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

Advancing Delirium Treatment Trials in Older Adults: Recommendations for Future Trials From the Network for Investigation of Delirium: Unifying Scientists (NIDUS)

OBJECTIVES

To summarize the delirium treatment trial literature, identify the unique challenges in delirium treatment trials, and formulate recommendations to address each in older adults.

DESIGN

A 39-member interprofessional and international expert working group of clinicians (physicians, nurses, and pharmacists) and nonclinicians (biostatisticians, epidemiologists, and trial methodologists) was convened. Four expert panels were assembled to explore key subtopics (pharmacological/nonpharmacologic treatment, methodological challenges, and novel research designs).

METHODS

To provide background and context, a review of delirium treatment randomized controlled trials (RCTs) published between 2003 and 2023 was conducted and evidence gaps were identified. The four panels addressed the identified subtopics. For each subtopic, research challenges were identified and recommendations to address each were proposed through virtual discussion before a live, full-day, and in-person conference. General agreement was reached for each proposed recommendation across the entire working group via moderated conference discussion. Recommendations were synthesized across panels and iteratively discussed through rounds of virtual meetings and draft reviews.

RESULTS

We identified key evidence gaps through a systematic literature review, yielding 43 RCTs of delirium treatments. From this review, eight unique challenges for delirium treatment trials were identified, and recommendations to address each were made based on panel input. The recommendations start with design of interventions that consider the multifactorial nature of delirium, include both pharmacological and nonpharmacologic approaches, and target pathophysiologic pathways where possible. Selecting appropriate at-risk patients with moderate vulnerability to delirium may maximize effectiveness. Targeting patients with at least moderate delirium severity and duration will include those most likely to experience adverse outcomes. Delirium severity should be the primary outcome of choice; measurement of short- and long-term clinical outcomes will maximize clinical relevance. Finally, plans for handling informative censoring and missing data are key.

CONCLUSIONS

By addressing key delirium treatment challenges and research gaps, our recommendations may serve as a roadmap for advancing delirium treatment research in older adults.

The Pathophysiology and Biomarkers of Delirium

Delirium is a major disturbance in the mental state characterized by fluctuations in arousal, deficits in attention, distorted perception, and disruptions in memory and cognitive processing. Delirium affects approximately 18% to 25% of hospital inpatients, with even higher rates observed during critical illness. To develop therapies to shorten the duration and limit the adverse effects of delirium, it is important to understand the mechanisms underlying its presentation. Neuroimaging modalities such as magnetic resonance imaging (MRI), positron emission tomography, functional MRI, and near-infrared spectroscopy point to global atrophy, white matter changes, and disruptions in cerebral blood flow, oxygenation, metabolism, and connectivity as key correlates of delirium pathogenesis. Electroencephalography demonstrates generalized slowing of normal background activity, with pathologic decreases in variability of oscillatory patterns and disruptions in functional connectivity among specific brain regions. Elevated serum biomarkers of inflammation, including interleukin-6, C-reactive protein, and S100B, suggest a role of dysregulated inflammatory processes and cellular metabolism, particularly in perioperative and sepsis-related delirium. Emerging animal models that can mimic delirium-like clinical states will reveal further insights into delirium pathophysiology. The combination of clinical and basic science methods of exploring delirium shows great promise in elucidating its underlying mechanisms and revealing potential therapeutic targets.

The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.

International Delirium Pathophysiology & Electrophysiology Network for Data sharing (iDEPEND)

In an era of 'big data', we propose that a collaborative network approach will drive a better understanding of the mechanisms of delirium, and more rapid development of therapies. We have formed the International Delirium Pathophysiology & Electrophysiology Network for Data sharing (iDEPEND) group with a key aim to 'facilitate the study of delirium pathogenesis with electrophysiology, imaging, and biomarkers including data acquisition, analysis, and interpretation'. Our initial focus is on studies of electrophysiology as we anticipate this methodology has great potential to enhance our understanding of delirium. Our article describes this principle and is used to highlight the endeavour to the wider community as we establish key stakeholders and partnerships.

Electroencephalography as a tool for assessing delirium in hospitalized patients: A single-center tertiary hospital experience

Background

Delirium is a prevalent yet underdiagnosed disorder characterized by acute cognitive impairment. Various screening tools are available, including the Confusion Assessment Method (CAM) and 4 A's test (4AT). However, the results of these assessments may vary among raters. Therefore, we investigated the objective use of electroencephalography (EEG) in delirium and its clinical associations and predictive value.

Method

This cross-sectional observational study was conducted at Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan, Malaysia, from April 2021 to April 2023. This study included patients aged ≥18 years with a preliminary diagnosis of delirium. Demographic and clinical data were collected along with EEG recordings evaluated by certified neurologists to classify abnormalities and compare the associated factors between patients with delirium with or without EEG abnormalities.

Results

One hundred and twenty patients were recruited, with 80.0% displaying EEG abnormalities, mostly generalized slowing (moderate to severe) and primarily generalized slowing (mild to severe), and were characterized by theta activity. Age was significantly associated with EEG abnormalities, with patients aged 75 and older demonstrating the highest incidence (88.2%). The CAM scores were strongly correlated with EEG abnormalities (r = 0.639, P < 0.001) and was a predictor of EEG abnormalities (P < 0.012), indicating that EEG can complement clinical assessments for delirium. The Richmond Agitation and Sedation Scale (RASS) scores (r = -0.452, P < 0.001) and Barthel index (BI) (r = -0.582, P < 0.001) were negatively correlated with EEG abnormalities. Additionally, a longer hospitalization duration was associated with EEG abnormalities (r = 0.250, P = 0.006) and emerged as a predictor of such changes (P = 0.030).

Conclusion

EEG abnormalities are prevalent in patients with delirium, particularly in elderly patients. CAM scores and the duration of hospitalization are valuable predictors of EEG abnormalities. EEG can be an objective tool for enhancing delirium diagnosis and prognosis, thereby facilitating timely interventions.

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.

Supervised deep learning with vision transformer predicts delirium using limited lead EEG

As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standard but is resource intensive thus not feasible for widespread delirium monitoring. This study evaluated the use of limited-lead rapid-response EEG and supervised deep learning methods with vision transformer to predict delirium. This proof-of-concept study used a prospective design to evaluate use of supervised deep learning with vision transformer and a rapid-response EEG device for predicting delirium in mechanically ventilated critically ill older adults. Fifteen different models were analyzed. Using all available data, the vision transformer models provided 99.9%+ training and 97% testing accuracy across models. Vision transformer with rapid-response EEG is capable of predicting delirium. Such monitoring is feasible in critically ill older adults. Therefore, this method has strong potential for improving the accuracy of delirium detection, providing greater opportunity for individualized interventions. Such an approach may shorten hospital length of stay, increase discharge to home, decrease mortality, and reduce the financial burden associated with delirium.

American Delirium Society 2022 Year in Review: Highlighting the Year's Most Impactful Delirium Research

Background

Since 2015, the American Delirium Society (ADS) Research Committee has conducted an annual survey of the delirium literature for presentation in its year-in-review session. Our objectives were to describe the review process used for the 2021-2022 and to summarise the selected publications.

Methods

Each member of the ADS Research Committee nominated up to 6 publications considered to be the most impactful primary delirium research published from September 1, 2021, to July 31, 2022. The 24 nominated studies were divided into three categories balanced by number of articles: medical intervention trials, non-medical intervention trials, and delirium detection/basic science studies. Each ADS Research Committee member ranked all studies in their assigned category for methodological rigor and for impact, each being scored as 0-10, for a total score of 0-20. It was decided a priori to select the top three highest-scoring articles in each category for presentation, with ties adjudicated by Committee consensus.

Results

Nineteen Research Committee members served as reviewers. Scores for each category were similar: medical interventions mean (standard deviation) 12.8 (1.1), non-medical interventions 13.1 (1.1), and detection/basic science 12.6 (1.0). We summarise the results of the papers presented in the 2022 ADS year-in-review session.

Conclusion

The diversity of studies presented for the 2022 ADS year-in-review session illustrates the breadth of the delirium field and the growing number of clinical trials. The dissemination of publications across a broad, diverse array of journals provides further justification of the need for delirium-specific journals.

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.

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

Objective

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

Methods

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

Results

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

Conclusion

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

Monitoring of sedation in mechanically ventilated patients using remote technology

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

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.