A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends

Sustainability of a Pediatric Neurointensive Care Unit Model Within a Mixed Pediatric Intensive Care Unit and Its Effect on Nursing Sentiment

ABSTRACT

BACKGROUND: Pediatric neurocritical care (PNCC) and pediatric neurointensive care units (neuro-PICU) are growing fields. Although some institutions have established independent neuro-PICUs meeting most Neurocritical Care Society (NCS) standards for neurocritical care units, many centers lack the resources to do so. We describe an alternative neuro-PICU model as a designated unit within a mixed pediatric intensive care unit (PICU) and its effects on nursing sentiment. METHODS: We established a 6-bed neuro-PICU within a 36-bed noncardiac PICU. Charge nurses were tasked with admitting PNCC patients into these beds. For nursing expertise, we used a core group of 12 PNCC specialty nurses and instituted PNCC nursing education to PICU nurses. We observed the number of PNCC patients admitted to neuro-PICU beds and surveyed charge nurses to identify barriers to assigning patients. We surveyed PICU nursing staff to explore sentiment regarding PNCC before and after establishing the neuro-PICU. Nursing criteria were compared with NCS standards. RESULTS: In the 40-month period, our PICU saw 2060 PNCC admissions. Overall, occupied neuro-PICU beds housed PNCC patients 74.1% of the time. The biggest barriers to patient placement were too many competing placement requests, not enough neuro-PICU beds when specialty census was high, and difficulty assigning one nurse to two PNCC patients. In surveys after establishing the neuro-PICU, compared to before, experienced nurses reported being more interested in obtaining Emergency Neurological Life Support certification (94.2% vs 80.6%, P = .0495), and inexperienced nurses reported being more familiar with PNCC clinical pathways (53.5% vs 31.7%, P = .0263). Most NCS criteria related to nursing organization were met. CONCLUSIONS: Focused neuro-PICUs should be developed to complement advances in the field of PNCC. Alternative neuro-PICU models are possible and can increase nursing interest in further education and awareness of clinical pathways, but barriers exist that require institutional commitment to nursing development to sustain the delivery of specialized care to this population.

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.

Multidisciplinary Consensus on Curricular Priorities for Pediatric Neurocritical Care Nursing Education: A Modified Delphi Study in the United States

BACKGROUND

Nurses are vital partners in the development of pediatric neurocritical care (PNCC) programs. Nursing expertise is acknowledged to be an integral component of high-quality specialty patient care in the field, but little guidance exists regarding educational requirements to build that expertise. We sought to obtain expert consensus from nursing professionals and physicians on curricular priorities for specialized PNCC nursing education in pediatric centers across the United States.

METHODS

We used a modified Delphi study technique surveying a multidisciplinary expert panel of nursing professionals and physicians. Online surveys were distributed to 44 panelists over three rounds to achieve consensus on curricular topics deemed essential for PNCC nursing education. During each round, panelists were asked to rate topics as essential or not essential, as well as given opportunities to provide feedback and suggest changes. Feedback was shared anonymously to the panelist group throughout the process.

RESULTS

From 70 initial individual topics, the consensus process yielded 19 refined topics that were confirmed to be essential for a PNCC nursing curriculum by the expert panel. Discrepancies existed regarding how universally to recommend topics of advanced neuromonitoring, such as brain tissue oxygenation; specialized neurological assessments, such as the serial neurological assessment in pediatrics or National Institutes of Health Stroke Scale; and some disease-based populations. Panelists remarked that not all centers see specific diseases, and not all centers currently employ advanced neuromonitoring technologies and skills.

CONCLUSIONS

We report 19 widely accepted curricular priorities that can serve as a standard educational base for PNCC nursing. Developing education for nurses in PNCC will complement PNCC programs with targeted nursing expertise that extends comprehensive specialty care to the bedside. Further work is necessary to effectively execute educational certification programs, implement nursing standards in the field, and evaluate the impact of nursing expertise on patient care and outcomes.

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.

Neuromonitoring in Children with Cerebrovascular Disorders

BACKGROUND

Cerebrovascular disorders are an important cause of morbidity and mortality in children. The acute care of a child with an ischemic or hemorrhagic stroke or cerebral sinus venous thrombosis focuses on stabilizing the patient, determining the cause of the insult, and preventing secondary injury. Here, we review the use of both invasive and noninvasive neuromonitoring modalities in the care of pediatric patients with arterial ischemic stroke, nontraumatic intracranial hemorrhage, and cerebral sinus venous thrombosis.

METHODS

Narrative review of the literature on neuromonitoring in children with cerebrovascular disorders.

RESULTS

Neuroimaging, near-infrared spectroscopy, transcranial Doppler ultrasonography, continuous and quantitative electroencephalography, invasive intracranial pressure monitoring, and multimodal neuromonitoring may augment the acute care of children with cerebrovascular disorders. Neuromonitoring can play an essential role in the early identification of evolving injury in the aftermath of arterial ischemic stroke, intracranial hemorrhage, or sinus venous thrombosis, including recurrent infarction or infarct expansion, new or recurrent hemorrhage, vasospasm and delayed cerebral ischemia, status epilepticus, and intracranial hypertension, among others, and this, is turn, can facilitate real-time adjustments to treatment plans.

CONCLUSIONS

Our understanding of pediatric cerebrovascular disorders has increased dramatically over the past several years, in part due to advances in the neuromonitoring modalities that allow us to better understand these conditions. We are now poised, as a field, to take advantage of advances in neuromonitoring capabilities to determine how best to manage and treat acute cerebrovascular disorders in children.

Tonic Seizures in a Patient With Lennox-Gastaut Syndrome Manifest as "Icicles" Rather Than "Flames" on Quantitative EEG Analysis

SUMMARY

Quantitative analysis of continuous electroencephalography (QEEG) is increasingly being used to augment seizure detection in critically ill patients. Typically, seizures manifest on QEEG as abrupt increases in power and frequency, a visual pattern often called "flames." Here, we present a case of a 16-year-old patient with intractable Lennox-Gastaut syndrome secondary to a pathogenic variant in the SCN2A gene who had tonic seizures that manifest as abrupt decreases in power on QEEG, a visual pattern we term "icicles." Recognition of QEEG patterns representative of different seizure types is important as QEEG use becomes more widespread including in pediatric populations.

Macroperiodic Oscillations Are Associated With Seizures Following Acquired Brain Injury in Young Children

PURPOSE

Seizures occur in 10% to 40% of critically ill children. We describe a phenomenon seen on color density spectral array but not raw EEG associated with seizures and acquired brain injury in pediatric patients.

METHODS

We reviewed EEGs of 541 children admitted to an intensive care unit between October 2015 and August 2018. We identified 38 children (7%) with a periodic pattern on color density spectral array that oscillates every 2 to 5 minutes and was not apparent on the raw EEG tracing, termed macroperiodic oscillations (MOs). Internal validity measures and interrater agreement were assessed. We compared demographic and clinical data between those with and without MOs.

RESULTS

Interrater reliability yielded a strong agreement for MOs identification (kappa: 0.778 [0.542-1.000]; P < 0.0001). There was a 76% overlap in the start and stop times of MOs among reviewers. All patients with MOs had seizures as opposed to 22.5% of the general intensive care unit monitoring population ( P < 0.0001). Macroperiodic oscillations occurred before or in the midst of recurrent seizures. Patients with MOs were younger (median of 8 vs. 208 days; P < 0.001), with indications for EEG monitoring more likely to be clinical seizures (42 vs. 16%; P < 0.001) or traumatic brain injury (16 vs. 5%, P < 0.01) and had fewer premorbid neurologic conditions (10.5 vs. 33%; P < 0.01).

CONCLUSIONS

Macroperiodic oscillations are a slow periodic pattern occurring over a longer time scale than periodic discharges in pediatric intensive care unit patients. This pattern is associated with seizures in young patients with acquired brain injuries.

Utilization of quantitative electroencephalogram in China: an online questionnaire survey

Background

Quantitative electroencephalogram (QEEG) is a tool that uses a computer to analyze brain activity monitored by electroencephalogram (EEG) according to measurements such as frequency, amplitude, and slope. The purpose of this study was to understand the current situation of QEEG utilization in China and further compare the situations among different regions and different levels of hospitals.

Methods

An online questionnaire comprising 14 questions was designed. Statistical description and analysis were made for the results of the questionnaire survey.

Results

A total of 158 people from 134 medical institutions participated in the survey. The participants came from 21 provinces, accounting for 61.76% (21/34) of the 34 provincial administrative regions in China. The Eastern China region accounted for 66.42% (89/134) of all the medical institutions that participated in this survey. Among the institutions surveyed, QEEG was routinely used in only 23.88% (32/134) of them. Among the medical institutions in which QEEG was routinely used, 87.50% (28/32) of them were 3A-grade hospitals. Among the institutions with routine use of QEEG, 56.25% (18/32) were affiliated hospitals of medical schools. There was a significant difference in the utilization of QEEG between the 3A-grade and non-3A-grade hospitals (P = 0.040) and between the hospitals affiliated to medical schools and those non-affiliated to medical schools (P = 0.020).

Conclusions

The utilization of QEEG is still limited in China. There are differences in the use of QEEG among different hospitals and regions.

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.

Real-time seizure detection in paediatric intensive care patients: the RESET child brain protocol

INTRODUCTION

Approximately 20%-40% of comatose children with risk factors in intensive care have electrographic-only seizures; these go unrecognised due to the absence of continuous electroencephalography (EEG) monitoring (cEEG). Utility of cEEG with high-quality assessment is currently limited due to high-resource requirements. New software analysis tools are available to facilitate bedside cEEG assessment using quantitative EEG (QEEG) trends. The primary aim of this study is to describe accuracy of interpretation of QEEG trends by paediatric intensive care unit (PICU) nurses compared with cEEG assessment by neurologist (standard clinical care) in children at risk of seizures and status epilepticus utilising diagnostic test statistics. The secondary aims are to determine time to seizure detection for QEEG users compared with standard clinical care and describe impact of confounders on accuracy of seizure detection.

METHODS AND ANALYSIS

This will be a single-centre, prospective observational cohort study evaluating a paediatric QEEG programme utilising the full 19 electrode set. The setting will be a 36-bed quaternary PICU with medical, cardiac and general surgical cases. cEEG studies in PICU patients identified as 'at risk of seizures' will be analysed. Trained bedside clinical nurses will interpret the QEEG. Seizure events will be marked as seizures if >3 QEEG criteria occur. Post-hoc dedicated neurologists, who remain blinded to the QEEG analysis, will interpret the cEEG. Determination of standard test characteristics will assess the primary hypothesis. To calculate 95% (CIs) around the sensitivity and specificity estimates with a CI width of 10%, the sample size needed for sensitivity is 80 patients assuming each EEG will have approximately 9 to 18 1-hour epochs.

ETHICS AND DISSEMINATION

The study has received approval by the Children's Health Queensland Human Research Ethics Committee (HREC/19/QCHQ/58145). Results will be made available to the funders, critical care survivors and their caregivers, the relevant societies, and other researchers.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clinical Trials Registry (ANZCTR) 12621001471875.

Quantitative EEG-Based Seizure Estimation in Super-Refractory Status Epilepticus

BACKGROUND

The objective of this study was to evaluate the accuracy of seizure burden in patients with super-refractory status epilepticus (SRSE) by using quantitative electroencephalography (qEEG).

METHODS

EEG recordings from 69 patients with SRSE (2009-2019) were reviewed and annotated for seizures by three groups of reviewers: two board-certified neurophysiologists using only raw EEG (gold standard), two neurocritical care providers with substantial experience in qEEG analysis (qEEG experts), and two inexperienced qEEG readers (qEEG novices) using only a qEEG trend panel.

RESULTS

Raw EEG experts identified 35 (51%) patients with seizures, accounting for 2950 seizures (3,126 min). qEEG experts had a sensitivity of 93%, a specificity of 61%, a false positive rate of 6.5 per day, and good agreement (κ = 0.64) between both qEEG experts. qEEG novices had a sensitivity of 98.5%, a specificity of 13%, a false positive rate of 15 per day, and fair agreement (κ = 0.4) between both qEEG novices. Seizure burden was not different between the qEEG experts and the gold standard (3,257 vs. 3,126 min), whereas qEEG novices reported higher burden (6066 vs. 3126 min).

CONCLUSIONS

Both qEEG experts and novices had a high sensitivity but a low specificity for seizure detection in patients with SRSE. qEEG could be a useful tool for qEEG experts to estimate seizure burden in patients with SRSE.

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.

Implementing Quantitative Electroencephalogram Monitoring by Nurses in a Pediatric Intensive Care Unit

BACKGROUND

Nonconvulsive seizures occur frequently in pediatric intensive care unit patients and can be impossible to detect clinically without electroencephalogram monitoring. Quantitative electroencephalography uses mathematical signal analysis to compress data, monitoring trends over time. Nonneurologists can identify seizures with quantitative electroencephalography, but data on its use in the clinical setting are limited.

LOCAL PROBLEM

Bedside quantitative electroencephalography was implemented and nurses received education on its use for seizure detection. This quality improvement project aimed to describe the time between nurses' recognition of electrographic seizures and seizure treatment.

METHODS

Education was provided in phases over several months. Retrospective medical record review evaluated quantitative electroencephalograms and medication interventions from September 2019 through March 2020. A bedside form was used to measure nurses' use of quantitative electroencephalograms, change recognition, clinician notification, and seizure treatment. A nurse survey evaluated the education after implementation.

RESULTS

Data included 44 electroencephalograms from 30 pediatric intensive care unit patients aged 18 years or less with electroencephalogram monitoring durations of 4 hours or longer. Nurses monitored quantitative electroencephalograms in 73% of cases, documented at least 1 change in the quantitative electroencephalogram display in 28% of these cases, and contacted the neurocritical care team in 78% of cases in which they documented a change. Seizure treatment was initiated in response to the nursing call in 1 patient. Time to treatment was approximately 20 minutes.

CONCLUSIONS

An education program for quantitative electroencephalogram interpretation by nurse providers is feasible yet complex, requiring multiple reeducation cycles.

IGNITE Status Epilepticus Survey: A Nationwide Interrogation about the Current Management of Status Epilepticus in Germany

We aimed to evaluate the current management of status epilepticus (SE) in intensive care units (ICUs) in Germany, depending on the different hospital levels of care and the ICU specialty. We performed a nationwide web-based anonymized survey, including all German ICUs registered with the German Society for Neurointensive and Emergency Care (Deutsche Gesellschaft für Neurointensiv- und Notfallmedizin; DGNI). The response rate was 83/232 (36%). Continuous EEG monitoring (cEEG) was available in 86% of ICUs. Regular written cEEG reports were obtained in only 50%. Drug management was homogeneous with a general consensus regarding substance order: benzodiazepines—anticonvulsants—sedatives. Thereunder first choice substances were lorazepam (90%), levetiracetam (91%), and propofol (73%). Data suggest that network structures for super-refractory SE are not permeable, as 75% did not transfer SE patients. Our survey provides “real world data” concerning the current management of SE in Germany. Uniform standards in the implementation of cEEG could help further improve the overall quality. Initial therapy management is standardized. For super-refractory SE, a concentration of highly specialized centers establishing network structures analogous to neurovascular diseases seems desirable to apply rescue therapies with low evidence carefully, ideally collecting data on this rare condition in registries and clinical trials.

Using EEG in Resource-Limited Areas: Comparing Qualitative and Quantitative Interpretation Methods in Cerebral Malaria

BACKGROUND

Our goal was to compare the strength of association and predictive ability of qualitative and quantitative electroencephalographic (EEG) factors with the outcomes of death and neurological disability in pediatric cerebral malaria (CM).

METHODS

We enrolled children with a clinical diagnosis of CM admitted to Queen Elizabeth Central Hospital (Blantyre, Malawi) between 2012 and 2017. A routine-length EEG was performed within four hours of admission. EEG data were independently interpreted using qualitative and quantitative methods by trained pediatric neurophysiologists. EEG interpreters were unaware of patient discharge outcome.

RESULTS

EEG tracings from 194 patients were reviewed. Multivariate modeling revealed several qualitative and quantitative EEG variables that were independently associated with outcomes. Quantitative methods modeled on mortality had better goodness of fit than qualitative ones. When modeled on neurological morbidity in survivors, goodness of fit was better for qualitative methods. When the probabilities of an adverse outcome were calculated using multivariate regression coefficients, only the model of quantitative EEG variables regressed on the neurological sequelae outcome showed clear separation between outcome groups.

CONCLUSIONS

Multiple qualitative and quantitative EEG factors are associated with outcomes in pediatric CM. It may be possible to use quantitative EEG factors to create automated methods of study interpretation that have similar predictive abilities for outcomes as human-based interpreters, a rare resource in many malaria-endemic areas. Our results provide a proof-of-concept starting point for the development of quantitative EEG interpretation and prediction methodologies useful in resource-limited settings.

Electrographic Seizure Detection by Neuroscience Intensive Care Unit Nurses via Bedside Real-Time Quantitative EEG

Objective

Our primary objective was to determine the performance of real-time neuroscience intensive care unit (neuro-ICU) nurse interpretation of quantitative EEG (qEEG) at the bedside for seizure detection. Secondary objectives included determining nurse time to seizure detection and assessing factors that influenced nurse accuracy.

Methods

Nurses caring for neuro-ICU patients undergoing continuous EEG (cEEG) were trained using a 1-hour qEEG panel (rhythmicity spectrogram and amplitude-integrated EEG) bedside display. Nurses' hourly interpretations were compared with post hoc cEEG review by 2 neurophysiologists as the gold standard. Diagnostic performance, time to seizure detection compared with standard of care (SOC), and effects of other factors on nurse accuracy were calculated.

Results

A total of 109 patients and 65 nurses were studied. Eight patients had seizures during the study period (7%). Nurse sensitivity and specificity for the detection of seizures were 74% and 92%, respectively. Mean nurse time to seizure detection was significantly shorter than SOC by 132 minutes (Cox proportional hazard ratio 6.96). Inaccurate nurse interpretation was associated with increased hours monitored and presence of brief rhythmic discharges.

Conclusions

This prospective study of real-time nurse interpretation of qEEG for seizure detection in neuro-ICU patients showed clinically adequate sensitivity and specificity. Time to seizure detection was less than that of SOC.

Trial Registration Information

Clinical trial registration number NCT02082873.

Classification of Evidence

This study provides Class I evidence that neuro-ICU nurse interpretation of qEEG detects seizures in adults with a sensitivity of 74% and a specificity of 92% compared with traditional cEEG review.

Suppression of Electrographic Seizures Is Associated with Amelioration of QTc Interval Prolongation in Patients with Traumatic Brain Injury

Introduction: Disorders in electroencephalography (EEG) are commonly noted in patients with traumatic brain injury (TBI) and may be associated with electrocardiographic disturbances. Electrographic seizures (ESz) are the most common features in these patients. This study aimed to explore the relationship between ESz and possible changes in QTc interval and spatial QRS-T angle both during ESz and after ESz resolution. Methods: Adult patients with TBI were studied. Surface 12-lead ECGs were recorded using a Cardiax device during ESz events and 15 min after their effective suppression using barbiturate infusion. The ESz events were diagnosed using Masimo Root or bispectral index (BIS) devices. Results: Of the 348 patients considered for possible inclusion, ESz were noted in 72, with ECG being recorded in 21. Prolonged QTc was noted during ESz but significantly ameliorated after ESz suppression (540.19 ± 60.68 ms vs. 478.67 ± 38.52 ms, p < 0.001). The spatial QRS-T angle was comparable during ESz and after treatment. Regional cerebral oximetry increased following ESz suppression (from 58.4% ± 6.2 to 60.5% ± 4.2 (p < 0.01) and from 58.2% ± 7.2 to 60.8% ± 4.8 (p < 0.05) in the left and right hemispheres, respectively). Conclusion: QTc interval prolongation occurs during ESz events in TBI patients but both it and regional cerebral oximetry are improved after suppression of seizures.

Utility of Quantitative EEG for Seizure Detection in Adults

SUMMARY

Traditional review of EEG for seizure detection requires time and the expertise of a trained neurophysiologist; therefore, it is time- and resource-intensive. Quantitative EEG (qEEG) encompasses a variety of methods to make EEG review more efficient and allows for nonexpert review. Literature supports that qEEG is commonly used by neurophysiologists and nonexperts in clinical practice. In this review, the different types of qEEG trends and spectrograms used for seizure detection in adults, from basic concepts to clinical applications, are discussed. The merits and drawbacks of the most common qEEG trends are detailed. The authors detail the retrospective literature on qEEG sensitivity, specificity, and false alarm rate as interpreted by experts and nonexperts alike. Finally, the authors discuss the future of qEEG as a useful screening tool and speculate on the trajectory of future investigations in the field.

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.

Continuous Electroencephalographic Training for Neuroscience Intensive Care Unit Nurses: A Feasibility Study

BACKGROUND

Use of continuous electroencephalographic (cEEG) monitoring has more than doubled at our institution for the last 4 years. Although intensive care unit cEEG is reviewed remotely by board-certified epileptologists every 4 to 6 hours, there are inherent delays between occurrence, recognition, and treatment of epileptiform activity. Neuroscience intensive care unit (NSICU) nurses are uniquely positioned to monitor cEEG in real time yet do not receive formal training. The purpose of this study was to evaluate the effectiveness of an education program to teach nurses to monitor cEEG, identify a burst suppression pattern, and measure the duration of suppression.

METHODS

We performed a retrospective analysis of pretest and posttest data. All NSICU nurses (40) were invited to complete the pretest (PT-0), with 25 participating. Learning style/preference, demographics, comfort with cEEG, and knowledge of EEG fundamentals were assessed. A convenience cohort of NSICU nurses (13) were selected to undergo EEG training. Posttests evaluating EEG fundamental knowledge were completed immediately after training (PT-1), at 3 months (PT-3), and at 6 months (PT-6). The cohort also completed a burst suppression module after the training, which assessed ability to quantify the duration of suppression.

RESULTS

Mean cohort test scores significantly improved after the training (P < .001). All nurses showed improvement in test scores, and 76.9% passed PT-1 (a score of 80% or higher). Reported mean comfort level with EEG also significantly improved after the training (P = .001). There was no significant difference between mean cohort scores between PT-1, PT-3, and PT-6 (all 88.6%; P = 1.000). Mean cohort score from the bust suppression module was 73%, with test scores ranging from 31% to 93%.

CONCLUSIONS

NSICU nurses can be taught fundamentals of cEEG, to identify a burst suppression pattern, and to quantify the duration of suppression. Further research is needed to determine whether this knowledge can be translated into clinical competency and affect patient care.

Structure and Outcomes of Educational Programs for Training Non-electroencephalographers in Performing and Screening Adult EEG: A Systematic Review

OBJECTIVE

To qualitatively and quantitatively summarize curricula, teaching methods, and effectiveness of educational programs for training bedside care providers (non-experts) in the performance and screening of adult electroencephalography (EEG) for nonconvulsive seizures and other patterns.

METHODS

PRISMA methodological standards were followed. MEDLINE, EMBASE, Cochrane, CINAHL, WOS, Scopus, and MedEdPORTAL databases were searched from inception until February 26, 2020 with no restrictions. Abstract and full-text review was completed in duplicate. Studies were included if they were original research; involved non-experts performing, troubleshooting, or screening adult EEG; and provided qualitative descriptions of curricula and teaching methods and/or quantitative assessment of non-experts (vs gold standard EEG performance by neurodiagnostic technologists or interpretation by neurophysiologists). Data were extracted in duplicate. A content analysis and a meta-narrative review were performed.

RESULTS

Of 2430 abstracts, 35 studies were included. Sensitivity and specificity of seizure identification varied from 38 to 100% and 65 to 100% for raw EEG; 40 to 93% and 38 to 95% for quantitative EEG, and 95 to 100% and 65 to 85% for sonified EEG, respectively. Non-expert performance of EEG resulted in statistically significant reduced delay (86 min, p < 0.0001; 196 min, p < 0.0001; 667 min, p < 0.005) in EEG completion and changes in management in approximately 40% of patients. Non-experts who were trained included physicians, nurses, neurodiagnostic technicians, and medical students. Numerous teaching methods were utilized and often combined, with instructional and hands-on training being most common.

CONCLUSIONS

Several different bedside providers can be educated to perform and screen adult EEG, particularly for the purpose of diagnosing nonconvulsive seizures. While further rigorous research is warranted, this review demonstrates several potential bridges by which EEG may be integrated into the care of critically ill patients.

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.

Educational initiatives and implementation of electroencephalography into the acute care environment: a protocol of a systematic review

BACKGROUND

Use of electroencephalography (EEG) is currently recommended by the American Clinical Neurophysiology Society for a wide range of indications, including diagnosis of nonconvulsive status epilepticus and evaluation of unexplained disorders of consciousness. Data interpretation usually occurs by expert personnel (e.g., epileptologists, neurophysiologists), with information relayed to the primary care team. However, data cannot always be read in time-sensitive fashion, leading to potential delays in EEG interpretation and patient management. Multiple training programs have recently been described to enable non-experts to rapidly interpret EEG at the bedside. A comprehensive review of these training programs, including the tools used, outcomes obtained, and potential pitfalls, is currently lacking. Therefore, the optimum training program and implementation strategy remain unknown.

METHODS

We will conduct a systematic review of descriptive studies, case series, cohort studies, and randomized controlled trials assessing training programs for EEG interpretation by non-experts. Our primary objective is to comprehensively review educational programs in this domain and report their structure, patterns of implementation, limitations, and trainee feedback. Our secondary objective will be to compare the performance of non-experts for EEG interpretation with a gold standard (e.g., interpretation by a certified electroencephalographers). Studies will be limited to those performed in acute care settings in both adult and pediatric populations (intensive care unit, emergency department, or post-anesthesia care units). Comprehensive search strategies will be developed for MEDLINE, EMBASE, WoS, CINAHL, and CENTRAL to identify studies for review. The gray literature will be scanned for further eligible studies. Two reviewers will independently screen the search results to identify studies for inclusion. A standardized data extraction form will be used to collect important data from each study. If possible, we will attempt to meta-analyze the quantitative data. If heterogeneity between studies is too high, we will present meaningful quantitative comparisons of secondary outcomes as per the synthesis without meta-analysis (SWiM) reporting guidelines.

DISCUSSION

We will aim to summarize the current literature in this domain to understand the structure, patterns, and pitfalls of EEG training programs for non-experts. This review is undertaken with a view to inform future education designs, potentially enabling rapid detection of EEG abnormalities, and timely intervention by the treating physician.

PROSPERO REGISTRATION

Submitted and undergoing review. Registration ID: CRD42020171208 .

Detecting Seizures and Epileptiform Abnormalities in Acute Brain Injury

PURPOSE OF REVIEW

Acute brain injury (ABI) is a broad category of pathologies, including traumatic brain injury, and is commonly complicated by seizures. Electroencephalogram (EEG) studies are used to detect seizures or other epileptiform patterns. This review seeks to clarify EEG findings relevant to ABI, explore practical barriers limiting EEG implementation, discuss strategies to leverage EEG monitoring in various clinical settings, and suggest an approach to utilize EEG for triage.

RECENT FINDINGS

Current literature suggests there is an increased morbidity and mortality risk associated with seizures or patterns on the ictal-interictal continuum (IIC) due to ABI. Further, increased use of EEG is associated with better clinical outcomes. However, there are many logistical barriers to successful EEG implementation that prohibit its ubiquitous use. Solutions to these limitations include the use of rapid EEG systems, non-expert EEG analysis, machine learning algorithms, and the incorporation of EEG data into prognostic models.

Continuous Electroencephalography Monitoring in Critically Ill Infants and Children

Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.

A standardized nomenclature for spectrogram EEG patterns: Inter-rater agreement and correspondence with common intensive care unit EEG patterns

OBJECTIVE

To determine the inter-rater agreement (IRA) of a standardized nomenclature for EEG spectrogram patterns, and to estimate the probability distribution of ictal-interictal continuum (IIC) patterns vs. other EEG patterns within each category in this nomenclature.

METHODS

We defined seven spectrogram categories: "Solid Flames", "Irregular Flames", "Broadband-monotonous", "Narrowband-monotonous", "Stripes", "Low power", and "Artifact". Ten electroencephalographers scored 115 spectrograms and the corresponding raw EEG samples. Gwet's agreement coefficient was used to calculate IRA.

RESULTS

Solid Flames represented seizures or IIC patterns 69.4% of the time. Irregular Flames represented seizures or IIC patterns 38.7% of the time. Broadband-monotonous primarily corresponded with seizures or IIC (54.3%) and Narrowband-monotonous with focal or generalized slowing (43.8%). Stripes were associated with burst-suppression (37.2%) and generalized suppression (34.4%). Low Power category was associated with generalized suppression (94%). There was "near perfect" agreement for Solid Flames (κ = 94.36), Low power (κ = 92.61), and Artifact (κ = 93.72). There was "substantial agreement" for all other categories (κ = 74.65-79.49).

CONCLUSIONS

This EEG spectrogram nomenclature has high IRA among electroencephalographers.

SIGNIFICANCE

The nomenclature can be a useful tool for EEG screening. Future studies are needed to determine if using this nomenclature shortens time to IIC identification, and how best to use it in practice to reduce time to intervention.

Teaching Important Basic EEG Patterns of Bedside Electroencephalography to Critical Care Staffs: A Prospective Multicenter Study

BACKGROUND

Continuous electroencephalography (cEEG) is commonly recommended for neurocritical care patients. Routine implementation of such monitoring requires the specific training of professionals. The aim of this research was to evaluate the effectiveness of a training program on initiation of the basic interpretation of cEEG for critical care staff in a prospective multicenter study.

METHODS

After completion of a pretest, participants (senior physicians, fellows, residents, medical students, and nurses) recruited in six French ICUs participated in a face-to-face electroencephalogram (EEG) training program followed by additional e-learning sessions at day 1 (post-course), day 15, day 30, and day 90, based on training tests followed by illustrated and commented answers. Each test was designed to evaluate knowledge and skills through correct recognition of ten predefined EEG sequences covering the most common normal and abnormal patterns. The primary objective was to achieve a success rate > 80% correct answers at day 90 by at least 75% of the participants.

RESULTS

Among 250 participants, 77/108 (71.3%) who completed the full training program achieved at least 80% correct answers at day 90. Paired comparisons between the scores obtained at each evaluation showed an increase over time. The rate of correct answers at day 90 was > 80% for all common predefined EEG sequences, except for the recognition of periodic and burst-suppression patterns and reactivity, which were identified in only 42.6% (95% CI 36.4-48.8), 60.2% (54.1-66.3), and 70.4% (64.7-76.1) of the tests, respectively.

CONCLUSIONS

A training strategy for the basic interpretation of EEG in ICUs, consisting of a face-to-face EEG course supplemented with reinforcement of knowledge by e-learning, was associated with significant resignation and an effectiveness of training allowing 71% of learners to accurately recognize important basic EEG patterns encountered in critically ill patients.

TRIAL REGISTRATION

ClinicalTrials.gov number: NCT03545776.