Continuous Electroencephalography in the Critically Ill: Clinical and Continuous Electroencephalography Markers for Targeted Monitoring

Cyclic Alternating Patterns of Encephalopathy (CAPE) in Acute Brain Injury Through a Quantitative Electroencephalogram (QEEG) Perspective: A Case Series

Continuous EEG (cEEG) is a non-invasive bedside tool used to detect causative or contributory conditions of the encephalopathic state. By continuously recording electrical brain activity, it provides insights into background patterns, seizures, and dynamic cerebral activity, thereby aiding in the management of critically ill patients with acute brain injury. The term 'cyclic alternating pattern of encephalopathy' (CAPE) was recently introduced to describe alternating changes in brain electrical activity observed on EEG in critically ill patients. CAPE is characterized by electrocerebral background pattern shifts lasting at least ten seconds and repeating regularly for a minimum of six cycles. Quantitative EEG (QEEG) facilitates the interpretation of extensive cEEG datasets by applying mathematical algorithms to transform raw EEG data into time-compressed, frequency- or amplitude-based visualizations. Through Fourier analysis, QEEG decomposes the EEG signals, plotting the amplitude of different frequency bands over time, enabling easier identification of state changes such as CAPE across extended periods. This case series highlights four critically ill patients exhibiting CAPE on cEEG, with corresponding findings illustrated via QEEG. These cases demonstrate that QEEG effectively identifies CAPE by detecting changes in spectral power density and rhythmicity across distinct states. Adjusting the temporal resolution on QEEG enhances the visibility of CAPE patterns, facilitating their recognition.

The Role of Electroencephalography in Children with Acute Altered Mental Status of Unknown Etiology: A Prospective Study

INTRODUCTION

Acute altered mental status (AAMS) is often a challenge for clinicians, since the underlying etiologies cannot always easily be inferred based on the patient's clinical presentation, medical history, or early examinations. The aim of this study is to evaluate the role of electroencephalogram (EEG) as a diagnostic tool in AAMS of unknown etiology in children.

MATERIALS AND METHODS

We conducted a prospective study involving EEG assessments on children presenting with AAMS between May 2017 and October 2019. Inclusion criteria were age 1 month to 18 years and acute (<1 week) and persistent (>5 minutes) altered mental status. Patients with a known etiology of AAMS were excluded. A literature review was also performed.

RESULTS

Twenty patients (median age: 7.7 years, range: 0.5-15.4) were enrolled. EEG contributed to the diagnosis in 14/20 cases, and was classified as diagnostic in 9/20 and informative in 5/20. Specifically, EEG was able to identify nonconvulsive status epilepticus (NCSE) in five children and psychogenic events in four. EEG proved to be a poorly informative diagnostic tool at AAMS onset in six children; however, in five of them, it proved useful during follow-up.

CONCLUSIONS

Limited data exist regarding the role of EEG in children with AAMS of unknown etiology. In our population, EEG proved to be valuable tool, and was especially useful in the prompt identification of NCSE and psychogenic events.

Associations between Electroencephalographic Variables, Early Post-Traumatic Seizure Risk, and Outcomes following Pediatric Severe Traumatic Brain Injury

Early post-traumatic seizures (PTS) are associated with worse outcomes in children with traumatic brain injury (TBI). Our aim was to identify the association between continuous electroencephalogram (cEEG) characteristics and early PTS risk following pediatric severe TBI. We also evaluated the relationship between cEEG background features and outcomes. A single-center retrospective cohort study was performed on children between 0 and 18 years of age admitted to the pediatric intensive care unit from 2016 to 2019 with severe TBI and cEEG monitoring within 7 days of injury. Raw cEEG tracings were reviewed by an epileptologist in accordance with American Clinical Neurophysiology Society (ACNS) Critical Care EEG terminology. Univariate comparisons were made between children with and without early PTS, as well as between those with and without varying cEEG background features. Eighteen children (31%) of the 59 included had early PTS. Interictal abnormalities, inclusive of sporadic spikes and sharp waves, rhythmic delta activity, or lateralized periodic discharges (LPDs) were more common among children with seizures (100 vs. 22%; p  < 0.01). LPDs were also more common in the seizure group (44 vs. 2%; p  < 0.01). Background discontinuity was associated with worse Glasgow Outcome Scale-Extended Pediatric Version (GOS-E Peds) scores at discharge and 3-, 6-, and 12-month post-discharge ( p  < 0.01). Lack of reactivity was also associated with worse GOS-E Peds scores at 3-, 6-, and 12-month post-discharge ( p  < 0.01). Interictal abnormalities and LPDs were each associated with early PTS following pediatric severe TBI. Larger studies should evaluate if high-risk patients would benefit from prolonged cEEG monitoring and/or more aggressive anti-seizure prophylaxis. Discontinuity and lack of variability were associated with worse outcomes. Future studies should attempt to clarify their role as potential early markers of prognosis.

Prevalence of Electrographic Seizures in Hospitalized Patients With Altered Mental Status With No Significant Seizure Risk Factors Who Underwent Continuous EEG Monitoring: A Retrospective Study

OBJECTIVES

The objective of this study is to evaluate the prevalence of electrographic seizures in hospitalized patients with altered mental status and no significant risk factors for seizures.

METHODS

We retrospectively reviewed over a six-year period (2013-2019) the medical records of all adults admitted at Ohio State University Wexner Medical Center (OSUWMC), who underwent continuous electroencephalography (cEEG) monitoring for > 48 hours. Our primary objective was to identify the prevalence of electrographic seizures in patients with altered mental status and no significant acute or remote risk factors for seizures.

RESULTS

A total of 1966 patients were screened for the study, 1892 were excluded (96.2%) and 74 patients met inclusion criteria. Electrographic seizures were identified in seven of 74 patients (9.45%). We found a significant correlation between electrographic seizures and a history of hepatic cirrhosis, n= 4 (57%), (p=0.035), acute chronic hepatic failure during admission, 71% (n=5), (p=0.027), and hyperammonemia (p =0.009).

CONCLUSION

In this retrospective study of patients with altered mental status and no significant acute or remote risk factors for seizures who underwent cEEG monitoring for > 48 hours, electrographic seizures were identified in 9.45%. Electrographic seizures were associated with hepatic dysfunction and hyperammonemia. Based on our results, cEEG monitoring should be considered in patients with altered mental status and hepatic dysfunction even in the absence of other seizure risk factors.

EEG: Current relevance and promising quantitative analyses

Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.

Continuous electroencephalography in the intensive care unit: A critical review and position statement from an Australian and New Zealand perspective

Objectives

This article aims to critically review the literature on continuous electroencephalography (cEEG) monitoring in the intensive care unit (ICU) from an Australian and New Zealand perspective and provide recommendations for clinicians.

Design and review methods

A taskforce of adult and paediatric neurologists, selected by the Epilepsy Society of Australia, reviewed the literature on cEEG for seizure detection in critically ill neonates, children, and adults in the ICU. The literature on routine EEG and cEEG for other indications was not reviewed. Following an evaluation of the evidence and discussion of controversial issues, consensus was reached, and a document that highlighted important clinical, practical, and economic considerations regarding cEEG in Australia and New Zealand was drafted.

Results

This review represents a summary of the literature and consensus opinion regarding the use of cEEG in the ICU for detection of seizures, highlighting gaps in evidence, practical problems with implementation, funding shortfalls, and areas for future research.

Conclusion

While cEEG detects electrographic seizures in a significant proportion of at-risk neonates, children, and adults in the ICU, conferring poorer neurological outcomes and guiding treatment in many settings, the health economic benefits of treating such seizures remain to be proven. Presently, cEEG in Australian and New Zealand ICUs is a largely unfunded clinical resource that is subsequently reserved for the highest-impact patient groups. Wider adoption of cEEG requires further research into impact on functional and health economic outcomes, education and training of the neurology and ICU teams involved, and securement of the necessary resources and funding to support the service.

Prevalence, electroclinical spectrum and effect on the outcome of non-convulsive status epilepticus in critically ill patients; the utility of routine electroencephalogram

OBJECTIVES

To highlight the importance of routine electroencephalogram (rEEG) in detecting non-convulsive status epilepticus (NCSE), describing the electroclinical spectrum and effect on outcome in critically ill patients with altered mental status (CIPAMS).

METHODS

This retrospective study was conducted at King Fahd University Hospital. Clinical data and EEG recordings of CIPAMS to rule out NCSE were reviewed. All patients had at least 30 minutes of EEG recording. The Salzburg Consensus criteria (SCC) were applied to diagnose NCSE. The data analysis was performed using SPSS version 22.0. The chi-squared test was used to compare categorical variables such as etiologies, EEG findings, and functional outcomes. Multivariable analysis was performed to identify the predictors of unfavorable outcomes.

RESULTS

A total of 323 CIPAMS referred to rule out NCSE were enrolled (mean age 57.8 ± 20 years). Nonconvulsive status epilepticus was diagnosed in 54 (16.7%) patients. A significant association was found between subtle clinical features and NCSE (P =< 0.01). Acute ischemic stroke (18.5%), sepsis (18.5%), and hypoxic brain injury (22.2%) were the main etiologies. The previous history of epilepsy was significantly associated with NCSE (P = 0.01). Acute stroke, cardiac arrest, mechanical ventilation, and NCSE were statistically associated with unfavorable outcomes. Nonconvulsive status epilepticus was an independent predictor of unfavorable outcomes (P = 0.02, OR = 2.75, CI = 1.16-6.48) during multivariable analysis. Sepsis was associated with higher mortality (P =< 0.01, OR = 2.4, CI = 1.4-4.0).

SIGNIFICANCE

Our study findings suggest that the utility of rEEG in detecting NCSE in CIPAMS should not be underestimated. Important observations further indicate that repeating rEEG is advisable, as this would increase the likelihood of identifying NCSE. Thus, physicians should consider and repeat rEEG when evaluating CIPAMS in order to detect NCSE, which is an independent predictor of unfavorable outcomes. Nonetheless, further studies comparing rEEG and cEEG yields are required to augment the current understanding of the electroclinical spectrum and better describe NCSE in CIPAMS.

Indications for continuous electroencephalographic (cEEG) monitoring: What do they tell us?

OBJECTIVE

While studies have explored clinical and EEG predictors of seizures on continuous EEG (cEEG), the role of cEEG indications as predictors of seizures has not been studied. Our study aims to fill this knowledge gap.

METHODS

We used the prospective cEEG database at Cleveland Clinic for the 2016 calendar year. Patients ≥ 18 years who underwent cEEG for the indication of altered mental status (AMS) and seizure-like events (SLE: motor or patient-reported events) were included. Baseline characteristics and EEG findings were compared between the two groups. Multivariable regression was used to compare the two groups and identify seizure detection risk factors.

RESULTS

Of 2227 patients (mean age 59.4 years) who met the inclusion criteria, 882 (50% females) underwent cEEG for AMS and 1345(51% females) for SLE. SLE patients were younger(OR: 0.988, CI: 0.98-0.99, p < 0.001), had longer monitoring(OR:1.04, CI:1.00-1.07, p = 0.033), were more likely to have epilepsy-related-breakthrough seizures(OR:25.9, CI:0.5.89-115, p < 0.001), psychogenic non-epileptic spells (OR:6.85, CI:1.60-29.3, p = 0.008), were more awake (p < 0.001) and more likely to be on anti-seizure medications(OR:1.60, CI:1.29-1.98, p < 0.001). On multivariable analysis, SLE was an independent predictor of seizure detection (OR: 2.60, CI: 1.77-3.88, p < 0.001).

SIGNIFICANCE

Our findings highlight the differences in patients undergoing cEEG for AMS vs. SLE. SLE as a cEEG indication represents an independent predictor of seizures on cEEG and, therefore, deserves special attention. Future multicenter studies are needed to validate our findings.

Electrographic Seizures in the Critically Ill

Identifying and treating critically ill patients with seizures can be challenging. In this article, the authors review the available data on patient populations at risk, seizure prognostication with tools such as 2HELPS2B, electrographic seizures and the various ictal-interictal continuum patterns with their latest definitions and associated risks, ancillary testing such as imaging studies, serum biomarkers, and invasive multimodal monitoring. They also illustrate 5 different patient scenarios, their treatment and outcomes, and propose recommendations for targeted treatment of electrographic seizures in critically ill patients.

Personalized model to predict seizures based on dynamic and static continuous EEG monitoring data

BACKGROUND/OBJECTIVE

Early recognition of patients who may be at risk of developing acute symptomatic seizures would be useful. We aimed to determine whether continuous electroencephalography (cEEG) data using machine learning techniques such as neural networks and decision trees could predict seizure occurrence in hospitalized patients.

METHODS

This was a single center retrospective cohort analysis of cEEG data in patients aged 18-90 years who were admitted and underwent cEEG monitoring between 2010 and 2019 limited to 72 h excluding those who were seizing at the onset of recording. A total of 41,491 patients were reviewed; of these, 3874 were used to develop the static model and 1687 to develop the dynamic model (half with seizure and half without seizure in each cohort). Of these, 80% were randomly selected as derivation cohorts for each model and 20% were randomly selected as validation cohorts. Dynamic and static machine learning models (long short term memory (LSTM) and Extreme Gradient Boosting algorithm (XGBoost)) based on day-to-day dynamic EEG changes and binary static EEG features over the prior 72 h or until seizure, which ever was earlier, were used.

RESULTS

The static model was able to predict seizure occurrence based on cEEG data with sensitivity and specificity of 0.81 and 0.59, respectively, with an AUC of 0.70. The dynamic model was able to predict seizure occurrence with sensitivity and specificity of 0.72 and 0.80, respectively, and AUC of 0.81.

CONCLUSIONS

Machine learning models could be applied to cEEG data to predict seizure occurrence based on available cEEG data. Dynamic day-to-day EEG data are more useful in predicting seizures than binary static EEG data. These models could potentially be used to determine the need for ongoing cEEG monitoring and to prioritize resources.

Intravenous Brivaracetam in the Management of Acute Seizures in the Hospital Setting: A Scoping Review

BACKGROUND

Clinical considerations for drug treatment of acute seizures involve variables such as safety, tolerability, drug-drug interactions, dosage, route of administration, and alterations in pharmacokinetics because of critical illness. Therapy options that are easily and quickly administered without dilution, well tolerated, and effective are needed for the treatment of acute seizures. The objective of this review is to focus on the clinical considerations relating to the use of intravenous brivaracetam (IV BRV) for the treatment of acute seizures in the hospital, focusing on critically ill patients.

METHODS

This was a scoping literature review of PubMed from inception to April 13, 2021, and search of the American Academy of Neurology (AAN) 2021 Annual Meeting website for English language publications/conference abstracts reporting the results of IV BRV use in hospitalized patients, particularly in the critical care setting. Outcomes of interest relating to the clinical pharmacology, safety, tolerability, efficacy, and effectiveness of IV BRV were reviewed and are discussed.

RESULTS

Twelve studies were included for analysis. One study showed that plasma concentrations of IV BRV 15 min after the first dose were similar between patients receiving IV BRV as bolus or infusion. IV BRV was generally well tolerated in patients with acute seizures in the hospital setting, with a low incidence of individual TEAEs classified as behavioral disorders. IV BRV demonstrated efficacy and effectiveness and had a rapid onset, with clinical and electrophysiological improvement in seizures observed within minutes. Although outside of the approved label, findings from several studies suggest that IV BRV reduces seizures and is generally well tolerated in patients with status epilepticus.

CONCLUSIONS

IV BRV shows effectiveness, and is generally well tolerated in the management of acute seizures in hospitalized patients where rapid administration is needed, representing a clinically relevant antiseizure medication for potential use in the critical care setting.

Continuous Electroencephalogram Evaluation of Paroxysmal Events in Critically Ill Patients: Diagnostic Yield and Impact on Clinical Decision Making

BACKGROUND

Continuous electroencephalogram (cEEG) monitoring has been widely used in the intensive care unit (ICU) for the evaluation of patients in the ICU with altered consciousness to detect nonconvulsive seizures. We investigated the yield of cEEG when used to evaluate paroxysmal events in patients in the ICU and assessed the predictors of a diagnostic findings. The clinical impact of cEEG was also evaluated in this study.

METHODS

We identified patients in the ICU who underwent cEEG monitoring (> 6 h) to evaluate paroxysmal events between January 1, 2018, and December 31, 2019. We extracted patient demographics, medical history, neurological examination, brain imaging results, and the description of the paroxysmal events that necessitated the monitoring. We dichotomized the cEEG studies into those that captured habitual nonepileptic events or revealed epileptiform discharges (ictal or interictal), i.e., those considered to be of positive diagnostic yield (Y +), and those studies that did not show those findings (negative diagnostic yield, Y -). We also assessed the clinical impact of cEEG by documenting changes in administered antiseizure medication (ASM) before and after the cEEG.

RESULTS

We identified 159 recordings that were obtained for the indication of paroxysmal events, of which abnormal movements constituted the majority (n = 123). For the remaining events (n = 36), descriptions included gaze deviations, speech changes, and sensory changes. Twenty-nine percent (46 of 159) of the recordings were Y + , including the presence of ictal or interictal epileptiform discharges (n = 33), and captured habitual nonepileptic events (n = 13). A history of epilepsy was the only predictor of the study outcome. Detection of abnormal findings occurred within 6 h of the recording in most patients (30 of 46, 65%). Overall, cEEG studies led to 49 (31%) changes in ASM administration. The changes included dosage increases or initiation of ASM in patients with epileptiform discharges (n = 28) and reduction or elimination of ASM in patients with either habitual nonepileptic events (n = 5) or Y - cEEG studies (n = 16).

CONCLUSIONS

Continuous electroencephalogram monitoring is valuable in evaluating paroxysmal events, with a diagnostic yield of 29% in critically ill patients. A history of epilepsy predicts diagnostic studies. Both Y + and Y - cEEG studies may directly impact clinical decisions by leading to ASMs changes.

Electroencephalography at the height of a pandemic: EEG findings in patients with COVID-19

OBJECTIVE

To characterize continuous video electroencephalogram (VEEG) findings of hospitalized COVID-19 patients.

METHODS

We performed a retrospective chart review of patients admitted at three New York City hospitals who underwent VEEG at the peak of the COVID-19 pandemic. Demographics, comorbidities, neuroimaging, VEEG indications and findings, treatment, and outcomes were collected.

RESULTS

Of 93 patients monitored, 77% had severe COVID-19 and 40% died. Acute ischemic or hemorrhagic stroke was present in 26% and 15%, respectively. Most common VEEG indications were encephalopathy/coma (60%) and seizure-like movements (38%). Most common VEEG findings were generalized slowing (97%), generalized attenuation (31%), generalized periodic discharges (17%) and generalized sharp waves (15%). Epileptiform abnormalities were present in 43% and seizures in 8% of patients, all of whom had seizure risk factors. Factors associated with an epileptiform VEEG included increasing age (OR 1.07, p = 0.001) and hepatic/renal failure (OR 2.99, p = 0.03).

CONCLUSIONS

Most COVID-19 patients who underwent VEEG monitoring had severe COVID-19 and over one-third had acute cerebral injury (e.g., stroke, anoxia). Seizures were uncommon. VEEG findings were nonspecific.

SIGNIFICANCE

VEEG findings in this cohort of hospitalized COVID-19 patients were those often seen in critical illness. Seizures were uncommon and occurred in the setting of common seizure risk factors.

How to diagnose and manage neurological toxicities of immune checkpoint inhibitors: an update

As the use of cancer immunotherapy with immune checkpoint inhibitors (ICIs) is expanding rapidly for the treatment of many tumor types, it is crucial that both neurologists and oncologists become familiar with the diagnosis and treatment of neurological immune-related adverse events (n-irAEs). These are rare complications, developing in their severe forms in only 1-3% of the patients, but are highly relevant due to their mortality and morbidity burden. The diagnosis of n-irAEs is-however-challenging, as many alternative diagnoses need to be considered in the complex scenario of a patient with advanced cancer developing neurological problems. A tailored diagnostic approach is advisable according to the presentation, clinical history, and known specificities of n-irAEs. Several patterns characterized by distinct clinical, immunological, and prognostic characteristics are beginning to emerge. For example, myasthenia gravis is more likely to develop after anti-programmed cell death protein 1 (PD-1) or anti-programmed cell death ligand 1 (PD-L1) treatment, while meningitis appears more frequently after anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) therapy. Also, peripheral neuropathy and Guillain-Barré syndrome seem to be more common in patients with an underlying melanoma. Central nervous system disorders (CNS) are less frequent and are more often associated with lung cancer, and some of them (especially those with limbic encephalitis and positive onconeural antibodies) have a poor prognosis. Herein, we provide an update of the recent advances in the diagnosis and treatment of neurological toxicities related to ICI use, focusing on the exclusion of alternative diagnoses, diagnostic specificities, and treatment of n-irAEs.

Long-term continuation of anti-seizure medications after acute stroke

Objective

To investigate the factors associated with the long‐term continuation of anti‐seizure medications (ASMs) in acute stroke patients.

Methods

We performed a retrospective cohort study of stroke patients with concern for acute symptomatic seizures (ASySs) during hospitalization who subsequently visited the poststroke clinic. All patients had continuous EEG (cEEG) monitoring. We generated a multivariable logistic regression model to analyze the factors associated with the primary outcome of continued ASM use after the first poststroke clinic visit.

Results

A total of 507 patients (43.4% ischemic stroke, 35.7% intracerebral hemorrhage, and 20.9% aneurysmal subarachnoid hemorrhage) were included. Among them, 99 (19.5%) suffered from ASySs, 110 (21.7%) had epileptiform abnormalities (EAs) on cEEG, and 339 (66.9%) had neither. Of the 294 (58%) patients started on ASMs, 171 (33.7%) were discharged on them, and 156 (30.3% of the study population; 53.1% of patients started on ASMs) continued ASMs beyond the first poststroke clinic visit [49.7 (±31.7) days after cEEG]. After adjusting for demographical, stroke‐ and hospitalization‐related variables, the only independent factors associated with the primary outcome were admission to the NICU [Odds ratio (OR) 0.37 (95% CI 0.15–0.9)], the presence of ASySs [OR 20.31(95% CI 9.45–48.43)], and EAs on cEEG [OR 2.26 (95% CI 1.14–4.58)].

Interpretation

Almost a third of patients with poststroke ASySs concerns may continue ASMs for the long term, including more than half started on them acutely. Admission to the NICU may lower the odds, and ASySs (convulsive or electrographic) and EAs on cEEG significantly increase the odds of long‐term ASM use.

Severe Neurologic Complications of SARS-CoV-2

Purpose of Review

This review presents an overview of the known neurocritical care complications of severe acute respiratory virus 2 (SARS-CoV-2). We present readers with a review of the literature of severe neurologic complications of SARS-CoV-2 and cases from our institution to illustrate these conditions.

Recent Findings

Neurologic manifestations are being increasingly recognized in the literature. Some patients can have severe neurologic manifestations, though the true prevalence is unknown.

Summary

Severe neurologic complications of COVID-19 include large vessel occlusion ischemic stroke, intracranial hemorrhage, encephalitis, myelitis, Guillain-Barre syndrome, status epilepticus, posterior reversible encephalopathy syndrome, and hypoxic-ischemic encephalopathy. These conditions can manifest in COVID-19 patients even in the absence of risk factors and must be promptly identified as they can have a high mortality if left untreated.

Practicality and cost-effectiveness of using MRI compatible EEG system in the critical care setting

BACKGROUND

Continuous video-EEG (cvEEG) monitoring is a vastly utilized tool for monitoring critically ill patients in the intensive care unit. Our study investigates the clinical utility and cost-effectiveness of using MRI Compatible EEG electrode system for patients being monitored in the intensive care unit.

METHODS

This retrospective study included 14 critically ill patients who underwent cvEEG between March 2019 to March 2020. They were classified into 2 subgroups: Group 1- 'MRI-compatible EEG' (mean age: 56.00 ± 19.99 years; M:F = 2:5; N = 7), Group 2 - 'Conventional EEG' (mean age: 49.14 ± 24.76 years; M:F = 4:3; N = 7). The EEG monitoring times as well as cost-effectiveness of cvEEG between the groups were compared using Mann-Whitney Test (p ≤ 0.05). We also compared the MRI quality between the groups using Chi-squared test (p ≤ 0.05).

RESULTS

The EEG non-monitored time in Group 2 (7.62 ± 6.45 h) was significantly higher than Group 1 (2.71 ± 2.34 h)] (p = 0.025). The average daily cost for cvEEG in Group 1 ($2098.53 ± 493.58) and Group 2 ($2230.58 ± 142.73) was comparable (p = 0.896). The quality of MRI scans between Group 1 (6/7) and 2 (6/7) were also comparable (p = 1.000).

CONCLUSIONS

The monitoring time lost in patients with MRI Compatible EEG electrodes was significantly lower than the patients with Conventional EEG electrodes. The daily cost of monitoring and the quality of MRI scans were comparable between the 2 groups. We conclude that the use of MRI Compatible EEG electrodes is a practical and cost-effective method to improve the quality of monitoring in critically ill patients.

Acute Symptomatic Seizures in Critically Ill Patients with COVID-19: Is There an Association?

Background

The coronavirus disease of 2019 (COVID-19) emerged as a global pandemic. Historically, the group of human coronaviruses can also affect the central nervous system leading to neurological symptoms; however, the causative mechanisms of the neurological manifestations of COVID-19 disease are not well known. Seizures have not been directly reported as a part of COVID-19 outside of patients with previously known brain injury or epilepsy. We report two cases of acute symptomatic seizures, in non-epileptic patients, associated with severe COVID-19 disease.

Case Presentations

Two advanced-age, non-epileptic, male patients presented to our northeast Ohio-based health system with concern for infection in Mid-March 2020. Both had a history of lung disease and during their hospitalization tested positive for SARS-CoV-2. They developed acute encephalopathy days into their hospitalization with clinical and electrographic seizures. Resolution of seizures was achieved with levetiracetam.

Discussion

Patients with COVID-19 disease are at an elevated risk for seizures, and the mechanism of these seizures is likely multifactorial. Clinical (motor) seizures may not be readily detected in this population due to the expansive utilization of sedatives and paralytics for respiratory optimization strategies. Many of these patients are also not electrographically monitored for seizures due to limited resources, multifactorial risk for acute encephalopathy, and the risk of cross-contamination. Previously, several neurological symptoms were seen in patients with more advanced COVID-19 disease, and these were thought to be secondary to multi-system organ failure and/or disseminated intravascular coagulopathy-related brain injury. However, these patients may also have an advanced breakdown of the blood–brain barrier precipitated by pro-inflammatory cytokine reactions. The neurotropic effect and neuroinvasiveness of SARS-Coronavirus-2 have not been directly established.

Conclusions

Acute symptomatic seizures are possible in patients with COVID-19 disease. These seizures are likely multifactorial in origin, including cortical irritation due to blood–brain barrier breakdown, precipitated by the cytokine reaction as a part of the viral infection. Patients with clinical signs of seizures or otherwise unexplained encephalopathy may benefit from electroencephalography monitoring and/or empiric anti-epileptic therapy. Further studies are needed to elucidate the risk of seizures and benefit of monitoring in this population.

Does continuous electroencephalography influence therapeutic decisions in neurocritical care?

OBJECTIVES

In the neurocritical care unit (neuro-ICU), the impact of continuous EEG (cEEG) on therapeutic decisions and prognostication, including outcome prediction using the Status Epilepticus Severity Score (STESS), is poorly investigated. We studied to what extent cEEG contributes to treatment decisions, and how this relates to clinical outcome and the use of STESS in neurocritical care.

METHODS

We included patients admitted to the neuro-ICU or neurological step-down unit of a tertiary referral hospital between 05/2013 and 06/2015. Inclusion criteria were ≥20 h of cEEG monitoring and age ≥15 years. Exclusion criteria were primary epileptic and post-cardiac arrest encephalopathies.

RESULTS

Ninety-eight patients met inclusion criteria, 80 of which had status epilepticus, including 14 with super-refractory status. Median length of cEEG monitoring was 50 h (range 21-374 h). Mean STESS was lower in patients with favorable outcome 1 year after discharge (modified Rankin Scale [mRS] 0-2) compared to patients with unfavorable outcome (mRS 3-6), albeit not statistically significant (mean STESS 2.3 ± 2.1 vs 3.6 ± 1.7, p = 0.09). STESS had a sensitivity of 80%, a specificity of 42%, and a negative predictive value of 93% for outcome. cEEG results changed treatment decisions in 76 patients, including escalation of antiepileptic treatment in 65 and reduction in 11 patients.

CONCLUSION

Status Epilepticus Severity Score had a high negative predictive value but low sensitivity, suggesting that STESS should be used cautiously. Of note, cEEG results altered clinical decision-making in three of four patients, irrespective of the presence or absence of status epilepticus, confirming the clinical value of cEEG in neurocritical care.

EEG Monitoring After Convulsive Status Epilepticus

After convulsive status epilepticus, patients of all ages may have ongoing EEG seizures identified by continuous EEG monitoring. Furthermore, high EEG seizure exposure has been associated with unfavorable neurobehavioral outcomes. Thus, recent guidelines and consensus statements recommend many patients with persisting altered mental status after convulsive status epilepticus undergo continuous EEG monitoring. This review summarizes the available epidemiologic data and related recommendations provided by recent guidelines and consensus statements.

Continuous EEG in ICU: Not a Luxury After All

Continuous vs Routine Electroencephalogram in Critically Ill Adults With Altered Consciousness and No Recent Seizure: A Multicenter Randomized Clinical Trial

Rossetti AO, Schindler K, Sutter R, Rüegg S, Zubler F, Novy J, Oddo M, Warpelin-Decrausaz L, Alvarez V. JAMA Neurol. 2020;77(10):1-8. doi:10.1001/jamaneurol.2020.2264

Importance:

In critically ill patients with altered consciousness, continuous electroencephalogram (cEEG) improves seizure detection but is resource-consuming compared with routine EEG (rEEG). It is also uncertain whether cEEG has an effect on outcome.

Objective:

To assess whether cEEG is associated with reduced mortality compared with rEEG.

Design, Setting, and Participants:

The pragmatic multicenter Continuous EEG Randomized Trial in Adults was conducted between 2017 and 2018, with follow-up of 6 months. Outcomes were assessed by interviewers blinded to interventions. The study took place at 4 tertiary hospitals in Switzerland (intensive and intermediate care units). Depending on investigators’ availability, we pragmatically recruited critically ill adults having Glasgow Coma Scale scores of 11 or less or Full Outline of Responsiveness score of 12 or less, without recent seizures or status epilepticus. They had cerebral (eg, brain trauma, cardiac arrest, hemorrhage, or stroke) or noncerebral conditions (eg, toxic-metabolic or unknown etiology), and EEG was requested as part of standard care. An independent physician provided emergency informed consent.

Interventions:

Participants were randomized 1:1 to cEEG for 30 to 48 hours versus 2 rEEGs (20 minutes each), interpreted according to standardized American Clinical Neurophysiology Society guidelines.

Main Outcomes and Measures:

Mortality at 6 months represented the primary outcome. Secondary outcomes included interictal and ictal features detection and change in therapy.

Results:

We analyzed 364 (33% women; mean [SD] age, 63 [15] years) patients. At 6 months, mortality was 89 of 182 in those with cEEG and 88 of 182 in those with rEEG (adjusted relative risk [RR]: 1.02; 95% CI: 0.83-1.26; P = .85). Exploratory comparisons within subgroups stratifying patients according to age, premorbid disability, comorbidities on admission, deeper consciousness reduction, and underlying diagnoses revealed no significant effect modification. Continuous EEG was associated with increased detection of interictal features and seizures (adjusted RR: 1.26; 95% CI: 1.08-1.15; P = .004 and 3.37; 95% CI: 1.63-7.00; P = .001, respectively) and more frequent adaptations in anti-seizure therapy (RR: 1.84; 95% CI: 1.12-3.00; P = .01).

Conclusions and Relevance:

This pragmatic trial shows that in critically ill adults with impaired consciousness and no recent seizure, cEEG leads to increased seizure detection and modification of anti-seizure treatment but is not related to improved outcome compared with repeated rEEG. Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources.

Continuous electroencephalography characteristics and acute symptomatic seizures in COVID-19 patients

OBJECTIVE

As concerns regarding neurological manifestations in COVID-19 (coronavirus disease 2019) patients increase, limited data exists on continuous electroencephalography (cEEG) findings in these patients. We present a retrospective cohort study of cEEG monitoring in COVID-19 patients to better explore this knowledge gap.

METHODS

Among 22 COVID-19 patients, 19 underwent cEEGs, and 3 underwent routine EEGs (<1 h). Demographic and clinical variables, including comorbid conditions, discharge disposition, survival and cEEG findings, were collected.

RESULTS

cEEG was performed for evaluation of altered mental status (n = 17) or seizure-like events (n = 5). Five patients, including 2 with epilepsy, had epileptiform abnormalities on cEEG. Two patients had electrographic seizures without a prior epilepsy history. There were no acute neuroimaging findings. Periodic discharges were noted in one-third of patients and encephalopathic EEG findings were not associated with IV anesthetic use.

CONCLUSIONS

Interictal epileptiform abnormalities in the absence of prior epilepsy history were rare. However, the discovery of asymptomatic seizures in two of twenty-two patients was higher than previously reported and is therefore of concern.

SIGNIFICANCE

cEEG monitoring in COVID-19 patients may aid in better understanding an epileptogenic potential of SARS-CoV2 infection. Nevertheless, larger studies utilizing cEEG are required to better examine acute epileptic risk in COVID-19 patients.

COVID-19 infection in known epileptic and non-epileptic children: what is the place of chloroquine sulfate? (a case report)

The coronavirus 19 (COVID-19) disease, which was declared in China in December 2019, very early on became a pandemic, claiming more than 28 million victims worldwide to date. Its impact on the central nervous system is still poorly understood. The objective of this work is to assess the involvement of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in the aggravation of seizures in children known to have epilepsy and in the epileptogenesis of children hitherto seizure-free. Prior to conducting this work, we had obtained informed consent from patients and parents. We report the cases of three (3) patients, one known epileptic and the other two apparently healthy, who presented a febrile seizure in a context of COVID-19 infection. The aggravation of the epileptic seizure was indicative of a SARS-CoV-2 infection in the first patient, while the seizure occurred after induction of chloroquine sulfate treatment in the 2 other patients. Although our current concern is to limit the spread of the disease to COVID-19, it is crucial to address its possible complications. Notably, the worsening of seizures in children with epilepsy and the occurrence of first seizures in children without epilepsy following drug treatment. Equipping our COVID-19 patient management facilities with electroencephalogram (EEG) equipment could facilitate continuous electroencephalographic monitoring of children for proper management.

Ictal-interictal continuum: a review of recent advancements

Continuous electroencephalogram (cEEG) has become an indispensable technique in the management of critically ill patients for early detection and treatment of non-convulsive seizures (NCS) and non-convulsive status epilepticus (NCSE). It has also brought about a renaissance in a wide range of rhythmic and periodic patterns with heterogeneous frequency and morphology. These patterns share the rhythmic and sharp appearances of electrographic seizures, but often lack the necessary frequency, spatiotemporal evolution and clinical accompaniments to meet the definitive criteria for ictal patterns. They may be associated with cerebral metabolic crisis and neuronal injury, therefore not clearly interictal either, but lie along an intervening spectrum referred to as ictal-interictal continuum (IIC). Generally speaking, rhythmic and periodic patterns are categorized as interictal patterns when occurring at a rate of <1Hz, and are categorized as NCS and NCSE when occurring at a rate of >2.5 Hz with spatiotemporal evolution. As such, IIC commonly includes the rhythmic and periodic patterns occurring at a rate of 1–2.5 Hz without spatiotemporal evolution and clinical correlates. Currently there are no evidence-based guidelines on when and if to treat patients with IIC patterns, and particularly how aggressively to treat, presenting a challenging electrophysiological and clinical conundrum. In practice, a diagnostic trial with preferably a non-sedative anti-seizure medication (ASM) can be considered with the end point being both clinical and electrographic improvement. When available and necessary, correlation of IIC with biomarkers of neuronal injury, such as neuronal specific enolase (NSE), neuroimaging, depth electrode recording, cerebral microdialysis and oxygen measurement, can be assessed for the consideration of ASM treatment. Here we review the recent advancements in their clinical significance, risk stratification and treatment algorithm.

Patient-Reported Outcomes (PROs) in Acute Symptomatic Seizure (ASyS) Versus Patients With Established Epilepsy

Purpose

Acute symptomatic seizures (ASyS) are common in critically ill patients. It is unknown how ASyS affect posthospitalization self-reported health compared to patients with established epilepsy.

Methods

This is a retrospective cohort study from 2010 to 2018. Patients were identified by an institutional epilepsy database (Ebase). Patient-reported outcome measures (PROMs) were completed as part of standard of care and included the number of seizures in the prior 4 weeks, Liverpool Seizure Severity Scale (LSSS) ictal score, quality of life in epilepsy (QOLIE)-10, Patient Health Questionnaire-9 scales, and the PROM Information System Global Health (PROMIS-GH) scale. Mixed-effects models were created to adjust for age, sex, and race and to examine score trajectory over the 1 year after baseline.

Results

A total of 15 311 established epilepsy patients and 317 patients with ASyS were identified. When compared to patients with epilepsy, patients with ASyS were older, mostly male, more often black, and had worse baseline scores on the QOLIE-10 (P < .001), PROMIS-GH Physical Health (P = .037), and LSSS Ictal (P = .006) scales. Patient-Reported Outcomes Measurement Information System Mental and Physical Health T-scores were worse than the general population (T-score = 50) for patients with both ASyS (44 and 42.5, respectively) and epilepsy (44.2 and 44.6, respectively). After adjusting for age, sex, and race, patients with ASyS reported 38% fewer seizures (P = .006) yet worse QOLIE-10 score (P = .034). We found that scores improved over time for all PROMs except for PROMIS-GH Mental Health.

Conclusion

Compared to patients with epilepsy, patients with ASyS had fewer seizures but worse epilepsy-specific quality of life. Independent of group status, scores generally improved over time.

Electrographic seizures and ictal-interictal continuum (IIC) patterns in critically ill patients

Critical care long-term continuous electroencephalogram (cEEG) monitoring has expanded dramatically in the last several decades spurned by technological advances in EEG digitalization and several key clinical findings: 1-Seizures are relatively common in the critically ill-large recent observational studies suggest that around 20% of critically ill patients placed on cEEG have seizures. 2-The majority (~75%) of patients who have seizures have exclusively "electrographic seizures", that is, they have no overt ictal clinical signs. Along with the discovery of the unexpectedly high incidence of seizures was the high prevalence of EEG patterns that share some common features with archetypical electrographic seizures but are not uniformly considered to be "ictal". These EEG patterns include lateralized periodic discharges (LPDs) and generalized periodic discharges (GPDs)-patterns that at times exhibit ictal-like behavior and at other times behave more like an interictal finding. Dr. Hirsch and colleagues proposed a conceptual framework to describe this spectrum of patterns called the ictal-interictal continuum (IIC). In the following years, investigators began to answer some of the key pragmatic clinical concerns such as which patients are at risk of seizures and what is the optimal duration of cEEG use. At the same time, investigators have begun probing the core questions for critical care EEG-what is the underlying pathophysiology of these patterns, at what point do these patterns cause secondary brain injury, what are the optimal treatment strategies, and how do these patterns affect clinical outcomes such as neurological disability and the development of epilepsy. In this review, we cover recent advancements in both practical concerns regarding cEEG use, current treatment strategies, and review the evidence associating IIC/seizures with poor clinical outcomes.

Validation of the 2HELPS2B Seizure Risk Score in Acute Brain Injury Patients

BACKGROUND AND OBJECTIVE

Seizures are common after traumatic brain injury (TBI), aneurysmal subarachnoid hemorrhage (aSAH), subdural hematoma (SDH), and non-traumatic intraparenchymal hemorrhage (IPH)-collectively defined herein as acute brain injury (ABI). Most seizures in ABI are subclinical, meaning that they are only detectable with EEG. A method is required to identify patients at greatest risk of seizures and thereby in need of prolonged continuous EEG monitoring. 2HELPS2B is a simple point system developed to address this need. 2HELPS2B estimates seizure risk for hospitalized patients using five EEG findings and one clinical finding (pre-EEG seizure). The initial 2HELPS2B study did not specifically assess the ABI subpopulation. In this study, we aim to validate the 2HELPS2B score in ABI and determine its relative predictive accuracy compared to a broader set of clinical and electrographic factors.

METHODS

We queried the Critical Care EEG Monitoring Research Consortium database for ABI patients age ≥ 18 with > 6 h of continuous EEG monitoring; data were collected between February 2013 and November 2018. The primary outcome was electrographic seizure. Clinical factors considered were age, coma, encephalopathy, ABI subtype, and acute suspected or confirmed pre-EEG clinical seizure. Electrographic factors included 18 EEG findings. Predictive accuracy was assessed using a machine-learning paradigm with area under the receiver operator characteristic (ROC) curve as the primary outcome metric. Three models (clinical factors alone, EEG factors alone, EEG and clinical factors combined) were generated using elastic-net logistic regression. Models were compared to each other and to the 2HELPS2B model. All models were evaluated by calculating the area under the curve (AUC) of a ROC analysis and then compared using permutation testing of AUC with bootstrapping to generate confidence intervals.

RESULTS

A total of 1528 ABI patients were included. Total seizure incidence was 13.9%. Seizure incidence among ABI subtype varied: IPH 17.2%, SDH 19.1%, aSAH 7.6%, TBI 9.2%. Age ≥ 65 (p = 0.015) and pre-cEEG acute clinical seizure (p < 0.001) positively affected seizure incidence. Clinical factors AUC = 0.65 [95% CI 0.60-0.71], EEG factors AUC = 0.82 [95% CI 0.77-0.87], and EEG and clinical factors combined AUC = 0.84 [95% CI 0.80-0.88]. 2HELPS2B AUC = 0.81 [95% CI 0.76-0.85]. The 2HELPS2B AUC did not differ from EEG factors (p = 0.51), or EEG and clinical factors combined (p = 0.23), but was superior to clinical factors alone (p < 0.001).

CONCLUSIONS

Accurate seizure risk forecasting in ABI requires the assessment of EEG markers of pathologic electro-cerebral activity (e.g., sporadic epileptiform discharges and lateralized periodic discharges). The 2HELPS2B score is a reliable and simple method to quantify these EEG findings and their associated risk of seizure.

Development of a model to predict electroencephalographic seizures in critically ill children

OBJECTIVE

Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone.

METHODS

We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG.

RESULTS

The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES.

SIGNIFICANCE

A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy.

Determinants and outcome of repeat continuous electroencephalogram monitoring-A case-control study

OBJECTIVE

A retrospective, single-center study to analyze the determinants of a repeat continuous EEG (cEEG) monitoring during hospitalization and its outcomes using a matched case-control study design.

METHODS

Adults with a repeat cEEG session (cases) were matched by age (±3 years), gender, and mental status to patients with a single cEEG (controls) during hospitalization. Several clinical and EEG characteristics were analyzed to identify predictors of repeat cEEG. Repeat cEEG outcomes were analyzed based on its yield of electrographic seizure. We investigated the predictors of finding increased epileptic potential (degree of association with electrographic seizures) on the repeat cEEG, a marker for possible anti-epileptic drugs (AEDs) management change.

RESULTS

A total of 213 (8.6% of all unique cEEG patients) cases were included. A multivariable conditional logistic regression model comparing cases and controls showed that the presence of acute brain insult [odds ratio (OR) = 3.36, 95% CI = 1.26-8.94, P = .015], longer hospital admission (OR = 1.11, 95% CI = 1.07-1.15, P < .001) and being on AEDs at the end of index cEEG (OR = 4.0, 95% CI = 1.8-8.87, P < .001) was determinants of a repeat cEEG. Among cases, 17 (8%) had electrographic seizures on repeat cEEG. Increased epileptic potential on repeat cEEG was noted in 34 (16%) cases. The latter is associated with change in etiology after the index cEEG (P = .03) and duration of repeat cEEG (P = .003) based on multivariable logistic regression model. AEDs were changed in 46 (21.6%) patients based on repeat cEEG findings.

SIGNIFICANCE

Repeat cEEG is not an uncommon practice. It leads to the diagnosis of electrographic seizures in a significant percentage of patients. With the potential of impacting AED management in 16%-21% patients, it should be considered in high-risk patients suffering acute brain insults undergoing prolonged hospitalization.

Electroencephalographic biomarkers of epilepsy development in patients with acute brain injury: a matched, parallel cohort study

OBJECTIVE

This study was designed to investigate if highly epileptic electroencephalogram (EEG) findings in patients with acute brain injury increase the long-term risk of epilepsy development.

METHODS

Adults patients, lacking epilepsy history, with electrographic seizures or lateralized periodic discharges (LPDs) (cases) were identified and matched based on age, mental status, and etiology with the ones lacking any epileptiform activity (controls) on continuous EEG (cEEG) during hospitalization. The primary outcome of clinical seizures after hospital discharge and their antiepileptic drug (AED) status was determined using a telephonic interview. Logistic regression models using generalized estimating equations to account for the matched nature of the data were performed.

RESULTS

A total of 70 cases [16 (22.9%) "LPDs only," 34 (48.6%) "electrographic seizure only," and 20 (28.6%) "both"] and controls were enrolled. A total of 22 (31.4%) cases developed epilepsy after a mean follow-up duration of 20.6 ± 5.0 months compared to three (4.3%) controls. After adjusting for cEEG indication and follow-up duration, the odds of cases developing epilepsy were almost 15 times higher compared to the controls (OR = 14.8, 95% CI = 2.4-92.3, P = 0.004). This elevated risk was despite a 10 times higher likelihood of cases to be taking AEDs at the last follow-up (OR = 10.34, 95% CI = 3.7-29, P < 0.001).

INTERPRETATION

Highly epileptic EEG findings in patients with acute brain injury may serve as prognostic biomarkers of epilepsy development. Although prospective studies are required to confirm our findings, it seems that with epilepsy developing in almost one-third cases in less than 2-year follow-up period, such patients may potentially be ideal candidates for epilepsy prevention clinical trials.

Super-Refractory Status Epilepticus Treated with High Dose Perampanel: Case Series and Review of the Literature

Introduction

Acute symptomatic seizures are frequent in the critically ill patient and can be difficult to treat. The novel anticonvulsant perampanel may be effective in the treatment of status epilepticus considering its mechanism of action of being an AMPA antagonist. We present four cases of super refractory status epilepticus treated with high dose perampanel.

Method

Case report.

Cases

Four patients were treated with perampanel for their refractory status epilepticus. One patient had new onset refractory status epilepticus of unknown etiology. Three other patients had status epilepticus as a result of their cardiac arrest. Two of the cardiac arrest patients had myoclonus. In all patients, the additional of perampanel resulted in a reduction of seizure burden without affecting hemodynamics or hepatic or renal function.

Conclusion

Perampanel may be effective in the treatment of super-refractory status epilepticus of varying etiologies. A larger, prospective study is needed to further assess this therapy.

The diagnostic value of continuous EEG for the detection of non-convulsive status epilepticus in neurosurgical patients - A prospective cohort study

•

The incidence of seizure activity was 10%.

•

Seizure activity was detected within the first 30 min in three of the five patients.

•

Markers for patient selection for cEEG in neurosurgical patients are needed.

Objective

To prospectively compare the diagnostic yields of standard EEG and continuous EEG (cEEG) monitoring for the diagnosis of non-convulsive status epilepticus (NCSE) in neurosurgical patients in the intensive care unit.

Methods

We included 50 consecutive patients with clinical suspicion of NCSE due to unexplained coma or subtle clinical phenomena such as discrete myoclonus. The initial 30-minute EEG recording and the following cEEG were analyzed separately for seizure activity. Data were collected on neurosurgical diagnosis, previous diagnosis of epilepsy, current medication, level of consciousness, and outcome at discharge from the neurosurgical department.

Results

Recurrent electrographic seizure activity was detected in five patients. This was within the first 30 mins for three patients and on the following cEEG for two patients. Antiepileptic treatment had been initiated in three of these patients. Most of the 50 patients had severe newly acquired neurological disability at discharge.

Conclusions

The prospective finding of a 10% seizure incidence was lower than reports from retrospective studies.

Significance

Use of cEEG led to detection of seizure activity in 2 of 50 patients (4%) and was thus a low-yield method in neurosurgical patients with suspicion of NCSE. Specific markers for patient selection for cEEG are needed.

Monitoring for seizures in the intensive care unit

Acute symptomatic seizures have been known to occur in critically ill patients for many years. It was not until the widespread use of continuous EEG (cEEG) in the critically ill did we appreciate the incidence of electrographic seizures and status epilepticus in the ICU (Newey and Kinzy, 2018). Many of the seizures that occur are without any apparent clinical signs at the time of the recording. The patients often have convulsive seizures at onset then over the next few hours they lose the ability to have a generalized tonic clonic convulsion. They may then have subtle clinical signs (ictal nystagmus, facial twitching, etc.) or lose any apparent motor response. The end result is that many of the patients lose any clinical signs for their seizures by the time they are in the ICU and their seizures are termed "nonconvulsive." The recognition of seizures in the ICU is important for the effects the seizures have on outcome, particularly in morbidity and mortality and the risk of developing epilepsy after the acute symptomatic event. The use of cEEG in the ICU population has not only highlighted the high incidence of seizure activity but has also been used to assess overall cerebral function with applications in ischemia monitoring and prognostication, and to assess the degree of encephalopathy. This chapter will illustrate the core principles of cEEG monitoring in the critical care population including the incidence of seizures, determining who is at highest risk for seizures, how long patients should be monitored and ICU EEG seizure.