Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes

Valine and Inflammation Drive Epilepsy in a Mouse Model of ECHS1 Deficiency

ECHS1 Deficiency (ECHS1D) is a rare and devastating pediatric disease that currently has no defined treatments. This disorder results from missense loss-of-function mutations in the ECHS1 gene that result in severe developmental delays, encephalopathy, hypotonia, and early death. ECHS1 enzymatic activity is necessary for the beta-oxidation of fatty acids and the oxidation of branched-chain amino acids within the inner mitochondrial matrix. The pathogenesis of disease remains unknown, however it is hypothesized that disease is driven by an accumulation of toxic metabolites from impaired valine oxidation. To expand our knowledge on disease mechanisms, a novel mouse model of ECHS1D was generated that possesses a disease-associated knock-in (KI) allele and a knock-out (KO) allele. To investigate the behavioral phenotype, a battery of testing was performed at multiple time points, which included assessments of learning, motor function, endurance, sensory responses, and anxiety. Neurological abnormalities were assessed using wireless telemetry EEG recordings, pentylenetetrazol (PTZ) seizure induction, and immunohistochemistry. Metabolic perturbations were measured within the liver, serum, and brain using mass spectrometry and magnetic resonance spectroscopy. To test disease mechanisms, mice were subjected to disease pathway stressors and then survival, body weight gain, and epilepsy were assessed. Mice containing KI/KI or KI/KO alleles were viable with normal development and survival, and the presence of KI and KO alleles resulted in a significant reduction in ECHS1 protein. ECHS1D mice displayed reduced exercise capacity and pain sensation. EEG analysis revealed increased slow wave power that was associated with perturbations in sleep. ECHS1D mice had significantly increased epileptiform EEG discharges, and were sensitive to seizure induction, which resulted in death of 60% of ECHS1D mice. Under basal conditions, brain structure was grossly normal, although histological analysis revealed increased microglial activation in aged ECHS1D mice. Increased dietary valine only affected ECHS1D mice, which significantly exacerbated seizure susceptibility and resulted in death. Lastly, acute inflammatory challenge drove regression and early lethality in ECHS1D mice. In conclusion, we developed a novel model of ECHS1D that may be used to further knowledge on disease mechanisms and to develop therapeutics. Our data suggests altered metabolic signaling and inflammation may contribute to epilepsy in ECHS1D, and these alterations may be attributed to impaired valine metabolism.

Prevalence of clinical electroencephalography findings in stroke patients with delirium

OBJECTIVE

Delirium is an acute cognitive disorder associated with multiple electroencephalographic (EEG) abnormalities in non-neurological patients, though specific EEG characteristics in patients with stroke remain unclear. We aimed to compare the prevalence of EEG abnormalities in stroke patients during delirium episodes with periods that did not correspond to delirium.

METHODS

We retrospectively analyzed clinical EEG reports for stroke patients who received daily delirium assessments as part of a prospective study. We compared the prevalence of EEG features corresponding to patient-days with vs. without delirium, including focal and generalized slowing, and focal and generalized epileptiform abnormalities (EAs).

RESULTS

Among 58 patients who received EEGs, there were 192 days of both EEG and delirium monitoring (88% [n = 169] corresponding to delirium). Generalized slowing was significantly more prevalent on days with vs. without delirium (96% vs. 57%, p = 0.03), as were bilateral or generalized EAs (38% vs. 13%, p = 0.03). In contrast, focal slowing (53% vs. 74%, p = 0.11) and focal EAs were less prevalent on days with delirium (38% vs. 48%, p = 0.37), though these differences were not statistically significant.

CONCLUSIONS

We found a higher prevalence of generalized but not focal EEG abnormalities in stroke patients with delirium.

SIGNIFICANCE

These findings may reinforce the diffuse nature of delirium-associated encephalopathy, even in patients with discrete structural lesions.

Precise prediction of cerebrospinal fluid amyloid beta protein for early Alzheimer's disease detection using multimodal data

Alzheimer's disease (AD) constitutes a neurodegenerative disorder marked by a progressive decline in cognitive function and memory capacity. The accurate diagnosis of this condition predominantly relies on cerebrospinal fluid (CSF) markers, notwithstanding the associated burdens of pain and substantial financial costs endured by patients. This study encompasses subjects exhibiting varying degrees of cognitive impairment, encompassing individuals with subjective cognitive decline, mild cognitive impairment, and dementia, constituting a total sample size of 82 participants. The primary objective of this investigation is to explore the relationships among brain atrophy measurements derived from magnetic resonance imaging, atypical electroencephalography (EEG) patterns, behavioral assessment scales, and amyloid β-protein (Aβ) indicators. The findings of this research reveal that individuals displaying reduced Aβ1-42/Aβ-40 levels exhibit significant atrophy in the frontotemporal lobe, alongside irregularities in various parameters related to EEG frequency characteristics, signal complexity, inter-regional information exchange, and microstates. The study additionally endeavors to estimate Aβ1-42/Aβ-40 content through the application of a random forest algorithm, amalgamating structural data, electrophysiological features, and clinical scales, achieving a remarkable predictive precision of 91.6%. In summary, this study proposes a cost-effective methodology for acquiring CSF markers, thereby offering a valuable tool for the early detection of AD.

Increased A1 astrocyte activation-driven hippocampal neural network abnormality mediates delirium-like behavior in aged mice undergoing cardiac surgery

Delirium is the most common neurological complication after cardiac surgery with adverse impacts on surgical outcomes. Advanced age is an independent risk factor for delirium occurrence but its underlying mechanisms are not fully understood. Although increased A1 astrocytes and abnormal hippocampal networks are involved in neurodegenerative diseases, whether A1 astrocytes and hippocampal network changes are involved in the delirium-like behavior of aged mice remains unknown. In the present study, a mice model of myocardial ischemia-reperfusion mimicking cardiac surgery and various assessments were used to investigate the different susceptibility of the occurrence of delirium-like behavior between young and aged mice and the underlying mechanisms. The results showed that surgery significantly increased hippocampal A1 astrocyte activation in aged compared to young mice. The high neuroinflammatory state induced by surgery resulted in glutamate accumulation in the extrasynaptic space, which subsequently decreased the excitability of pyramidal neurons and increased the PV interneurons inhibition through enhancing N-methyl-D-aspartate receptors' tonic currents in the hippocampus. These further induced the abnormal activities of the hippocampal neural networks and consequently contributed to delirium-like behavior in aged mice. Notably, the intraperitoneal administration of exendin-4, a glucagon-like peptide-1 receptor agonist, downregulated A1 astrocyte activation and alleviated delirium-like behavior in aged mice, while IL-1α, TNF-α, and C1q in combination administered intracerebroventricularly upregulated A1 astrocyte activation and induced delirium-like behavior in young mice. Therefore, our study suggested that cardiac surgery increased A1 astrocyte activation which subsequently impaired the hippocampal neural networks and triggered delirium development.

DeltaScan for the Assessment of Acute Encephalopathy and Delirium in ICU and non-ICU Patients, a Prospective Cross-Sectional Multicenter Validation Study

OBJECTIVES

To measure the diagnostic accuracy of DeltaScan: a portable real-time brain state monitor for identifying delirium, a manifestation of acute encephalopathy (AE) detectable by polymorphic delta activity (PDA) in single-channel electroencephalograms (EEGs).

DESIGN

Prospective cross-sectional study.

SETTING

Six Intensive Care Units (ICU's) and 17 non-ICU departments, including a psychiatric department across 10 Dutch hospitals.

PARTICIPANTS

494 patients, median age 75 (IQR:64-87), 53% male, 46% in ICUs, 29% delirious.

MEASUREMENTS

DeltaScan recorded 4-minute EEGs, using an algorithm to select the first 96 seconds of artifact-free data for PDA detection. This algorithm was trained and calibrated on two independent datasets.

METHODS

Initial validation of the algorithm for AE involved comparing its output with an expert EEG panel's visual inspection. The primary objective was to assess DeltaScan's accuracy in identifying delirium against a delirium expert panel's consensus.

RESULTS

DeltaScan had a 99% success rate, rejecting 6 of the 494 EEG's due to artifacts. Performance showed and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.86 (95% CI: 0.83-0.90) for AE (sensitivity: 0.75, 95%CI=0.68-0.81, specificity: 0.87 95%CI=0.83-0.91. The AUC was 0.71 for delirium (95%CI=0.66-0.75, sensitivity: 0.61 95%CI=0.52-0.69, specificity: 72, 95%CI=0.67-0.77). Our validation aim was an NPV for delirium above 0.80 which proved to be 0.82 (95%CI: 0.77-0.86). Among 84 non-delirious psychiatric patients, DeltaScan differentiated delirium from other disorders with a 94% (95%CI: 87-98%) specificity.

CONCLUSIONS

DeltaScan can diagnose AE at bedside and shows a clear relationship with clinical delirium. Further research is required to explore its role in predicting delirium-related outcomes.

Electroencephalography as a Biomarker of Prognosis in Acute Brain Injury

Electroencephalography (EEG) is a noninvasive tool that allows the monitoring of cerebral brain function in critically ill patients, aiding with diagnosis, management, and prognostication. Specific EEG features have shown utility in the prediction of outcomes in critically ill patients with status epilepticus, acute brain injury (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, and traumatic brain injury), anoxic brain injury, and toxic-metabolic encephalopathy. Studies have also found an association between particular EEG patterns and long-term functional and cognitive outcomes as well as prediction of recovery of consciousness following acute brain injury. This review summarizes these findings and demonstrates the value of utilizing EEG findings in the determination of prognosis.

Automated detection of immune effector cell-associated neurotoxicity syndrome via quantitative EEG

OBJECTIVE

To develop an automated, physiologic metric of immune effector cell-associated neurotoxicity syndrome among patients undergoing chimeric antigen receptor-T cell therapy.

METHODS

We conducted a retrospective observational cohort study from 2016 to 2020 at two tertiary care centers among patients receiving chimeric antigen receptor-T cell therapy with a CD19 or B-cell maturation antigen ligand. We determined the daily neurotoxicity grade for each patient during EEG monitoring via chart review and extracted clinical variables and outcomes from the electronic health records. Using quantitative EEG features, we developed a machine learning model to detect the presence and severity of neurotoxicity, known as the EEG immune effector cell-associated neurotoxicity syndrome score.

RESULTS

The EEG immune effector cell-associated neurotoxicity syndrome score significantly correlated with the grade of neurotoxicity with a median Spearman's R2 of 0.69 (95% CI of 0.59-0.77). The mean area under receiving operator curve was greater than 0.85 for each binary discrimination level. The score also showed significant correlations with maximum ferritin (R2 0.24, p = 0.008), minimum platelets (R2 -0.29, p = 0.001), and dexamethasone usage (R2 0.42, p < 0.0001). The score significantly correlated with duration of neurotoxicity (R2 0.31, p < 0.0001).

INTERPRETATION

The EEG immune effector cell-associated neurotoxicity syndrome score possesses high criterion, construct, and predictive validity, which substantiates its use as a physiologic method to detect the presence and severity of neurotoxicity among patients undergoing chimeric antigen receptor T-cell therapy.

Bispectral EEG (BSEEG) Algorithm Captures High Mortality Risk Among 1,077 Patients: Its Relationship to Delirium Motor Subtype

OBJECTIVE

Delirium is dangerous and a predictor of poor patient outcomes. We have previously reported the utility of the bispectral EEG (BSEEG) with a novel algorithm for the detection of delirium and prediction of patient outcomes including mortality. The present study employed a normalized BSEEG (nBSEEG) score to integrate the previous cohorts to combine their data to investigate the prediction of patient outcomes. We also aimed to test if the BSEEG method can be applicable regardless of age, and independent of delirium motor subtypes.

METHODS

We calculated nBSEEG score from raw BSEEG data in each cohort and classified patients into BSEEG-positive and BSEEG-negative groups. We used log-rank test and Cox proportional hazards models to predict 90-day and 1-year outcomes for the BSEEG-positive and -negative groups in all subjects and motor subgroups.

RESULTS

A total of 1,077 subjects, the BSEEG-positive group showed significantly higher 90-day (hazard ratio 1.33 [95% CI 1.16-1.52] and 1-year (hazard ratio 1.22 [95% CI 1.06-1.40] mortality rates than the negative group after adjustment for covariates such as age, sex, CCI, and delirium status. Among patients with different motor subtypes of delirium, the hypoactive group showed significantly higher 90-day (hazard ratio 1.41 [95% CI 1.12-1.76] and 1-year mortality rates (hazard ratio 1.32 [95% CI 1.05-1.67], which remained significant after adjustment for the same covariates.

CONCLUSION

We found that the BSEEG method is capable of capturing patients at high mortality risk.

Sleep Fragmentation, Electroencephalographic Slowing, and Circadian Disarray in a Mouse Model for Intensive Care Unit Delirium

BACKGROUND

We aimed to further validate our previously published animal model for delirium by testing the hypothesis that in aged mice, Anesthesia, Surgery and simulated ICU conditions (ASI) induce sleep fragmentation, electroencephalographic (EEG) slowing, and circadian disarray consistent with intensive care unit (ICU) patients with delirium.

METHODS

A total of 41 mice were used. Mice were implanted with EEG electrodes and randomized to ASI or control groups. ASI mice received laparotomy, anesthesia, and simulated ICU conditions. Controls did not receive ASI. Sleep was recorded at the end of ICU conditions, and hippocampal tissue was collected on EEG recording. Arousals, EEG dynamics, and circadian gene expression were compared with t tests. Two-way repeated measures analysis of variance (RM ANOVA) was used to assess sleep according to light.

RESULTS

ASI mice experienced frequent arousals (36.6 ± 3.2 vs 26.5 ± 3.4; P = .044; 95% confidence interval [CI], 0.29-19.79; difference in mean ± SEM, 10.04 ± 4.62) and EEG slowing (frontal theta ratio, 0.223 ± 0.010 vs 0.272 ± 0.019; P = .026; 95% CI, -0.091 to -0.007; difference in mean ± SEM, -0.05 ± 0.02) relative to controls. In ASI mice with low theta ratio, EEG slowing was associated with a higher percentage of quiet wakefulness (38.2 ± 3.6 vs 13.4 ± 3.8; P = .0002; 95% CI, -35.87 to -13.84; difference in mean ± SEM, -24.86 ± 5.19). ASI mice slept longer during the dark phases of the circadian cycle (nonrapid eye movement [NREM], dark phase 1 [D1]: 138.9 ± 8.1 minutes vs 79.6 ± 9.6 minutes, P = .0003, 95% CI, -95.87 to -22.69, predicted mean difference ± SE: -59.28 ± 13.89; NREM, dark phase 2 (D2): 159.3 ± 7.3 minutes vs 112.6 ± 15.5 minutes, P = .006, 95% CI, -83.25 to -10.07, mean difference ± SE, -46.66 ± 13.89; rapid eye movement (REM), D1: 20.5 ± 2.1 minutes vs 5.8 ± 0.8 minutes, P = .001, 95% CI, -24.60 to -4.71, mean difference ± SE, -14. 65 ± 3.77; REM, D2: 21.0 ± 2.2 minutes vs 10.3 ± 1.4 minutes, P = .029, 95% CI, -20.64 to -0.76, mean difference ± SE, -10.70 ± 3.77). The expression of essential circadian genes was also lower in ASI mice (basic helix-loop-helix ARNT like [BMAL1] : -1.3 fold change; circadian locomotor output cycles protein kaput [CLOCK] : -1.2).

CONCLUSIONS

ASI mice experienced EEG and circadian changes mimicking those of delirious ICU patients. These findings support further exploration of this mouse approach to characterize the neurobiology of delirium.

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis

OBJECTIVE

This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach.

METHODS

We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature.

RESULTS

For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs.

CONCLUSIONS

Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD.

SIGNIFICANCE

Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.

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.

Preclinical and translational models for delirium: Recommendations for future research from the NIDUS delirium network

Delirium is a common, morbid, and costly syndrome that is closely linked to Alzheimer's disease (AD) and AD-related dementias (ADRD) as a risk factor and outcome. Human studies of delirium have advanced our knowledge of delirium incidence and prevalence, risk factors, biomarkers, outcomes, prevention, and management. However, understanding of delirium neurobiology remains limited. Preclinical and translational models for delirium, while challenging to develop, could advance our knowledge of delirium neurobiology and inform the development of new prevention and treatment approaches. We discuss the use of preclinical and translational animal models in delirium, focusing on (1) a review of current animal models, (2) challenges and strategies for replicating elements of human delirium in animals, and (3) the utility of biofluid, neurophysiology, and neuroimaging translational markers in animals. We conclude with recommendations for the development and validation of preclinical and translational models for delirium, with the goal of advancing awareness in this important field.

Ultrathin, Soft, Bioresorbable Organic Electrochemical Transistors for Transient Spatiotemporal Mapping of Brain Activity

A critical challenge lies in the development of the next-generation neural interface, in mechanically tissue-compatible fashion, that offer accurate, transient recording electrophysiological (EP) information and autonomous degradation after stable operation. Here, an ultrathin, lightweight, soft and multichannel neural interface is presented based on organic-electrochemical-transistor-(OECT)-based network, with capabilities of continuous high-fidelity mapping of neural signals and biosafety active degrading after performing functions. Such platform yields a high spatiotemporal resolution of 1.42 ms and 20 µm, with signal-to-noise ratio up to ≈37 dB. The implantable OECT arrays can well establish stable functional neural interfaces, designed as fully biodegradable electronic platforms in vivo. Demonstrated applications of such OECT implants include real-time monitoring of electrical activities from the cortical surface of rats under various conditions (e.g., narcosis, epileptic seizure, and electric stimuli) and electrocorticography mapping from 100 channels. This technology offers general applicability in neural interfaces, with great potential utility in treatment/diagnosis of neurological disorders.

Cohort profile of PLUTO: a perioperative biobank focusing on prediction and early diagnosis of postoperative complications

PURPOSE

Although elective surgery is generally safe, some procedures remain associated with an increased risk of complications. Improved preoperative risk stratification and earlier recognition of these complications may ameliorate postoperative recovery and improve long-term outcomes. The perioperative longitudinal study of complications and long-term outcomes (PLUTO) cohort aims to establish a comprehensive biorepository that will facilitate research in this field. In this profile paper, we will discuss its design rationale and opportunities for future studies.

PARTICIPANTS

Patients undergoing elective intermediate to high-risk non-cardiac surgery are eligible for enrolment. For the first seven postoperative days, participants are subjected to daily bedside visits by dedicated observers, who adjudicate clinical events and perform non-invasive physiological measurements (including handheld spirometry and single-channel electroencephalography). Blood samples and microbiome specimens are collected at preselected time points. Primary study outcomes are the postoperative occurrence of nosocomial infections, major adverse cardiac events, pulmonary complications, acute kidney injury and delirium/acute encephalopathy. Secondary outcomes include mortality and quality of life, as well as the long-term occurrence of psychopathology, cognitive dysfunction and chronic pain.

FINDINGS TO DATE

Enrolment of the first participant occurred early 2020. During the inception phase of the project (first 2 years), 431 patients were eligible of whom 297 patients consented to participate (69%). Observed event rate was 42% overall, with the most frequent complication being infection.

FUTURE PLANS

The main purpose of the PLUTO biorepository is to provide a framework for research in the field of perioperative medicine and anaesthesiology, by storing high-quality clinical data and biomaterials for future studies. In addition, PLUTO aims to establish a logistical platform for conducting embedded clinical trials.

TRIAL REGISTRATION NUMBER

NCT05331118.

Delirium-related psychiatric and neurocognitive impairment and the association with post-intensive care syndrome-A narrative review

INTRODUCTION

Delirium is common among patients admitted to the intensive care unit (ICU) and its impact on the neurocognitive and psychiatric state of survivors is of great interest. These new-onset or worsening conditions, together with physical alterations, are called post-intensive care syndrome (PICS). Our aim is to update on the latest screening and follow-up options for psychological and cognitive sequelae of PICS.

METHOD

This narrative review discusses the occurrence of delirium in ICU settings and the relatively new concept of PICS. Psychiatric and neurocognitive morbidities that may occur in survivors of critical illness following delirium are addressed. Future perspectives for practice and research are discussed.

RESULTS

There is no "gold standard" for diagnosing delirium in the ICU, but two extensively validated tools, the confusion assessment method for the ICU and the intensive care delirium screening checklist, are often used. PICS complaints are frequent in ICU survivors who have suffered delirium and have been recognized as an important public health and socio-economic problem worldwide. Depression, anxiety, post-traumatic stress disorder, and long-term cognitive impairment are recurrently exhibited. Screening tools for these deficits are discussed, as well as the suggestion of early assessment after discharge and at 3 and 12 months.

CONCLUSIONS

Delirium is a complex but common phenomenon in the ICU and a risk factor for PICS. Its diagnosis is challenging with potential long-term adverse outcomes, including psychiatric and cognitive difficulties. The implementation of screening and follow-up protocols for PICS sequelae is warranted to ensure early detection and appropriate management.

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.

The Usefulness of Quantitative Electroencephalography in Diagnosis and Severity Evaluation of Delirium: A Retrospective Study

OBJECTIVE

Incontrovertible disease markers are absent in delirium. This study investigated the usefulness of quantitative electroencephalography (qEEG) in diagnosing delirium.

METHODS

This retrospective case-control study reviewed medical records and qEEG data of 69 age/sex-matched patients (delirium group, n=30; control group, n=39). The first minute of artifact-free EEG data with eyes closed was selected. Nineteen electrodes' sensitivity, specificity, and correlation with delirium rating scale-revised-98 were analyzed.

RESULTS

On comparing the means of absolute power by frontal, central, and posterior regions, the delta and theta powers showed significant differences (p<0.001) in all regions, and the magnitude of the absolute power was higher in the delirium group than in the control group; only the posterior region showed a significant (p<0.001) difference in beta power. The spectral power of theta at the frontal region (area under the curve [AUC]=0.84) and theta at the central and posterior regions (AUC=0.83) showed 90% sensitivity and 79% specificity, respectively, in differentiating delirious patients and controls. The beta power of the central region showed a significant negative correlation with delirium severity (R=-0.457, p=0.011).

CONCLUSION

Power spectrum analysis of qEEG showed high accuracy in screening delirium among patients. The study suggests qEEG as a potential aid in diagnosing delirium.

Altered EEG, disrupted hippocampal long-term potentiation and neurobehavioral deficits implicate a delirium-like state in a mouse model of sepsis

Sepsis and systemic inflammation are often accompanied by severe encephalopathy, sleep disruption and delirium that strongly correlate with poor clinical outcomes including long-term cognitive deficits. The cardinal manifestations of delirium are fluctuating altered mental status and inattention, identified in critically ill patients by interactive bedside assessment. The lack of analogous assessments in mouse models or clear biomarkers is a challenge to preclinical studies of delirium. In this study, we utilized concurrent measures of telemetric EEG recordings and neurobehavioral tasks in mice to characterize inattention and persistent cognitive deficits following polymicrobial sepsis. During the 24-hour critical illness period for the mice, slow-wave EEG dominance, sleep disruption, and hypersensitivity to auditory stimuli in neurobehavioral tasks resembled clinical observations in delirious patients in which alterations in similar outcome measurements, although measured differently in mice and humans, are reported. Mice were tested for nest building ability 7 days after sepsis induction, when sickness behaviors and spontaneous activity had returned to baseline. Animals that showed persistent deficits determined by poor nest building at 7 days also exhibited molecular changes in hippocampal long-term potentiation compared to mice that returned to baseline cognitive performance. Together, these behavioral and electrophysiological biomarkers offer a robust mouse model with which to further probe molecular pathways underlying brain and behavioral changes during and after acute illness such as sepsis.

Delirium in a Child in Pediatric Intensive Care Unit

Delirium is a syndrome with an acute onset that is accompanied by fluctuation and is associated with behaviors that indicate impaired consciousness and cognition. It is common and costly and is associated with severe functional decline and distress in an adult. However, its detection and diagnosis are so challenging in children. Herein, we report a 2-year-old girl who was admitted in the pediatric intensive care unit (PICU) with pneumonia and was intubated because of respiratory failure. She needed a lot of benzodiazepine and opioid drugs to be sedated. During hospital course after extubation, she developed by agitation and restlessness and dissociation from environment. Electroencephalography was done and diffuse generalized slow wave was observed. Finally, by environmental factors' correction, benzodiazepine decreasing, and risperidone administering, she became well and discharged. Delirium should be considered as an important, underdiagnosed, and common condition in the PICU. It should be considered in altered cognition, consciousness, and circadian rhythm disturbance situation in children.

Postoperative delirium in geriatric patients with hip fractures

Postoperative delirium (POD) is a frequent complication in geriatric patients with hip fractures, which is linked to poorer functional recovery, longer hospital stays, and higher short-and long-term mortality. Patients with increased age, preoperative cognitive impairment, comorbidities, perioperative polypharmacy, and delayed surgery are more prone to develop POD after hip fracture surgery. In this narrative review, we outlined the latest findings on postoperative delirium in geriatric patients with hip fractures, focusing on its pathophysiology, diagnosis, prevention, and treatment. Perioperative risk prediction, avoidance of certain medications, and orthogeriatric comprehensive care are all examples of effective interventions. Choices of anesthesia technique may not be associated with a significant difference in the incidence of postoperative delirium in geriatric patients with hip fractures. There are few pharmaceutical measures available for POD treatment. Dexmedetomidine and multimodal analgesia may be effective for managing postoperative delirium, and adverse complications should be considered when using antipsychotics. In conclusion, perioperative risk intervention based on orthogeriatric comprehensive care is the most effective strategy for preventing postoperative delirium in geriatric patients with hip fractures.

EEG-based grading of immune effector cell-associated neurotoxicity syndrome

CAR-T cell therapy is an effective cancer therapy for multiple refractory/relapsed hematologic malignancies but is associated with substantial toxicity, including Immune Effector Cell Associated Neurotoxicity Syndrome (ICANS). Improved detection and assessment of ICANS could improve management and allow greater utilization of CAR-T cell therapy, however, an objective, specific biomarker has not been identified. We hypothesized that the severity of ICANS can be quantified based on patterns of abnormal brain activity seen in electroencephalography (EEG) signals. We conducted a retrospective observational study of 120 CAR-T cell therapy patients who had received EEG monitoring. We determined a daily ICANS grade for each patient through chart review. We used visually assessed EEG features and machine learning techniques to develop the Visual EEG-Immune Effector Cell Associated Neurotoxicity Syndrome (VE-ICANS) score and assessed the association between VE-ICANS and ICANS. We also used it to determine the significance and relative importance of the EEG features. We developed the Visual EEG-ICANS (VE-ICANS) grading scale, a grading scale with a physiological basis that has a strong correlation to ICANS severity (R = 0.58 [0.47-0.66]) and excellent discrimination measured via area under the receiver operator curve (AUC = 0.91 for ICANS ≥ 2). This scale shows promise as a biomarker for ICANS which could help to improve clinical care through greater accuracy in assessing ICANS severity.

Quantitative EEG During Critical Illness Correlates with Patterns of Long-Term Cognitive Impairment

OBJECTIVE

Many intensive care unit (ICU) survivors suffer disabling long-term cognitive impairment (LTCI) after critical illness. We compared EEG characteristics during critical illness with patients' 1-year neuropsychological outcomes.

METHODS

We performed a post hoc analysis of patients in the BRAIN-ICU study who had undergone EEG for clinical purposes during admission (n = 10). All survivors underwent formal cognitive assessments at 12-month follow-up. We evaluated EEGs by conventional visual inspection and computed 10 quantitative features. We explored associations between EEG and patterns of LTCI using Wilcoxon rank-sum tests and Spearman's rank correlations.

RESULTS

Of 521 Vanderbilt patients enrolled in the parent study, 24 had EEG recordings during admission. Ten survivors had EEG tracings available and completed follow-up cognitive testing. All but one inpatient EEG showed generalized background slowing. All patients demonstrated cognitive impairment in at least one domain at follow-up. The most common deficits occurred in delayed memory (DM-median index 62) and visuospatial/constructional (VC-median index 69) domains. Relative alpha power correlated with VC score (ρ = 0.78, P = .008). Peak interhemispheric coherence correlated negatively with DM (ρ = -0.81, P = .018).

CONCLUSIONS

Quantitative EEG features during critical illness correlated with domain-specific cognitive performance in our small cohort of ICU survivors. Further study in larger prospective cohorts is required to determine whether these relationships hold.

SIGNIFICANCE

EEG may serve as a prognostic biomarker predicting patterns of long-term cognitive impairment.

High incidence of epileptiform activity in adults undergoing extracorporeal membrane oxygenation

OBJECTIVE

The prevalence of seizures and other types of epileptiform brain activity in patients undergoing extracorporeal membrane oxygenation (ECMO) is unknown. We aimed to estimate the prevalence of seizures and ictal-interictal continuum patterns in patients undergoing electroencephalography (EEG) during ECMO.

METHODS

Retrospective review of a prospective ECMO registry from 2011-2018 in a university-affiliated academic hospital. Adult subjects who had decreased level of consciousness and underwent EEG monitoring for seizure screening were included. EEG classification followed the American Clinical Neurophysiology Society criteria. Poor neurological outcome was defined as a Cerebral Performance Category of 3-5 at hospital discharge.

RESULTS

Three hundred and ninety-five subjects had ECMO, and one hundred and thirteen (28.6%) had EEG monitoring. Ninety-two (23.3%) subjects had EEG performed during ECMO and were included in the study (average EEG duration 54 h). Veno-arterial ECMO was the most common cannulation strategy (83%) and 26 (28%) subjects had extracorporeal cardiopulmonary resuscitation. Fifty-eight subjects (63%) had epileptiform activity or ictal-interictal continuum patterns on EEG, including three (3%) subjects with nonconvulsive status epilepticus, 33 (36%) generalized periodic discharges, and 4 (5%) lateralized periodic discharges. Comparison between subjects with or without epileptiform activity showed comparable in-hospital mortality (57% vs. 47%, p = 0.38) and poor neurological outcome (and 56% and 36%, p = 0.23). Twenty-seven subjects (33%) had acute neuroimaging abnormalities (stroke N = 21).

CONCLUSIONS

Seizures and ictal-interictal continuum patterns are commonly observed in patients managed with ECMO. Further studies are needed to evaluate whether epileptiform activity is an actionable target for interventions.

SIGNIFICANCE

Epileptiform and ictal-interictal continuum abnormalities are frequently observed in patients supported with ECMO undergoing EEG monitoring.

The future of intensive care: delirium should no longer be an issue

In the ideal intensive care unit (ICU) of the future, all patients are free from delirium, a syndrome of brain dysfunction frequently observed in critical illness and associated with worse ICU-related outcomes and long-term cognitive impairment. Although screening for delirium requires limited time and effort, this devastating disorder remains underestimated during routine ICU care. The COVID-19 pandemic brought a catastrophic reduction in delirium monitoring, prevention, and patient care due to organizational issues, lack of personnel, increased use of benzodiazepines and restricted family visitation. These limitations led to increases in delirium incidence, a situation that should never be repeated. Good sedation practices should be complemented by novel ICU design and connectivity, which will facilitate non-pharmacological sedation, anxiolysis and comfort that can be supplemented by balanced pharmacological interventions when necessary. Improvements in the ICU sound, light control, floor planning, and room arrangement can facilitate a healing environment that minimizes stressors and aids delirium prevention and management. The fundamental prerequisite to realize the delirium-free ICU, is an awake non-sedated, pain-free comfortable patient whose management follows the A to F (A-F) bundle. Moreover, the bundle should be expanded with three additional letters, incorporating humanitarian care: gaining (G) insight into patient needs, delivering holistic care with a 'home-like' (H) environment, and redefining ICU architectural design (I). Above all, the delirium-free world relies upon people, with personal challenges for critical care teams to optimize design, environmental factors, management, time spent with the patient and family and to humanize ICU care.

Using Electroencephalogram Biosignal Changes for Delirium Detection in Intensive Care Units

ABSTRACT

BACKGROUND: Biosignal data acquired during quantitative electroencephalography (QEEG) research may ultimately be used to develop algorithms for more accurate detection of delirium. This study investigates the biosignal changes during delirium states by using the QEEG data of patients in a medical intensive care unit. METHODS: This observational study was conducted between September 2018 and December 2019 at a tertiary hospital in South Korea. Delirium was measured using the Korean version of Confusion Assessment Method for the Intensive Care Unit in intensive care unit patients. Quantitative EEG measurements were recorded for 20 minutes in a natural state without external treatment or stimuli, and QEEG data measured in the centroparietal and parietal regions with eyes open were selected for analysis. Power spectrum analysis with a 5-minute epoch was conducted on the selected 65 cases. RESULTS: QEEG changes in the presence of delirium indicated that alpha, beta, gamma, and spectral edge frequency 50% waves showed significantly lower absolute power spectra than the corresponding findings in the absence of delirium. Brain-mapping results showed that these brain waves were inactivated in delirious states. CONCLUSION: QEEG assessments can potentially detect the changes in the centroparietal and parietal regions of delirium patients. QEEG changes, including lower power spectra of alpha, beta, and gamma waves, and spectral edge frequency 50%, can be successfully used to distinguish delirium from the absence of delirium.

Electroencephalography in delirium assessment: a scoping review

Background

Delirium is a common disorder affecting around 31% of patients in the intensive care unit (ICU). Delirium assessment scores such as the Confusion Assessment Method (CAM) are time-consuming, they cannot differentiate between different types of delirium and their etiologies, and they may have low sensitivities in the clinical setting. While today, electroencephalography (EEG) is increasingly being applied to delirious patients in the ICU, a lack of clear cut EEG signs, leads to inconsistent assessments.

Methods

We therefore conducted a scoping review on EEG findings in delirium. One thousand two hundred thirty-six articles identified through database search on PubMed and Embase were reviewed. Finally, 33 original articles were included in the synthesis.

Results

EEG seems to offer manifold possibilities in diagnosing delirium. All 33 studies showed a certain degree of qualitative or quantitative EEG alterations in delirium. Thus, normal routine (rEEG) and continuous EEG (cEEG) make presence of delirium very unlikely. All 33 studies used different research protocols to at least some extent. These include differences in time points, duration, conditions, and recording methods of EEG, as well as different patient populations, and diagnostic methods for delirium. Thus, a quantitative synthesis and common recommendations are so far elusive.

Conclusion

Future studies should compare the different methods of EEG recording and evaluation to identify robust parameters for everyday use. Evidence for quantitative bi-electrode delirium detection based on increased relative delta power and decreased beta power is growing and should be further pursued. Additionally, EEG studies on the evolution of a delirium including patient outcomes are needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02557-w.

VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations With Clinical Outcomes

Supplemental Digital Content is available in the text.

To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes.

Evaluation and Management of Delirium

Delirium, sometimes referred to as encephalopathy, is an acute confusional state that is both common in hospitalized patients and associated with poor outcomes. For patients, families, and caregivers, delirium can be a traumatic experience. While delirium is one of the most common diagnoses encountered by the consulting neurologist, the majority of the time it will have been previously unrecognized as such by the care team. Neurologic syndromes such as dementia or aphasia can either be misdiagnosed as delirium or may coexist with it, necessitating careful neurologic assessment. Once the diagnosis of delirium has been established, a careful evaluation for predisposing and precipitating factors can help uncover modifiable contributors, which should be addressed as part of a multicomponent, primarily nonpharmacologic intervention. Importantly, delirium management, which begins with comprehensive prevention, should emphasize the humanity of the delirious patient and the challenges of caring for this vulnerable population. When considered, delirium represents an important opportunity for the neurologist to substantially enhance patient care.

Seizures, Status Epilepticus, and Continuous EEG in the Intensive Care Unit

PURPOSE OF REVIEW

This article discusses the evolving definitions of seizures and status epilepticus in the critical care environment and the role of critical care EEG in both diagnosing seizure activity and serving as a predictive biomarker of clinical trajectory.

RECENT FINDINGS

Initial screening EEG has been validated as a tool to predict which patients are at risk of future seizures. However, accepted definitions of seizures and nonconvulsive status epilepticus encourage a treatment trial when the diagnosis on EEG is indeterminate because of periodic or rhythmic patterns or uncertain clinical correlation. Similarly, recent data have demonstrated the diagnostic utility of intracranial EEG in increasing the yield of seizure detection. EEG has additionally been validated as a diagnostic biomarker of covert consciousness, a predictive biomarker of cerebral ischemia and impending neurologic deterioration, and a prognostic biomarker of coma recovery and status epilepticus resolution. A recent randomized trial concluded that patients allocated to continuous EEG had no difference in mortality than those undergoing intermittent EEG but could not demonstrate whether this lack of difference was because of studying heterogeneous conditions, examining a monitoring tool rather than a therapeutic approach, or examining an outcome measure (mortality) perhaps more strongly associated with early withdrawal of life-sustaining therapy than to a sustained response to pharmacotherapy.

SUMMARY

Seizures and status epilepticus are events of synchronous hypermetabolic activity that are either discrete and intermittent or, alternatively, continuous. Seizures and status epilepticus represent the far end of a continuum of ictal-interictal patterns that include lateralized rhythmic delta activity and periodic discharges, which not only predict future seizures but may be further classified as status epilepticus on the basis of intracranial EEG monitoring or a diagnostic trial of antiseizure medication therapy. In particularly challenging cases, neuroimaging or multimodality neuromonitoring may be a useful adjunct documenting metabolic crisis. Specialized uses of EEG as a prognostic biomarker have emerged in traumatic brain injury for predicting language function and covert consciousness, cardiac arrest for predicting coma recovery, and subarachnoid hemorrhage for predicting neurologic deterioration due to delayed cerebral ischemia.

Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S)

OBJECTIVES

Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

DESIGN

Retrospective cohort study.

SETTING

Single-center tertiary academic medical center.

PATIENTS

Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity.

CONCLUSIONS

The E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.

Electroencephalography of mechanically ventilated patients at high risk of delirium

Objective

Neurophysiological exploration of ICU delirium is limited. Here, we examined EEG characteristics of medical‐surgical critically ill patients with new‐onset altered consciousness state at high risk for ICU delirium.

Materials and methods

Pre‐planned analysis of non‐neurological mechanically ventilated medical‐surgical ICU subjects, who underwent a prospective multicenter randomized, controlled EEG study (NCT03129438, April 2017–November 2018). EEG characteristics, according to the 2012 ACNS nomenclature, included background activity, rhythmic periodic patterns/epileptic activity, amplitude, frequency, stimulus‐induced discharges, triphasic waves, reactivity, and NREM sleep. We explored EEG findings in delirious versus non‐delirious patients, specifically focusing on the presence of burst‐suppression and rhythmic periodic patterns (ictal‐interictal continuum), and ictal activity.

Results

We analyzed 91 patients (median age, 66 years) who underwent EEG because of new‐onset altered consciousness state at a median 5 days from admission; 42 patients developed delirium (46%). Burst‐suppression (10 vs 0%, p = .02), rhythmic/periodic patterns (43% vs 22%, p = .03) and epileptiform activity (7 vs 0%, p = .05) were more frequent in delirious versus non‐delirious patients. The presence of at least one of these abnormal EEG findings (32/91 patients; 35%) was associated with a significant increase in the likelihood of delirium (42 vs 15%, p = .006). Cumulative dose of sedatives and analgesics, as well as all other EEG characteristics, did not differ significantly between the two groups.

Conclusion

In mechanically ventilated non‐neurological critically ill patients with new‐onset alteration of consciousness, EEG showing burst‐suppression, rhythmic or periodic patterns, or seizures/status epilepticus indicate an increased risk of ICU delirium.

Can We Distinguish Triphasic Waves From Other Generalized Periodic Discharges? Do We Need to?

SUMMARY

Triphasic waves are intuitively distinctive waveforms that fall under the umbrella of generalized periodic discharges. The ability to distinguish these waveforms consistently could be helpful if a specific underlying pathophysiology could be identified. However, scalp EEG and clinical observation have been limited in their ability to elucidate the underlying cortical physiology that leads to triphasic waves. Evidence from intracranial physiologic data and computational modeling suggest that these and other periodic discharges should be viewed not as strictly ictal nor non-ictal but rather on the spectrum between these two. Triphasic waves in particular appear to result from an abnormal balance between cortical excitation and synaptic transmission with input from functionally connected brain networks, such as the thalamocortical pathways involved in arousal. The practical implication of triphasic waves begins with acknowledgement of uncertainty and a rational approach should ask whether the pattern-or its treatment-might be creating harm.

Delirium in critical illness: clinical manifestations, outcomes, and management

Delirium is the most common manifestation of brain dysfunction in critically ill patients. In the intensive care unit (ICU), duration of delirium is independently predictive of excess death, length of stay, cost of care, and acquired dementia. There are numerous neurotransmitter/functional and/or injury-causing hypotheses rather than a unifying mechanism for delirium. Without using a validated delirium instrument, delirium can be misdiagnosed (under, but also overdiagnosed and trivialized), supporting the recommendation to use a monitoring instrument routinely. The best-validated ICU bedside instruments are CAM-ICU and ICDSC, both of which also detect subsyndromal delirium. Both tools have some inherent limitations in the neurologically injured patients, yet still provide valuable information about delirium once the sequelae of the primary injury settle into a new post-injury baseline. Now it is known that antipsychotics and other psychoactive medications do not reliably improve brain function in critically ill delirious patients. ICU teams should systematically screen for predisposing and precipitating factors. These include exacerbations of cardiac/respiratory failure or sepsis, metabolic disturbances (hypoglycemia, dysnatremia, uremia and ammonemia) receipt of psychoactive medications, and sensory deprivation through prolonged immobilization, uncorrected vision and hearing deficits, poor sleep hygiene, and isolation from loved ones so common during COVID-19 pandemic. The ABCDEF (A2F) bundle is a means to facilitate implementation of the 2018 Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS) Guidelines. In over 25,000 patients across nearly 100 institutions, the A2F bundle has been shown in a dose-response fashion (i.e., greater bundle compliance) to yield improved survival, length of stay, coma and delirium duration, cost, and less ICU bounce-backs and discharge to nursing homes.

Evaluation of point-of-care thumb-size bispectral electroencephalography device to quantify delirium severity and predict mortality

BACKGROUND

We have developed the bispectral electroencephalography (BSEEG) method for detection of delirium and prediction of poor outcomes.

AIMS

To improve the BSEEG method by introducing a new EEG device.

METHOD

In a prospective cohort study, EEG data were obtained and BSEEG scores were calculated. BSEEG scores were filtered on the basis of standard deviation (s.d.) values to exclude signals with high noise. Both non-filtered and s.d.-filtered BSEEG scores were analysed. BSEEG scores were compared with the results of three delirium screening scales: the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), the Delirium Rating Scale-Revised-98 (DRS) and the Delirium Observation Screening Scale (DOSS). Additionally, the 365-day mortalities and the length of stay (LOS) in the hospital were analysed.

RESULTS

We enrolled 279 elderly participants and obtained 620 BSEEG recordings; 142 participants were categorised as BSEEG-positive, reflecting slower EEG activity. BSEEG scores were higher in the CAM-ICU-positive group than in the CAM-ICU-negative group. There were significant correlations between BSEEG scores and scores on the DRS and the DOSS. The mortality rate of the BSEEG-positive group was significantly higher than that of the BSEEG-negative group. The LOS of the BSEEG-positive group was longer compared with that of the BSEEG-negative group. BSEEG scores after s.d. filtering showed stronger correlations with delirium screening scores and more significant prediction of mortality.

CONCLUSIONS

We confirmed the usefulness of the BSEEG method for detection of delirium and of delirium severity, and prediction of patient outcomes with a new EEG device.

EEG findings in CART T associated neurotoxicity: clinical and radiological correlations

BACKGROUND

While EEG is frequently reported as abnormal after CAR T cell therapy, its clinical significance remains unclear. We aim to systematically describe EEG features in a large single-center cohort and correlate them with clinical and radiological findings.

METHODS

We retrospectively identified patients undergoing CAR T cell therapy who had continuous EEG. Neurotoxicity grades, detailed neurological symptoms, and brain MRI or FDG-PET were obtained. Correlation between clinical and radiological findings and EEG features was assessed.

RESULTS

In 81 patients with median neurotoxicity grade 3 (IQR 2-3), diffuse EEG background slowing was the most common finding and correlated with neurotoxicity severity (p <0.001). A total of 42 patients had rhythmic or periodic patterns, 16 of them within the ictal-interictal-continuum (IIC), 5 with clinical seizures, and 3 with only electrographic seizures. Focal EEG abnormalities, consisting of lateralized periodic discharges (LPD, n=1), lateralized rhythmic delta activity (LRDA, n=6), or focal slowing (n=19), were found in 22 patients. All patients with LRDA, LPD, and 10/19 patients with focal slowing had focal clinical symptoms concordant with these EEG abnormalities. In addition, these focal EEG changes often correlated with PET hypometabolism or MRI hypoperfusion, in absence of a structural lesion.

CONCLUSION

In adult patients experiencing neurotoxicity after CAR T cell infusion, EEG degree of background disorganization correlated with neurotoxicity severity. IIC patterns and focal EEG abnormalities are frequent and often correlate with focal clinical symptoms and with PET-hypometabolism/MRI-hypoperfusion, without structural lesion. The etiology of these findings remains to be elucidated.

Delirium and long term cognition in critically ill patients

Delirium, a form of acute brain dysfunction, is very common in the critically ill adult patient population. Although its pathophysiology is poorly understood, multiple factors associated with delirium have been identified, many of which are coincident with critical illness. To date, no drug or non-drug treatments have been shown to improve outcomes in patients with delirium. Clinical trials have provided a limited understanding of the contributions of multiple triggers and processes of intensive care unit (ICU) acquired delirium, making identification of therapies difficult. Delirium is independently associated with poor long term outcomes, including persistent cognitive impairment. A longer duration of delirium is associated with worse long term cognition after adjustment for age, education, pre-existing cognitive function, severity of illness, and exposure to sedatives. Interestingly, differences in prevalence are seen between ICU survivor populations, with survivors of acute respiratory distress syndrome experiencing higher rates of cognitive impairment at early follow-up compared with mixed ICU survivor populations. Although cognitive performance improves over time for some ICU survivors, impairment is persistent in others. Studies have so far been unable to identify patients at higher risk of long term cognitive impairment; this is an active area of scientific investigation.

Why is delirium more frequent in the elderly?

An aging-related reduction in the brain's functional reserve may explain why delirium is more frequent in the elderly than in younger people insofar as the reserve becomes inadequate to cover the metabolic requirements that are critically increased by stressors. The aim of this paper is to review the normal aging-related changes that theoretically compromise complex mental activities, neuronal and synaptic densities, and the neurocomputational flexibility of the functional reserve. A pivotal factor is diminished connectivity, which is substantially due to the loss of synapses and should specifically affect association systems and cholinergic fibres in delirious patients. However, micro-angiopathy with impaired blood flow autoregulation, increased blood/brain barrier permeability, changes in cerebrospinal fluid dynamics, weakened mitochondrial performance, and a pro-inflammatory involution of the immune system may also jointly affect neurons and their synaptic assets, and even cause the progression of delirium to dementia regardless of the presence of co-existing plaques, tangles, or other pathological markers. On the other hand, the developmental growth in functional reserve during childhood and adolescence makes the brain increasingly resistant to delirium, and residual reserve can allow the elderly to recover. These data support the view that functional reserve is the variable that confronts stressors and governs the risk and intensity of and recovery from delirium. Although people of any age are at risk of delirium, the elderly are at greater risk because aging and age-dependent structural changes inevitably affect the brain's functional reserve.

Current Challenges in the Recognition and Management of Delirium Superimposed on Dementia

Delirium occurring in a patient with preexisting dementia is referred to as delirium superimposed on dementia (DSD). DSD commonly occurs in older hospitalized patients and is associated with worse outcomes, including higher rates of mortality and institutionalization, compared to inpatients with delirium or dementia alone. This narrative review summarizes the screening, diagnosis, management, and pathophysiology of DSD and concludes by highlighting opportunities for future research. Studies were identified via Medline and PsycINFO keyword search, and handsearching reference lists. Conceptually, DSD could be considered an "acute exacerbation" of dementia precipitated by a noxious insult akin to an acute exacerbation of heart failure or acute on chronic renal failure. However, unlike other organ systems, there are no established biomarkers for delirium, so DSD is diagnosed and monitored clinically. Because cognitive dysfunction is common to both delirium and dementia, the diagnosis of DSD can be challenging. Inattention, altered levels of arousal, and motor dysfunction may help distinguish DSD from dementia alone. An informant history suggestive of an acute change in cognition or alertness should be investigated and managed as delirium until proven otherwise. The key management principles include prevention, identifying and treating the underlying precipitant(s), implementing multicomponent interventions to create an ideal environment for brain recovery, preventing complications, managing distress, and monitoring for resolution. Informing and involving family members or caregivers throughout the patient journey are essential because there is significant prognostic uncertainty, including the risk of persistent cognitive and functional decline following DSD and relapse. Furthermore, informal carers can provide significant assistance in management. Emerging evidence demonstrates that increased exposure to delirium is associated with neuronal injury and worse cognitive outcomes although the mechanisms through which this occurs remain unclear. Given the clinical overlap between delirium and dementia, studying shared pathophysiological pathways may uncover diagnostic tests and is an essential step in therapeutic innovation.

Dexmedetomidine with sufentanil in intravenous patient-controlled analgesia for relief from postoperative pain, inflammation and delirium after esophageal cancer surgery

BACKGROUND AND AIMS

Postoperative pain can cause serious adverse reactions that severely affect postoperative outcome. The present study evaluated the effect of dexmedetomidine (DEX) added to sufentanil in intravenous patient-controlled analgesia (PCA) on the relief of pain and inflammatory responses during postoperative recovery of patients undergoing a combined thoracoscopic-laparoscopic esophagectomy (TLE).

METHODS

Sixty patients undergoing TLE were randomly allocated to receive 1 μg/ml of sufentanil alone (Group S) or 1 μg/ml of sufentanil plus 2.5 μg/ml of DEX (Group D) for postoperative intravenous (IV) PCA. Postoperative pain relief, cumulative PCA requirements, inflammatory marker levels, delirium and recovery were assessed.

RESULTS

A joint DEX and sufentanil regimen significantly reduced the area under the curve of numerical rating scores for pain at rest (NRSR) and coughing (NRSC) at 1-48 h postoperatively (P = 0.000) that were associated with lower PCA-delivered cumulative sufentanil consumption and less PCA frequency until 48 h postoperatively (P < 0.05 and P < 0.0001, respectively). The simultaneous administration of DEX and sufentanil significantly reduced plasma IL-6 and TNF-α concentrations and increased IL-10 level (P < 0.0001, P = 0.0003 and P = 0.0345, respectively), accompanied by better postoperative delirium categories and health statuses of patients (P = 0.024 and P < 0.05, respectively). There was no hypotension, bradycardia, respiratory depression or oversedation in Group D.

CONCLUSION

Patients receiving DEX in addition to IV PCA sufentanil for TLE exhibited better postoperative analgesia, fewer inflammatory responses and lower postoperative delirium categories and better health statuses.

Mortality prediction by bispectral electroencephalography among 502 patients: its role in dementia

Complications of delirium and dementia increase mortality; however, it is difficult to diagnose delirium accurately, especially among dementia patients. The bispectral electroencephalography score can detect delirium and predict mortality in elderly patients. We aimed to develop an efficient and reliable bispectral electroencephalography device for high-throughput screening. We also hypothesized that bispectral electroencephalography score can predict mortality among dementia patients. A prospective cohort study was conducted between January 2016 and December 2018 to measure bispectral electroencephalography from elderly patients and correlate with outcomes. A total of 502 elderly (55 years old or older) patients with and without dementia were enrolled. For a replication of the utility of bispectral electroencephalography, mortalities between bispectral electroencephalography-positive and bispectral electroencephalography-negative group were compared. In addition, patients with and without dementia status were added to examine the utility of bispectral electroencephalography among dementia patients. The mortality within 180 days in the bispectral electroencephalography-positive group was higher than that of the bispectral electroencephalography-negative group in both the replication and the total cohorts. Mortality of those in the bispectral electroencephalography-positive group showed a dose-dependent increase in both cohorts. When the dementia patients showed bispectral electroencephalography positive, their mortality was significantly higher than those with dementia but who were bispectral electroencephalography-negative. Mortality within 30 days in the bispectral electroencephalography-positive group was significantly higher than that of the bispectral electroencephalography-negative group. The utility of the bispectral electroencephalography to predict mortality among large sample of 502 elderly patients was shown. The bispectral electroencephalography score can predict mortality among elderly patients in general, and even among dementia patients, as soon as 30 days.

Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium

Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analysis using larger sample size and performed topological data analysis to improve the BSEEG method. Data from 274 subjects included in the previous study were analyzed as a 1st cohort. Subjects were enrolled at the University of Iowa Hospitals and Clinics (UIHC) between January 30, 2016, and October 30, 2017. A second cohort with 265 subjects was recruited between January 16, 2019, and August 19, 2019. The BSEEG score was calculated as a power ratio between low frequency to high frequency using our newly developed algorithm. Additionally, Topological data analysis (TDA) score was calculated by applying TDA to our EEG data. The BSEEG score and TDA score were compared between those patients with delirium and without delirium. Among the 274 subjects from the first cohort, 102 were categorized as delirious. Among the 206 subjects from the second cohort, 42 were categorized as delirious. The areas under the curve (AUCs) based on BSEEG score were 0.72 (1st cohort, Fp1-A1), 0.76 (1st cohort, Fp2-A2), and 0.67 (2nd cohort). AUCs from TDA were much higher at 0.82 (1st cohort, Fp1-A1), 0.84 (1st cohort, Fp2-A2), and 0.78 (2nd cohort). When sensitivity was set to be 0.80, the TDA drastically improved specificity to 0.66 (1st cohort, Fp1-A1), 0.72 (1st cohort, Fp2-A2), and 0.62 (2nd cohort), compared to 0.48 (1st cohort, Fp1-A1), 0.54 (1st cohort, Fp2-A2), and 0.46 (2nd cohort) with BSEEG. BSEEG has the potential to detect delirium, and TDA is helpful to improve the performance.

Pseudodelirium: Psychiatric Conditions to Consider on the Differential for Delirium

OBJECTIVE

The phenotypes of several psychiatric conditions can very closely resemble delirium; the authors describe such presentations as pseudodelirium. However, because the clinical management of these conditions differs markedly from that of delirium, prompt differentiation is essential. The authors provide an educational review to assist clinicians in identifying and managing psychiatric conditions that may be especially challenging to differentiate from delirium.

METHODS

Based on clinical experience, the authors identified four psychiatric conditions as among the most difficult to differentiate from delirium: disorganized psychosis, Ganser syndrome, delirious mania, and catatonia. An overview of each condition, description of clinical features, differentiation of specific phenotypes from delirium, and review of clinical management are also provided.

RESULTS

The thought and behavioral disorganization in disorganized psychosis can be mistaken for the clouded sensorium and behavioral dysregulation encountered in delirium. The fluctuating alertness and apparent confusion in Ganser syndrome resemble delirium's altered arousal and cognitive features. As its name suggests, delirious mania presents as a mixture of hyperactive delirium and mania; additional features may include psychosis, autonomic activation, and catatonia. Both delirium and catatonia have hypokinetic and hyperkinetic variants, and the two syndromes can also co-occur.

CONCLUSIONS

The clinical presentations of several psychiatric conditions can blend with the phenotype of delirium, at times even co-occurring with it. Detailed evaluation is often required to differentiate such instances of pseudodelirium from delirium proper.

Clinical Electroencephalography Findings and Considerations in Hospitalized Patients With Coronavirus SARS-CoV-2

Background and Purpose

Reports have suggested that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes neurologic manifestations including encephalopathy and seizures. However, there has been relatively limited electrophysiology data to contextualize these specific concerns and to understand their associated clinical factors. Our objective was to identify EEG abnormalities present in patients with SARS-CoV-2, and to determine whether they reflect new or preexisting brain pathology.

Methods

We studied a consecutive series of hospitalized patients with SARS-CoV-2 who received an EEG, obtained using tailored safety protocols. Data from EEG reports and clinical records were analyzed to identify EEG abnormalities and possible clinical associations, including neurologic symptoms, new or preexisting brain pathology, and sedation practices.

Results

We identified 37 patients with SARS-CoV-2 who underwent EEG, of whom 14 had epileptiform findings (38%). Patients with epileptiform findings were more likely to have preexisting brain pathology (6/14, 43%) than patients without epileptiform findings (2/23, 9%; p = 0.042). There were no clear differences in rates of acute brain pathology. One case of nonconvulsive status epilepticus was captured, but was not clearly a direct consequence of SARS-CoV-2. Abnormalities of background rhythms were common, as may be seen in systemic illness, and in part associated with recent sedation (p = 0.022).

Conclusions

Epileptiform abnormalities were common in patients with SARS-CoV-2 referred for EEG, but particularly in the context of preexisting brain pathology and sedation. These findings suggest that neurologic manifestations during SARS-CoV-2 infection may not solely relate to the infection itself, but rather may also reflect patients' broader, preexisting neurologic vulnerabilities.