Electroencephalography in delirium assessment: a scoping review

Personalizing ICU liberation for critically Ill children: shaping the future of the ABCDEF bundle

PURPOSE OF REVIEW

Intensive care unit (ICU) liberation bundles aim to reduce acquired morbidities in the growing population of pediatric ICU survivors. This review will explore emerging precision medicine strategies to personalize the implementation of the ICU liberation bundle.

RECENT FINDINGS

The ICU liberation bundle, also known as the ABCDEF bundle, consists of six interrelated components: A: assess, prevent, and manage pain; B: spontaneous breathing trials; C: choice of analgesia and sedation; D: delirium assessment, prevention, and management; E: early mobility and exercise; F: family engagement and empowerment. Emerging research and technology are advancing personalized approaches to each component, enabling researchers and clinicians to optimize implementation based on patient-specific factors. Key innovations include automated screening and risk stratification using computational models, wearable technologies that detect bundle elements, and genetic assessments to evaluate disease risk and medication responses.

SUMMARY

This review provides an overview of the ICU liberation bundle for critically ill children and highlights opportunities to personalize each bundle element to enhance survivorship. By integrating advancements in precision medicine, clinicians and researchers can improve the quality of care for the growing population of PICU survivors, ultimately leading to better long-term outcomes.

Developing an Electroencephalogram-based Model to Predict Awakening after Cardiac Arrest Using Partial Processing with the BIS Engine

BACKGROUND

Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. The authors sought to determine whether internal electroencephalogram (EEG) subparameters extracted by the BIS monitor (Medtronic, USA), a device commonly used to estimate depth of anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest.

METHODS

In this retrospective cohort study, a three-layer neural network was trained to predict recovery of consciousness to the point of command following versus not based on 48 h of continuous EEG recordings in 315 comatose patients admitted to a single U.S. academic medical center after cardiac arrest (derivation cohort, n = 181; validation cohort, n = 134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine ( i.e. , the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. The model was trained on hourly averaged measurements of these internal subparameters. This model's performance was compared to the modified Westhall qualitative EEG scoring framework.

RESULTS

Maximum prognostic accuracy in the derivation cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, the model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristics curve, 0.86; accuracy, 0.87; sensitivity, 0.83; specificity, 0.88; positive predictive value, 0.71; negative predictive value, 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest.

CONCLUSIONS

In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative accepted standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

Dynamic reconstruction of electroencephalogram data using RBF neural networks

Introduction

Electroencephalography (EEG) is widely used for analyzing brain activity; however, the nonlinear and nature of EEG signals presents significant challenges for traditional analysis methods. Machine has shown great promise in addressing these limitations. This study proposes a novel approach using Radial Function (RBF) neural networks optimized by Particle Swarm Optimization (PSO) to reconstruct EEG dynamics and extract age-related neural characteristics.

Methods

EEG recordings were collected from 142 participants spanning multiple age groups. Signals were preprocessed through bandpass filtering (1-35 Hz) and Independent Component Analysis (ICA) for artifact removal. neural network was trained on EEG time-series data with PSO employed to optimize model parameters identify fixed points in the reconstructed neural system. Statistical analyses including ANOVA and Kruskal-Wallis tests were performed to assess age-related differences in fixed-point coordinates.

Results

The RBF network demonstrated high accuracy in EEG signal reconstruction across different frequency a normalized root mean square error (NRMSE) of 0.0671 ± 0.0074 and a Pearson correlation coefficient ± 0.0678. Spectral and time-frequency analyses confirmed the model s capability to accurately capture oscillations. Importantly analysis of RBF network fixed-point coordinates revealed distinct age-related.

Discussion

These findings suggest that fixed-point coordinates of RBF networks can serve as quantitative markers aging providing new insights into age-dependent changes in brain dynamics. The proposed method offers computationally efficient and interpretable approach for EEG analysis with potential applications in neurological diagnosis and cognitive research.

Neurological diagnostic tests for patients with and without delirium: a prospective observational study

Since most of the precipitating factors of delirium are not due to neurological disorders, neurological diagnostic tests (NDTs) may be of limited value. We hypothesized that delirium has a high burden of NDTs with a low diagnostic yield. All patients admitted to the internal medicine wards of a single secondary teaching hospital between November 2019 and January 2020 were eligible. Within the first 48 h of their admission, they had a formal evaluation by a neuropsychologist to screen for presence of delirium. NDTs (brain MRI, brain CT, electroencephalography (EEG), and lumbar puncture) performed during the hospital stay were compared between patients with and without delirium using a logistic regression model stratified by a propensity score. The proportions of diagnostic yield (acute anomalies that changed the treatment management) provided by each type of examination were compared. Of 217 patients included, 19/32 patients (61%) with delirium had one or more NDTs, compared to 48/185 (26%) without delirium (adjusted OR 2.7; 95%CI 1.1-6.7; p = 0.027). The proportions of NDT results affecting management for patients with and without delirium were 13 and 20% for brain CT scans (p = 0.71), 29 and 38% for brain MRI (p = 0.99), and 20 and 10% for EEGs (p = 0.99), respectively. The higher proportion of NDTs performed on patients with delirium was associated with a low diagnostic yield, although not statistically different from those performed among inpatients without delirium. There is a need for restrictive, evidence-based guidelines to help with the work-up for patients with delirium.

Electroencephalography based delirium screening in acute supratentorial stroke

BACKGROUND

Up to 25% of patients suffering from an acute stroke are diagnosed with delirium during the hospital stay, with older age increasing the risk. Generalized slowing in the electroencephalogram (EEG) supports the diagnosis of delirium. We examined the potential of single-channel EEG (DeltaScan®) as an easy-to-use device on intensive care units for detecting delirium. Our aim was to investigate characteristics of bihemispheric EEG recordings and single-channel EEG in patients suffering from strokes with and without delirium and to analyze the diagnostic accuracy of EEG-based diagnoses.

METHODS

Within the first five days after stroke onset, patients received single-channel EEG DeltaScan® and a routine 21-channel EEG. The DeltaScan® analyzes right sided fronto-parietal EEG using a proprietary algorithm focusing on polymorphic delta activity (PDA). In routine EEG the power spectral density (PSD) in predefined frequency bands was analyzed based on 2-minute eyes-closed resting state segments. EEG-analyses were conducted in MNE (v1.3.1) in Python (3.10) and RStudio (v4.2.1).

RESULTS

In 9 of 53 patients (52-90 years) delirium was diagnosed according to DSM-V criteria. Sensitivity of DeltaScan® was 44% (95% CI = 15.3-77.3%), while specificity was 71% (95% CI = 57-83%). We found patients with right hemispheric stroke having a higher probability to be false positive in DeltaScan® (p = 0.01). The 21-channel EEG based power analysis revealed significant differences in frontal delta and theta power between patients with and without delirium (p < 0.05).

CONCLUSIONS

When EEG is used in clinical practice to support a delirium diagnosis in stroke patients, bihemispheric recordings are likely preferable over unilateral recordings. Slowing in the delta- or theta-frequency spectrum over the site of stroke may lead to false-positive results in single channel EEG based delirium scoring.

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

Background

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

Methods

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

Results

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

Conclusions

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

Management of delirium in acute stroke patients: a position paper by the Austrian Stroke Society on prevention, diagnosis, and treatment

Delirium is a common complication in acute stroke patients, occurring in 15-35% of all stroke unit admissions and is associated with prolonged hospital stay and a poor post-stroke prognosis. Managing delirium in acute stroke patients necessitates an intensive and multiprofessional therapeutic approach, placing a significant burden on healthcare staff. However, dedicated practical recommendations for delirium management developed for the population of acute stroke patients are lacking. For this purpose, the Austrian Stroke Society, in cooperation with the Austrian Society of Neurology, the Austrian Society of Neurorehabilitation, and the Austrian Society of Psychiatry, Psychotherapy, and Psychosomatics has formulated an evidence-based position paper addressing the management of delirium in acute stroke patients. The paper outlines practical recommendations on the three pillars of care in stroke patients with delirium: (a) Key aspects of delirium prevention including stroke-specific delirium risk factors and delirium prediction scores are described. Moreover, a non-pharmacological delirium prevention bundle is presented. (b) The paper provides recommendations on timing and frequency of delirium screening to ensure early diagnosis of delirium in acute stroke patients. Moreover, it reports on the use of different delirium screening tools in stroke populations. (c) An overview of non-pharmacological and pharmacological treatment strategies in patients with delirium and acute stroke is presented and summarized as key recommendation statements.

Resting state EEG relates to short- and long-term cognitive functioning after cardiac arrest

BACKGROUND

Approximately half of cardiac arrest survivors have persistent cognitive impairment. Guidelines recommend early screening to identify patients at risk for cognitive impairment, but there is no consensus on the best screening method. We aimed to identify quantitative EEG measures relating with short- and long-term cognitive function after cardiac arrest for potential to cognitive outcome prediction.

METHODS

We analyzed data from a prospective longitudinal multicenter cohort study designed to develop a prediction model for cognitive outcome after cardiac arrest. For the current analysis, we used twenty-minute EEG registrations from 80 patients around one week after cardiac arrest. We calculated power spectral density, normalized alpha-to-theta ratio (nATR), peak frequency, and center of gravity (CoG) of this peak frequency. We related these with global cognitive functioning (scores on the Montreal Cognitive Assessment (MoCA)) at one week, three and twelve months follow-up with multivariate mixed effect models, and with performance on standard neuropsychological examination at twelve months using Pearson correlation coefficients.

RESULTS

Each individual EEG parameter related to MoCA at one week (βnATR = 7.36; P < 0.01; βpeak frequency = 1.73, P < 0.01; βCoG = -9.88, P < 0.01). The nATR also related with the MoCA at three months ((βnATR = 2.49; P 0.01). No EEG metrics significantly related to the MoCA score at twelve months. nATR and peak frequency related with memory performance at twelve months. Results were consistent in sensitivity analyses.

CONCLUSION

Early resting-state EEG parameters relate with short-term global cognitive functioning and with memory function at one year after cardiac arrest. Additional predictive values in multimodal prediction models need further study.

Delirium in psychiatric settings: risk factors and assessment tools in patients with psychiatric illness: a scoping review

BACKGROUND

Delirium is a common disorder affecting patients' psychiatric illness, characterized by a high rate of underdiagnosis, misdiagnosis, and high risks. However, previous studies frequently excluded patients with psychiatric illness, leading to limited knowledge about risk factors and optimal assessment tools for delirium in psychiatric settings.

OBJECTIVES

The scoping review was carried out to (1) identify the risk factors associated with delirium in patients with psychiatric illness; (2) synthesize the performance of assessment tools for detecting delirium in patients with psychiatric illness in psychiatric settings.

DESIGN

Scoping review.

DATA SOURCES

PubMed, Web of Science, and Embase were searched to identify primary studies on delirium in psychiatric settings from inception to Dec 2023 inclusive. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

RESULTS

A final set of 36 articles meeting the inclusion criteria, two main themes were extracted: risk factors associated with delirium in patients with psychiatric illness and assessment tools for detecting delirium in psychiatric settings. The risk factors associated with delirium primarily included advanced age, physical comorbid, types of psychiatric illness, antipsychotics, anticholinergic drug, Electroconvulsive therapy, and the combination of lithium and Electroconvulsive therapy. Delirium Rating Scale-Revised-98, Memorial Delirium Assessment Scale, and Delirium Diagnostic Tool-Provisional might be valuable for delirium assessment in patients with psychiatric illness in psychiatric settings.

CONCLUSIONS

Delirium diagnosis in psychiatric settings is complex due to the overlapping clinical manifestations between psychiatric illness and delirium, as well as their potential co-occurrence. It is imperative to understand the risk factors and assessment methods related to delirium in this population to address diagnostic delays, establish effective prevention and screening strategies. Future research should focus on designing, implementing, and evaluating interventions that target modifiable risk factors, to prevent and manage delirium in patients with psychiatric illness.

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.

Single-channel qEEG characteristics distinguish delirium from no delirium, but not postoperative from non-postoperative delirium

OBJECTIVE

This exploratory study examined quantitative electroencephalography (qEEG) changes in delirium and the use of qEEG features to distinguish postoperative from non-postoperative delirium.

METHODS

This project was part of the DeltaStudy, a cross-sectional,multicenterstudy in Intensive Care Units (ICUs) and non-ICU wards. Single-channel (Fp2-Pz) four-minutes resting-state EEG was analyzed in 456 patients. After calculating 98 qEEG features per epoch, random forest (RF) classification was used to analyze qEEG changes in delirium and to test whether postoperative and non-postoperative delirium could be distinguished.

RESULTS

An area under the receiver operatingcharacteristic curve (AUC) of 0.76 (95% Confidence Interval (CI) 0.71-0.80) was found when classifying delirium with a sensitivity of 0.77 and a specificity of 0.63 at the optimal operating point. The classification of postoperative versus non-postoperative delirium resulted in an AUC of 0.50 (95%CI 0.38-0.61).

CONCLUSIONS

RF classification was able to discriminate delirium from no delirium with reasonable accuracy, while also identifying new delirium qEEG markers like autocorrelation and theta peak frequency. RF classification could not distinguish postoperative from non-postoperative delirium.

SIGNIFICANCE

Single-channel EEG differentiates between delirium and no delirium with reasonable accuracy. We found no distinct EEG profile for postoperative delirium, which may suggest that delirium is one entity, whether it develops postoperatively or not.

Intraoperative EEG-based monitors: are we looking under the lamppost?

PURPOSE OF REVIEW

While electroencephalogram (EEG)-based depth of anesthesia monitors have been in use clinically for decades, there is still a major debate concerning their efficacy for detecting awareness under anesthesia (AUA). Further utilization of these monitors has also been discussed vividly, for example, reduction of postoperative delirium (POD).It seems that with regard to reducing AUA and POD, these monitors might be applicable, under specific anesthetic protocols. But in other settings, such monitoring might be less contributive and may have a 'built-it glass ceiling'.Recent advances in other venues of electrophysiological monitoring might have a strong theoretical rationale, and early supporting results, to offer a breakthrough out of this metaphorical glass ceiling. The purpose of this review is to present this possibility.

RECENT FINDINGS

Following previous findings, it might be concluded that for some anesthesia protocols, the prevailing depth of anesthesia monitors may prevent incidences of AUA and POD. However, in other settings, which may involve other anesthesia protocols, or specifically for POD - other perioperative causes, they may not. Attention-related processes measured by easy-to-use real-time electrophysiological markers are becoming feasible, also under anesthesia, and might be applicable for more comprehensive prevention of AUA, POD and possibly other perioperative complications.

SUMMARY

Attention-related monitoring might have a strong theoretical basis for the prevention of AUA, POD, and potentially other distressing postoperative outcomes, such as stroke and postoperative neurocognitive disorder. There seems to be already some initial supporting evidence in this regard.

Hospital admission and the occurrence of delirium in older adults with physical frailty: cross-sectional study

OBJECTIVE

To analyze the relationship between hospitalization and the occurrence of delirium in older adults with physical frailty.

METHOD

Cross-sectional study carried out in a public hospital in southern Brazil. Hospitalized older adults aged ≥ 60 years participated. Sociodemographic and clinical data were collected, physical frailty phenotype tests were performed and the Confusion Assessment Method was used. Descriptive analyzes were carried out and odds ratio values were estimated for the frailty and delirium variables.

RESULTS

Of the 320 older adults evaluated, 21.14% presented delirium, 49% were identified as pre-frail and 36.2% as frail. Of those affected by delirium, 71.6% were classified as frail and 28.3% as pre-frail (p < 0.001). An association was observed between the occurrence of delirium and frailty (OR 1.22; 95% CI 1.07 to 1.38), age ≥ 80 years (OR 1.14; 95% CI 1.01 to 1.32), epilepsy (OR 1.38; 95% CI 1.09 to 1.76), dementia (OR 1.58; 95% CI 1.37 to 1.82), and history of stroke (OR 1.14; 95% CI 1.03 to 1.26).

CONCLUSION

There was a high frequency of pre-frail and frail older adults, and the occurrence of delirium in frail was significantly higher. Special attention should be paid to frail older adults to prevent the occurrence of delirium during hospitalization.

Precision-based approaches to delirium in critical illness: A narrative review

Delirium occurs in critical illness and is associated with poor clinical outcomes, having a longstanding impact on survivors. Understanding the complexity of delirium in critical illness and its deleterious outcome has expanded since early reports. Delirium is a culmination of predisposing and precipitating risk factors that result in a transition to delirium. Known risks range from advanced age, frailty, medication exposure or withdrawal, sedation depth, and sepsis. Because of its multifactorial nature, different clinical phenotypes, and potential neurobiological causes, a precise approach to reducing delirium in critical illness requires a broad understanding of its complexity. Refinement in the categorization of delirium subtypes or phenotypes (i.e., psychomotor classifications) requires attention. Recent advances in the association of clinical phenotypes with clinical outcomes expand our understanding and highlight potentially modifiable targets. Several delirium biomarkers in critical care have been examined, with disrupted functional connectivity being precise in detecting delirium. Recent advances reinforce delirium as an acute, and partially modifiable, brain dysfunction, and place emphasis on the importance of mechanistic pathways including cholinergic activity and glucose metabolism. Pharmacologic agents have been assessed in randomized controlled prevention and treatment trials, with a disappointing lack of efficacy. Antipsychotics remain widely used after "negative" trials, yet may have a role in specific subtypes. However, antipsychotics do not appear to improve clinical outcomes. Alpha-2 agonists perhaps hold greater potential for current use and future investigation. The role of thiamine appears promising, yet requires evidence. Looking forward, clinical pharmacists should prioritize the mitigation of predisposing and precipitating risk factors as able. Future research is needed within individual delirium psychomotor subtypes and clinical phenotypes to identify modifiable targets that hold the potential to improve not only delirium duration and severity, but long-term outcomes including cognitive impairment.

Quantitative electroencephalography predicts postoperative delirium in cardiac surgical patients after cardiopulmonary bypass: a prospective observational study

Objective

Despite its frequency and associated negative effect, delirium remains poorly recognized in postoperative patients after ICU admission, especially among those who have undergone cardiac surgery with cardiopulmonary bypass. Postoperative delirium is triggered by a wide variety of acute medical conditions associated with impaired neuronal network connectivity. The lack of objective biomarkers primarily hinders the early detection of delirium. Seeking early biomarkers for tracking POD could potentially assist in predicting the onset of delirium and assessing the severity of delirium and response to interventions.

Methods

QEEGs were taken from 46 sedated postoperative patients, with 24 of them having undergone cardiac surgery. The assessment of delirium was performed twice daily using the Confusion Assessment Method for the ICU (CAM-ICU) to screen for postoperative delirium (POD). QEEG data were interpreted clinically by neurophysiologists and processed by open-source EEGLAB to identify features in patients who had or did not have POD after cardiac or non-cardiac surgery.

Results

The incidence of delirium in patients after undergoing cardiac surgery was nine times greater than in those after non-cardiac surgeries (41.7% vs. 4.5%; p = 0.0046). Patients with delirium experienced longer use of mechanical ventilation (118 h (78,323) compared to 20 h (18,23); p < 0.0001) and an extended ICU length of stay (7 days (6, 20) vs. 2 days (2, 4); p < 0.0001). The depth of anesthesia, as measured by RASS scores (p = 0.3114) and spectral entropy (p = 0.1504), showed no significant difference. However, notable differences were observed between delirious and non-delirious patients in terms of the amplitude-integrated EEG (aEEG) upper limit, the relative power of the delta band, and spectral edge frequency 95 (SEF95) (p = 0.0464, p = 0.0417, p = 0.0337, respectively).

Conclusion

In a homogenous population of sedated postoperative patients, robust qEEG parameters strongly correlate with delirium and could serve as valuable biomarkers for early detection of delirium and assist in clinical decision-making.

COVID-19 psychosis versus psychosis due to cytotoxic lesion of the corpus callosum (CLOCC): A case report and review

Background

Cytotoxic lesions of the corpus callosum syndrome (CLOCC) is an inflammatory disorder caused by various etiologies such as medications, malignancies, seizure, metabolic abnormalities, and infections, especially COVID-19. It presents on MRI as an area of restricted diffusion in the corpus callosum. We present a case of psychosis and CLOCC in a patient with mild active COVID-19 infection.

Case

A 25-year-old male with a history of asthma and unclear past psychiatric history presented to the emergency room with shortness of breath, chest pain, and disorganized behavior. His-COVID-19 PCR was negative, and he was voluntarily admitted to psychiatry for management of unspecified psychosis. Overnight, he spiked a fever and was diaphoretic with headache and altered mental status. Repeat COVID-19 PCR at this time was positive and cycle threshold indicated infectivity. A brain MRI showed a new restricted diffusion within the midline of the splenium of the corpus callosum. Lumbar puncture was unremarkable. He continued to have flat affect and exhibit disorganized behavior with unspecified grandiosity, unclear auditory hallucinations, echopraxia, and poor attention and working memory. He was started on risperidone, with an MRI after 8 days showing complete resolution of the lesion in the corpus callosum and symptoms.

Conclusion

This case discusses diagnostic difficulties and treatment options for a patient presenting with psychotic symptoms and disorganized behavior in the context of active COVID-19 infection and CLOCC and highlights differences between delirium, COVID-19 psychosis and neuropsychiatric symptoms of CLOCC. Future research directions are also discussed.

Cortical reactivity to transcranial magnetic stimulation predicts risk of post-stroke delirium

OBJECTIVE

Post-stroke delirium (PSD) is a frequent and with regard to outcome unfavorable complication in acute stroke. The neurobiological mechanisms predisposing to PSD remain poorly understood, and biomarkers predicting its risk have not been established. We tested the hypothesis that hypoexcitable or disconnected brain networks predispose to PSD by measuring brain reactivity to transcranial magnetic stimulation with electroencephalography (TMS-EEG).

METHODS

We conducted a cross-sectional study in 33 acute stroke patients within 48 hours of stroke onset. Brain reactivity to single-pulse TMS of dorsolateral prefrontal cortex, primary motor cortex and superior parietal lobule of the right hemisphere was quantified by response intensity, effective connectivity, perturbational complexity index (PCI^ST), and natural frequency of the TMS-EEG response. PSD development was clinically tracked every 8 hours before and for 7 days following TMS-EEG.

RESULTS

Fourteen patients developed PSD while 19 patients did not. The PSD group showed lower excitability, effective connectivity, PCI^ST and natural frequency compared to the non-PSD group. The maximum PCI^ST over all three TMS sites demonstrated largest classification accuracy with a ROC-AUC of 0.943. This effect was independent of lesion size, affected hemisphere and stroke severity. Maximum PCI^ST and maximum natural frequency correlated inversely with delirium duration.

CONCLUSIONS

Brain reactivity to TMS-EEG can unravel brain network states of reduced excitability, effective connectivity, perturbational complexity and natural frequency that identify acute stroke patients at high risk for development of delirium.

SIGNIFICANCE

Findings provide novel insight into the pathophysiology of pre-delirium brain states and may promote effective delirium prevention strategies in those patients at high risk.

Approach to New-Onset Psychosis in Pediatrics: A Review of Current Practice and an Interdisciplinary Consensus-Driven Clinical Pathway at a Single-Center Institution

New-onset psychosis in the pediatric population poses many diagnostic challenges. Given the diversity of underlying causes, which fall under the purview of multiple medical specialties, a timely, targeted, yet thorough workup requires a systematic and coordinated approach. A committee of expert pediatric physicians from the divisions of emergency medicine, psychiatry, neurology, hospitalist medicine, and radiology convened to create and implement a novel clinical pathway and approach to the pediatric patient presenting with new-onset psychosis. Here we provide background and review the evidence supporting the investigations recommended in our pathway to screen for a comprehensive range of etiologies of pediatric psychosis.

Sepsis-Associated Delirium: A Narrative Review

Delirium is characterized by an acutely altered mental status accompanied by reductions in cognitive function and attention. Delirium in septic patients, termed sepsis-associated delirium (SAD), differs in several specific aspects from the other types of delirium that are typically encountered in intensive care units. Since sepsis and delirium are both closely associated with increased morbidity and mortality, it is important to not only prevent but also promptly diagnose and treat SAD. We herein reviewed the etiology, pathogenesis, risk factors, prevention, diagnosis, treatment, and prognosis of SAD, including coronavirus disease 2019 (COVID-19)-related delirium. Delirium by itself not only worsens long-term prognosis, but it is also regarded as an important factor affecting the outcome of post-intensive care syndrome. In COVID-19 patients, the difficulties associated with adequately implementing the ABCDEF bundle (Assess, prevent, and manage pain; Both spontaneous awakening and breathing trials: Choice of analgesia and sedation; Delirium assess, prevent, and manage; Early mobility and exercise; Family engagement/empowerment) and the need for social isolation are issues that require the development of conventional care for SAD.

The Association between Electroencephalography with Auditory Steady-State Response and Postoperative Delirium

Delirium is a disorder of consciousness and a risk factor for cognitive dysfunction and poor prognosis. We hypothesized that preoperative gamma activities would be linked to postoperative delirium. We enrolled 71 subjects for elective surgery and recorded auditory steady-state response (ASSR) by electroencephalography (EEG) before the surgery and examined postoperative delirium with DSM-5. The EEG data were analyzed for baseline power, and ASSR evoked power (EP) and phase-locking factor (PLF) within the gamma range. Postoperative delirium was found in 18 patients (delirium group) but not in 53 patients (non-delirium group). There were no significant differences in the 40-Hz EP or PLF between the two groups. The baseline gamma activity negatively correlated with the 40-Hz PLF in the non-delirium group (ρ = −0.444, p < 0.01). The correlation between baseline gamma activity and 40-Hz EP was not significant in either the delirium or non-delirium group. In all patients, both preoperative PLF and EP had no significant correlations with the Delirium Rating Scale Revised-98 and the Memorial Delirium Assessment Measure at the post-operation, respectively. The disruption of the neurophysiological relationship between baseline gamma activity before sound stimuli and the PLF of the 40-Hz ASSR may be one of the potential neurophysiological indicators associated with postoperative delirium.