Perioperative Electroencephalogram Spectral Dynamics Related to Postoperative Delirium in Older Patients

Intraoperative electroencephalogram features related to frailty in older patients: an exploratory prospective observational study

Frailty is an independent risk factor for the increased incidence of postoperative delirium (POD). To date, the effect of frailty on intraoperative electroencephalogram (EEG) changes remains unexplored. The present study, an exploratory analysis of a prospective cohort study, aimed to investigate the differences in EEG characteristics between frail and robust patients. This prospective observational study was conducted between December 2020 and November 2021. The preoperative frailty status was assessed using the FRAIL scale. The patients' baseline (before anesthesia) and intraoperative EEG data were collected using a brain function monitor. Finally, 20 robust and 26 frail older patients scheduled for elective spinal surgery or transurethral prostatectomy under propofol-based general anesthesia were included in the final analysis. Baseline and intraoperative EEG spectrogram and power spectra were compared between the frail and robust groups. No differences were observed in baseline EEG between the frail and robust groups. When the intraoperative EEG spectral parameters were compared, the alpha peak frequency (10.56 ± 0.49 vs. 10.14 ± 0.36 Hz, P = 0.002) and alpha peak, delta, theta, alpha, and beta powers were lower in the frail group. After adjusting for age, Charlson Comorbidity Index (CCI), and mini-mental state examination (MMSE) score, the FRAIL score was still negatively associated with total, delta, theta, alpha, and beta powers. Frail patients had reduced EEG (0-30 Hz) power after the induction of propofol-based general anesthesia. After adjusting for age, CCI, and MMSE score, frail patients still showed evidence of reduced δ, θ, α, and β power.

Intraoperative electroencephalogram patterns as predictors of postoperative delirium in older patients: a systematic review and meta-analysis

Background

Postoperative delirium (POD) significantly affects patient outcomes after surgery, leading to increased morbidity, extended hospital stays, and potential long-term cognitive decline. This study assessed the predictive value of intraoperative electroencephalography (EEG) patterns for POD in adults.

Methods

This systematic review and meta-analysis followed the PRISMA and Cochrane Handbook guidelines. A thorough literature search was conducted using PubMed, Medline, and CENTRAL databases focusing on intraoperative native EEG signal analysis in adult patients. The primary outcome was the relationship between the burst suppression EEG pattern and POD development.

Results

From the initial 435 articles identified, 19 studies with a total of 7,229 patients were included in the systematic review, with 10 included in the meta-analysis (3,705 patients). In patients exhibiting burst suppression, the POD incidence was 22.1% vs. 13.4% in those without this EEG pattern (p=0.015). Furthermore, an extended burst suppression duration associated with a higher likelihood of POD occurrence (p = 0.016). Interestingly, the burst suppression ratio showed no significant association with POD.

Conclusions

This study revealed a 41% increase in the relative risk of developing POD in cases where a burst suppression pattern was present. These results underscore the clinical relevance of intraoperative EEG monitoring in predicting POD in older patients, suggesting its potential role in preventive strategies.

Systematic Review Registration

This study was registered on International Platform for Registered Protocols for Systematic Reviews and Meta-Analyses: INPLASY202420001, https://doi.org/10.37766/inplasy2024.2.0001.

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

The Association of Preoperative Diabetes With Postoperative Delirium in Older Patients Undergoing Major Orthopedic Surgery: A Prospective Matched Cohort Study

BACKGROUND

Postoperative delirium (POD) is a common form of postoperative brain dysfunction, especially in the elderly. However, its risk factors remain largely to be determined. This study aimed to investigate whether (1) preoperative diabetes is associated with POD after elective orthopedic surgery and (2) intraoperative frontal alpha power is a mediator of the association between preoperative diabetes and POD.

METHODS

This was a prospective matched cohort study of patients aged 60 years or more, with a preoperative diabetes who underwent elective orthopedic surgery. Nondiabetic patients were matched 1:1 to diabetic patients in terms of age, sex, and type of surgery. Primary outcome was occurrence of POD, assessed using the 3-minute Diagnostic Confusion Assessment Method (3D-CAM) once daily from 6 pm to 8 pm during the postoperative days 1-7 or until discharge. Secondary outcome was the severity of POD which was assessed for all participants using the short form of the CAM-Severity. Frontal electroencephalogram (EEG) was recorded starting before induction of anesthesia and lasting until discharge from the operating room. Intraoperative alpha power was calculated using multitaper spectral analyses. Mediation analysis was used to estimate the proportion of the association between preoperative diabetes and POD that could be explained by intraoperative alpha power.

RESULTS

A total of 138 pairs of eligible patients successfully matched 1:1. After enrollment, 6 patients in the diabetes group and 4 patients in the nondiabetes group were excluded due to unavailability of raw EEG data. The final analysis included 132 participants with preoperative diabetes and 134 participants without preoperative diabetes, with a median age of 68 years and 72.6% of patients were female. The incidence of POD was 16.7% (22/132) in patients with preoperative diabetes vs 6.0% (8/134) in patients without preoperative diabetes. Preoperative diabetes was associated with increased odds of POD after adjustment of age, sex, body mass index, education level, hypertension, arrhythmia, coronary heart disease, and history of stroke (odds ratio, 3.2; 95% confidence interval [CI], 1.4-8.0; P = .009). The intraoperative alpha power accounted for an estimated 20% (95% CI, 2.6-60%; P = .021) of the association between diabetes and POD.

CONCLUSIONS

This study suggests that preoperative diabetes is associated with an increased risk of POD in older patients undergoing major orthopedic surgery, and that low intraoperative alpha power partially mediates such association.

Differential effects of sevoflurane and desflurane on frontal intraoperative electroencephalogram dynamics associated with postoperative delirium

STUDY OBJECTIVE

Intraoperative electroencephalogram (EEG) patterns associated with postoperative delirium (POD) development have been studied, but the differences in EEG recordings between sevoflurane- and desflurane-induced anesthesia have not been clarified. We aimed to distinguish the EEG characteristics of sevoflurane and desflurane in relation to POD development.

DESIGN AND PATIENTS

We collected frontal four-channel EEG data during the maintenance of anesthesia from 148 elderly patients who received sevoflurane (n = 77) or desflurane (n = 71); 30 patients were diagnosed with delirium postoperatively. The patients were divided into four subgroups based on anesthetics and delirium status: sevoflurane delirium (n = 17), sevoflurane non-delirium (n = 60), desflurane delirium (n = 13), and desflurane non-delirium (n = 58). We compared spectral power, coherence, and pairwise phase consistency (PPC) between sevoflurane and desflurane, and between non-delirium and delirium groups for each anesthetic.

MAIN RESULTS

In patients without POD, the sevoflurane non-delirium group exhibited higher EEG spectral power across 8.5-35 Hz (99.5% CI bootstrap analysis) and higher PPC from alpha to gamma bands (p < 0.005) compared to the desflurane non-delirium group. Conversely, in patients with POD, no significant EEG differences were observed between the sevoflurane and desflurane delirium groups. For the sevoflurane-induced patients, the sevoflurane delirium group had significantly lower power within 7.5-31.5 Hz (99.5% CI bootstrap analysis), reduced coherence over 8.9-23.8 Hz (99.5% CI bootstrap analysis), and lower PPC values in the alpha band (p < 0.005) compared with the sevoflurane non-delirium group. For the desflurane-induced patients, there were no significant differences in the EEG patterns between delirium and non-delirium groups.

CONCLUSIONS

In normal patients without POD, sevoflurane demonstrates a higher power spectrum and prefrontal connectivity than desflurane. Furthermore, reduced frontal alpha power, coherence, and connectivity of intraoperative EEG could be associated with an increased risk of POD. These intraoperative EEG characteristics associated with POD are more noticeable in sevoflurane-induced anesthesia than in desflurane-induced anesthesia.

Intraoperative monitoring of the central and peripheral nervous systems: a narrative review

The central and peripheral nervous systems are the primary target organs during anaesthesia. At the time of the inception of the British Journal of Anaesthesia, monitoring of the central nervous system comprised clinical observation, which provided only limited information. During the 100 yr since then, and particularly in the past few decades, significant progress has been made, providing anaesthetists with tools to obtain real-time assessments of cerebral neurophysiology during surgical procedures. In this narrative review article, we discuss the rationale and uses of electroencephalography, evoked potentials, near-infrared spectroscopy, and transcranial Doppler ultrasonography for intraoperative monitoring of the central and peripheral nervous systems.

Update of the European Society of Anaesthesiology and Intensive Care Medicine evidence-based and consensus-based guideline on postoperative delirium in adult patients

Postoperative delirium (POD) remains a common, dangerous and resource-consuming adverse event but is often preventable. The whole peri-operative team can play a key role in its management. This update to the 2017 ESAIC Guideline on the prevention of POD is evidence-based and consensus-based and considers the literature between 01 April 2015, and 28 February 2022. The search terms of the broad literature search were identical to those used in the first version of the guideline published in 2017. POD was defined in accordance with the DSM-5 criteria. POD had to be measured with a validated POD screening tool, at least once per day for at least 3 days starting in the recovery room or postanaesthesia care unit on the day of surgery or, at latest, on postoperative day 1. Recent literature confirmed the pathogenic role of surgery-induced inflammation, and this concept reinforces the positive role of multicomponent strategies aimed to reduce the surgical stress response. Although some putative precipitating risk factors are not modifiable (length of surgery, surgical site), others (such as depth of anaesthesia, appropriate analgesia and haemodynamic stability) are under the control of the anaesthesiologists. Multicomponent preoperative, intra-operative and postoperative preventive measures showed potential to reduce the incidence and duration of POD, confirming the pivotal role of a comprehensive and team-based approach to improve patients' clinical and functional status.

Preoperative electroencephalographic alpha-power changes with eyes opening are associated with postoperative attention impairment and inattention-related delirium severity

BACKGROUND

In the eyes-closed, awake condition, EEG oscillatory power in the alpha band (7-13 Hz) dominates human spectral activity. With eyes open, however, EEG alpha power substantially decreases. Less alpha attenuation with eyes opening has been associated with inattention; thus, we analysed whether reduced preoperative alpha attenuation with eyes opening is associated with postoperative inattention, a delirium-defining feature.

METHODS

Preoperative awake 32-channel EEG was recorded with eyes open and eyes closed in 71 non-neurological, noncardiac surgery patients aged ≥ 60 years. Inattention and other delirium features were assessed before surgery and twice daily after surgery until discharge. Eyes-opening EEG alpha-attenuation magnitude was analysed for associations with postoperative inattention, primarily, and with delirium severity, secondarily, using multivariate age- and Mini-Mental Status Examination (MMSE)-adjusted logistic and proportional-odds regression analyses.

RESULTS

Preoperative alpha attenuation with eyes opening was inversely associated with postoperative inattention (odds ratio [OR] 0.73, 95% confidence interval [CI]: 0.57, 0.94; P=0.038). Sensitivity analyses showed an inverse relationship between alpha-attenuation magnitude and inattention chronicity, defined as 'never', 'newly', or 'chronically' inattentive (OR 0.76, 95% CI: 0.62, 0.93; P=0.019). In addition, preoperative alpha-attenuation magnitude was inversely associated with postoperative delirium severity (OR 0.79, 95% CI: 0.65, 0.95; P=0.040), predominantly as a result of the inattention feature.

CONCLUSIONS

Preoperative awake, resting, EEG alpha attenuation with eyes opening might represent a neural biomarker for risk of postoperative attentional impairment. Further, eyes-opening alpha attenuation could provide insight into the neural mechanisms underlying postoperative inattention risk.

EEG-Messung in Narkose

Based on the existing literature, the application of designated, processed EEG-monitors to measure anesthetic depth and the associated clinical implications are explained. EEG-monitors quantify the hypnotic portion of anesthesia, but not the nociceptive properties of anesthetics. Depth of anesthesia monitoring is common practice in many German hospitals and helps to visualize the interindividual variability of anesthetics, especially of propofol. Although deep anesthesia is associated with increased long-term mortality, this relation seems not to be causally related. Nevertheless, depth of anesthesia monitors help to identify patients being especially susceptible to anesthetics. Moreover, they have shown to reduce the incidence of intraoperative awareness and postoperative delirium. The application of processed EEG-monitors to reduce the incidence of postoperative delirium is currently recommended by the European Society of Anaesthesiology and Intensive Care.

Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations

Introduction

Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice.

Objective

We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device.

Methods

We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery.

Results

Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93.

Discussion

This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients.

Electroencephalographic Biomarkers, Cerebral Oximetry, and Postoperative Cognitive Function in Adult Noncardiac Surgical Patients: A Prospective Cohort Study

BACKGROUND

Perioperative neurocognitive disorders are a major public health issue, although there are no validated neurophysiologic biomarkers that predict cognitive function after surgery. This study tested the hypothesis that preoperative posterior electroencephalographic alpha power, alpha frontal-parietal connectivity, and cerebral oximetry would each correlate with postoperative neurocognitive function.

METHODS

This was a single-center, prospective, observational study of adult (older than 18 yr) male and female noncardiac surgery patients. Whole-scalp, 16-channel electroencephalography and cerebral oximetry were recorded in the preoperative, intraoperative, and immediate postoperative settings. The primary outcome was the mean postoperative T-score of three National Institutes of Health Toolbox Cognition tests-Flanker Inhibitory Control and Attention, List Sorting Working Memory, and Pattern Comparison Processing Speed. These tests were obtained at preoperative baseline and on the first two postoperative mornings. The lowest average score from the first two postoperative days was used for the primary analysis. Delirium was a secondary outcome (via 3-min Confusion Assessment Method) measured in the postanesthesia care unit and twice daily for the first 3 postoperative days. Last, patient-reported outcomes related to cognition and overall well-being were collected 3 months postdischarge.

RESULTS

Sixty-four participants were recruited with a median (interquartile range) age of 59 (48 to 66) yr. After adjustment for baseline cognitive function scores, no significant partial correlation (ρ) was detected between postoperative cognition scores and preoperative relative posterior alpha power (%; ρ = -0.03, P = 0.854), alpha frontal-parietal connectivity (via weight phase lag index; ρ = -0.10, P = 0.570, respectively), or preoperative cerebral oximetry (%; ρ = 0.21, P = 0.246). Only intraoperative frontal-parietal theta connectivity was associated with postoperative delirium (F[1,6,291] = 4.53, P = 0.034). No electroencephalographic or oximetry biomarkers were associated with cognitive or functional outcomes 3 months postdischarge.

CONCLUSIONS

Preoperative posterior alpha power, frontal-parietal connectivity, and cerebral oximetry were not associated with cognitive function after noncardiac surgery.

EDITOR’S PERSPECTIVE

EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis

Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.

Intra-operative electroencephalogram frontal alpha-band spectral analysis and postoperative delirium in cardiac surgery: A prospective cohort study

BACKGROUND

Postoperative delirium (POD) remains a frequent complication after cardiac surgery, with pre-operative cognitive status being one of the main predisposing factors. However, performing complete pre-operative neuropsychological testing is challenging. The magnitude of frontal electroencephalographic (EEG) α oscillations during general anaesthesia has been related to pre-operative cognition and could constitute a functional marker for brain vulnerability.

OBJECTIVE

We hypothesised that features of intra-operative α-band activity could predict the occurrence of POD.

DESIGN

Single-centre prospective observational study.

SETTING

University hospital, from 15 May 2019 to 15 December 2021.

PATIENTS

Adult patients undergoing elective cardiac surgery.

MAIN OUTCOME MEASURES

Pre-operative cognitive status was assessed by neuropsychological tests and scored as a global z score. A 5-min EEG recording was obtained 30 min after induction of anaesthesia. Anaesthesia was maintained with sevoflurane. Power and peak frequency in the α-band were extracted from the frequency spectra. POD was assessed using the Confusion Assessment Method for Intensive Care Unit, the Confusion Assessment Method and a chart review.

RESULTS

Sixty-five (29.5%) of 220 patients developed POD. Delirious patients were significantly older with median [IQR] ages of 74 [64 to 79] years vs. 67 [59 to 74] years; P  < 0.001) and had lower pre-operative cognitive z scores (-0.52 ± 1.14 vs. 0.21 ± 0.84; P  < 0.001). Mean α power (-14.03 ± 4.61 dB vs. -11.59 ± 3.37 dB; P  < 0.001) and maximum α power (-11.36 ± 5.28 dB vs. -8.85 ± 3.90 dB; P  < 0.001) were significantly lower in delirious patients. Intra-operative mean α power was significantly associated with the probability of developing POD (adjusted odds ratio, 0.88; 95% confidence interval (CI), 0.81 to 0.96; P  = 0.007), independently of age and only whenever cognitive status was not considered.

CONCLUSION

A lower intra-operative frontal α-band power is associated with a higher incidence of POD after cardiac surgery. Intra-operative measures of α power could constitute a means of identifying patients at risk of this complication.

TRIAL REGISTRATION

NCT03706989.

The Thalamus in Perioperative Neurocognitive Disorders

Thalamus function and structure are known predictors of individual differences in the risk of age-related neurocognitive disorders (NCD), such as dementia. However, to date, little is known about their role in the perioperative setting. Here, we provide a narrative review of brain-imaging studies of preoperative and postoperative thalamus scanning parameters associated with risks of developing perioperative NCD, such as postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) during the postoperative phase. These findings are discussed in light of the concept of reserve capacity.

Differences in the EEG Power Spectrum and Cross-Frequency Coupling Patterns between Young and Elderly Patients during Sevoflurane Anesthesia

Electroencephalography (EEG) is widely used for monitoring the depth of anesthesia in surgical patients. Distinguishing age-related EEG features under general anesthesia will help to optimize anesthetic depth monitoring during surgery for elderly patients. This retrospective cohort study included 41 patients aged from 18 to 79 years undergoing noncardiac surgery under general anesthesia. We compared the power spectral signatures and phase-amplitude coupling patterns of the young and elderly groups under baseline and surgical anesthetic depth. General anesthesia by sevoflurane significantly increased the spectral power of delta, theta, alpha, and beta bands and strengthened the cross-frequency coupling both in young and elderly patients. However, the variation in EEG power spectral density and the modulation of alpha amplitudes on delta phases was relatively weaker in elderly patients. In conclusion, the EEG under general anesthesia using sevoflurane exhibited similar dynamic features between young and elderly patients, and the weakened alteration of spectral power and cross-frequency coupling patterns could be utilized to precisely quantify the depth of anesthesia in elderly patients.

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.

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

STUDY OBJECTIVE

Delirium in the post-anesthesia care unit (PACU-D) presents a serious condition with a high medical and socioeconomic impact. In particular, PACU-D is among common postoperative complications of elderly patients. As PACU-D may be associated with postoperative delirium, early detection of at-risk patients and strategies to prevent PACU-D are important. We characterized EEG baseline signatures of patients who developed PACU-D following surgery and general anesthesia and patients who did not.

DESIGN AND SETTING

We conducted a post-hoc analysis of preoperative EEG recordings between patients with and without PACU-D, as indicated by positive bCAM scores post general anesthesia and surgery.

PATIENTS AND MEASUREMENTS

Preoperative baseline EEG recordings from 89 patients were recorded at controlled eyes-open (focused wakefulness) and eyes-closed (relaxed wakefulness) conditions. We computed power spectral densities, permutation entropy, spectral entropy and spectral edge frequency to see if these parameters can reflect potential baseline EEG differences between PACU-D (31.5%) and noPACU-D (68.5%) patients. Wilcoxon's Rank Sum Test as well as AUC values were used to determine statistical significance.

MAIN RESULTS

Baseline EEG recordings showed significant differences between PACU-D and noPACU-D patients preoperatively. Compared to the noPACU-D group, PACU-D patients presented with lower power in higher frequencies during relaxed and focused wakefulness alike. These differences in power led to AUC values of 0.73 [0.59;0.85] (permutation entropy) and 0.72 [0.61;0.83] (spectral edge frequency) indicative of a "fair" performance to separate patients with and without PACU-D.

CONCLUSIONS

The baseline EEG of relaxed wakefulness as well as focused wakefulness may be used to assess the risk of developing PACU-D following surgery under general anesthesia. Moreover, routinely used monitoring parameters capture these differences as well, potentially allowing an easy transfer to clinical settings.

CLINICAL TRIAL NUMBER

NCT03775356.

Oscillatory and aperiodic neuronal activity in working memory following anesthesia

OBJECTIVE

Anesthesia and surgery are associated with cognitive impairment, particularly memory deficits. So far, electroencephalography markers of perioperative memory function remain scarce.

METHODS

We included male patients >60 years scheduled for prostatectomy under general anesthesia. We obtained neuropsychological assessments and a visual match-to-sample working memory task with simultaneous 62-channel scalp electroencephalography 1 day before and 2 to 3 days after surgery.

RESULTS

Twenty-six patients completed both pre- and postoperative sessions. Compared with preoperative performance, verbal learning deteriorated after anesthesia (California Verbal Learning Test total recall; t₂₅ = -3.25, p = 0.015, d = -0.902), while visual working memory performance showed a dissociation between match and mismatch accuracy (match*session F_1,25 = 3.866, p = 0.060). Better verbal learning was associated with an increase of aperiodic brain activity (total recall r = 0.66, p = 0.029, learning slope r = 0.66, p = 0.015), whereas visual working memory accuracy was tracked by oscillatory theta/alpha (7 - 9 Hz), low beta (14 - 18 Hz) and high beta/gamma (34 - 38 Hz) activity (matches: p < 0.001, mismatches: p = 0.022).

CONCLUSIONS

Oscillatory and aperiodic brain activity in scalp electroencephalography track distinct features of perioperative memory function.

SIGNIFICANCE

Aperiodic activity provides a potential electroencephalographic biomarker to identify patients at risk for postoperative cognitive impairments.

Unveiling age-independent spectral markers of propofol-induced loss of consciousness by decomposing the electroencephalographic spectrum into its periodic and aperiodic components

Background

Induction of general anesthesia with propofol induces radical changes in cortical network organization, leading to unconsciousness. While perioperative frontal electroencephalography (EEG) has been widely implemented in the past decades, validated and age-independent EEG markers for the timepoint of loss of consciousness (LOC) are lacking. Especially the appearance of spatially coherent frontal alpha oscillations (8-12 Hz) marks the transition to unconsciousness.Here we explored whether decomposing the EEG spectrum into its periodic and aperiodic components unveiled markers of LOC and investigated their age-dependency. We further characterized the LOC-associated alpha oscillations by parametrizing the adjusted power over the aperiodic component, the center frequency, and the bandwidth of the peak in the alpha range.

Methods

In this prospective observational trial, EEG were recorded in a young (18-30 years) and an elderly age-cohort (≥ 70 years) over the transition to propofol-induced unconsciousness. An event marker was set in the EEG recordings at the timepoint of LOC, defined with the suppression of the lid closure reflex. Spectral analysis was conducted with the multitaper method. Aperiodic and periodic components were parametrized with the FOOOF toolbox. Aperiodic parametrization comprised the exponent and the offset. The periodic parametrization consisted in the characterization of the peak in the alpha range with its adjusted power, center frequency and bandwidth. Three time-segments were defined: preLOC (105 - 75 s before LOC), LOC (15 s before to 15 s after LOC), postLOC (190 - 220 s after LOC). Statistical significance was determined with a repeated-measures ANOVA.

Results

Loss of consciousness was associated with an increase in the aperiodic exponent (young: p = 0.004, elderly: p = 0.007) and offset (young: p = 0.020, elderly: p = 0.004) as well as an increase in the adjusted power (young: p < 0.001, elderly p = 0.011) and center frequency (young: p = 0.008, elderly: p < 0.001) of the periodic alpha peak. We saw age-related differences in the aperiodic exponent and offset after LOC as well as in the power and bandwidth of the periodic alpha peak during LOC.

Conclusion

Decomposing the EEG spectrum over induction of anesthesia into its periodic and aperiodic components unveiled novel age-independent EEG markers of propofol-induced LOC: the aperiodic exponent and offset as well as the center frequency and adjusted power of the power peak in the alpha range.

Machine-learning model predicting postoperative delirium in older patients using intraoperative frontal electroencephalographic signatures

Objective

In older patients receiving general anesthesia, postoperative delirium (POD) is the most frequent form of cerebral dysfunction. Early identification of patients at higher risk to develop POD could provide the opportunity to adapt intraoperative and postoperative therapy. We, therefore, propose a machine learning approach to predict the risk of POD in elderly patients, using routine intraoperative electroencephalography (EEG) and clinical data that are readily available in the operating room.

Methods

We conducted a retrospective analysis of the data of a single-center study at the Charité-Universitätsmedizin Berlin, Department of Anesthesiology [ISRCTN 36437985], including 1,277 patients, older than 60 years with planned surgery and general anesthesia. To deal with the class imbalance, we used balanced ensemble methods, specifically Bagging and Random Forests and as a performance measure, the area under the ROC curve (AUC-ROC). We trained our models including basic clinical parameters and intraoperative EEG features in particular classical spectral and burst suppression signatures as well as multi-band covariance matrices, which were classified, taking advantage of the geometry of a Riemannian manifold. The models were validated with 10 repeats of a 10-fold cross-validation.

Results

Including EEG data in the classification resulted in a robust and reliable risk evaluation for POD. The clinical parameters alone achieved an AUC-ROC score of 0.75. Including EEG signatures improved the classification when the patients were grouped by anesthetic agents and evaluated separately for each group. The spectral features alone showed an AUC-ROC score of 0.66; the covariance features showed an AUC-ROC score of 0.68. The AUC-ROC scores of EEG features relative to patient data differed by anesthetic group. The best performance was reached, combining both the EEG features and the clinical parameters. Overall, the AUC-ROC score was 0.77, for patients receiving Propofol it was 0.78, for those receiving Sevoflurane it was 0.8 and for those receiving Desflurane 0.73. Applying the trained prediction model to an independent data set of a different clinical study confirmed these results for the combined classification, while the classifier on clinical parameters alone did not generalize.

Conclusion

A machine learning approach combining intraoperative frontal EEG signatures with clinical parameters could be an easily applicable tool to early identify patients at risk to develop POD.

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.

Spectral edge frequency during general anaesthesia: A narrative literature review

Previous studies have attempted to determine the depth of anaesthesia with different anaesthetic agents using electroencephalogram (EEG) measurements with variable success. Measuring depth of anaesthesia is confounded by the complexity of the EEG and the fact that different agents create different pattens. A narrative review was undertaken to examine the available research evidence on the effect and reliability of spectral edge frequency (SEF) for assessing the depth of anaesthesia in adult patients under general anaesthesia. A systematic search of the PubMed®, Scopus®, CINAHL and Cochrane databases identified six randomized controlled trials and five observational studies. The findings of these studies suggest that SEF varies according to the anaesthetic drugs used. Remifentanil and age are two factors that can affect SEF, while other opioids and benzodiazepine (administered separately) seem to have no effect. No patients experienced intraoperative awareness. However, this does not indicate that SEF can provide full protection against it and the number of articles in which intraoperative awareness was studied was too small to afford any certainty. None of the studies demonstrated a reliable SEF interval associated with adequate general anaesthesia. SEF must be adapted to the anaesthetic drug used, the patient’s age and state while under general anaesthesia.

[Intraoperative Neuromonitoring: Electroencephalography]

Intraoperative neuromonitoring using electroencephalography (EEG) enables anaesthesiologists to monitor the depth of anaesthesia. It is intended to reduce the occurrence of intraoperative wakefulness, postoperative delirium and postoperative cognitive deficits and to shorten process times in the operating room. This article shows how to interpret the raw EEG, spectrograms and processed indices for different age groups and anaesthetics and summarizes the resulting clinical benefits. While propofol and volatile anesthetics produce characteristic frontal EEG signatures with a high activity of coherent α- and δ-waves, ketamine triggers an increase in rapid γ-waves, which leads to incorrectly high indices (BIS, PSI, NI) despite deep anaesthetic levels.In children, frontal α-waves do not appear until the age of approx. 6 months and valid indices (BIS, PSI, NI) can only be derived starting at an age of approx. 12 months. Furthermore, children of preschool and elementary school age often show epileptiform discharges in the EEG during induction of anaesthesia, what is linked to emergence delirium. In adults, the intraoperative frontal α-power decreases significantly with increasing age and older patients tend to have an increased occurrence of burst suppression patterns during anaesthesia. Clinical benefits of EEG-based neuromonitoring comprise reduced doses of anaesthesia, shorter wake-up times after surgery and a lower incidence of intraoperative awareness during total intravenous anaesthesia. Moreover, anaesthesia guided by processed EEG indices can reduce the incidence of postoperative delirium and postoperative cognitive deficits in older patients. In-depth knowledge about intraoperative EEG changes that go beyond the interpretation of processed indices could lead to a further reduction in intra- and postoperative complications in the future.