Evaluating the Clinical Impact of Rapid Response Electroencephalography: The DECIDE Multicenter Prospective Observational Clinical Study

Feasibility Assessment of Rapid Response EEG in the Identification of Nonconvulsive Seizures During Military Medical Air Transport

INTRODUCTION

Traumatic brain injury often requires neurologic care and specialized equipment, not often found downrange. Nonconvulsive seizures (NCSs) and nonconvulsive status epilepticus (NCSE) occur in up to 30% of patients with moderate or severe traumatic brain injury and is associated with a 39% morbidity and an 18% mortality. It remains difficult to identify at bedside because of the heterogeneous clinical manifestations. The primary diagnostic tool is an electroencephalogram (EEG) which is large, requires an external power source, and requires a specialized technician and neurologist to collect and interpret the data. Rapid response EEG (rr-EEG) is an FDA-approved device that is pocket sized and battery powered and uses a disposable 10-electrode headset. Prior studies have demonstrated the noninferiority of rr-EEG in the identification of NCSE and NCS as compared to conventional EEG in hospitals. An unanswered question is whether rr-EEG could be used in the identification of NCSE and NCS by medics.

MATERIALS AND METHODS

In conjunction with the Critical Care Air Transport (CCAT) team, a simulation was created and implemented on a CCAT training mission. The simulation team included a neurology resident, who oversaw the simulation, a pulmonary critical care fellow, an intensive care unit nurse, and a respiratory therapy. A survey was provided before and after the simulation. The team was expected to review the rr-EEG to make clinical decisions during ground transport, takeoff, and landing. The neurology resident monitored and recorded the team's ability to distinguish between NCS and a normal EEG. In between, the neurology resident monitored the quality of the EEG for potential interference and loss of quality.

RESULTS

The CCAT team was able to efficiently set up the rr-EEG on a patient manikin, correctly identify visual EEG wave forms of a patient in NCS, and utilize the proprietary audio program of a simulated patient in NCS. The team reported that the device was easily set up in the environment, and the data were interpretable despite vibration, aircraft auditory and electrical noise, and the ergonomics of the aircraft medical section.

CONCLUSIONS

This pilot study has validated a potentially revolutionary technology in medical transport. The rr-EEG technology is measurably user-friendly and will improve patient outcomes. This device and simulation can reduce time to an EEG by hours to days allowing for immediate treatment and intervention, which can significantly reduce morbidity and mortality.

Evaluating the feasibility of prehospital point-of-care EEG: The prehospital implementation of rapid EEG (PHIRE) study

Background

Point-of-care electroencephalography (EEG) devices can be rapidly applied and do not require specialized technologists, creating new opportunities to use EEG during prehospital care. We evaluated the feasibility of point-of-care EEG during ambulance transport for 911 calls.

Methods

This mixed-methods study was conducted between May 28, 2022 and October 28, 2023. Emergency Medical Services (EMS) clinicians identified eligible individuals, provided emergent treatment, applied EEG, and obtained an EEG recording during ambulance transport. Eligible patients were aged 6 years or older and evaluated for seizure, stroke, or altered mental status. EMS clinicians completed a survey and a brief phone interview following every enrollment. Two epileptologists reviewed EEG recordings for interpretability and artifact.

Results

There were 34 prehospital encounters in which EEG was applied. Patients had a mean age of 69 years, and 15 (44%) were female. EEG recordings had a median duration of 10 min 30 s. It took EMS clinicians an average of 2.5 min to apply the device and begin EEG recording. There were 14 (47%) recordings where clinicians achieved a high-quality connection for all 10 electrodes and 32 (94%) recordings that were sufficient in quality to interpret. There were 24 (71%) recordings with six or more channels free of artifact for 5 min or more. All clinicians agreed or strongly agreed that the device was easy to use.

Conclusion

Among real-world prehospital encounters for patients with neurologic symptoms, point-of-care EEG was rapidly applied and yielded EEG recordings that could be used for clinical interpretation, demonstrating the feasibility of point-of-care EEG in future prehospital care.

New wearable and portable EEG modalities in epilepsy: The views of hospital-based healthcare professionals

BACKGROUND

Novel mobile and portable EEG solutions, designed for short and long-term monitoring of individuals with epilepsy have been developed in recent years but, they are underutilized, lacking full integration into clinical routine. Exploring the opinions of hospital-based healthcare professionals regarding their potential application, technical requirements and value would be crucial for future device development and increase their clinical application.

PURPOSE

To evaluate professionals' opinions on novel EEG systems, focusing on their potential application in various clinical settings, professionals' interest in non-invasive solutions for ultra-long monitoring of people with epilepsy (PWE) and factors which could increase future use of novel EEG systems.

MATERIALS AND METHODS

We conducted an online survey where Hospital-based professionals shared opinions on potential advantages, clinical value, and key features of novel wearable EEG systems in five different clinical settings. Additionally, insights were gathered on the need for future research and, the need for additional information about devices from companies and researchers.

RESULTS

Respondents (n = 40) prioritized high performance, data quality, easy patient mobility, and comfort as crucial features for novel devices. Advantages were highlighted, including more natural settings, reduced application time, earlier epilepsy diagnosis, and decreased support requirements. Novel EEG devices were seen as valuable for epilepsy diagnosis, seizure monitoring, automatic seizure documentation, seizure alarms, and seizure forecasting. Interest in integrating these new systems into clinical practice was high, particularly for supervising drug-resistant epilepsy, reducing SUDEP, and detecting nocturnal seizures. Professionals emphasized the need for more research studies and highlighted the need for increased information from companies and researchers.

CONCLUSIONS

Professionals underscore specific technical and practical features, along with potential clinical advantages and value of novel EEG devices that could drive their development. While interest in integrating these solutions in clinical practice exists, further validation studies and enhanced communication between researchers, companies, and clinicians are crucial for overcoming potential scepticism and facilitating widespread adoption.

Improving Quality of Care for Status Epilepticus: Putting Protocols into Practice

PURPOSE OF REVIEW

Timely treatment of status epilepticus (SE) improves outcomes, however gaps between recommended and implemented care are common. This review analyzes obstacles and explores interventions to optimize effective, evidence-based treatment of SE.

RECENT FINDINGS

Seizure action plans, rescue medications, and noninvasive wearables with seizure detection capabilities can facilitate early intervention for prolonged seizures in the home and school. In the field, standardized EMS protocols, EMS education, and screening tools can address variability in SE definitions and treatment, particularly benzodiazepine dosing. In the emergency room and hospital, provider education, SE order sets and alerts, and rapid EEG technologies, can shorten time to first-line therapy, second-line therapy, and EEG initiation. Widespread, sustained improvement in SE care remains challenging. A multipronged approach including emphasis on pre-hospital intervention, treatment protocols adapted to local contexts, and SE databases to systematically collect process and outcome metrics have the potential to transform SE treatment and outcomes.

Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study

BACKGROUND AND OBJECTIVES

Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers. Brief EEG screening with the validated 2HELPS2B algorithm was proposed as a method to triage cEEG resources, but it is hampered by cEEG requirements, primarily EEG technologists. Seizure risk-stratification using reduced time-to-application rapid response-EEG (rrEEG) systems (∼5 minutes) could be a solution. We assessed the noninferiority of the 2HELPS2B score on a 1-hour rrEEG compared to cEEG.

METHODS

A multicenter retrospective EEG diagnostic accuracy study was conducted from October 1, 2021, to July 31, 2022. Chart and EEG review performed with consecutive sampling at 4 tertiary care centers, included records of patients ≥18 years old, from January 1, 2018, to June 20, 2022. Monte Carlo simulation power analysis yielded n = 500 rrEEG; for secondary outcomes n = 500 cEEG and propensity-score covariate matching was planned. Primary outcome, noninferiority of rrEEG for seizure risk prediction, was assessed per area under the receiver operator characteristic curve (AUC). Noninferiority margin (0.05) was based on the 2HELPS2B validation study.

RESULTS

A total of 240 rrEEG with follow-on cEEG were obtained. Median age was 64 (interquartile range 22); 42% were female. 2HELPS2B on a 1-hour rrEEG met noninferiority to cEEG (AUC 0.85, 95% CI 0.78-0.90, p = 0.001). Secondary endpoints of comparison with a matched contemporaneous cEEG showed no significant difference in AUC (0.89, 95% CI 0.83-0.94, p = 0.31); in false negative rate for the 2HELPS2B = 0 group (p = 1.0) rrEEG (0.021, 95% CI 0-0.062), cEEG (0.016, 95% CI 0-0.048); nor in survival analyses.

DISCUSSION

2HELPS2B on 1-hour rrEEG is noninferior to cEEG for seizure prediction. Patients with low-risk (2HELPS2B = 0) may be able to forgo prolonged cEEG, allowing for increased monitoring of at-risk patients.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that rrEEG is noninferior to cEEG in calculating the 2HELPS2B score to predict seizure risk.

Utility and rationale for continuous EEG monitoring: a primer for the general intensivist

This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.

Evaluating the Impact of Point-of-Care Electroencephalography on Length of Stay in the Intensive Care Unit: Subanalysis of the SAFER-EEG Trial

BACKGROUND

Electroencephalography (EEG) is needed to diagnose nonconvulsive seizures. Prolonged nonconvulsive seizures are associated with neuronal injuries and deleterious clinical outcomes. However, it is uncertain whether the rapid identification of these seizures using point-of-care EEG (POC-EEG) can have a positive impact on clinical outcomes.

METHODS

In a retrospective subanalysis of the recently completed multicenter Seizure Assessment and Forecasting with Efficient Rapid-EEG (SAFER-EEG) trial, we compared intensive care unit (ICU) length of stay (LOS), unfavorable functional outcome (modified Rankin Scale score ≥ 4), and time to EEG between adult patients receiving a US Food and Drug Administration-cleared POC-EEG (Ceribell, Inc.) and those receiving conventional EEG (conv-EEG). Patient records from January 2018 to June 2022 at three different academic centers were reviewed, focusing on EEG timing and clinical outcomes. Propensity score matching was applied using key clinical covariates to control for confounders. Medians and interquartile ranges (IQRs) were calculated for descriptive statistics. Nonparametric tests (Mann-Whitney U-test) were used for the continuous variables, and the χ2 test was used for the proportions.

RESULTS

A total of 283 ICU patients (62 conv-EEG, 221 POC-EEG) were included. The two populations were matched using demographic and clinical characteristics. We found that the ICU LOS was significantly shorter in the POC-EEG cohort compared to the conv-EEG cohort (3.9 [IQR 1.9-8.8] vs. 8.0 [IQR 3.0-16.0] days, p = 0.003). Moreover, modified Rankin Scale functional outcomes were also different between the two EEG cohorts (p = 0.047).

CONCLUSIONS

This study reveals a significant association between early POC-EEG detection of nonconvulsive seizures and decreased ICU LOS. The POC-EEG differed from conv-EEG, demonstrating better functional outcomes compared with the latter in a matched analysis. These findings corroborate previous research advocating the benefit of early diagnosis of nonconvulsive seizure. The causal relationship between the type of EEG and metrics of interest, such as ICU LOS and functional/clinical outcomes, needs to be confirmed in future prospective randomized studies.

Precision emergency medicine

BACKGROUND

Precision health is a burgeoning scientific discipline that aims to incorporate individual variability in biological, behavioral, and social factors to develop personalized health solutions. To date, emergency medicine has not deeply engaged in the precision health movement. However, rapid advances in health technology, data science, and medical informatics offer new opportunities for emergency medicine to realize the promises of precision health.

METHODS

In this article, we conceptualize precision emergency medicine as an emerging paradigm and identify key drivers of its implementation into current and future clinical practice. We acknowledge important obstacles to the specialty-wide adoption of precision emergency medicine and offer solutions that conceive a successful path forward.

RESULTS

Precision emergency medicine is defined as the use of information and technology to deliver acute care effectively, efficiently, and authentically to individual patients and their communities. Key drivers and opportunities include leveraging human data, capitalizing on technology and digital tools, providing deliberate access to care, advancing population health, and reimagining provider education and roles. Overcoming challenges in equity, privacy, and cost is essential for success. We close with a call to action to proactively incorporate precision health into the clinical practice of emergency medicine, the training of future emergency physicians, and the research agenda of the specialty.

CONCLUSIONS

Precision emergency medicine leverages new technology and data-driven artificial intelligence to advance diagnostic testing, individualize patient care plans and therapeutics, and strategically refine the convergence of the health system and the community.

Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures

OBJECTIVE

Strategies are needed to optimally deploy continuous EEG monitoring (CEEG) for electroencephalographic seizure (ES) identification and management due to resource limitations. We aimed to construct an efficient multi-stage prediction model guiding CEEG utilization to identify ES in critically ill children using clinical and EEG covariates.

METHODS

The largest prospective single-center cohort of 1399 consecutive children undergoing CEEG was analyzed. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed.

RESULTS

When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model.

CONCLUSIONS

Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.

The Cost of After-Hour Electroencephalography

Background and Objectives

High costs associated with after-hour electroencephalography (EEG) constitute a barrier for financially constrained hospitals to provide this neurodiagnostic procedure outside regular working hours. Our study aims to deepen our understanding of the cost elements involved in delivering EEG services during after-hours.

Methods

We accessed publicly available data sets and created a cost model depending on 3 most commonly seen staffing scenarios: (1) technologist on-site, (2) technologist on-call from home, and (3) a hybrid of the two.

Results

Cost of EEG depends on the volume of testing and the staffing plan. Within the various cost elements, labor cost of EEG technologists is the predominant expenditure, which varies across geographic regions and urban areas.

Discussion

We provide a model to explain why access to EEGs during after-hours has a substantial expense. This model provides a cost calculator tool (made available as part of this publication in eAppendix 1, links.lww.com/CPJ/A513) to estimate the cost of EEG platform based on site-specific staffing scenarios and annual volume.

Estimate of Patients With Missed Seizures Because of Delay in Conventional EEG

PURPOSE

There is frequent delay between ordering and placement of conventional EEG. Here we estimate how many patients had seizures during this delay.

METHODS

Two hundred fifty consecutive adult patients who underwent conventional EEG monitoring at the University of Wisconsin Hospital were retrospectively chart reviewed for demographics, time of EEG order, clinical and other EEG-related information. Patients were stratified by use of anti-seizure medications before EEG and into low-risk, medium-risk, and high-risk groups based on 2HELPS2B score (0, 1, or >1). Monte Carlo simulations (500 trials) were performed to estimate seizures during delay.

RESULTS

The median delay from EEG order to performing EEG was 2.00 hours (range of 0.5-8.00 hours) in the total cohort. For EEGs ordered after-hours, it was 2.00 hours (range 0.5-8.00 hours), and during business hours, it was 2.00 hours (range 0.5-6.00 hours). The place of EEG, intensive care unit, emergency department, and general floor, did not show significant difference (P = 0.84). Anti-seizure medication did not affect time to first seizure in the low-risk (P = 0.37), medium-risk (P = 0.44), or high-risk (P = 0.12) groups. The estimated % of patients who had a seizure in the delay period for low-risk group (2HELPS2B = 0) was 0.8%, for the medium-risk group (2HELPS2B = 1) was 10.3%, and for the high-risk group (2HELPS2B > 1) was 17.6%, and overall risk was 7.2%.

CONCLUSIONS

The University of Wisconsin Hospital with 24-hour in-house EEG technologists has a median delay of 2 hours from order to start of EEG, shorter than published reports from other centers. Nonetheless, seizures were likely missed in about 7.2% of patients.

Reducing Time to Electroencephalography in Pediatric Convulsive Status Epilepticus: A Quality Improvement Initiative

BACKGROUND

Pediatric convulsive status epilepticus (CSE) is a neurological emergency utilizing electroencephalography (EEG) to guide therapeutic interventions. Guidelines recommend EEG initiation within one hour of seizure onset, but logistic and structural barriers often lead to significant delays. We aimed to reduce the time to EEG in pediatric CSE.

METHODS

From 2017 to 2022, we implemented process improvements, including EEG order sets with priority-based timing guidance, technologist workflow changes, a satisfaction survey, and feedback from key stakeholder groups, over five plan-do-study-act (PDSA) cycles. Seizure start time, time of EEG order, and time to EEG initiation were extracted. Time to interpretable EEG was determined from manual review of the EEG tracing.

RESULTS

Time from EEG order to interpretable EEG decreased by nearly 50%, from a median of 90 minutes to 48 minutes. There were clinically and statistically significant improvements in time from EEG order to EEG initiation, time from EEG order to interpretable EEG, and EEG start to interpretable EEG. Ongoing provider education and guidance enabled improvements, whereas a new electronic health care record negatively impacted electronic ordering. EEG technologists reported that they understood the importance of emergent EEG for clinical care and did not find that the new workflow caused excessive disruption.

CONCLUSIONS

Timely access to EEG for pediatric patients with CSE can be improved through clinical processes that use existing devices and that maintain the benefits of full-montage EEG recordings. Similar process improvement efforts may be generalizable to other institutions to increase adherence to guidelines and provide improved care.

Economic review of point-of-care EEG

Aims: Point-of-care electroencephalogram (POC-EEG) is an acute care bedside screening tool for the identification of nonconvulsive seizures (NCS) and nonconvulsive status epilepticus (NCSE). The objective of this narrative review is to describe the economic themes related to POC-EEG in the United States (US).Materials and methods: We examined peer-reviewed, published manuscripts on the economic findings of POC-EEG for bedside use in US hospitals, which included those found through targeted searches on PubMed and Google Scholar. Conference abstracts, gray literature offerings, frank advertisements, white papers, and studies conducted outside the US were excluded.Results: Twelve manuscripts were identified and reviewed; results were then grouped into four categories of economic evidence. First, POC-EEG usage was associated with clinical management amendments and antiseizure medication reductions. Second, POC-EEG was correlated with fewer unnecessary transfers to other facilities for monitoring and reduced hospital length of stay (LOS). Third, when identifying NCS or NCSE onsite, POC-EEG was associated with greater reimbursement in Medical Severity-Diagnosis Related Group coding. Fourth, POC-EEG may lower labor costs via decreasing after-hours requests to EEG technologists for conventional EEG (convEEG).Limitations: We conducted a narrative review, not a systematic review. The studies were observational and utilized one rapid circumferential headband system, which limited generalizability of the findings and indicated publication bias. Some sample sizes were small and hospital characteristics may not represent all US hospitals. POC-EEG studies in pediatric populations were also lacking. Ultimately, further research is justified.Conclusions: POC-EEG is a rapid screening tool for NCS and NCSE in critical care and emergency medicine with potential financial benefits through refining clinical management, reducing unnecessary patient transfers and hospital LOS, improving reimbursement, and mitigating burdens on healthcare staff and hospitals. Since POC-EEG has limitations (i.e. no video component and reduced montage), the studies asserted that it did not replace convEEG.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

Automated Seizure Detection in Patients with Cardiac Arrest: A Retrospective Review of Ceribell™ Rapid-EEG Recordings

BACKGROUND

In patients with cardiac arrest who remain comatose after return of spontaneous circulation, seizures and other abnormalities on electroencephalogram (EEG) are common. Thus, guidelines recommend urgent initiation of EEG for the evaluation of seizures in this population. Point-of-care EEG systems, such as Ceribell™ Rapid Response EEG (Rapid-EEG), allow for prompt initiation of EEG monitoring, albeit through a reduced-channel montage. Rapid-EEG incorporates an automated seizure detection software (Clarity™) to measure seizure burden in real time and alert clinicians at the bedside when a high seizure burden, consistent with possible status epilepticus, is identified. External validation of Clarity is still needed. Our goal was to evaluate the real-world performance of Clarity for the detection of seizures and status epilepticus in a sample of patients with cardiac arrest.

METHODS

This study was a retrospective review of Rapid-EEG recordings from all the patients who were admitted to the medical intensive care unit at Kent Hospital (Warwick, RI) between 6/1/2021 and 3/18/2022 for management after cardiac arrest and who underwent Rapid-EEG monitoring as part of their routine clinical care (n = 21). Board-certified epileptologists identified events that met criteria for seizures or status epilepticus, as per the 2021 American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology, and evaluated any seizure burden detections generated by Clarity.

RESULTS

In this study, 4 of 21 patients with cardiac arrest (19.0%) who underwent Rapid-EEG monitoring had multiple electrographic seizures, and 2 of those patients (9.5%) had electrographic status epilepticus within the first 24 h of the study. None of these ictal abnormalities were detected by the Clarity seizure detection system. Clarity showed 0% seizure burden throughout the entirety of all four Rapid-EEG recordings, including the EEG pages that showed definite seizures or status epilepticus.

CONCLUSIONS

The presence of frequent electrographic seizures and/or status epilepticus can go undetected by Clarity. Timely and careful review of all raw Rapid-EEG recordings by a qualified human EEG reader is necessary to guide clinical care, regardless of Clarity seizure burden measurements.

Rapid-Response Electroencephalography in Seizure Diagnosis and Patient Care: Lessons From a Community Hospital

ABSTRACT

BACKGROUND: Nonconvulsive seizures are a major source of in-hospital morbidity and a cause of unexplained encephalopathy in critically ill patients. Electroencephalography (EEG) is essential to confirm nonconvulsive seizures and can guide patient-specific workup, treatment, and prognostication. In a 208-bed community hospital, EEG services were limited to 1 part-time EEG technician and 1 EEG machine shared between inpatient and outpatient settings. Its use was restricted to typical business hours. A nursing-led quality improvement (QI) project endeavored to enhance access to EEG by introducing a point-of-care rapid-response EEG program. METHODS: For this project, a multidisciplinary protocol was developed to deploy a Food and Drug Administration-cleared, point-of-care rapid-response EEG platform (Ceribell Inc) in a community hospital's emergency department and inpatient units to streamline neurodiagnostic workups. This QI project compared EEG volume, study location, time-to-EEG, number of cases with seizures captured on EEG, and hospital-level financial metrics of diagnosis-related group reimbursements and length of stay for the 6 months before (pre-QI, using conventional EEG) and 6 months after implementing the rapid-response protocol (post-QI). RESULTS: Electroencephalography volume increased from 35 studies pre-QI to 115 post-QI (3.29-fold increase), whereas the median time from EEG order to EEG start decreased 7.6-fold (74 [34-187] minutes post-QI vs 562 [321-1034] minutes pre-QI). Point-of-care EEG was also associated with more confirmed seizure diagnoses compared with conventional EEG (27/115 post-QI vs 0/35 pre-QI). This resulted in additional diagnosis-related group reimbursements and hospital revenue. Availability of point-of-care EEG was also associated with a shorter median length of stay. CONCLUSION: A nurse-led, rapid-response EEG protocol at a community hospital resulted in significant improvements in EEG accessibility and seizure diagnosis with hospital-level financial benefits. By expanding access to EEG, confirming nonconvulsive seizures, and increasing care efficiency, rapid-response EEG protocols can enhance patient care.

Implementation and impact of a point of care electroencephalography platform in a community hospital: a cohort study

Objective

To determine the clinical and financial feasibility of implementing a poc-EEG system in a community hospital.

Design

Data from a prospective cohort displaying abnormal mentation concerning for NCSE or rhythmic movements due to potential underlying seizure necessitating EEG was collected and compared to a control group containing patient data from 2020.

Setting

A teaching community hospital with limited EEG support.

Patients

The study group consisted of patients requiring emergent EEG during hours when conventional EEG was unavailable. Control group is made up of patients who were emergently transferred for EEG during the historical period.

Interventions

Application and interpretation of Ceribell®, a poc-EEG system.

Measurement and main results

88 patients were eligible with indications for poc-EEG including hyperkinetic movements post-cardiac arrest (19%), abnormal mentation after possible seizure (46%), and unresponsive patients with concern for NCSE (35%). 21% had seizure burden on poc-EEG and 4.5% had seizure activity on follow-up EEG. A mean of 1.1 patients per month required transfer to a tertiary care center for continuous EEG. For the control period, a total of 22 patients or a mean of 2 patients per month were transferred for emergent EEG. Annually, we observed a decrease in the number of transferred patients in the post-implementation period by 10.8 (95% CI: -2.17-23.64, p = 0.1). Financial analysis of the control found the hospital system incurred a loss of $3,463.11 per patient transferred for an annual loss of $83,114.64. In the study group, this would compute to an annual loss of $45,713.05 for an overall decrease in amount lost of $37,401.59. We compared amount lost per patient between historical controls and study patients. Implementation of poc-EEG resulted in an overall decrease in annual amount lost of $37,401.59 by avoidance of transfer fees. We calculated the amount gained per patient in the study group to be $13,936.44. To cover the cost of the poc-EEG system, 8.59 patients would need to avoid transfer annually.

Conclusion

A poc-EEG system can be safely implemented in a community hospital leading to an absolute decrease in transfers to tertiary hospital. This decrease in patient transfers can cover the cost of implementing the poc-EEG system. The additional benefits from transfer avoidance include clinical benefits such as rapid appropriate treatment of seizures and avoidance of unnecessary treatment as well as negating transfer risk and keeping the patient at their local hospital.

Point-of-care electroencephalography enables rapid evaluation and management of non-convulsive seizures and status epilepticus in the emergency department

Objectives

To describe our institutional experience with point-of-care electroencephalography (pocEEG) and its impact on the evaluation/management of suspected non-convulsive seizures in the emergency department (ED).

Methods

We retrospectively identified 157 adults who underwent pocEEG monitoring in our community hospital ED in 1 year. We calculated the time to obtain pocEEG in the ED (door-to-EEG time) and examined the impact of pocEEG findings (categorized as seizure, highly epileptiform patterns, slowing, or normal activity) on antiseizure medication treatment.

Results

PocEEG revealed seizures (14%, n = 22), highly epileptiform patterns (22%, n = 34), slowing (44%, n = 69), and normal activity (20%, n = 32). The median door-to-EEG time (from initial ED evaluation to pocEEG monitoring) was only 1.2 hours (interquartile range 0.1-2.1) even though 55% of studies were performed after-hours (5 pm-9 am). Most patients were admitted (54% to the intensive care unit, 41% to floor). Antiseizure medication treatment occurred pre-pocEEG in 93 patients (59%) and post-pocEEG in 88 patients (56%). By reviewing the relationship between pocEEG monitoring and antiseizure medication management, we found a significant association between pocEEG findings and changes in management (P < 0.001). Treatment escalation occurred more frequently in patients with epileptiform activity (seizures or highly epileptiform patterns, 52%) than patients with non-epileptiform activity (normal or slow, 25%, P < 0.001), and avoidance of treatment escalation occurred more frequently in patients with normal or slow activity (27%) than patients with seizures or highly epileptiform patterns (2%, P < 0.001).

Conclusion

Our study, the largest to date describing the real-world use of pocEEG in emergency medicine, found that rapid EEG acquisition in the ED was feasible in a community hospital and significantly affected the management of suspected non-convulsive seizures.

State-of-the-Art Evaluation of Acute Adult Disorders of Consciousness for the General Intensivist

OBJECTIVES

To provide a concise review of knowledge and practice pertaining to the diagnosis and initial management of unanticipated adult patient disorders of consciousness (DoC) by the general intensivist.

DATA SOURCES

Detailed search strategy using PubMed and OVID Medline for English language articles describing adult patient acute DoC diagnostic evaluation and initial management strategies including indications for transfer.

STUDY SELECTION

Descriptive and interventional studies that address acute adult DoC, their evaluation and initial management, indications for transfer, as well as outcome prognostication.

DATA EXTRACTION

Relevant descriptions or studies were reviewed, and the following aspects of each manuscript were identified, abstracted, and analyzed: setting, study population, aims, methods, results, and relevant implications for adult critical care practice.

DATA SYNTHESIS

Acute adult DoC may be categorized by etiology including structural, functional, infectious, inflammatory, and pharmacologic, the understanding of which drives diagnostic investigation, monitoring, acute therapy, and subsequent specialist care decisions including team-based local care as well as intra- and inter-facility transfer.

CONCLUSIONS

Acute adult DoC may be initially comprehensively addressed by the general intensivist using an etiology-driven and team-based approach. Certain clinical conditions, procedural expertise needs, or resource limitations inform transfer decision-making within a complex care facility or to one with greater complexity. Emerging collaborative science helps improve our current knowledge of acute DoC to better align therapies with underpinning etiologies.

Critical care EEG monitoring: improving access and unravelling potentially epileptic patterns

PURPOSE OF REVIEW

The major advances in critical care EEG have been the development of rapid response EEG, major revision of the American Clinical Neurophysiology Society's (ACNS) standardized critical care EEG terminology, and the commencement of treatment trials on rhythmic and periodic patterns (RPPs) that do not qualify as seizures.

RECENT FINDINGS

Rapid response EEG (rEEG) has proven an important supplement to full montage continuous EEG monitoring (cEEG). This EEG can be applied in a few minutes and provides excellent ability to exclude seizures, selecting those where conversion to cEEG would have the greatest diagnostic yield. Once cEEG has been commenced, the durations required to adequately exclude seizures have been refined. The ACNS provided major revision and expansion to the standardized critical care EEG terminology, which paved the way for determining with great accuracy the RPPs that are associated with seizures and that are capable of causing neurologic symptoms and/or secondary neuronal injury. The current limitations to multicenter treatment trials of these patterns have been highlighted.

SUMMARY

Novel methods of EEG in critical care have been expanding access to all patients where clinically indicated. Standardized EEG terminology has provided the framework to determine what patterns in which presenting causes warrant treatment vs. those that do not.

The EEG pen-on-paper sound: History and recent advances

The electroencephalogram (EEG) is one of the most useful technologies for brain research and clinical neurology, characterized by non-invasiveness and high time resolution. The acquired traces are visibly displayed, but various studies investigate the translation of brain waves in sound (i.e., a process called sonification). Several articles have been published since 1934 about the sonification of EEG traces, in the attempt to identify the "brain-sound." However, for a long time this sonification technique was not used for clinical purposes. The analog EEG was in fact already equipped with an auditory output, although rarely mentioned in scientific papers: the pen-on-paper noise made by the writer unit. EEG technologists often relied on the sound that pens made on paper to facilitate the diagnosis. This article provides a sample of analog video-EEG recordings with audio support representing the strengths of a combined visual-and-auditory detection of different types of seizures. The purpose of the present article is to illustrate how the analog EEG "sounded," as well as to highlight the advantages of this pen-writing noise. It was considered so useful that early digital EEG devices could be equipped with special software to duplicate it digitally. Even in the present days, the sonification can be considered as an attempt to modify the EEG practice using auditory neurofeedback with applications in therapeutic interventions, cognitive improvement, and basic research.

Detect, predict, and prevent acute seizures and status epilepticus

Status epilepticus is one of the most frequent pediatric neurological emergencies. While etiology is often influencing the outcome, more easily modifiable risk factors of outcome include detection of prolonged convulsive seizures and status epilepticus and appropriately dosed and timely applied medication treatment. Unpredictability and delayed or incomplete treatment may at times lead to longer seizures, thereby affecting outcomes. Barriers in the care of acute seizures and status epilepticus include the identification of patients at greatest risk of convulsive status epilepticus, potential stigma, distrust, and uncertainties in acute seizure care, including caregivers, physicians, and patients. Furthermore, unpredictability, detection capability, and identification of acute seizures and status epilepticus, limitations in access to obtaining and maintaining appropriate treatment, and rescue treatment options pose challenges. Additionally, timing and dosing of treatment and related acute management algorithms, potential variations in care due to healthcare and physician culture and preference, and factors related to access, equity, diversity, and inclusion of care. We outline strategies for the identification of patients at risk of acute seizures and status epilepticus, improved status epilepticus detection and prediction, and acute closed-loop treatment and status epilepticus prevention. This paper was presented at the 8th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures held in September 2022.

Risk of Developing Seizures in Children With Abnormal EEG Findings During Polysomnography

BACKGROUND

Polysomnography (PSG) utilizes abbreviated electroencephalogram (EEG) to stage sleep. The aim of this study was to determine whether epileptiform abnormalities on this limited EEG coverage correlated with abnormalities on routine EEG (rEEG) and an increased risk for seizures in children without a prior diagnosis of epilepsy.

METHODS

A six-year retrospective chart review was performed assessing children with abnormalities on EEG during PSG. Children who underwent subsequent rEEG were included; children with a prior diagnosis of seizures were excluded. The main outcome measures were rEEG results and subsequent diagnosis of epilepsy.

RESULTS

A total of 67 children met inclusion criteria. Average age was six years, and 43 (64%) were male. rEEG was normal in 16 (24%). Epileptiform abnormalities were focal in 36 (54%), generalized in eight (12%), and mixed in five (8%). An additional two (3%) had slow background rhythm without epileptiform discharges. Thirty-one patients had neurology clinic follow-up with an average duration of 31 months (range 4 to 65 months). Of these, nine (29%) developed seizures, including all three with generalized epileptiform discharges, four of 19 (21%) with focal epileptiform discharges, and two of five (40%) with mixed epileptiform discharges or background slowing. None of the four patients with a normal rEEG had seizures. Eight of the nine patients with seizures were treated with antiepileptic drugs.

CONCLUSIONS

Children with no history of seizures found to have abnormal EEG during PSG are likely to have an abnormal rEEG. Additionally, they have an increased risk for developing seizures.

Rapid response EEG with needle electrodes in an intensive care unit with limited resources

Objective

Continuous EEG (cEEG) is the gold standard for detecting seizures and rhythmic and periodic patterns (RPPs) in critically ill patients but is often not available in health systems with limited resources. The current study aims to determine the feasibility and utility of low-cost, practical, limited montage, sub-dermal needle electrode EEG in a setting where otherwise no EEG would be available.

Methods

The study included all adult patients admitted to the intensive care unit of a single center over a 24-month period. Members of the existing ICU care team, mostly nurses, were trained to place 8 sub-dermal needle EEG electrodes to achieve rapid, limited montage-EEG recording. Clinical outcomes were recorded, including any reported major complications; and the EEG findings documented, including background characterization, RPPs, and seizures.

Results

One hundred twenty-three patients, mean age 68 years, underwent an average of 15.6 min of EEG recording. There were no complications of electrode placement. Overall, 13.0% had seizures (8.1% qualifying as status epilepticus [SE]), 18.7 % had generalized periodic discharges (GPDs), 4.9% had lateralized periodic discharges (LPDs), and 11.4 % sporadic epileptiform discharges (sEDs). Greater mortality was observed in patients with worse background EEGs, seizures, LPDs, or sEDs.

Conclusions

Rapid, limited montage EEG could be achieved safely and inexpensively in a broad population of critically ill patients following minimal training of existing care teams.

Significance

For resource poor centers or centers outside of major metropolitan areas who otherwise have no access to EEG, this may prove a useful method for screening for non-convulsive seizures and status epilepticus.

Interfacility Transfers for Seizure-Related Emergencies in the United States

BACKGROUND AND OBJECTIVES

Interfacility transfer protocols are important for seizure-related emergencies, the cause of approximately 1% of all emergency department (ED) visits in the United States, but data on current practices are lacking. We assessed the prevalence, temporal trends, and patterns of interfacility transfers following seizure-related ED visits.

METHODS

We performed a retrospective longitudinal cross-sectional analysis of ED dispositions for seizure-related emergencies among adult and pediatric populations using the Nationwide Emergency Department Sample (NEDS). We used joinpoint regression to analyze annual trends in ED visits and transfer rates from 2007 to 2018. Logistic regression models using data from 2016 to 2018 explored the patient- and hospital-level factors associated with transfer vs admission. Sampling weights were applied to account for the complex survey design of the NEDS.

RESULTS

Using nationally representative data from 2007 to 2018, there were 7,372,065 weighted ED visits for seizure-related emergencies, including 419,368 (5.6%) visits for a primary diagnosis of status epilepticus. We found that 2.3%-5.6% of all these seizure-related ED visits resulted in an interfacility transfer and that the rate of transfer increased significantly over time. Among ED visits specifically for status epilepticus, interfacility transfers resulted from 19.8% to 23.24% of visits, which also increased over time. Multivariable logistic regression of adult and pediatric visits for status epilepticus revealed that transferring hospitals were more likely to be nonmetropolitan (adjusted odds ratio [aOR] 2.2, 95% CI 1.6-2.9) and less likely to have continuous electroencephalography (cEEG) capabilities (aOR 0.3, 98% CI 0.3-0.4). Transferred patients were more likely to be children (aOR 1.5, 95% CI 1.3-1.6 for those 1-4 years old; aOR 1.5 (95% CI 1.3-1.7) for ages 5-14 years), have acute cerebrovascular disease (aOR 1.4, 95% CI 1.1-1.8), and have received mechanical ventilation (aOR 1.5, 95% CI 1.4-1.7).

DISCUSSION

By 2018, approximately 1 in 19 seizure-related and 1 in 5 status epilepticus ED visits resulted in interfacility transfers. In order of strength of association, illness severity, ED seizure volume, comorbid meningitis and traumatic brain injury, nonrural location, cEEG capabilities, and pediatric age favored admission. Rural location, lack of cEEG capabilities, and comorbid stroke favored transfer. Thoughtful deployment of novel EEG technologies and teleneurology tools may help optimize triage and prevent unnecessary ED transfers.

Electroencephalogram in the intensive care unit: a focused look at acute brain injury

Over the past decades, electroencephalography (EEG) has become a widely applied and highly sophisticated brain monitoring tool in a variety of intensive care unit (ICU) settings. The most common indication for EEG monitoring currently is the management of refractory status epilepticus. In addition, a number of studies have associated frequent seizures, including nonconvulsive status epilepticus (NCSE), with worsening secondary brain injury and with worse outcomes. With the widespread utilization of EEG (spot and continuous EEG), rhythmic and periodic patterns that do not fulfill strict seizure criteria have been identified, epidemiologically quantified, and linked to pathophysiological events across a wide spectrum of critical and acute illnesses, including acute brain injury. Increasingly, EEG is not just qualitatively described, but also quantitatively analyzed together with other modalities to generate innovative measurements with possible clinical relevance. In this review, we discuss the current knowledge and emerging applications of EEG in the ICU, including seizure detection, ischemia monitoring, detection of cortical spreading depolarizations, assessment of consciousness and prognostication. We also review some technical aspects and challenges of using EEG in the ICU including the logistics of setting up ICU EEG monitoring in resource-limited settings.

Predicting Early Seizures After Intracerebral Hemorrhage with Machine Learning

BACKGROUND

Seizures are a harmful complication of acute intracerebral hemorrhage (ICH). "Early" seizures in the first week after ICH are a risk factor for deterioration, later seizures, and herniation. Ideally, seizure medications after ICH would only be administered to patients with a high likelihood to have seizures. We developed and validated machine learning (ML) models to predict early seizures after ICH.

METHODS

We used two large datasets to train and then validate our models in an entirely independent test set. The first model ("CAV") predicted early seizures from a subset of variables of the CAVE score (a prediction rule for later seizures)-cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL-whereas early seizure was the dependent variable. We attempted to improve on the "CAV" model by adding anticoagulant use, antiplatelet use, Glasgow Coma Scale, international normalized ratio, and systolic blood pressure ("CAV + "). For each model we used logistic regression, lasso regression, support vector machines, boosted trees (Xgboost), and random forest models. Final model performance was reported as the area under the receiver operating characteristic curve (AUC) using receiver operating characteristic models for the test data. The setting of the study was two large academic institutions: institution 1, 634 patients; institution 2, 230 patients. There were no interventions.

RESULTS

Early seizures were predicted across the ML models by the CAV score in test data, (AUC 0.72, 95% confidence interval 0.62-0.82). The ML model that predicted early seizure better in the test data was Xgboost (AUC 0.79, 95% confidence interval 0.71-0.87, p = 0.04) compared with the CAV model AUC.

CONCLUSIONS

Early seizures after ICH are predictable. Models using cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL had a good accuracy rate, and performance improved with more independent variables. Additional methods to predict seizures could improve patient selection for monitoring and prophylactic seizure medications.

Adult Critical Care Electroencephalography Monitoring for Seizures: A Narrative Review

Electroencephalography (EEG) is an important and relatively inexpensive tool that allows intensivists to monitor cerebral activity of critically ill patients in real time. Seizure detection in patients with and without acute brain injury is the primary reason to obtain an EEG in the Intensive Care Unit (ICU). In response to the increased demand of EEG, advances in quantitative EEG (qEEG) created an approach to review large amounts of data instantly. Finally, rapid response EEG is now available to reduce the time to detect electrographic seizures in limited-resource settings. This review article provides a concise overview of the technical aspects of EEG monitoring for seizures, clinical indications for EEG, the various available modalities of EEG, common and challenging EEG patterns, and barriers to EEG monitoring in the ICU.

Comparing Seizures Captured by Rapid Response EEG and Conventional EEG Recordings in a Multicenter Clinical Study

Objective

A recent multicenter prospective study (DECIDE trial) examined the use of Ceribell Rapid Response EEG (Rapid-EEG) in the emergent evaluation and management of critically ill patients suspected to have non-convulsive seizures. We present a detailed, patient-level examination of seizures detected either on initial Rapid-EEG or subsequent conventional EEG within 24 h to investigate whether seizures were missed on Rapid-EEG due to the exclusion of midline/parasagittal coverage.

Methods

We identified from 164 patients studied in the DECIDE trial those who had seizures detected on Rapid-EEG but not conventional EEG (n = 6), conventional EEG but not Rapid-EEG (n = 4), or both Rapid-EEG and conventional EEG (n = 9). We examined the electrographic characteristics of ictal and interictal findings on both devices, especially their detection in lateral or midline/parasagittal chains, and patient clinical histories to identify contributors toward discordant seizure detection.

Results

Seizures detected on both EEG systems had similar electrographic appearance and laterality. Seizures detected only on conventional EEG (within 24 h following Rapid-EEG) were visible in the temporal chains, and external clinical factors (e.g., treatment with anti-seizure medications, sedation, and duration of recordings) explained the delayed presentation of seizures. Patients with seizures detected only by Rapid-EEG were treated with anti-seizure medications, and subsequent conventional EEG detected interictal highly epileptiform patterns with similar laterality.

Conclusions

Our case series demonstrates that electrographic data obtained from initial Rapid-EEG and subsequent conventional EEG monitoring are largely concordant relative to morphology and laterality. These findings are valuable to inform future investigation of abbreviated EEG systems to optimize management of suspected non-convulsive seizures and status epilepticus. Future, larger studies could further investigate the value of Rapid-EEG findings for forecasting and predicting seizures in long-term EEG recordings.

Improved access to rapid electroencephalography at a community hospital reduces inter-hospital transfers for suspected non-convulsive seizures

OBJECTIVE

Patients with suspected non-convulsive seizures are optimally evaluated with EEG. However, limited EEG infrastructure at community hospitals often necessitates transfer for long-term EEG monitoring (LTM). Novel point-of-care EEG systems could expedite management of nonconvulsive seizures and reduce unnecessary transfers. We aimed to describe the impact of rapid access to EEG using a novel EEG device with remote expert interpretation (tele-EEG) on rates of transfer for LTM.

METHODS

We retrospectively identified a cohort of patients who underwent Rapid-EEG (Ceribell Inc., Mountain View, CA) monitoring as part of a new standard-of-care at a community hospital. Rapid-EEGs were initially reviewed on-site by a community hospital neurologist before transitioning to tele-EEG review by epileptologists at an affiliated academic hospital. We compared the rate of transfer for LTM after Rapid-EEG/tele-EEG implementation to the expected rate if rapid access to EEG was unavailable.

RESULTS

Seventy-four patients underwent a total of 118 Rapid-EEG studies (10 with seizure, 18 with highly epileptiform patterns, 90 with slow/normal activity). Eighty-one studies (69%), including 9 of 10 studies that detected seizures, occurred after-hours when EEG was previously unavailable. Based on historical practice patterns, we estimated that Rapid-EEG potentially obviated transfer for LTM in 31 of 33 patients (94%); both completed transfers occurred before the transition to tele-EEG review.

SIGNIFICANCE

Rapid access to EEG led to the detection of seizures that would otherwise have been missed and reduced inter-hospital transfers for LTM. We estimate that the reduction in inter-hospital transportation costs alone would be in excess of $39,000 ($1,274 per patient). Point-of-care EEG systems may support a hub-and-spoke model for managing non-convulsive seizures (similar to that utilized in this study and analogous to existing acute stroke infrastructures), with increased EEG capacity at community hospitals and tele-EEG interpretation by specialists at academic hospitals that can accept transfers for LTM.

The Past, Present, and Future of Tele-EEG

Tele-electroencephalogram (EEG) has become more pervasive over the last 20 years due to advances in technology, both independent of and driven by personnel shortages. The professionalization of EEG services has both limited growth and controlled the quality of tele-EEG. Growing data on the conditions that benefit from brain monitoring have informed increased critical care EEG and ambulatory EEG utilization. Guidelines that marshal responsible use of still-limited resources and changes in broadband and billing practices have also shaped the tele-EEG landscape. It is helpful to characterize the drivers of tele-EEG to navigate barriers to sustainable growth and to build dynamic systems that anticipate challenges in any of the domains that expand access and enhance quality of these diagnostic services. We explore the historical factors and current trends in tele-EEG in the United States in this review.

Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review

In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

Epileptic seizures in the emergency room: clinical and electroencephalographic findings associated with brain perfusion patterns on computed tomography

Background

Diagnosis of epileptic seizures, particularly regarding status epilepticus (SE), may be challenging in an emergency room setting. The aim of the study was to study the diagnostic yield of perfusion computed tomography (pCT) in patients with single epileptic seizures and SE.

Methods

We retrospectively reviewed the records of patients who followed an acute ischemic stroke pathway during a 9-month period and who were finally diagnosed with a single epileptic seizure or SE. Perfusion maps were visually analyzed for the presence of hyperperfusion and hypoperfusion. Clinical data, EEG patterns, and neuroimaging findings were compared.

Results

We included 47 patients: 20 (42.5%) with SE and 27 (57.5%) with single epileptic seizure. Of 18 patients who showed hyperperfusion on pCT, 12 were ultimately diagnosed with SE and eight had EEG findings compatible with an SE pattern. Focal hyperperfusion on pCT had a sensitivity of 60% (95% CI 36.4–80.2) and a specificity of 77.8% (95% CI 57.2–90.6) for predicting a final diagnosis of SE. The presence of cerebral cortical and thalamic hyperperfusion had a high specificity for predicting SE presence. Of note, 96% of patients without hyperperfusion on pCT did not show an SE pattern on early EEG.

Conclusions

In acute settings, detection by visual analysis of focal cerebral cortical hyperperfusion on pCT in patients with epileptic seizures, especially if accompanied by the highly specific feature of thalamic hyperperfusion, is suggestive of a diagnosis of SE and requires clinical and EEG confirmation. The absence of focal hyperperfusion makes a diagnosis of SE unlikely.

TeleNeuroICU: Expanding the Reach of Subspecialty Neurocritical Care

Telemedicine is a rapidly growing field of medicine due to a combination of high-speed global telecommunication systems and accessibility of small, fast mobile computing platforms with bidirectional audiovisual camera capabilities. Teleneurology is a subset of telemedicine. TeleNeuroICU, one form of teleneurology, is the practice of virtually consulting on patients in the ICU setting with neurological and neurosurgical conditions. Given the current and future shortage of neurologists and neurointensivists, there is a high demand for TeleNeuroICU services around the globe and this is expected to increase in the future. This review summarizes the state of the art around the TeleNeuroICU practice for practitioners in the field, emerging research in this area, and new technologies and integrations that enhance the value of TeleNeuroICU to health care systems.

Rapid Handheld Continuous Electroencephalogram (EEG) Has the Potential to Detect Delirium in Older Adults

BACKGROUND

Delirium-related biochemical derangements lead to electrical changes that can be detected in electroencephalographic (EEG) patterns followed by behavioral signs and symptoms. Studies using limited lead EEG show a large difference between patients with and without delirium while discriminating delirium from other causes. Handheld rapid EEG devices may be capable of detecting delirium before symptom onset, thus providing an objective physiological method to detect delirium when it is most amenable to interventions.

OBJECTIVE

The aim of this study was to explore the potential for rapid EEG to detect waveform pattern changes consistent with delirium status.

METHODS

This prospective exploratory pilot study used a correlational design and mixed models to explore the relationships between handheld portable EEG data and delirium status.

RESULTS

While being under powered minimized opportunities to detect statistical differences in EEG-derived ratios using spectral density analysis, sleep-to-wake ratios tended to be higher in patients with delirium.

CONCLUSIONS

Limited lead EEG may be useful in predicting adverse outcomes and risk for delirium in older critically ill patients. Although this population is at the highest risk for mortality, delirium is not easily identified by current clinical assessments. Therefore, further investigation of limited lead EEG for delirium detection is warranted.

Rapid Response EEG: Current State and Future Directions

PURPOSE OF REVIEW

To critically appraise the literature on the application, methods, and advances in emergency electroencephalography (EEG).

RECENT FINDINGS

The development of rapid EEG (rEEG) technologies and other reduced montage approaches, along with advances in machine learning over the past decade, has increased the rate and access to EEG acquisition. These achievements have made EEG in the emergency setting a practical diagnostic technique for detecting seizures, suspected nonconvulsive status epilepticus (NCSE), altered mental status, stroke, and in the setting of sedation. Growing evidence supports using EEG to expedite medical decision-making in the setting of suspected acute neurological injury. This review covers approaches to acquiring EEG in the emergency setting in the adult and pediatric populations. We also cover the clinical impact of this data, the time associated with emergency EEG, and the costs of acquiring EEG in these settings. Finally, we discuss the advances in artificial intelligence for rapid electrophysiological interpretation.

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.

Educational initiatives for electroencephalography in the critical care setting: a systematic review and meta-analysis

PURPOSE

We systematically reviewed existing critical care electroencephalography (EEG) educational programs for non-neurologists, with the primary goal of reporting the content covered, methods of instruction, overall duration, and participant experience. Our secondary goals were to assess the impact of EEG programs on participants' core knowledge, and the agreement between non-experts and experts for seizure identification.

SOURCE

Major databases were searched from inception to 30 August 2020. Randomized controlled trials, cohort studies, and descriptive studies were all considered if they reported an EEG curriculum for non-neurologists in a critical care setting. Data were presented thematically for the qualitative primary outcome and a meta-analysis using a random effects model was performed for the quantitative secondary outcomes.

PRINCIPAL FINDINGS

Twenty-nine studies were included after reviewing 7,486 citations. Twenty-two studies were single centre, 17 were from North America, and 16 were published after 2016. Most EEG studies were targeted to critical care nurses (17 studies), focused on processed forms of EEG with amplitude-integrated EEG being the most common (15 studies), and were shorter than one day in duration (24 studies). In pre-post studies, EEG programs significantly improved participants' knowledge of tested material (standardized mean change, 1.79; 95% confidence interval [CI], 0.86 to 2.73). Agreement for seizure identification between non-experts and experts was moderate (Cohen's kappa = 0.44; 95% CI, 0.27 to 0.60).

CONCLUSIONS

It is feasible to teach basic EEG to participants in critical care settings from different clinical backgrounds, including physicians and nurses. Brief training programs can enable bedside providers to recognize high-yield abnormalities such as non-convulsive seizures.

Evaluating the utility of Rapid Response EEG in emergency care

BACKGROUND

Timely management of non-convulsive status epilepticus (NCSE) is critical to improving patient outcomes. However, NCSE can only be confirmed using electroencephalography (EEG), which is either significantly delayed or entirely unavailable in emergency departments (EDs). We piloted the use of a new bedside EEG device, Rapid Response EEG (Rapid-EEG, Ceribell), in the ED and evaluated its impact on seizure management when used by emergency physicians.

METHODS

Patients who underwent Rapid-EEG to rule out NCSE were prospectively enrolled in a pilot project conducted at two ED sites (an academic hospital and a community hospital). Physicians were surveyed on the perceived impact of the device on seizure treatment and patient disposition, and we calculated physicians' sensitivity and specificity (with 95% CI) for diagnosing NCSE using Rapid-EEG's Brain Stethoscope function.

RESULTS

Of the 38 patients enrolled, the one patient with NCSE was successfully diagnosed and treated within minutes of evaluation. Physicians reported that Rapid-EEG changed clinical management for 20 patients (53%, 95% CI 37% to 68%), primarily by ruling out seizures and avoiding antiseizure treatment escalation, and expedited disposition for 8 patients (21%, 95% CI 11% to 36%). At the community site, physicians diagnosed seizures by their sound using Brain Stethoscope with 100% sensitivity (95% CI 5% to 100%) and 92% specificity (95% CI 62% to 100%).

CONCLUSION

Rapid-EEG was successfully deployed by emergency physicians at academic and community hospitals, and the device changed management in a majority of cases. Widespread adoption of Rapid-EEG may lead to earlier diagnosis of NCSE, reduced unnecessary treatment and expedited disposition of seizure mimics.

Pick Your Poison but Pick It Wisely: Choosing a Second-Line Medication for the Management of Status Epilepticus

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Early Neurologic Recovery, Practice Pattern Variation, and the Risk of Endotracheal Intubation Following Established Status Epilepticus

OBJECTIVE

To quantify the association between early neurologic recovery, practice pattern variation, and endotracheal intubation during established status epilepticus, we performed a secondary analysis within the cohort of patients enrolled in the Established Status Epilepticus Treatment Trial (ESETT).

METHODS

We evaluated factors associated with the endpoint of endotracheal intubation occurring within 120 minutes of ESETT study drug initiation. We defined a blocked, stepwise multivariate regression, examining 4 phases during status epilepticus management: (1) baseline characteristics, (2) acute treatment, (3) 20-minute neurologic recovery, and (4) 60-minute recovery, including seizure cessation and improving responsiveness.

RESULTS

Of 478 patients, 117 (24.5%) were intubated within 120 minutes. Among high-enrolling sites, intubation rates ranged from 4% to 32% at pediatric sites and 19% to 39% at adult sites. Baseline characteristics, including seizure precipitant, benzodiazepine dosing, and admission vital signs, provided limited discrimination for predicting intubation (area under the curve [AUC] 0.63). However, treatment at sites with an intubation rate in the highest (vs lowest) quartile strongly predicted endotracheal intubation independently of other treatment variables (adjusted odds ratio [aOR] 8.12, 95% confidence interval [CI] 3.08-21.4, model AUC 0.70). Site-specific variation was the factor most strongly associated with endotracheal intubation after adjustment for 20-minute (aOR 23.4, 95% CI 6.99-78.3, model AUC 0.88) and 60-minute (aOR 14.7, 95% CI 3.20-67.5, model AUC 0.98) neurologic recovery.

CONCLUSIONS

Endotracheal intubation after established status epilepticus is strongly associated with site-specific practice pattern variation, independently of baseline characteristics, and early neurologic recovery and should not alone serve as a clinical trial endpoint in established status epilepticus.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov Identifier: NCT01960075.

What's new on EEG monitoring in the ICU

Continuous video-EEG (cEEG, lasting hours to several days) is increasingly used in ICU patients, as it is more sensitive than routine video-EEG (rEEG, lasting 20-30 min) to detect seizures or status epilepticus, and allows more frequent changes in therapeutic regimens. However, cEEG is more resource-consuming, and its relationship to outcome compared to repeated rEEG has only been formally assessed very recently in a randomized controlled trial, which did not show any significant difference in terms of long-term mortality or functional outcome. Awaiting more refined trials, it seems therefore that using repeated rEEG in ICU patients may represent a reasonable alternative in resource-limited settings. Prolonged EEG has been used recently in patients with severe COVID-19 infection, the proportion of seizures seems albeit relatively low, and similar to ICU patients with medical conditions. As in any case a timely EEG recording is recommended in the ICU, r ecent technical developments may ease its use in clinical practice.

Modeling the economic value of Ceribell Rapid Response EEG in the inpatient hospital setting

AIMS

Potentially life-threatening diagnosis of non-convulsive status epilepticus (NCSE) can only be confirmed with electroencephalography (EEG). When access to EEG is limited, physicians may empirically treat, risking unnecessary sedation and intubation, or not treat, increasing risk of refractory seizures. Either may prolong hospital length of stay (LOS). The current study aimed to examine the effect of a new EEG system (Ceribell Rapid Response EEG, Rapid-EEG) on hospital costs by enabling easy access to EEG and expedited seizure diagnosis and treatment.

MATERIALS AND METHODS

We built a two-armed decision-analytic cost-benefit model comparing Rapid-EEG with clinical suspicion alone for NCSE. Diagnostic parameters were informed by a multicenter clinical trial (DECIDE, NCT03534258), while LOS and cost parameters were from public US inpatient data, published literature, and Center for Medicare and Medicaid Services fee schedules. We calculated reference case estimates from mean values, while uncertainty was assessed using 95% prediction intervals (PI) generated by probabilistic sensitivity analysis (PSA) and ANCOVA sum of squares. All costs were indexed to 2019 US$.

RESULTS

Each use case of Rapid-EEG saved $3,971 to $17,290 as it led to reduction in the hospital LOS by 1.2 days (6.1 vs. 7.4 days) and ICU LOS by 0.4 days (1.5 vs. 1.9 days). Using PSA, Rapid-EEG saving was $5,633 per use case (95% PI: $($4,649 to $6,617), as it led to diminished hospital LOS by 1.1 days (95% PI: 0.9-1.4 days) and reduced ICU LOS by 0.5 days (95% PI: 0.4-0.6 days). Cost-savings were demonstrated in 75% of replications. Sixty-four percent of variance in total costs was attributable to LOS for persons incorrectly diagnosed with seizures.

LIMITATIONS

Results were obtained from the analysis of existing data and not a prospective outcome trial.

CONCLUSIONS

Rapid-EEG alters the treatment course for patients with suspected seizures and will result in cost savings per patient.

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

INTRODUCTION

Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients' clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE).

METHODS

We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ's real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists.

RESULTS

Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%.

DISCUSSION

Claritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity.

CONCLUSIONS

Ruling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ's high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.