Low Frontal Alpha Power Is Associated With the Propensity for Burst Suppression: An Electroencephalogram Phenotype for a "Vulnerable Brain"

Optimizing anesthesia management based on early identification of electroencephalogram burst suppression risk in non-cardiac surgery patients: a visualized dynamic nomogram

BACKGROUND

Burst suppression (BS) is a specific electroencephalogram (EEG) pattern that may contribute to postoperative delirium and negative outcomes. Few prediction models of BS are available and some factors such as frailty and intraoperative hypotension (IOH) which have been reported to promote the occurrence of BS were not included. Therefore, we look forward to creating a straightforward, precise, and clinically useful prediction model by incorporating new factors, such as frailty and IOH.

MATERIALS AND METHODS

We retrospectively collected 540 patients and analyzed the data from 418 patients. Univariate analysis and backward stepwise logistic regression were used to select risk factors to develop a dynamic nomogram model, and then we developed a web calculator to visualize the process of prediction. The performance of the nomogram was evaluated in terms of discrimination, calibration, and clinical utility.

RESULTS

According to the receiver operating characteristic (ROC) analysis, the nomogram showed good discriminative ability (AUC = 0.933) and the Hosmer-Lemeshow goodness-of-fit test demonstrated the nomogram had good calibration (p = 0.0718). Age, Clinical Frailty Scale (CFS) score, midazolam dose, propofol induction dose, total area under the hypotensive threshold of mean arterial pressure (MAP_AUT), and cerebrovascular diseases were the independent risk predictors of BS and used to construct nomogram. The web-based dynamic nomogram calculator was accessible by clicking on the URL: https://eegbsnomogram.shinyapps.io/dynnomapp/ or scanning a converted Quick Response (QR) code.

CONCLUSIONS

Incorporating two distinctive new risk factors, frailty and IOH, we firstly developed a visualized nomogram for accurately predicting BS in non-cardiac surgery patients. The model is expected to guide clinical decision-making and optimize anesthesia management.

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.

Artificial intelligence and nonoperating room anesthesia

PURPOSE OF REVIEW

The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future.

RECENT FINDINGS

AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems.

SUMMARY

The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

Emergence Electroencephalography in an Unresponsiveness Geriatric Patient in the Postanesthesia Care Unit: A Case Report

Incomplete neurological awakening manifested as aberrant patterns of electroencephalography (EEG) at emergence may be responsible for an unresponsive patient in the postanesthesia care unit (PACU). We describe a case of an individual who remained unresponsive but awake in the PACU. Retrospective, intraoperative EEG analysis showed low alpha power and a sudden shift from deep delta to arousal preextubation. We explored parallels with diminished motivation disorders and anesthesia-induced sleep paralysis due to imbalances in anesthetic drug sensitivity between brain regions. Our findings highlight the relevance of end-anesthesia EEG patterns in diagnosing delayed awakening.

Intraoperative pediatric electroencephalography monitoring: an updated review

Intraoperative electroencephalography (EEG) monitoring under pediatric anesthesia has begun to attract increasing interest, driven by the availability of pediatric-specific EEG monitors and the realization that traditional dosing methods based on patient movement or changes in hemodynamic response often lead to imprecise dosing, especially in younger infants who may experience adverse events (e.g., hypotension) due to excess anesthesia. EEG directly measures the effects of anesthetics on the brain, which is the target end-organ responsible for inducing loss of consciousness. Over the past ten years, research on anesthesia and computational neuroscience has improved our understanding of intraoperative pediatric EEG monitoring and expanded the utility of EEG in clinical practice. We now have better insights into neurodevelopmental changes in the developing pediatric brain, functional connectivity, the use of non-proprietary EEG parameters to guide anesthetic dosing, epileptiform EEG changes during induction, EEG changes from spinal/regional anesthesia, EEG discontinuity, and the use of EEG to improve clinical outcomes. This review article summarizes the recent literature on EEG monitoring in perioperative pediatric anesthesia, highlighting several of the topics mentioned above.

Lower alpha frequency of intraoperative frontal EEG is associated with postoperative delirium: A secondary propensity-matched analysis

BACKGROUND

Postoperative delirium (POD) is a serious complication of surgery, especially in the elderly patient population. It has been proposed that decreasing the amount of anesthetics by titrating to an EEG index will lower POD rate, but clear evidence is missing. A strong age-dependent negative correlation has been reported between the peak oscillatory frequency of alpha waves and end-tidal anesthetic concentration, with older patients generating slower alpha frequencies. We hypothesized, that slower alpha oscillations are associated with a higher rate of POD.

METHOD

Retrospective analysis of patients` data from a prospective observational study in cardiac surgical patients approved by the Bernese Ethics committee. Frontal EEG was recorded during Isoflurane effect-site concentrations of 0.7 to 0.8 and peak alpha frequency was measured at highest power between 6 and 17 Hz. Delirium was assessed by chart review. Demographic and clinical characteristics were compared between POD and non-POD groups. Selection bias was addressed using nearest neighbor propensity score matching (PSM) for best balance. This incorporated 18 variables, whereas patients with missing variable information or without an alpha oscillation were excluded.

RESULT

Of the 1072 patients in the original study, 828 were included, 73 with POD, 755 without. PSM allowed 328 patients into the final analysis, 67 with, 261 without POD. Before PSM, 8 variables were significantly different between POD and non-POD groups, none thereafter. Mean peak alpha frequency was significantly lower in the POD in contrast to non-POD group before and after matching (7.9 vs 8.9 Hz, 7.9 vs 8.8 Hz respectively, SD 1.3, p < 0.001).

CONCLUSION

Intraoperative slower frontal peak alpha frequency is independently associated with POD after cardiac surgery and may be a simple intraoperative neurophysiological marker of a vulnerable brain for POD. Further studies are needed to investigate if there is a causal link between alpha frequency and POD.

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.

Effect of Repeated Exposure to Sevoflurane on Electroencephalographic Alpha Oscillation in Pediatric Patients Undergoing Radiation Therapy: A Prospective Observational Study

BACKGROUND

Pharmacological tolerance is defined as a decrease in the effect of a drug over time, or the need to increase the dose to achieve the same effect. It has not been established whether repeated exposure to sevoflurane induces tolerance in children.

METHODS

We conducted an observational study in children younger than 6 years of age scheduled for multiple radiotherapy sessions with sevoflurane anesthesia. To evaluate the development of sevoflurane tolerance, we analyzed changes in electroencephalographic spectral power at induction, across sessions. We fitted individual and group-level linear regression models to evaluate the correlation between the outcomes and sessions. In addition, a linear mixed-effect model was used to evaluate the association between radiotherapy sessions and outcomes.

RESULTS

Eighteen children were included and the median number of radiotherapy sessions per child was 28 (interquartile range: 10 to 33). There was no correlation between induction time and radiotherapy sessions. At the group level, the linear mixed-effect model showed, in a subgroup of patients, that alpha relative power and spectral edge frequency 95 were inversely correlated with the number of anesthesia sessions. Nonetheless, this subgroup did not differ from the other subjects in terms of age, sex, or the total number of radiotherapy sessions.

CONCLUSIONS

Our results suggest that children undergoing repeated anesthesia exposure for radiotherapy do not develop tolerance to sevoflurane. However, we found that a group of patients exhibited a reduction in the alpha relative power as a function of anesthetic exposure. These results may have implications that justify further studies.

Closed-loop anesthesia: foundations and applications in contemporary perioperative medicine

A closed-loop automatically controls a variable using the principle of feedback. Automation within anesthesia typically aims to improve the stability of a controlled variable and reduce workload associated with simple repetitive tasks. This approach attempts to limit errors due to distractions or fatigue while simultaneously increasing compliance to evidence based perioperative protocols. The ultimate goal is to use these advantages over manual care to improve patient outcome. For more than twenty years, clinical studies in anesthesia have demonstrated the superiority of closed-loop systems compared to manual control for stabilizing a single variable, reducing practitioner workload, and safely administering therapies. This research has focused on various closed-loops that coupled inputs and outputs such as the processed electroencephalogram with propofol, blood pressure with vasopressors, and dynamic predictors of fluid responsiveness with fluid therapy. Recently, multiple simultaneous independent closed-loop systems have been tested in practice and one study has demonstrated a clinical benefit on postoperative cognitive dysfunction. Despite their advantages, these tools still require that a well-trained practitioner maintains situation awareness, understands how closed-loop systems react to each variable, and is ready to retake control if the closed-loop systems fail. In the future, multiple input multiple output closed-loop systems will control anesthetic, fluid and vasopressor titration and may perhaps integrate other key systems, such as the anesthesia machine. Human supervision will nonetheless always be indispensable as situation awareness, communication, and prediction of events remain irreplaceable human factors.

Cognitive function mediates the relationship between age and anaesthesia-induced oscillatory-specific alpha power

Cognitive decline is common among older individuals, and although the underlying brain mechanisms are not entirely understood, researchers have suggested using EEG frontal alpha activity during general anaesthesia as a potential biomarker for cognitive decline. This is because frontal alpha activity associated with GABAergic general anaesthetics has been linked to cognitive function. However, oscillatory-specific alpha power has also been linked with chronological age. We hypothesize that cognitive function mediates the association between chronological age and (oscillatory-specific) alpha power. We analysed data from 380 participants (aged over 60) with baseline screening assessments and intraoperative EEG. We utilized the telephonic Montreal Cognitive Assessment to assess cognitive function. We computed total band power, oscillatory-specific alpha power, and aperiodics to measure anaesthesia-induced alpha activity. To test our mediation hypotheses, we employed structural equation modelling. Pairwise correlations between age, cognitive function and alpha activity were significant. Cognitive function mediated the association between age and classical alpha power [age → cognitive function → classical alpha; β = -0.0168 (95% confidence interval: -0.0313 to -0.00521); P = 0.0016] as well as the association between age and oscillatory-specific alpha power [age → cognitive function → oscillatory-specific alpha power; β = -0.00711 (95% confidence interval: -0.0154 to -0.000842); P = 0.028]. However, cognitive function did not mediate the association between age and aperiodic activity (1/f slope, P = 0.43; offset, P = 0.0996). This study is expected to provide valuable insights for anaesthesiologists, enabling them to make informed inferences about a patient's age and cognitive function from an analysis of anaesthetic-induced EEG signals in the operating room. To ensure generalizability, further studies across different populations are needed.

The Perioperative Frontal Relative Ratio of the Alpha Power of Electroencephalography for Predicting Postoperative Delirium After Highly Invasive Surgery: A Prospective Observational Study

BACKGROUND

We investigated the associations between postoperative delirium (POD) and both the relative ratio of the alpha (α)-power of electroencephalography (EEG) and inflammatory markers in a prospective, single-center observational study.

METHODS

We enrolled 84 patients who underwent radical cancer surgeries with reconstruction for esophageal cancer, oral floor cancer, or pharyngeal cancer under total intravenous anesthesia. We collected the perioperative EEG data and the perioperative data of the inflammatory markers, including neutrophil gelatinase-associated lipocalin, presepsin, procalcitonin, C-reactive protein, and the neutrophil-lymphocyte ratio (NLR). The existence of POD was evaluated based on the Intensive Care Delirium Screening Checklist. We compared the time-dependent changes in the relative ratio of the EEG α-power and inflammatory markers between the patients with and without POD.

RESULTS

Four of the 84 patients were excluded from the analysis. Of the remaining 80 patients, 25 developed POD and the other 55 did not. The relative ratio of the α-power at baseline was significantly lower in the POD group than the non-POD group (0.18 ± 0.08 vs 0.28 ± 0.11, P < .001). A time-dependent decline in the relative ratio of α-power in the EEG during surgery was observed in both groups. There were significant differences between the POD and non-POD groups in the baseline, 3-h, 6-h, and 9-h values of the relative ratio of α-power. The preoperative NLR of the POD group was significantly higher than that of the non-POD group (2.88 ± 1.04 vs 2.22 ± 1.00, P < .001), but other intraoperative inflammatory markers were comparable between the groups. Two multivariable logistic regression models demonstrated that the relative ratio of the α-power at baseline was significantly associated with POD.

CONCLUSIONS

Intraoperative frontal relative ratios of the α-power of EEG were associated with POD in patients who underwent radical cancer surgery. Intraoperative EEG monitoring could be a simple and more useful tool for predicting the development of postoperative delirium than measuring perioperative acute inflammatory markers. A lower relative ratio of α-power might be an effective marker for vulnerability of brain and ultimately for the development of POD.

Intraoperative Electroencephalography Alpha-Band Power Is a Better Proxy for Preoperative Low MoCA Under Propofol Compared With Sevoflurane

BACKGROUND

Preoperative abnormal cognitive status is a risk factor for postoperative complications yet remains underdiagnosed. During propofol general anesthesia, intraoperative electroencephalography (EEG) variables, such as alpha band power (α-BP), correlate with cognitive status. This relationship under sevoflurane is unclear. We investigated whether EEG biomarkers of poor cognitive status found under propofol could be extended to sevoflurane.

METHODS

In this monocentric prospective observational study, 106 patients with intraoperative EEG monitoring were included (propofol/sevoflurane = 55/51). We administered the Montreal Cognitive Assessment (MoCA) scale to identify abnormal cognition (low MoCA) 1 day before intervention. EEG variables included delta to beta frequency band powers. Results were adjusted to age and drug dosage. We assessed depth of anesthesia (DoA) using the spectral edge frequency (SEF 95 ) and maintained it within (8-13) Hz.

RESULTS

The difference in α-BP between low and normal MoCA patients was significantly larger among propofol patients (propofol: 4.3 ± 4.8 dB versus sevoflurane: 1.5 ± 3.4 dB, P = .022). SEF 95 and age were not statistically different between sevoflurane and propofol groups. After adjusting to age and dose, low α-BP was significantly associated with low MoCA under propofol (odds ratio [OR] [confidence interval {CI}] = 0.39 [0.16-0.94], P = .034), but not under sevoflurane, where theta-band power was significantly associated with low MoCA (OR [CI] = 0.31 [0.13-0.73], P = .007).

CONCLUSIONS

We suggest that intraoperative EEG biomarkers of abnormal cognition differ between propofol and sevoflurane under general anesthesia.

Effect of raw electroencephalogram-guided anesthesia administration on postoperative outcomes in elderly patients undergoing abdominal major surgery: a randomized controlled trial

BACKGROUND

EEG monitoring techniques are receiving increasing clinical attention as a common method of reflecting the depth of sedation in the perioperative period. The influence of depth of sedation indices such as the bispectral index (BIS) generated by the processed electroencephalogram (pEEG) machine to guide the management of anesthetic depth of sedation on postoperative outcome remains controversial. This research was designed to decide whether an anesthetic agent exposure determined by raw electroencephalogram (rEEG) can influence anesthetic management and cause different EEG patterns and affect various patient outcomes.

METHODS

A total of 141 participants aged ≥ 60 years undergoing abdominal major surgery were randomized to rEEG-guided anesthesia or routine care group. The rEEG-guided anesthesia group had propofol titrated to keep the rEEG waveform at the C-D sedation depth during surgery, while in the routine care group the anesthetist was masked to the patient's rEEG waveform and guided the anesthetic management only through clinical experience. The primary outcome was the presence of postoperative complications, the secondary outcomes included intraoperative anesthetic management and different EEG patterns.

RESULTS

There were no statistically significant differences in the occurrence of postoperative respiratory, circulatory, neurological and gastrointestinal complications. Further EEG analysis revealed that lower frontal alpha power was significantly associated with a higher incidence of POD, and that rEEG-guidance not only reduced the duration of deeper anesthesia in patients with lower frontal alpha power, but also allowed patients with higher frontal alpha power to receive deeper and more appropriate depths of anesthesia than in the routine care group.

CONCLUSIONS

In elderly patients undergoing major abdominal surgery, rEEG-guided anesthesia did not reduce the incidence of postoperative respiratory, circulatory, neurological and gastrointestinal complications. rEEG-guided anesthesia management reduced the duration of intraoperative BS in patients and the duration of over-deep sedation in patients with lower frontal alpha waves under anesthesia, and there was a strong association between lower frontal alpha power under anesthesia and the development of POD. rEEG-guided anesthesia may improve the prognosis of patients with vulnerable brains by improving the early identification of frail elderly patients and providing them with a more effective individualized anesthetic managements.

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.

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.

Recovery From Acute Respiratory Distress Syndrome Is Associated With Increasing Alpha Power in the Frontal Electroencephalogram During Propofol Sedation: A Case Report

The effects of critical illness on electroencephalographic (EEG) signatures of sedatives have not been described, limiting the use of EEG-guided sedation in the intensive care unit (ICU). We report the case of a 36-year-old man recovering from acute respiratory distress syndrome (ARDS). Severe ARDS was characterized by slow-delta (0.1-4 Hz) and theta (4-8 Hz) oscillations but lacked the alpha (8-14 Hz) power expected during propofol sedation in a patient of this age. The alpha power emerged as ARDS resolved. This case raises the question of whether inflammatory states can alter EEG signatures during sedation.

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.

Frontal Electroencephalography Findings in Critically Ill COVID-19 Patients

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) negatively impacts the central nervous system, and studies using a full montage of electroencephalogram (EEG) electrodes have reported nonspecific EEG patterns associated with coronavirus disease 2019 (COVID-19) infection. The use of this technology is resource-intensive and limited in its implementation. In this descriptive pilot study, we report neurophysiological patterns and the potential prognostic capability of an abbreviated frontal EEG electrode montage in critically ill COVID-19 patients.

MATERIALS AND METHODS

Patients receiving mechanical ventilation for SARS-CoV-2 respiratory failure were monitored with Sedline Root Devices using EEG electrodes were placed over the forehead. Qualitative EEG assessments were conducted daily. The primary outcome was mortality, and secondary outcomes were duration of endotracheal intubation and lengths of intensive care and hospitalization stay.

RESULTS

Twenty-six patients were included in the study, and EEG discontinuity was identified in 22 (84.6%) patients. The limited sample size and patient heterogeneity precluded statistical analysis, but certain patterns were suggested by trends in the data. Survival was 100% (4/4) for those patients in which a discontinuous EEG pattern was not observed. The majority of patients (87.5%, 7/8) demonstrating activity in the low-moderate frequency range (7 to 17 Hz) survived compared with 61.1% (11/18) of those without this observation.

CONCLUSIONS

The majority of COVID-19 patients showed signs of EEG discontinuity during monitoring with an abbreviated electrode montage. The trends towards worse survival among those with EEG discontinuity support the need for additional studies to investigate these associations in COVID-19 patients.

Diversity of electroencephalographic patterns during propofol-induced burst suppression

Burst suppression is a brain state consisting of high-amplitude electrical activity alternating with periods of quieter suppression that can be brought about by disease or by certain anesthetics. Although burst suppression has been studied for decades, few studies have investigated the diverse manifestations of this state within and between human subjects. As part of a clinical trial examining the antidepressant effects of propofol, we gathered burst suppression electroencephalographic (EEG) data from 114 propofol infusions across 21 human subjects with treatment-resistant depression. This data was examined with the objective of describing and quantifying electrical signal diversity. We observed three types of EEG burst activity: canonical broadband bursts (as frequently described in the literature), spindles (narrow-band oscillations reminiscent of sleep spindles), and a new feature that we call low-frequency bursts (LFBs), which are brief deflections of mainly sub-3-Hz power. These three features were distinct in both the time and frequency domains and their occurrence differed significantly across subjects, with some subjects showing many LFBs or spindles and others showing very few. Spectral-power makeup of each feature was also significantly different across subjects. In a subset of nine participants with high-density EEG recordings, we noted that each feature had a unique spatial pattern of amplitude and polarity when measured across the scalp. Finally, we observed that the Bispectral Index Monitor, a commonly used clinical EEG monitor, does not account for the diversity of EEG features when processing the burst suppression state. Overall, this study describes and quantifies variation in the burst suppression EEG state across subjects and repeated infusions of propofol. These findings have implications for the understanding of brain activity under anesthesia and for individualized dosing of anesthetic drugs.

Anesthetic management of a patient with an electroencephalogram phenotype for a "vulnerable brain": a case report

BACKGROUND

Low frontal alpha power is an electroencephalogram phenotype suggesting vulnerability to anesthetics. This phenotype for a "vulnerable brain" carries risks for burst suppression at lower-than-expected anesthetic concentrations and therefore for postoperative delirium.

CASE PRESENTATION

A 73-year-old man underwent a laparoscopic Miles' operation. He was monitored with a bispectral index monitor. Before the skin incision, the fraction of age-adjusted minimum alveolar concentration of desflurane was 0.48, and a spectrogram showed slow-delta oscillation despite a bispectral index value of 38-48. Although the fraction of age-adjusted minimum alveolar concentration of desflurane decreased to 0.33, the EEG signature remained unchanged, along with a similar bispectral index value. No burst suppression patterns were observed throughout the whole procedure, and he did not experience postoperative delirium.

CONCLUSIONS

This case suggests that monitoring of electroencephalogram signatures is helpful for detecting patients with a "vulnerable brain" and for providing optimal anesthetic depth in such patients.

Time-trend analysis of the center frequency of the intrinsic mode function from the Hilbert-Huang transform of electroencephalography during general anesthesia: a retrospective observational study

BACKGROUND

Anesthesiologists are required to maintain an optimal depth of anesthesia during general anesthesia, and several electroencephalogram (EEG) processing methods have been developed and approved for clinical use to evaluate anesthesia depth. Recently, the Hilbert-Huang transform (HHT) was introduced to analyze nonlinear and nonstationary data. In this study, we assessed whether the changes in EEG characteristics during general anesthesia that are analyzed by the HHT are useful for monitoring the depth of anesthesia.

METHODS

This retrospective observational study enrolled patients who underwent propofol anesthesia. Raw EEG signals were obtained from a monitor through a previously developed software application. We developed an HHT analyzer to decompose the EEG signal into six intrinsic mode functions (IMFs) and estimated the instantaneous frequencies (HHT_IF) for each IMF. Changes over time in the raw EEG waves and parameters such as HHT_IF, BIS, spectral edge frequency 95 (SEF95), and electromyogram parameter (EMGlow) were assessed, and a Gaussian process regression model was created to assess the association between BIS and HHT_IF.

RESULTS

We analyzed EEG signals from 30 patients. The beta oscillation frequency range (13-25 Hz) was detected in IMF1 and IMF2 during the awake state, then after loss of consciousness, the frequency decreased and alpha oscillation (8-12 Hz) was detected in IMF2. At the emergence phase, the frequency increased and beta oscillations were detected in IMF1, IMF2, and IMF3. BIS and EMGlow changed significantly during the induction and emergence phases, whereas SEF95 showed a wide variability and no significant changes during the induction phase. The root mean square error between the observed BIS values and the values predicted by a Gaussian process regression model ranged from 4.69 to 9.68.

CONCLUSIONS

We applied the HHT to EEG analyses during propofol anesthesia. The instantaneous frequency in IMF1 and IMF2 identified changes in EEG characteristics during induction and emergence from general anesthesia. Moreover, the HHT_IF in IMF2 showed strong associations with BIS and was suitable for depicting the alpha oscillation. Our study suggests that the HHT is useful for monitoring the depth of anesthesia.

Evaluation of nociception in unconscious critically ill patients using a multimodal approach

This prospective observational study included 80 adults (>18 years) patients admitted to the intensive care unit who were unconscious (Glasgow Coma Scale [GCS] score <9 with a motor response <5) and receiving mechanical ventilation. A tetanic stimulation was used to assess nociception; automated pupillometry (Algiscan, ID-MED, France) was used to compute the pupillary pain index score (PPI), with a PPI > 4 considered as nociception. Concomitantly, the number of skin conductance fluctuations (NSCF) per second, measured using a Skin Conductance Algesimeter (SCA, MEDSTORM Innovation AS, Norway; > 0.27 fluctuations/sec indicating nociception), and the instantaneous Analgesia Nociception Index (iANI, MDoloris Medical Systems, France; <50 indicating nociception) were collected. Tetanic stimulation resulted in a median pupillary dilation of 16 [6-25]% and a PPI of 5 [2-7]. According to the PPI assessment, 44 patients (55%) had nociception, whereas 23 (29%) and 18 (23%) showed nociception according to the algesimeter and iANI assessment, respectively. No significant changes in measured physiologic variables were observed after the tetanic stimulation. There were no correlations between PPI, post-stimulation iANI, and SCA-derived variables. There were no differences in PPI, iANI, and SCA variables in patients with low and normal baseline EEG power at baseline. PERSPECTIVES: Detection of nociception varies across different devices in unconscious critically ill patients. Further studies are required to understand which method to implement for analgesic administration in this patient population.

Contribution of intraoperative electroencephalogram suppression to frailty-associated postoperative delirium: mediation analysis of a prospective surgical cohort

BACKGROUND

Frailty is a risk factor for postoperative delirium (POD), and has led to preoperative interventions that have reduced, but not eliminated, the risk. We hypothesised that EEG suppression, another risk factor for POD, mediates some of the frailty risk for POD.

METHODS

A prospective cohort study enrolled patients aged 65 yr or older, scheduled for noncardiac surgery under total intravenous anaesthesia. Frailty was assessed using the FRAIL scale. Cumulative duration of EEG suppression, defined as an amplitude between -5 and 5 μV for >0.5 s during anaesthesia, was measured. POD was diagnosed by either confusion assessment method (CAM), CAM-ICU, or medical records. The severity of POD was assessed using the Delirium Rating Scale - Revised-98 (DRS). Mediation analysis was used to estimate the relationships between frailty, EEG suppression, and severity of POD.

RESULTS

Among 252 enrolled patients, 51 were robust, 129 were prefrail, and 72 were frail. Patients classified as frail had higher duration of EEG suppression than either the robust (19 vs 0.57 s, P<0.001) or prefrail groups (19 vs 3.22 s, P<0.001). Peak delirium score was higher in the frail group than either the robust (17 vs 15, P<0.001) or prefrail groups (17 vs 16, P=0.007). EEG suppression time mediated 24.2% of the frailty-DRS scores association.

CONCLUSION

EEG suppression time mediated a statistically significant portion of the frailty-POD association in older noncardiac surgery patients. Trials directed at reducing EEG suppression time could result in intraoperative interventions to reduce POD in frail patients.

CLINICAL TRIAL REGISTRATION

ChiCTR2000041092 (Chinese Clinical Trial Registry).

Quantitative EEG signatures of delirium and coma in mechanically ventilated ICU patients

OBJECTIVE

To identify quantitative electroencephalography (EEG)-based indicators of delirium or coma in mechanically ventilated patients.

METHODS

We prospectively enrolled 28 mechanically ventilated intensive care unit (ICU) patients to undergo 24-hour continuous EEG, 25 of whom completed the study. We assessed patients twice daily using the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU). We evaluated the spectral profile, regional connectivity and complexity of 5-minute EEG segments after each assessment. We used penalized regression to select EEG metrics associated with delirium or coma, and compared mixed-effects models predicting delirium with and without the selected EEG metrics.

RESULTS

Delta variability, high-beta variability, relative theta power, and relative alpha power contributed independently to EEG-based identification of delirium or coma. A model with these metrics achieved better prediction of delirium or coma than a model with clinical variables alone (Akaike Information Criterion: 36 vs 43, p = 0.006 by likelihood ratio test). The area under the receiver operating characteristic curve for an ad hoc hypothetical delirium score using these metrics was 0.94 (95%CI 0.83-0.99).

CONCLUSIONS

We identified four EEG metrics that, in combination, provided excellent discrimination between delirious/comatose and non-delirious mechanically ventilated ICU patients.

SIGNIFICANCE

Our findings give insight to neurophysiologic changes underlying delirium and provide a basis for pragmatic, EEG-based delirium monitoring technology.

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.

Spectral EEG correlations from the different phases of general anesthesia

Introduction

Electroencephalography (EEG) signals contain transient oscillation patterns commonly used to classify brain states in responses to action, sleep, coma or anesthesia.

Methods

Using a time-frequency analysis of the EEG, we search for possible causal correlations between the successive phases of general anesthesia. We hypothesize that it could be possible to anticipate recovery patterns from the induction or maintenance phases. For that goal, we track the maximum power of the α-band and follow its time course.

Results and discussion

We quantify the frequency shift of the α-band during the recovery phase and the associated duration. Using Pearson coefficient and Bayes factor, we report non-significant linear correlation between the α-band frequency and duration shifts during recovery and the presence of the δ or the α rhythms during the maintenance phase. We also found no correlations between the α-band emergence trajectory and the total duration of the flat EEG epochs (iso-electric suppressions) induced by a propofol bolus injected during induction. Finally, we quantify the instability of the α-band using the mathematical total variation that measures possible deviations from a flat line. To conclude, the present correlative analysis shows that EEG dynamics extracted from the initial and maintenance phases of general anesthesia cannot anticipate both the emergence trajectory and the extubation time.

Decreased Electroencephalographic Alpha Power During Anesthesia Induction Is Associated With EEG Discontinuity in Human Infants

BACKGROUND

Electroencephalogram (EEG) discontinuity can occur at high concentrations of anesthetic drugs, reflecting suppression of electrocortical activity. This EEG pattern has been reported in children and reflects a deep state of anesthesia. Isoelectric events on the EEG, a more extreme degree of voltage suppression, have been shown to be associated with worse long-term neurologic outcomes in neonates undergoing cardiac surgery. However, the clinical significance of EEG discontinuities during pediatric anesthesia for noncardiac surgery is not yet known and merits further research. In this study, we assessed the incidence of EEG discontinuity during anesthesia induction in neurologically normal infants and the clinical factors associated with its development. We hypothesized that EEG discontinuity would be associated with sevoflurane-induced alpha (8-12 Hz) power during the period of anesthesia induction in infants.

METHODS

We prospectively recorded 26 channels of EEG during anesthesia induction in an observational cohort of 54 infants (median age, 7.6 months; interquartile range [IQR] [4.9-9.8 months]). We identified EEG discontinuity, defined as voltage amplitude <25 microvolts for >2 seconds, and assessed its association with sevoflurane-induced alpha power using spectral analysis and multivariable logistic regression adjusting for clinically important variables.

RESULTS

EEG discontinuity was observed in 20 of 54 subjects (37%), with a total of 25 discrete events. Sevoflurane-induced alpha power in the posterior regions of the head (eg, parietal or occipital regions) was significantly lower in the EEG discontinuity group (midline parietal channel on the electroencephalogram, International 10-20 System [Pz]; 8.3 vs 11.2 decibels [dBs]; P = .004), and this association remained after multivariable adjustment (adjusted odds ratio [aOR] = 0.51 per dB increase in alpha power [95% CI, 0.30-0.89]; P = .02). There were no differences in the baseline (unanesthetized) EEG between groups in alpha power or power in any other frequency band.

CONCLUSIONS

We demonstrate that EEG discontinuity is common during anesthesia induction and is related to the level of sevoflurane-induced posterior alpha power, a putative marker of cortical-thalamic circuit development in the first year of life. This association persisted even after adjusting for age and propofol coadministration. The fact that this difference was only observed during anesthesia and not in the baseline EEG suggests that otherwise hidden brain circuit properties are unmasked by general anesthesia. These neurophysiologic markers observed during anesthesia may be useful in identifying patients who may have a greater chance of developing discontinuity.

Effect of combined use of cerebral oximetry and electroencephalogram monitoring on the incidence of perioperative neurocognitive disorders in adult cardiac and non-cardiac surgery: A systematic review of randomized and non-randomized trials

Background: There is insufficient evidence to recommend using either intraoperative cerebral oximetry or (processed) electroencephalogram (EEG) alone for preventing perioperative neurocognitive disorders (PNDs). Objective: To evaluate the effectiveness of combined use of cerebral oximetry and electroencephalogram-guided anesthesia on the incidence of PNDs in adult patients undergoing cardiac and non-cardiac interventions. Methods: A PICOS - based systematic review of English articles using Pubmed and Embase (from inception to August 2022) was performed. There were no exclusion criteria regarding the type of the study. Abstract proceedings and new study protocols or ongoing studies were not included. Review articles were analyzed in search of eligible references. All possible terms that were illustrative of PNDs were used. Results: Among the 63 full manuscripts that were analyzed in detail, 15 met the inclusion criteria. We found 2 retrospective, 8 prospective observational and 5 randomized controlled trials of which 1 did not evaluate the use of neuromonitoring in the randomization process. The definition and the methods used to diagnose PNDs were very heterogeneous. Only 8 studies used an algorithm to avoid/treat cerebral oxygen desaturation and/or to treat EEG abnormalities. Overall, there was a tendency towards less PNDs in studies where such an algorithm was used. Conclusions: Our results suggest that integrating information obtained from cerebral oximetry and an EEG monitor may reduce the incidence of PNDs whenever an adapted algorithm is used to improve brain function.

Processed Electroencephalographic Use During Anesthesia and Outcomes: Analysis of The Society of Thoracic Surgeons Adult Cardiac Surgery Database

BACKGROUND

This study assessed associations between processed electroencephalographic (pEEG) use during anesthesia, surgery- and anesthesia-related risk factors, and neurologic outcomes and mortality after cardiac surgery.

METHODS

Drawing from The Society of Thoracic Surgeons Adult Cardiac Surgery Database and its Adult Cardiac Anesthesiology Section, we identified 42 932 records for elective, urgent, and emergency cardiac surgical procedures between July 1, 2017 and December 31, 2019. Using propensity score-weighted regression analysis, we analyzed the associations between pEEG use during anesthesia on the primary outcome, postoperative delirium (POD), and secondary outcomes of stroke, encephalopathy, coma, and operative mortality.

RESULTS

The rate of pEEG use during anesthesia use was 32.8% (n = 14 086), and its use was not associated with decreased odds for POD (odds ratio [OR], 0.88; 95% CI, 0.78-1.02) or encephalopathy (OR, 0.85; 95% CI, 0.70-1.03). Intraoperative pEEG monitoring use was also not associated with increased odds for stroke (OR, 1.17; 95% CI, 0.97-1.42) or coma (OR, 1.44; 95% CI, 0.82-2.52). In contrast, pEEG use during anesthesia was associated with higher odds for operative mortality (OR, 1.23; 95% CI, 1.05-1.44). This association remained significant after adjusting for POD (OR, 1.21; 95% CI, 1.03-1.41), stroke (OR, 1.21; 95% CI, 1.04-1.42), and encephalopathy (OR, 1.28; 95% CI, 1.07-1.52).

CONCLUSIONS

This large retrospective database study found no association between pEEG use during cardiac surgery and postoperative neurologic outcomes such as POD, stroke, encephalopathy, or coma. However, patients who underwent pEEG monitoring during anesthesia experienced higher mortality, even after adjustment for neurologic outcomes.

Electroencephalogram Features of Perioperative Neurocognitive Disorders in Elderly Patients: A Narrative Review of the Clinical Literature

Postoperative neurocognitive disorder (PND) is a common postoperative complication, particularly in older patients. Electroencephalogram (EEG) monitoring, a non-invasive technique with a high spatial-temporal resolution, can accurately characterize the dynamic changes in brain function during the perioperative period. Current clinical studies have confirmed that the power density of alpha oscillation during general anesthesia decreased with age, which was considered to be associated with increased susceptibility to PND in the elderly. However, evidence on whether general anesthesia under EEG guidance results in a lower morbidity of PND is still contradictory. This is one of the reasons that common indicators of the depth of anesthesia were limitedly derived from EEG signals in the frontal lobe. The variation of multi-channel EEG features during the perioperative period has the potential to highlight the occult structural and functional abnormalities of the subcortical-cortical neurocircuit. Therefore, we present a review of the application of multi-channel EEG monitoring to predict the incidence of PND in older patients. The data confirmed that the abnormal variation in EEG power and functional connectivity between distant brain regions was closely related to the incidence and long-term poor outcomes of PND in older adults.

Monitoring of sedation in mechanically ventilated patients using remote technology

PURPOSE OF REVIEW

Two years of coronavirus disease 2019 (COVID-19) pandemic highlighted that excessive sedation in the ICU leading to coma and other adverse outcomes remains pervasive. There is a need to improve monitoring and management of sedation in mechanically ventilated patients. Remote technologies that are based on automated analysis of electroencephalogram (EEG) could enhance standard care and alert clinicians real-time when severe EEG suppression or other abnormal brain states are detected.

RECENT FINDINGS

High rates of drug-induced coma as well as delirium were found in several large cohorts of mechanically ventilated patients with COVID-19 pneumonia. In patients with acute respiratory distress syndrome, high doses of sedatives comparable to general anesthesia have been commonly administered without defined EEG endpoints. Continuous limited-channel EEG can reveal pathologic brain states such as burst suppression, that cannot be diagnosed by neurological examination alone. Recent studies documented that machine learning-based analysis of continuous EEG signal is feasible and that this approach can identify burst suppression as well as delirium with high specificity.

SUMMARY

Preventing oversedation in the ICU remains a challenge. Continuous monitoring of EEG activity, automated EEG analysis, and generation of alerts to clinicians may reduce drug-induced coma and potentially improve patient outcomes.

Raspberry Pi-Based Data Archival System for Electroencephalogram Signals From the SedLine Root Device

BACKGROUND

The retrospective analysis of electroencephalogram (EEG) signals acquired from patients under general anesthesia is crucial in understanding the patient's unconscious brain's state. However, the creation of such database is often tedious and cumbersome and involves human labor. Hence, we developed a Raspberry Pi-based system for archiving EEG signals recorded from patients under anesthesia in operating rooms (ORs) with minimal human involvement.

METHODS

Using this system, we archived patient EEG signals from over 500 unique surgeries at the Emory University Orthopaedics and Spine Hospital, Atlanta, for about 18 months. For this, we developed a software package that runs on a Raspberry Pi and archives patient EEG signals from a SedLine Root EEG Monitor (Masimo) to a secure Health Insurance Portability and Accountability Act (HIPAA) compliant cloud storage. The OR number corresponding to each surgery was archived along with the EEG signal to facilitate retrospective EEG analysis. We retrospectively processed the archived EEG signals and performed signal quality checks. We also proposed a formula to compute the proportion of true EEG signal and calculated the corresponding statistics. Further, we curated and interleaved patient medical record information with the corresponding EEG signals.

RESULTS

We retrospectively processed the EEG signals to demonstrate a statistically significant negative correlation between the relative alpha power (8-12 Hz) of the EEG signal captured under anesthesia and the patient's age.

CONCLUSIONS

Our system is a standalone EEG archiver developed using low cost and readily available hardware. We demonstrated that one could create a large-scale EEG database with minimal human involvement. Moreover, we showed that the captured EEG signal is of good quality for retrospective analysis and combined the EEG signal with the patient medical records. This project's software has been released under an open-source license to enable others to use and contribute.

Ketamine Associated Intraoperative Electroencephalographic Signatures of Elderly Patients With and Without Preoperative Cognitive Impairment

BACKGROUND

Ketamine is typically used by anesthesiologists as an adjunct for general anesthesia and as a nonopioid analgesic. It has been explored for prevention of postoperative delirium, although results have been contradictory. In this study, we investigated the association of ketamine with postoperative delirium and specific encephalographic signatures. Furthermore, we examined these associations in the context of baseline neurocognition as measured by a validated assessment.

METHODS

We conducted a prospective observational study from January 2019 to December 2020. Ninety-eight patients aged ≥65 years and undergoing spine surgery scheduled for ≥3 hours were included in the study. All participants who completed the University of California San Francisco (UCSF) Brain Health Assessment preoperatively and postoperatively were assessed with the confusion assessment method for intensive care unit (CAM-ICU) and/or the Nursing Delirium Screening Scale (NuDESC). Patients had frontal electroencephalogram (EEG) recordings (SedLine Root, Masimo, Corp) quantitatively analyzed. We used 60 seconds of artifact-free EEG (without burst suppression) extracted from the middle of the maintenance period to calculate the normalized power spectral density (PSD). Comparisons were made between those who did or did not receive ketamine and according to results from neurocognitive assessments.

RESULTS

Ninety-eight patients (of a total of 155, enrolled and consented) had EEG of sufficient quality for analysis (42 women). Overall, we found a significant increase in the EEG power in the moderate frequency range (10-20 Hz) in patients that received ketamine. When the patients were divided by their preoperative cognitive status, this result in the ketamine group only held true for the cognitively normal patients. Patients that were cognitively impaired at baseline did not demonstrate a significant change in EEG characteristics based on ketamine administration, but impaired patients that received ketamine had a significantly higher rate of postoperative delirium (52% ketamine versus 20% no ketamine) (odds ratio [OR], 4.36; confidence interval [CI], 1.02-18.22; P = .048). In patients determined to be preoperatively cognitively normal, the incidence of postoperative delirium was not significantly associated with ketamine administration (19% ketamine versus 17% no ketamine) (OR, 1.10; CI, 0.30-4.04; P = .5833).

CONCLUSIONS

Ketamine-related changes in EEG are observed in a heterogeneous group of patients receiving spine surgery. This result was driven primarily by the effect of ketamine on cognitively normal patients and not observed in patients that were cognitively impaired at baseline. Furthermore, patients who were cognitively impaired at baseline and who had received ketamine were more likely to develop postoperative delirium, suggesting that cognitive vulnerability might be predicted by the lack of a neurophysiologic response to ketamine.

Burst Suppression During General Anesthesia and Postoperative Outcomes: Mini Review

In the last decade, burst suppression has been increasingly studied by many to examine whether it is a mechanism leading to postoperative cognitive impairment. Despite a lack of consensus across trials, the current state of research suggests that electroencephalogram (EEG) burst suppression, duration and EEG emergence trajectory may predict postoperative delirium (POD). A mini literature review regarding evidence about burst suppression impact and susceptibilities was conducted, resulting in conflicting studies. Primarily, studies have used different algorithm values to replace visual burst suppression examination, although many studies have since emerged showing that algorithms underestimate burst suppression duration. As these methods may not be interchangeable with visual analysis of raw data, it is a potential factor for the current heterogeneity between data. Even though additional research trials incorporating the use of raw EEG data are necessary, the data currently show that monitoring with commercial intraoperative EEG machines that use EEG indices to estimate burst suppression may help physicians identify burst suppression and guide anesthetic titration during surgery. These modifications in anesthetics could lead to preventing unfavorable outcomes. Furthermore, some studies suggest that brain age, baseline impairment, and certain medications are risk factors for burst suppression and postoperative delirium. These patient characteristics, in conjunction with intraoperative EEG monitoring, could be used for individualized patient care. Future studies on the feasibility of raw EEG monitoring, new technologies for anesthetic monitoring and titration, and patient-associated risk factors are crucial to our continued understanding of burst suppression and postoperative delirium.

Developing a Real-Time Electroencephalogram-Guided Anesthesia-Management Curriculum for Educating Residents: A Single-Center Randomized Controlled Trial

BACKGROUND

Different anesthetic drugs and patient factors yield unique electroencephalogram (EEG) patterns. Yet, it is unclear how best to teach trainees to interpret EEG time series data and the corresponding spectral information for intraoperative anesthetic titration, or what effect this might have on outcomes.

METHODS

We developed an electronic learning curriculum (ELC) that covered EEG spectrogram interpretation and its use in anesthetic titration. Anesthesiology residents at a single academic center were randomized to receive this ELC and given spectrogram monitors for intraoperative use versus standard residency curriculum alone without intraoperative spectrogram monitors. We hypothesized that this intervention would result in lower inhaled anesthetic administration (measured by age-adjusted total minimal alveolar concentration [MAC] fraction and age-adjusted minimal alveolar concentration [aaMAC]) to patients ≥60 old during the postintervention period (the primary study outcome). To study this effect and to determine whether the 2 groups were administering similar anesthetic doses pre- versus postintervention, we compared aaMAC between control versus intervention group residents both before and after the intervention. To measure efficacy in the postintervention period, we included only those cases in the intervention group when the monitor was actually used. Multivariable linear mixed-effects modeling was performed for aaMAC fraction and hospital length of stay (LOS; a non-prespecified secondary outcome), with a random effect for individual resident. A multivariable linear mixed-effects model was also used in a sensitivity analysis to determine if there was a group (intervention versus control group) by time period (post- versus preintervention) interaction for aaMAC. Resident EEG knowledge difference (a prespecified secondary outcome) was compared with a 2-sided 2-group paired t test.

RESULTS

Postintervention, there was no significant aaMAC difference in patients cared for by the ELC group (n = 159 patients) versus control group (N = 325 patients; aaMAC difference = -0.03; 95% confidence interval [CI], -0.09 to 0.03; P =.32). In a multivariable mixed model, the interaction of time period (post- versus preintervention) and group (intervention versus control) led to a nonsignificant reduction of -0.05 aaMAC (95% CI, -0.11 to 0.01; P = .102). ELC group residents (N = 19) showed a greater increase in EEG knowledge test scores than control residents (N = 20) from before to after the ELC intervention (6-point increase; 95% CI, 3.50-8.88; P < .001). Patients cared for by the ELC group versus control group had a reduced hospital LOS (median, 2.48 vs 3.86 days, respectively; P = .024).

CONCLUSIONS

Although there was no effect on mean aaMAC, these results demonstrate that this EEG-ELC intervention increased resident knowledge and raise the possibility that it may reduce hospital LOS.

Perioperative Electroencephalogram Spectral Dynamics Related to Postoperative Delirium in Older Patients

BACKGROUND

Intraoperative electroencephalography (EEG) signatures related to the development of postoperative delirium (POD) in older patients are frequently studied. However, a broad analysis of the EEG dynamics including preoperative, postinduction, intraoperative and postoperative scenarios and its correlation to POD development is still lacking. We explored the relationship between perioperative EEG spectra-derived parameters and POD development, aiming to ascertain the diagnostic utility of these parameters to detect patients developing POD.

METHODS

Patients aged ≥65 years undergoing elective surgeries that were expected to last more than 60 minutes were included in this prospective, observational single center study (Biomarker Development for Postoperative Cognitive Impairment [BioCog] study). Frontal EEGs were recorded, starting before induction of anesthesia and lasting until recovery of consciousness. EEG data were analyzed based on raw EEG files and downloaded excel data files. We performed multitaper spectral analyses of relevant EEG epochs and further used multitaper spectral estimate to calculate a corresponding spectral parameter. POD assessments were performed twice daily up to the seventh postoperative day. Our primary aim was to analyze the relation between the perioperative spectral edge frequency (SEF) and the development of POD.

RESULTS

Of the 237 included patients, 41 (17%) patients developed POD. The preoperative EEG in POD patients was associated with lower values in both SEF (POD 13.1 ± 4.6 Hz versus no postoperative delirium [NoPOD] 17.4 ± 6.9 Hz; P = .002) and corresponding γ-band power (POD -24.33 ± 2.8 dB versus NoPOD -17.9 ± 4.81 dB), as well as reduced postinduction absolute α-band power (POD -7.37 ± 4.52 dB versus NoPOD -5 ± 5.03 dB). The ratio of SEF from the preoperative to postinduction state (SEF ratio) was ~1 in POD patients, whereas NoPOD patients showed a SEF ratio >1, thus indicating a slowing of EEG with loss of unconscious. Preoperative SEF, preoperative γ-band power, and SEF ratio were independently associated with POD (P = .025; odds ratio [OR] = 0.892, 95% confidence interval [CI], 0.808-0.986; P = .029; OR = 0.568, 95% CI, 0.342-0.944; and P = .009; OR = 0.108, 95% CI, 0.021-0.568, respectively).

CONCLUSIONS

Lower preoperative SEF, absence of slowing in EEG while transitioning from preoperative state to unconscious state, and lower EEG power in relevant frequency bands in both these states are related to POD development. These findings may suggest an underlying pathophysiology and might be used as EEG-based marker for early identification of patients at risk to develop POD.

Current trends in anesthetic depth and antinociception monitoring: an international survey

Current trends in anesthetic depth (i.e., hypnosis) and antinociception monitoring are unclear. We thus aimed to determine contemporary perspectives on monitoring these components of anesthesia during general anesthesia. Participants received and responded anonymously to an internet-based international survey supported by the European Society of Anaesthesiology and Intensive Care. Comparisons, when applicable, were carried out using Chi² analysis or Fischer's exact test. A total of 564 respondents, predominantly from Europe (80.1%), participated. There was a strong participation from Belgium (11.5%). A majority (70.9%) of anesthetists considered hypnotic monitoring important on most occasions to always. In contrast, a majority (62.6%) never or only occasionally considered antinociception monitoring important. This difference in the perceived importance of anesthetic depth versus antinociception monitoring was significant (p < 0.0001). A majority of respondents (70.1%) believed that guiding hypnosis and antinociception using these monitors would improve patient care on most occasions to always. Nonetheless, a substantial number of participants were unsure if hypnotic (23%) or antinociception (32%) monitoring were recommended and there was a lack of knowledge (58%) of any published algorithms to titrate hypnotic and/or antinociceptive drugs based on the information provided by the monitors. In conclusion, current trends in European academic centers prioritize anesthesia depth over antinociception monitoring. Despite an agreement among respondents that applying strategies that optimize anesthetic depth and antinociception could improve outcome, there remains a lack of knowledge of appropriate algorithms. Future studies and recommendations should focus on clarifying goal-directed anesthetic strategies and determine their impact on perioperative patient outcome.

Electroencephalogram-Based Complexity Measures as Predictors of Post-operative Neurocognitive Dysfunction

Physiologic signals such as the electroencephalogram (EEG) demonstrate irregular behaviors due to the interaction of multiple control processes operating over different time scales. The complexity of this behavior can be quantified using multi-scale entropy (MSE). High physiologic complexity denotes health, and a loss of complexity can predict adverse outcomes. Since postoperative delirium is particularly hard to predict, we investigated whether the complexity of preoperative and intraoperative frontal EEG signals could predict postoperative delirium and its endophenotype, inattention. To calculate MSE, the sample entropy of EEG recordings was computed at different time scales, then plotted against scale; complexity is the total area under the curve. MSE of frontal EEG recordings was computed in 50 patients ≥ age 60 before and during surgery. Average MSE was higher intra-operatively than pre-operatively (p = 0.0003). However, intraoperative EEG MSE was lower than preoperative MSE at smaller scales, but higher at larger scales (interaction p < 0.001), creating a crossover point where, by definition, preoperative, and intraoperative MSE curves met. Overall, EEG complexity was not associated with delirium or attention. In 42/50 patients with single crossover points, the scale at which the intraoperative and preoperative entropy curves crossed showed an inverse relationship with delirium-severity score change (Spearman ρ = -0.31, p = 0.054). Thus, average EEG complexity increases intra-operatively in older adults, but is scale dependent. The scale at which preoperative and intraoperative complexity is equal (i.e., the crossover point) may predict delirium. Future studies should assess whether the crossover point represents changes in neural control mechanisms that predispose patients to postoperative delirium.

The raw and processed electroencephalogram in modern anesthesia practice: a brief primer on select clinical applications

The evidence supporting the intraoperative use of processed electroencephalography (pEEG) monitoring to guide anesthetic delivery is growing rapidly. This article reviews the key features of electroencephalography (EEG) waveforms and their clinical implications in select patient populations and anesthetic techniques. The first patient topic reviewed is the vulnerable brain. This term has emerged as a description of patients who may exhibit increased sensitivity to anesthetics and/or may develop adverse neurocognitive effects following anesthesia. pEEG monitoring of patients who are known to have or are suspected of having vulnerable brains, with focused attention on the suppression ratio, alpha band power, and pEEG indices, may prove useful. Second, pEEG monitoring along with vigilant attention to anesthetic delivery may minimize the risk of intraoperative awareness when administering a total intravenous anesthesia in combination with a neuromuscular blockade. Third, we suggest that processed EEG monitoring may play a role in anesthetic and resuscitative management when adverse changes in blood pressure occur. Fourth, pEEG monitoring can be used to better identify anesthesia requirements and guide anesthetic titration in patients with known or suspected substance use.

Frailty is associated with a higher risk of developing delirium and cognitive impairment among patients with diabetic kidney disease: A longitudinal population-based cohort study

AIMS

Delirium, a form of acute brain failure, exhibits a high incidence among older adults. Recent studies have implicated frailty as an under-recognized complication of diabetes mellitus. Whether the presence of frailty increases the risk of delirium/cognitive impairment among patients with diabetic kidney disease (DKD) remains unclear.

METHODS

From the longitudinal cohort of diabetes patients (LCDP) (n = 840,000) in Taiwan, we identified adults with DKD, dividing them into those without and with different severities of frailty based on a modified FRAIL scale. Cox proportional hazard regression was utilized to examine the frailty-associated risk of delirium/cognitive impairment, identified using approaches validated by others.

RESULTS

Totally 149,145 patients with DKD (mean 61.0 years, 44.2% female) were identified, among whom 31.0%, 51.7%, 16.0% and 1.3% did not have or had 1, 2 and >2 FRAIL items at baseline. After 3.68 years, 6613 (4.4%) developed episodes of delirium/cognitive impairment. After accounting for demographic/lifestyle factors, co-morbidities, medications and interventions, patients with DKD and 1, 2 and >2 FRAIL items had a progressively higher risk of developing delirium/cognitive impairment than those without (for those with 1, 2 and >2 items, hazard ratio 1.18, 1.26 and 1.30, 95% confidence interval 1.08-1.28, 1.14-1.39 and 1.10-1.55, respectively). For every FRAIL item increase, the associated risk rose by 9%.

CONCLUSIONS

Frailty significantly increased the risk of delirium/cognitive impairment among patients with DKD. Frailty screening in these patients may assist in delirium risk stratification.

The association of bispectral index values and metrics of cerebral perfusion during cardiopulmonary bypass

STUDY OBJECTIVE

Low bispectral index (BIS) values have been associated with adverse postoperative outcomes. However, trials of optimizing BIS by titrating anesthetic administration have reported conflicting results. One potential explanation is that cerebral perfusion may also affect BIS, but the extent of this relationship is not clear. Therefore, we examined whether BIS would be associated with cerebral perfusion during cardiopulmonary bypass, when anesthetic concentration was constant.

DESIGN

Observational cohort study.

SETTING

Cardiac operating room.

PATIENTS

Seventy-nine patients with cardiopulmonary bypass surgery were included.

MEASUREMENTS

Continuous BIS, mean arterial blood pressure (MAP), cerebral blood flow velocity (CBFV), and regional cerebral oxygen saturation (rSO₂) were monitored, with analysis during a period of constant anesthetic. Mean flow index (Mx) was calculated as Pearson correlation between MAP and CBFV. The lower limit of autoregulation (LLA) was identified as the MAP value at which Mx increased >0.4 with decreasing blood pressure. Postoperative delirium was assessed using the 3D-Confusion Assessment Method.

RESULTS

Mean BIS was lower during periods of MAP < LLA compared with BIS when MAP>LLA (mean 49.35 ± 10.40 vs. 50.72 ± 10.04, p = 0.002, mean difference = 1.38 [standard error: 0.42]). There was a dose response effect, with the BIS proportionately decreasing as MAP decreased below LLA (β = 0.15, 95% CI for the average slope across all patients 0.07 to 0.23, p < 0.001). In contrast, BIS was relatively unchanged when MAP was above LLA (β = 0.03, 95% CI for the average slope across all patients -0.02 to 0.09, p = 0.22). Additionally, increasing CBFV and rSO₂ were associated with increasing BIS. Patients with postoperative delirium had lower mean BIS and higher percentage of time duration with BIS <45 compared to patients without delirium.

CONCLUSIONS

There was an association of BIS and metrics of cerebral perfusion during a period of constant anesthetic administration, but the absolute magnitude of change in BIS as MAP decreased below the LLA was small.

Etiology of Burst Suppression EEG Patterns

Burst-suppression electroencephalography (EEG) patterns of electrical activity, characterized by intermittent high-power broad-spectrum oscillations alternating with isoelectricity, have long been observed in the human brain during general anesthesia, hypothermia, coma and early infantile encephalopathy. Recently, commonalities between conditions associated with burst-suppression patterns have led to new insights into the origin of burst-suppression EEG patterns, their effects on the brain, and their use as a therapeutic tool for protection against deleterious neural states. These insights have been further supported by advances in mechanistic modeling of burst suppression. In this Perspective, we review the origins of burst-suppression patterns and use recent insights to weigh evidence in the controversy regarding the extent to which burst-suppression patterns observed during profound anesthetic-induced brain inactivation are associated with adverse clinical outcomes. Whether the clinical intent is to avoid or maintain the brain in a state producing burst-suppression patterns, monitoring and controlling neural activity presents a technical challenge. We discuss recent advances that enable monitoring and control of burst suppression.

Neuroanesthesiology Update

This review summarizes the literature published in 2020 that is relevant to the perioperative care of neurosurgical patients and patients with neurological diseases as well as critically ill patients with neurological diseases. Broad topics include general perioperative neuroscientific considerations, stroke, traumatic brain injury, monitoring, anesthetic neurotoxicity, and perioperative disorders of cognitive function.

Does electroencephalographic burst suppression still play a role in the perioperative setting?

With the widespread use of electroencephalogram [EEG] monitoring during surgery or in the Intensive Care Unit [ICU], clinicians can sometimes face the pattern of burst suppression [BS]. The BS pattern corresponds to the continuous quasi-periodic alternation between high-voltage slow waves [the bursts] and periods of low voltage or even isoelectricity of the EEG signal [the suppression] and is extremely rare outside ICU and the operative room. BS can be secondary to increased anesthetic depth or a marker of cerebral damage, as a therapeutic endpoint [i.e., refractory status epilepticus or refractory intracranial hypertension]. In this review, we report the neurophysiological features of BS to better define its role during intraoperative and critical care settings.