Based Hardware Architecture for DOA Measurement from Mice EEG

OBJECTIVE

This research aims to design a hardware optimized machine learning based Depth of Anesthesia (DOA) measurement framework for mice and its FPGA implementation.

METHODS

Electroencephalography or EEG signal is acquired from 16 mice in the Neural Interface Research (NIR) Laboratory of the City University of Hong Kong. We present a logistic regression based approach with mathematically uncomplicated feature extraction techniques for efficient hardware implementation to estimate the DOA.

RESULTS

With the extraction of only two features, the proposed system can classify the state of consciousness with 94% accuracy for a 1 second EEG epoch, leading to a 100% accurate channel prediction after a 7 second run-time on average.

CONCLUSION

Through performance evaluation and comparative study confirmed the efficacy of the prototype.

SIGNIFICANCE

Traditionally the DOA is estimated by checking biophysical responses of a patient during the surgery. However, the physical symptoms can be misleading for a decisive conclusion due to the patient's health condition or as a side-effect of anesthetic drugs. Recently, several neuroscientific research works are correlating the EEG signal with conscious states, which is likely to have less interference with the patient's medical condition. This research presents the first-of-its-kind hardware implemented automatic DOA computation system for mice.

A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End

In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 μW per channel and has the input-referred noise of 0.29 μVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.

Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia

Accurate monitoring of the depth of anesthesia (DoA) is essential for intraoperative and postoperative patient's health. Commercially available electroencephalograph (EEG)-based DoA monitors are recommended only for certain anesthetic drugs and specific age-group patients. This paper presents a machine learning classification processor for accurate DoA estimation irrespective of the patient's age and anesthetic drug. The classification is solely based on six features extracted from EEG signal, i.e., spectral edge frequency (SEF), beta ratio, and four bands of spectral energy (FBSE). A machine learning fine decision tree classifier is adopted to achieve a four-class DoA classification (deep, moderate, and light DoA versus awake state). The feature selection and the classification processor are optimized to achieve the highest classification accuracy for the state of moderate anesthesia required for the surgical operations. The proposed 256-point fast Fourier transform accelerator is implemented to realize SEF, beta ratio, and FBSE that enables minimal latency and high accuracy feature extraction. The proposed DoA processor is implemented using a 65 nm CMOS technology and experimentally verified using field programming gate array (FPGA) based on the EEG recordings of 75 patients undergoing elective surgery with different types of anesthetic agents. The processor achieves an average accuracy of 92.2% for all DoA states, with a latency of 1s The 0.09 mm² DoA processor consumes 140nJ/classification.

Efficacy of bispectral index monitoring for prevention of anestheticawareness and complications during oocyte pick-up procedure

Background/aim: This study was planned by considering that the use of bispectral index (BIS) monitoring ensures sufficient depth of anesthesia and avoids anesthetic awareness and patient movement in the oocyte pick-up (OPU) procedure.Materials and methods: Ninety-eight patients undergoing OPU were randomly divided into 2 groups as the control group (n = 48) and BIS group (n = 50). After propofol and remifentanil induction, the control group was given additional propofol according to reaction response, while the BIS group was given propofol at BIS values of 60 and above with the aim that BIS values be 40?60. Total procedure time, recovery time, patient movement, additional propofol consumption, total number of oocytes, and awareness during anesthesia were recorded. Results: Demographic data were similar in the two groups (P > 0.05 for all). The recovery time in the BIS group was significantly shorter compared to the control group (P < 0.001) while additional propofol consumption was found to be significantly lower (P < 0.001). Baseline BIS values fell compared to all other times after induction significantly (P < 0.001). No patient had anesthesia awareness.Conclusion: During the OPU procedure BIS monitoring is considered to prevent anesthesia awareness, intraoperative movement, and complications caused by insufficient anesthetic use as it ensures optimal doses of anesthetic agents used and early recovery.

EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

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.

Perioperative management in order to minimise postoperative delirium and postoperative cognitive dysfunction: Results from a Swedish web-based survey

Cognitive side-effects such as emergence agitation (EA), postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are not infrequently complicating the postoperative care especially in elderly and fragile patients. The aim of the present survey was to gain insight regarding concern and interest in prevention and treatment strategies for postoperative delirium and dysfunction, and the use of EEG-based depth-of-anaesthesia monitoring possibly reducing the risk for cognitive side effects among anaesthesia personnel.

METHODS

A web-based validated questionnaire was sent to all Swedish anaesthesiologists and nurse anaesthetists during summer 2013. The questionnaire consisted of 3 sections, subjective preferences, routines and practices related to the perioperative handling of EA, POD, POCD.

RESULTS

The response rate was 52%. Cardiovascular/pulmonary risks where assessed as importance by 98, 97% of responders while 69% considered the risk of neurocognitive side-effects important. When asked explicitly around cognitive side-effects 89%, 37% and 44% assessed awareness, POC and POD respectively of importance. EEG-based depth-of-anaesthesia monitors were used in 50% of hospitals. The responders were not convinced about the benefits of such monitors even in at-risk patients. Structured protocols for the management of postoperative cognitive side-effects were available only in few hospitals.

CONCLUSION

Swedish anaesthesia personnel are concerned about the risk of postoperative cognitive side-effects but are more concerned about cardiovascular/pulmonary risks, pain, PONV and the rare event of awareness. Most respondents were not convinced about the use of depth-of-anaesthesia monitors. There is a need to improve knowledge around risk factors, prevention and management of postoperative cognitive side effects.

Awareness and recall during general anesthesia

Anesthesia awareness is defined as both consciousness and recall of surgical events. New research has been conducted out to test this phenomenon. However, testing methods have not proven reliable, including those using devices based on electroencephalographic techniques to detect and prevent intraoperative awareness. The limitations of a standard intraoperative brain monitor reflect our insufficient understanding of consciousness. Moreover, patients who experience an intraoperative awareness can develop serious post-traumatic stress disorders that should not be overlooked. In this review, we introduce the incidence of intraoperative awareness during general anesthesia and discuss the mechanisms of consciousness, as well as risk factors, various monitoring methods, outcome and prevention of intraoperative awareness.

Anaesthetic agents for thoracic surgery: what's best?

PURPOSE OF REVIEW

Pneumonectomy is still associated with a 5% 30-day mortality in a large series involving a variety of anaesthetic agents and techniques.

RECENT FINDINGS

As well as the immediate 'anaesthetic' complications of pain, nausea and vomiting and respiratory dysfunction, differences between anaesthetic agents have emerged in systemic inflammation, postoperative cognitive dysfunction, immune suppression and cell signalling after surgery.

SUMMARY

No one anaesthetic agent has emerged as best. There is a trade-off between initial success and long-term problems or vice versa.

Risk factor for intraoperative awareness

BACKGROUND AND OBJECTIVES

The intraoperative awareness is an adverse event in the general anesthesia, and may occur in approximately 20,000 cases per year, which justifies the study of the risk factors for this event. The objective of this study was to review this subject in order to reduce the incidence of intraoperative awareness and psychological sequelae incurring from this incident, which may result in post-traumatic stress disorder with negative repercussions on the surgical patient social, psychic and functional development.

CONTENT

It was conducted a review of the intraoperative awareness assessing its different phases during general anesthesia such as dreaming, wakefulness, explicit and implicit memory, as well as the analysis, consequences and prevention of its main related factors.

CONCLUSIONS

The reduction of awareness incidence during anesthesia is related to the anesthesiologist improved scientific and technical performance, involving issues such as monitoring, comprehension of the anesthesia activity components, hypnotic and analgesic drugs, neuromuscular blocking agents, autonomic and motor reflex control, in addition to the risk factors involved in this event.

Anesthesia awareness: narrative review of psychological sequelae, treatment, and incidence

Awareness during general anesthesia occurs when patients recall events or sensations during their surgeries, although the patients should have been unconscious at the time. Anesthesiologists are cognizant of this phenomenon, but few discussions occur outside the discipline. This narrative review summarizes the patient recollections, psychological sequelae, treatment and follow-up of psychological consequences, as well as incidence and etiology of awareness during general anesthesia. Recalled memories include noises, conversations, images, mental processes, feelings of pain and/or paralysis. Psychological consequences include anxiety, flashbacks, and posttraumatic stress disorder diagnosis. Limited discussion for therapeutic treatment after an anesthesia awareness experience exists. The incidence of anesthesia awareness ranges from 0.1 to 0.2% (e.g., 1-2/1000 patients). Increased recognition of awareness during general anesthesia within the psychological/counseling community, with additional research focusing on optimal therapeutic treatment, will improve the care of these patients.

Awareness under general anesthesia

BACKGROUND

Awareness while under general anesthesia, and the later recall of what happened during surgery, can be experienced by patients as horrific events that leave lasting mental trauma behind. Patients may have both auditory and tactile perception, potentially accompanied by feelings of helplessness, inability to move, pain, and panic ranging to an acute fear of death. For some patients, the experience of awareness under anesthesia has no sequelae; for others, however, it can lead to the development of post-traumatic stress disorder, consisting of complex psychopathological phenomena such as anxiety, insomnia, nightmares, irritability, and depression possibly leading to suicidality.

METHODS

The literature on the subject was selectively reviewed.

RESULTS

In the absence of risk factors awareness phenomena occur in one to two per 1000 operations under general anesthesia (0.1% to 0.2%) and are thus classed as an occasionally occurring critical event. In children, the risk of such phenomena occurring is 8 to 10 times higher. These phenomena are due to an inadequate depth of anesthesia with incomplete unconsciousness. They can be promoted by a number of risk factors that are either patient-related (ASA class III or above, medication abuse), surgery-related (Caesarean section, emergency procedures, surgery at night), or anesthesia-related (anesthesia without benzodiazepines, use of muscle relaxants).

CONCLUSION

Strategies for avoiding awareness phenomena under anesthesia include the training of staff to know about the problem and, specifically, the use of benzodiazepines, the avoidance of muscle relaxants if possible, and shielding the patient from excessive noise. EEG monitoring is effective but provides no guarantee against awareness. If awareness under anesthesia occurs despite these measures, the patient must be given expert, interdisciplinary treatment as soon after the event as possible in order to minimize its potential sequelae.

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.

Awareness during total intravenous anesthesia for endoscopic thyroidectomy: A case report

A 24-year-old woman underwent endoscopic thyroidectomy with total intravenous anesthesia technique. Immediately after surgery, she said that she had experienced intraoperative awareness for a while. She had vague auditory recall and some degree of pain at the incision site. She was given adequate doses of anesthetics during the operation and the drug delivery system was not compromised. We report awareness during total intravenous anesthesia for endoscopic thyroidectomy.