EEG and clinical assessment in delirium and acute encephalopathy

Automated EEG-Based Brainwave Analysis for the Detection of Postoperative Delirium Does Not Result in a Shorter Length of Stay in Geriatric Hip Fracture Patients: A Multicentre Randomized Controlled Trial

Introduction: Delirium in postoperative geriatric hip fracture patients is a serious and often preventable condition. If detected in time, it can be treated, but a delay in the diagnosis and initiation of treatment impairs outcomes. A novel approach to detect delirium is to use point-of-care electro-encephalogram (EEG) recording with automated analysis. In this study, the authors investigated whether screening for delirium with EEG recording and automated analysis resulted in reduced length of stay after surgery and superior screening performance in comparison to the Delirium Observation Screening Scale (DOS). Methods: This randomized control trial was conducted at two geriatric trauma centres in the Netherlands. Patients were eligible for inclusion if they were aged 70 years or above, were admitted to the geriatric trauma unit and undergoing surgery for a hip fracture. Patients were randomized to either the intervention (EEG-based brainwave analysis) or control group (DOSS screening tool). Participants were screened for delirium twice a day during three consecutive days starting at day 0 of the surgery, with the first measurement before the surgery. The primary outcome was length of stay. In addition, the screening performance for both automated EEG-based brainwave analysis and DOS was determined. Results: A total of 388 patients were included (189 per arm). There were no differences between groups in terms of median hospital length of stay (DOS 7 days (IQR 5.75-9) vs. EEG-based brainwave analysis 7 days (IQR 5-9); p = 0.867). The performance of EEG-based brainwave analysis was considerably lower than that of the DOSS in terms of discrimination between patients with and without postoperative delirium. Conclusions: Screening for postoperative delirium in geriatric hip fracture patients using automated EEG-based brainwave analysis did not result in a shorter length of stay. Additionally, the results of this study show no clear advantage in terms of the screening performance of EEG-based brainwave analysis over the current standard of care for geriatric patients with a hip fracture.

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.

An Interdisciplinary Reappraisal of Delirium and Proposed Subtypes

An interdisciplinary plenary session entitled "Rethinking and Rehashing Delirium" was held during the 2021 Annual Meeting of the Academy of Consultation-Liaison Psychiatry to facilitate dialog on the prevalent approach to delirium. Panel members included a psychiatrist, neurointensivist, and critical care specialist, and attendee comments were solicited with the goal of developing a statement. Discussion was focused on a reappraisal of delirium and, in particular, its disparate terminology and history in relation to acute encephalopathy. The authors endorse a recent joint position statement that describes acute encephalopathy as a rapidly evolving (<4 weeks) pathobiological brain process that presents as subsyndromal delirium, delirium, or coma and suggest the following points of refinement: (1) to suggest that "delirium disorder" describe the diagnostic construct including its syndrome, precipitant(s), and unique pathophysiology; (2) to restrict the term "delirium" to describing the clinical syndrome encountered at the bedside; (3) to clarify that the disfavored term "altered mental status" may occasionally be an appropriate preliminary designation where the diagnosis cannot yet be specified further; and (4) to provide rationale for rejecting the terms acute brain injury, failure, or dysfunction. The final common pathway of delirium appears to involve higher-level brain network dysfunction, but there are many insults that can disrupt functional connectivity. We propose that future delirium classification systems should seek to characterize the unique pathophysiological disturbances ("endotypes") that underlie delirium and delirium's individual neuropsychiatric symptoms. We provide provisional means of classification in hopes that novel subtypes might lead to specific intervention to improve patient experience and outcomes. This paper concludes by considering future directions for the field. Key areas of opportunity include interdisciplinary initiatives to harmonize efforts across specialties and settings, enhance underrepresented groups in research, integration of delirium and encephalopathy in coding, development of relevant quality and safety measures, and exploration of opportunities for translational science.

EEG background frequency is associated with discharge outcomes in non-ICU hospitalized patients with COVID-19

Objective

To assess risk factors for encephalopathy in non-ICU hospitalized patients with COVID-19 and the effect of encephalopathy on short-term outcomes.

Methods

We collected clinical and electrophysiological characteristics of fifty patients with COVID-19 infection admitted to a ward service and who had an electroencephalogram (EEG) performed. Associations with short-term outcomes including hospital length of stay and discharge disposition were determined from univariate and multivariate statistical analysis.

Results

Clinical delirium was associated with encephalopathy on EEG, cefepime use was associated with increased length of stay, and of all factors analyzed, background frequency on EEG alone was correlated with discharge disposition.

Conclusion

Encephalopathy is one of the major determinants of short-term outcomes in hospitalized non-ICU patients with COVID-19.

Comparison Between Deltascan Single Channel Electroencephalography (EEG), Confusion Assessment Method-Intensive Care Unit (CAM-ICU) Score and Clinical Assessment in Diagnosing Delirium in Intubated Patients in the Intensive Care Unit

Background

The aim of this article is to assess the feasibility of using single-channel electroencephalography (EEG) measurement for detecting delirium in intubated Intensive care (ICU) patients and to assess the level of agreement between the EEG measurements, the CAM-ICU score and the clinical diagnosis of delirium.

Materials and methods

This study was an exploratory pilot between May 2021 and September 2021 including intubated patients in the ICU. For this study the Prolira^® (Arnhem, The Netherlands) Deltascan single-channel EEG was used and compared with the Confusion Assessment Method (CAM)-ICU and the clinical diagnosis of delirium by ICU physicians.

Results

In total 23 patients were found eligible for this study, of which 20 were included in the final analysis. The patients mean age was 63.0 ± 8.8 years, and the majority (thirteen) was male (65%). In total 17 of the 20 patients (85%) received the diagnosis delirium by the medical team. There were no statistically significant differences between the Deltascan and CAM-ICU measurements in diagnosing delirium per time point (p values respectively 0.21; 0.90; 0.34; 0.11; 0.056 and 0.091). AUCs for the agreement between the CAM-ICU and the Deltascan measurements were respectively: 0.676 ± 0.205; 0.333 ± 0.224; 0.402 ± 0.146; 0.488 ± 0.202; 0.06 ± 0.077 and 0.06 ± 0.109 (all p>0.05). AUCs for the level of agreement between the clinical diagnosis delirium and Deltascan were: 0.676 ± 0.152; 0.686 ± 0.146; 0.711 ± 0.132; 0.688 ± 0.136; 0.500 ± 0.158 and 0.700 ± 0.211 (all p>0.05).

Conclusion

In this exploratory study, we showed that there is no statistical agreement between CAM-ICU and Delta scan measurements. Secondly, there is a higher agreement, although not statistically significant between the clinical diagnoses of a delirium (by a clinician) with the Deltascan measurements. Despite this small study we think that the Deltascan can be of additional value in intubated ICU patients and therefore larger studies are needed to substantiate our findings.

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.

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

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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.