Preoperative characterization of baseline EEG recordings for risk stratification of post-anesthesia care unit delirium

Electroencephalographic Measures of Delirium in the Perioperative Setting: A Systematic Review

Postoperative delirium (POD) is frequent in older adults and is associated with adverse cognitive and functional outcomes. In the last several decades, there has been an increased interest in exploring tools that easily allow the early recognition of patients at risk of developing POD. The electroencephalogram (EEG) is a widely available tool used to understand delirium pathophysiology, and its use in the perioperative setting has grown exponentially, particularly to predict and detect POD. We performed a systematic review to investigate the use of EEG in the pre-, intra-, and postoperative settings. We identified 371 studies, and 56 met the inclusion criteria. A range of techniques was used to obtain EEG data, from limited 1-4 channel setups to complex 256-channel systems. Power spectra were often measured preoperatively, yet the outcomes were inconsistent. During surgery, the emphasis was primarily on burst suppression (BS) metrics and power spectra, with a link between the frequency and timing of BS, and POD. The EEG patterns observed in POD aligned with those noted in delirium in different contexts, suggesting a reduction in EEG activity. Further research is required to investigate preoperative EEG indicators that may predict susceptibility to delirium.

Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures

Anesthesia plays a pivotal role in modern surgery by facilitating controlled states of unconsciousness. Precise control is crucial for safe and pain-free surgeries. Monitoring anesthesia depth accurately is essential to guide anesthesiologists, optimize drug usage, and mitigate postoperative complications. This study focuses on enhancing the classification performance of anesthesia-induced transitions between wakefulness and deep sleep into eight classes by leveraging advanced graph neural network (GNN). The research combines seven datasets into a single dataset comprising 290 samples and investigates key brain regions, to develop a robust classification framework. Initially, the dataset is augmented using the Synthetic Minority Over-sampling Technique (SMOTE) to expand the sample size to 1197. A graph-based approach is employed to get the intricate relationships between features, constructing a graph dataset with 1197 nodes and 714,610 edges, where nodes represent data samples and edges are the connections between the nodes. The connection (edge weight) is calculated using Spearman correlation coefficient matrix. An optimized GNN model is developed through an ablation study of eight hyperparameters, achieving an accuracy of 92.8%. The model's performance is further evaluated against one-dimensional (1D) CNN, and six machine learning models, demonstrating superior classification capabilities for small and imbalanced datasets. Additionally, we evaluated the proposed model on six different anesthesia datasets, observing no decline in performance. This work advances the understanding and classification of anesthesia states, providing a valuable tool for improved anesthesia management.

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 ).

Preoperative biomarkers associated with delayed neurocognitive recovery

To identify baseline biomarkers of delayed neurocognitive recovery (dNCR) using monitors commonly used in anesthesia. In this sub-study of observational prospective cohorts, we evaluated adult patients submitted to general anesthesia in a tertiary academic center in the United States. Electroencephalographic (EEG) features and cerebral oximetry were assessed in the perioperative period. The primary outcome was dNCR, defined as a decrease of 2 scores in the global Montreal Cognitive Assessment (MoCA) between the baseline and postoperative period. Forty-six adults (median [IQR] age, 65 [15]; 57% females; 65% American Society of Anesthesiologists (ASA) 3 were analyzed. Thirty-one patients developed dNCR (67%). Baseline higher EEG power in the lower alpha band (AUC = 0.73 (95% CI 0.48-0.93)) and lower alpha peak frequency (AUC = 0.83 (95% CI 0.48-1)), as well as lower cerebral oximetry (68 [5] vs 72 [3], p = 0.011) were associated with dNCR. Higher EEG power in the lower alpha band, lower alpha peak frequency, and lower cerebral oximetry values can be surrogates of baseline brain vulnerability.

Aperiodic component of the electroencephalogram power spectrum reflects the hypnotic level of anaesthesia

BACKGROUND

Aperiodic (nonoscillatory) electroencephalogram (EEG) activity can be characterised by its power spectral density, which decays according to an inverse power law. Previous studies reported a shift in the spectral exponent α from consciousness to unconsciousness. We investigated the impact of aperiodic EEG activity on parameters used for anaesthesia monitoring to test the hypothesis that aperiodic EEG activity carries information about the hypnotic component of general anaesthesia.

METHODS

We used simulated noise with varying inverse power law exponents α and the aperiodic component of EEGs recorded during wakefulness (n=62) and maintenance of general anaesthesia (n=125) in a diverse sample of surgical patients receiving sevoflurane, desflurane, or propofol, extracted using the Fitting Oscillations and One-Over-F algorithm. Four spectral EEG parameters (beta ratio, spectral edge frequency 95, spectral entropy, and alpha-to-delta ratio) and two time-series parameters (approximate [ApEn] and permutation entropy [PeEn]) were calculated from the simulated signals and human EEG data. Performance in distinguishing between consciousness and unconsciousness was evaluated with AUC values.

RESULTS

We observed an increase in the spectral exponent from consciousness to unconsciousness (AUC=0.98 (0.94-1)). The spectral parameters exhibited linear or nonlinear responses to changes in α. Using aperiodic EEG activity instead of the entire spectrum for spectral parameter calculation improved the separation between consciousness and unconsciousness for all parameters (AUCaperiodic=0.98 (0.94-1.00) vs AUCoriginal=0.71 (0.62-0.79) to AUCoriginal=0.95 (0.92-0.98)) up to the level of ApEn (AUC=0.96 (0.93-0.98)) and PeEn (AUC=0.94 (0.90-0.97)).

CONCLUSIONS

Aperiodic EEG activity could improve discrimination between consciousness and unconsciousness using spectral analyses.

Electroencephalogram monitoring during anesthesia and critical care: a guide for the clinician

Perioperative anesthetic, surgical and critical careinterventions can affect brain physiology and overall brain health. The clinical utility of electroencephalogram (EEG) monitoring in anesthesia and intensive care settings is multifaceted, offering critical insights into the level of consciousness and depth of anesthesia, facilitating the titration of anesthetic doses, and enabling the detection of ischemic events and epileptic activity. Additionally, EEG monitoring can aid in predicting perioperative neurocognitive disorders, assessing the impact of systemic insults on cerebral function, and informing neuroprognostication. This review provides a comprehensive overview of the fundamental principles of electroencephalography, including the foundations of processed and quantitative electroencephalography. It further explores the characteristic EEG signatures associated wtih anesthetic drugs, the interpretation of the EEG data during anesthesia, and the broader clinical benefits and applications of EEG monitoring in both anesthetic practice and intensive care environments.

The Presence/Absence of an Awake-State Dominant EEG Rhythm in Delirious Patients Is Related to Different Symptoms of Delirium Evaluated by the Intensive Care Delirium Screening Checklist (ICDSC)

(1) Background: Delirium is a serious condition in patients undergoing treatment for somatic diseases, leading to poor prognosis. However, the pathophysiology of delirium is not fully understood and should be clarified for its adequate treatment. This study analyzed the relationship between confusion symptoms in delirium and resting-state electroencephalogram (EEG) power spectrum (PS) profiles to investigate the heterogeneity. (2) Methods: The participants were 28 inpatients in a general hospital showing confusion symptoms with an Intensive Care Delirium Screening Checklist (ICDSC) score of 4 or above. EEG was measured at Pz in the daytime awake state for 100 s with the eyes open and 100 s with the eyes closed on the day of the ICDSC evaluation. PS analysis was conducted consecutively for each 10 s datum. (3) Results: Two resting EEG PS patterns were observed regarding the dominant rhythm: the presence or absence of a dominant rhythm, whereby the PS showed alpha or theta peaks in the former and no dominant rhythm in the latter. The patients showing a dominant EEG rhythm were frequently accompanied by hallucination or delusion (p = 0.039); conversely, those lacking a dominant rhythm tended to exhibit fluctuations in the delirium symptoms (p = 0.020). The other ICDSC scores did not differ between the participants with these two EEG patterns. (4) Discussion: The present study indicates that the presence and absence of a dominant EEG rhythm in delirious patients are related to different symptoms of delirium. Using EEG monitoring in the care of delirium will help characterize its heterogeneous pathophysiology, which requires multiple management strategies.

Prediction of Postoperative Delirium in Older Adults from Preoperative Cognition and Occipital Alpha Power from Resting-State Electroencephalogram

Background

Postoperative delirium is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. Electroencephalography (EEG) spectral slowing has frequently been observed during episodes of delirium, whereas intraoperative frontal alpha power is associated with postoperative delirium. We sought to identify preoperative predictors that could identify individuals at high risk for postoperative delirium, which could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and postoperative delirium-related complications.

Methods

In this prospective observational study, we used machine learning to evaluate whether baseline (preoperative) cognitive function and resting-state EEG could be used to identify patients at risk for postoperative delirium. Preoperative resting-state EEGs and the Montreal Cognitive Assessment were collected from 85 patients (age = 73 ± 6.4 years, 12 cases of delirium) undergoing elective surgery. The model with the highest f1-score was subsequently validated in an independent, prospective cohort of 51 older adults (age = 68 ± 5.2 years, 6 cases of delirium) undergoing elective surgery.

Results

Occipital alpha powers have higher f1-score than frontal alpha powers and EEG spectral slowing in the training cohort. Occipital alpha powers were able to predict postoperative delirium with AUC, specificity and accuracy all >90%, and sensitivity >80%, in the validation cohort. Notably, models incorporating transformed alpha powers and cognitive scores outperformed models incorporating occipital alpha powers alone or cognitive scores alone.

Conclusions

While requiring prospective validation in larger cohorts, these results suggest that strong prediction of postoperative delirium may be feasible in clinical settings using simple and widely available clinical tools. Additionally, our results suggested that the thalamocortical circuit exhibits different EEG patterns under different stressors, with occipital alpha powers potentially reflecting baseline vulnerabilities.

Construction of a nursing assessment framework for patients in anaesthesia recovery period: A modified Delphi study

AIM

To construct a nursing assessment framework for patients in anaesthesia recovery period.

DESIGN

A three-round modified Delphi method was employed to capture the consensus of 22 panellists.

METHODS

The initial items in the nursing assessment framework for patients in anaesthesia recovery period were developed based on the mini-clinical evaluation exercise (mini-CEX). A panel of 22 experts participated in this study. The panellists have more than 10 years of experience in either clinical anaesthesia, or post-anesthesia nursing, or operating room nursing, or surgical intensive nursing. Between March and April 2023, the panellists evaluated and recommended revisions to the initial framework.

RESULTS

This study resulted in the development of a nursing assessment framework for patients in anaesthesia recovery period. The initial version of the framework consisted of six dimensions with 27 items. Six items were modified after the first round of consultation. After the second round, five modifications and four deletions were made based on expert opinion. The third round resulted in a convergence of expert opinion. The framework, which consists of 24 items across five dimensions, was refined. The five dimensions are as follows: History-taking, Physical assessment, Clinical judgement, Organizational efficiency and Humanistic concern.

CONCLUSION

The nursing assessment framework for patients in anaesthesia recovery period was reached consensus between the 22 experts' opinions.

IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE

The assessment framework constructed in this study could be used for the process evaluation of post-anesthesia nursing. The framework may guide perianesthesia nurses in the timely and effective assessment of patients during this critical phase of care. It may be used for perianesthesia nursing education or to evaluate nurses' assessment skills.

REPORTING METHOD

The study is reported in accordance with the Guidance on Conducting and Reporting DElphi Studies (CREDES) recommendations.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Patient selection for nonoperating room anesthesia

PURPOSE OF REVIEW

Given the rapid growth of nonoperating room anesthesia (NORA) in recent years, it is essential to review its unique challenges as well as strategies for patient selection and care optimization.

RECENT FINDINGS

Recent investigations have uncovered an increasing prevalence of older and higher ASA physical status patients in NORA settings. Although closed claim data regarding patient injury demonstrate a lower proportion of NORA cases resulting in a claim than traditional operating room cases, NORA cases have an increased risk of claim for death. Challenges within NORA include site-specific differences, limitations in ergonomic design, and increased stress among anesthesia providers. Several authors have thus proposed strategies focusing on standardizing processes, site-specific protocols, and ergonomic improvements to mitigate risks.

SUMMARY

Considering the unique challenges of NORA settings, meticulous patient selection, risk stratification, and preoperative optimization are crucial. Embracing data-driven strategies and leveraging technological innovations (such as artificial intelligence) is imperative to refine quality control methods in targeted areas. Collaborative efforts led by anesthesia providers will ensure personalized, well tolerated, and improved patient outcomes across all phases of NORA care.

Electroencephalogram Biomarkers from Anesthesia Induction to Identify Vulnerable Patients at Risk for Postoperative Delirium

BACKGROUND

Postoperative delirium is a common complication in elderly patients undergoing anesthesia. Even though it is increasingly recognized as an important health issue, the early detection of patients at risk for postoperative delirium remains a challenge. This study aims to identify predictors of postoperative delirium by analyzing frontal electroencephalogram at propofol-induced loss of consciousness.

METHODS

This prospective, observational single-center study included patients older than 70 yr undergoing general anesthesia for a planned surgery. Frontal electroencephalogram was recorded on the day before surgery (baseline) and during anesthesia induction (1, 2, and 15 min after loss of consciousness). Postoperative patients were screened for postoperative delirium twice daily for 5 days. Spectral analysis was performed using the multitaper method. The electroencephalogram spectrum was decomposed in periodic and aperiodic (correlates to asynchronous spectrum wide activity) components. The aperiodic component is characterized by its offset (y intercept) and exponent (the slope of the curve). Computed electroencephalogram parameters were compared between patients who developed postoperative delirium and those who did not. Significant electroencephalogram parameters were included in a binary logistic regression analysis to predict vulnerability for postoperative delirium.

RESULTS

Of 151 patients, 50 (33%) developed postoperative delirium. At 1 min after loss of consciousness, postoperative delirium patients demonstrated decreased alpha (postoperative delirium: 0.3 μV2 [0.21 to 0.71], no postoperative delirium: 0.55 μV2 [0.36 to 0.74]; P = 0.019] and beta band power [postoperative delirium: 0.27 μV2 [0.12 to 0.38], no postoperative delirium: 0.38 μV2 [0.25 to 0.48]; P = 0.003) and lower spectral edge frequency (postoperative delirium: 10.45 Hz [5.65 to 15.04], no postoperative delirium: 14.56 Hz [9.51 to 16.65]; P = 0.01). At 15 min after loss of consciousness, postoperative delirium patients displayed a decreased aperiodic offset (postoperative delirium: 0.42 μV2 (0.11 to 0.69), no postoperative delirium: 0.62 μV2 [0.37 to 0.79]; P = 0.004). The logistic regression model predicting postoperative delirium vulnerability demonstrated an area under the curve of 0.73 (0.69 to 0.75).

CONCLUSIONS

The findings suggest that electroencephalogram markers obtained during loss of consciousness at anesthesia induction may serve as electroencephalogram-based biomarkers to identify at an early time patients at risk of developing postoperative delirium.

EDITOR’S PERSPECTIVE

Incidence of delirium after non-cardiac surgery in the Chinese elderly population: a systematic review and meta-analysis

Background

POD places a heavy burden on the healthcare system as the number of elderly people undergoing surgery is increasing annually because of the aging population. As a large country with a severely aging population, China's elderly population has reached 267 million. There has been no summary analysis of the pooled incidence of POD in the elderly Chinese population.

Methods

Systematic search databases included PubMed, Web of Science, EMBASE, Cochrane Library Databases, China Knowledge Resource Integrated Database (CNKI), Chinese Biomedical Database (CBM), WanFang Database, and Chinese Science and Technology Periodicals (VIP). The retrieval time ranged from the database's establishment to February 8, 2023. The pooled incidence of delirium after non-cardiac surgery was calculated using a random effects model. Meta-regression, subgroup, and sensitivity analyses were used to explore the source of heterogeneity.

Results

A total of 52 studies met the inclusion criteria, involving 18,410 participants. The pooled incidence of delirium after non-cardiac surgery in the elderly Chinese population was 18.6% (95% CI: 16.4-20.8%). The meta-regression results revealed anesthesia method and year of publication as a source of heterogeneity. In the subgroup analysis, the gender subgroup revealed a POD incidence of 19.6% (95% CI: 16.9-22.3%) in males and 18.3% (95% CI: 15.7-20.9%) in females. The year of publication subgroup analysis revealed a POD incidence of 20.3% (95% CI: 17.4-23.3%) after 2018 and 14.6 (95% CI: 11.6-17.6%) in 2018 and before. In the subgroup of surgical types, the incidence of hip fracture surgery POD was 20.7% (95% CI: 17.6-24.3%), the incidence of non-cardiac surgery POD was 18.4% (95% CI: 11.8-25.1%), the incidence of orthopedic surgery POD was 16.6% (95% CI: 11.8-21.5%), the incidence of abdominal neoplasms surgery POD was 14.3% (95% CI: 7.6-21.1%); the incidence of abdominal surgery POD was 13.9% (95% CI: 6.4-21.4%). The anesthesia methods subgroup revealed a POD incidence of 21.5% (95% CI: 17.9-25.1%) for general anesthesia, 15.0% (95% CI: 10.6-19.3%) for intraspinal anesthesia, and 8.3% (95% CI: 10.6-19.3%) for regional anesthesia. The measurement tool subgroup revealed a POD incidence of 19.3% (95% CI: 16.7-21.9%) with CAM and 16.8% (95% CI: 12.6-21.0%) with DSM. The sample size subgroup revealed a POD incidence of 19.4% (95% CI: 16.8-22.1%) for patients ≤ 500 and 15.3% (95% CI: 11.0-19.7%) for patients > 500. The sensitivity analysis suggested that the pooled incidence of postoperative delirium in this study was stable.

Conclusion

Our systematic review of the incidence of delirium after non-cardiac surgery in elderly Chinese patients revealed a high incidence of postoperative delirium. Except for cardiac surgery, the incidence of postoperative delirium was higher for hip fracture surgery than for other types of surgery. However, this finding must be further explored in future large-sample studies.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier: PROSPERO CRD42023397883.