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

Background and ObjectivesThe aim of this study was to identify predictors of a resective surgery and subsequent seizure freedom following intracranial EEG (ICEEG) for seizure-onset localization.MethodsThis is a retrospective chart review of 178 consecutive patients with medically refractory epilepsy who underwent ICEEG monitoring from 2002 to 2015. Univariable and multivariable regression analysis identified independent predictors of resection vs other options. Stepwise Akaike information criteria with the aid of clinical consideration were used to select the best multivariable model for predicting resection and outcome. Discrete time survival analysis was used to analyze the factors predicting seizure-free outcome. Cumulative probability of seizure freedom was analyzed using Kaplan-Meier curves and compared between resection and nonresection groups. Additional univariate analysis was performed on 8 select clinical scenarios commonly encountered during epilepsy surgical evaluations.ResultsMultivariable analysis identified the presence of a lesional MRI, presurgical hypothesis suggesting temporal lobe onset, and a nondominant hemisphere implant as independent predictors of resection (p < 0.0001, area under the receiver operating characteristic curve 0.80, 95% CI 0.73-0.87). Focal ICEEG onset and undergoing a resective surgery predicted absolute seizure freedom at the 5-year follow-up. Patients who underwent resective surgery were more likely to be seizure-free at 5 years compared with continued medical treatment or neuromodulation (60% vs 7%; p < 0.0001, hazard ratio 0.16, 95% CI 0.09-0.28). Even patients thought to have unfavorable predictors (nonlesional MRI or extratemporal lobe hypothesis or dominant hemisphere implant) had ≥50% chance of seizure freedom at 5 years if they underwent resection.DiscussionUnfavorable predictors, including having nonlesional extratemporal epilepsy, should not deter a thorough presurgical evaluation, including with invasive recordings, SCOPUS: ar.j, info:eu-repo/semantics/published

Objective: The aim was to determine the prevalence and risk factors for electrographic seizures and other electroencephalographic (EEG) patterns in patients with Coronavirus disease 2019 (COVID-19) undergoing clinically indicated continuous electroencephalogram (cEEG) monitoring and to assess whether EEG findings are associated with outcomes. Methods: We identified 197 patients with COVID-19 referred for cEEG at 9 participating centers. Medical records and EEG reports were reviewed retrospectively to determine the incidence of and clinical risk factors for seizures and other epileptiform patterns. Multivariate Cox proportional hazards analysis assessed the relationship between EEG patterns and clinical outcomes. Results: Electrographic seizures were detected in 19 (9.6%) patients, including nonconvulsive status epilepticus (NCSE) in 11 (5.6%). Epileptiform abnormalities (either ictal or interictal) were present in 96 (48.7%). Preceding clinical seizures during hospitalization were associated with both electrographic seizures (36.4% in those with vs 8.1% in those without prior clinical seizures, odds ratio [OR] 6.51, p = 0.01) and NCSE (27.3% vs 4.3%, OR 8.34, p = 0.01). A pre-existing intracranial lesion on neuroimaging was associated with NCSE (14.3% vs 3.7%; OR 4.33, p = 0.02). In multivariate analysis of outcomes, electrographic seizures were an independent predictor of in-hospital mortality (hazard ratio [HR] 4.07 [1.44–11.51], p < 0.01). In competing risks analysis, hospital length of stay increased in the presence of NCSE (30 day proportion discharged with vs without NCSE: HR 0.21 [0.03–0.33] vs 0.43 [0.36–0.49]). Interpretation: This multicenter retrospective cohort study demonstrates that seizures and other epileptiform abnormalities are common in patients with COVID-19 undergoing clinically indicated cEEG and are associated with adverse clinical outcomes. ANN NEUROL 2021;89:872–883., SCOPUS: ar.j, info:eu-repo/semantics/published

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Objective: The electroencephalographic (EEG) terms “brief potentially ictal rhythmic discharges” (BIRDs) and “paroxysmal fast activity” (PFA) are considered distinct entities; however, their definitions overlap, and they may have similar clinical significance. We investigated their clinical significance and their association with seizures and the seizure onset zone (SOZ). Methods: We retrospectively identified an adult cohort (July 2015 to March 2018) whose long-term (>12 h) EEGs in any setting reported BIRDs (>4 Hz, lasting.5–10 s) and/or PFA. Different frequency cutoffs for PFA (>13 Hz or ≥8 Hz) were tested to compare their clinical significance. Patient demographics, clinical history, and EEG features were recorded. Results: We identified 94 patients with BIRDs/PFA out of 3520 patients (3%); 36 were critically ill (12 with epilepsy), and 58 were noncritically ill (all with epilepsy). The frequency of BIRDs/PFA was largely dependent on EEG background: it tended to be slower (theta) in the absence of a posterior dominant rhythm or in the presence of continuous focal slowing in the same region (p =.01). Sixty-two of 94 patients (66%; 32/36 [89%] critically ill, 30/58 [52%] noncritically ill) had electrographic seizures during the recording. The scalp EEG SOZ colocalized with BIRDs/PFA in all cases. BIRDs with faster frequency (also qualifying as PFA by definition) had similar seizure risk to that of slower BIRDs (62%–71%), regardless of frequency cutoff used to define PFA. In addition, 30 of 30 (100%) patients with evolving BIRDs/PFA (which lasted a median of 6 s, range = 2–9.5 s) had electrographic seizures (>10 s), compared to 32 of 64 (50%) with nonevolving BIRDs (median = 1 s, range =.5–3.5 s; p <.01). Significance: A high proportion of patients with BIRDs/PFA had seizures on EEG, regardless of their frequency (i.e. whether they also qualified as PFA), and their location colocalized with scalp SOZ in all cases. BIRDs appear to be a scalp EEG biomarker of uncontr, SCOPUS: ar.j, info:eu-repo/semantics/published

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Importance: Seizure risk stratification is needed to boost inpatient seizure detection and to improve continuous electroencephalogram (cEEG) cost-effectiveness. 2HELPS2B can address this need but requires validation. Objective: To use an independent cohort to validate the 2HELPS2B score and develop a practical guide for its use. Design, Setting, and Participants: This multicenter retrospective medical record review analyzed clinical and EEG data from patients 18 years or older with a clinical indication for cEEG and an EEG duration of 12 hours or longer who were receiving consecutive cEEG at 6 centers from January 2012 to January 2019. 2HELPS2B was evaluated with the validation cohort using the mean calibration error (CAL), a measure of the difference between prediction and actual results. A Kaplan-Meier survival analysis was used to determine the duration of EEG monitoring to achieve a seizure risk of less than 5% based on the 2HELPS2B score calculated on first- hour (screening) EEG. Participants undergoing elective epilepsy monitoring and those who had experienced cardiac arrest were excluded. No participants who met the inclusion criteria were excluded. Main Outcomes and Measures: The main outcome was a CAL error of less than 5% in the validation cohort. Results: The study included 2111 participants (median age, 51 years; 1113 men [52.7%]; median EEG duration, 48 hours) and the primary outcome was met with a validation cohort CAL error of 4.0% compared with a CAL of 2.7% in the foundational cohort (P =.13). For the 2HELPS2B score calculated on only the first hour of EEG in those without seizures during that hour, the CAL error remained at less than 5.0% at 4.2% and allowed for stratifying patients into low- (2HELPS2B = 0; <5% risk of seizures), medium- (2HELPS2B = 1; 12% risk of seizures), and high-risk (2HELPS2B, ≥2; risk of seizures, >25%) groups. Each of the categories had an associated minimum recommended duration of EEG monitoring to achieve at least a less, SCOPUS: cp.j, info:eu-repo/semantics/published

OBJECTIVE The aim of this study was to investigate the performance of a metric of functional connectivity to classify and grade the excitability of brain regions based on evoked potentials in response to single-pulse electrical stimulation (SPES). METHODS Patients who underwent 1-Hz frequency stimulation at prospectively selected contacts between 2003 and 2014 at the Yale Comprehensive Epilepsy Center were included. The stimulated contacts were classified as the seizure onset zone (SOZ), highly irritative zone (possibly epileptogenic irritative zone [IZp]), and control contacts not involved in the epileptic activity. Response contacts were classified as SOZ, active interictal irritative zone (IZ), quiet, or other. The normalized number of responses was defined as the number of contacts with any evoked responses divided by the total number of recorded contacts, and the normalized distance is the ratio of the average distance between the site of stimulation and sites of evoked responses to the average distances between the site of stimulation and all other recording contacts. A new metric that the authors labeled the connectivity index (CI) is defined as the product of the 2 values. RESULTS A total of 57 stimulation sessions in 22 patients were analyzed. The CI of the SOZ was higher than for control contacts (median CI of 0.74 vs 0.16, p = 0.0002). The evoked responses after stimulation of SOZ were seen at further distances compared to control (median normalized distance 0.96 vs 0.62, p = 0.0005). It was 1.8 times more likely that a response would be recorded at the SOZ than in nonepileptic contacts after stimulation of a control site. Habitual seizures were triggered in 27% of patients and 35% of SOZ contacts (median stimulation intensity 4 mA) but in none of the control or IZp contacts. Non-SOZ contacts in multifocal or poor surgical outcome cases had a higher CI than non-SOZ contacts in patients with localizable onsets (median CI of 0.5 vs 0.12, p = 0.04). There wa, SCOPUS: ar.j, info:eu-repo/semantics/published

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, SCOPUS: ar.j, info:eu-repo/semantics/published

The clinical implementation of continuous electroencephalography (CEEG) monitoring in critically ill patients is hampered by the substantial burden of work that it entails for clinical neurophysiologists. Solutions that might reduce this burden, including by shortening the duration of EEG to be recorded, would help its widespread adoption. Our aim was to validate a recently described algorithm of time-dependent electro-clinical risk stratification for electrographic seizure (ESz) (TERSE) based on simple clinical and EEG features., SCOPUS: ar.j, info:eu-repo/semantics/published

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New-onset refractory status epilepticus (NORSE) is a clinical presentation, not a specific diagnosis, in a patient without active epilepsy or other preexisting relevant neurologic disorder, with new onset of refractory status epilepticus (RSE) that does not resolve after 2 or more rescue medications, without a clear acute or active structural, toxic, or metabolic cause. Febrile infection-related epilepsy syndrome is a subset of NORSE in which fever began at least 24 hours prior to the RSE. Both terms apply to all age groups. Until recently, NORSE was a poorly recognized entity without a consistent definition or approach to care. We review the current state of knowledge in NORSE and propose a roadmap for future collaborative research. Research investigating NORSE should prioritize the following 4 domains: (1) clinical features, etiology, and pathophysiology; (2) treatment; (3) adult and pediatric evaluation and management approaches; and (4) public advocacy, professional education, and family support. We consider international collaboration and multicenter research crucial in achieving these goals., SCOPUS: ar.j, info:eu-repo/semantics/published

OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. DESIGN: Retrospective. SETTING: ICUs at four academic medical centers in the United States. PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy.None. MEASUREMENTS AND MAIN RESULTS: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated. CONCLUSIONS: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance., SCOPUS: ar.j, info:eu-repo/semantics/published

Objective: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify low-risk patients (<5% seizures risk in 48 h). Methods: The Critical Care EEG Monitoring Research Consortium (CCEMRC) multicenter database contains 7716 continuous EEGs (cEEG). Neural networks (NN), elastic net logistic regression (EN), and sparse linear integer model (RiskSLIM) were trained to predict seizures. RiskSLIM was used previously to generate 2HELPS2B model of seizure predictions. Data were divided into training (60% for model fitting) and evaluation (40% for model evaluation) cohorts. Performance was measured using area under the receiver operating curve (AUC), mean risk calibration (CAL), and negative predictive value (NPV). A secondary analysis was performed using Monte Carlo simulation (MCS) to normalize all EEG recordings to 48 h and use only the first hour of EEG as a “screening EEG” to generate predictions. Results: RiskSLIM recreated the 2HELPS2B model. All models had comparable AUC: evaluation cohort (NN: 0.85, EN: 0.84, 2HELPS2B: 0.83) and MCS (NN: 0.82, EN; 0.82, 2HELPS2B: 0.81) and NPV (absence of seizures in the group that the models predicted to be low risk): evaluation cohort (NN: 97%, EN: 97%, 2HELPS2B: 97%) and MCS (NN: 97%, EN: 99%, 2HELPS2B: 97%). 2HELPS2B model was able to identify the largest proportion of low-risk patients. Interpretation: For seizure risk stratification of hospitalized patients, the RiskSLIM generated 2HELPS2B model compares favorably to the complex NN and EN generated models. 2HELPS2B is able to accurately and quickly identify low-risk patients with only a 1-h screening EEG., SCOPUS: ar.j, info:eu-repo/semantics/published

PURPOSE: The aim of this study was to assess the variability in EEG reactivity evaluation practices during cardiac arrest prognostication. METHODS: A survey of institutional representatives from North American academic hospitals participating in the Critical Care EEG Monitoring Research Consortium was conducted to assess practice patterns involving EEG reactivity evaluation. This 10-question multiple-choice survey evaluated metrics related to technical, interpretation, personnel, and procedural aspects of bedside EEG reactivity testing and interpretation specific to cardiac arrest prognostication. One response per hospital was obtained. RESULTS: Responses were received from 25 hospitals, including 7 pediatric hospitals. A standardized EEG reactivity protocol was available in 44% of centers. Sixty percent of respondents believed that reactivity interpretation was subjective. Reactivity bedside testing always (100%) started during hypothermia and was performed daily during monitoring in the majority (71%) of hospitals. Stimulation was performed primarily by neurodiagnostic technologists (76%). The mean number of activation procedures modalities tested was 4.5 (SD 2.1). The most commonly used activation procedures were auditory (83.3%), nail bed pressure (63%), and light tactile stimuli (63%). Changes in EEG amplitude alone were not considered consistent with EEG reactivity in 21% of centers. CONCLUSIONS: There is substantial variability in EEG reactivity evaluation practices during cardiac arrest prognostication among North American academic hospitals. Efforts are needed to standardize protocols and nomenclature according with national guidelines and promote best practices in EEG reactivity evaluation., SCOPUS: ar.j, info:eu-repo/semantics/published

Objective: To determine the clinical correlates bilateral independent periodic discharges (BIPDs) and their association with electrographic seizures and outcome. Methods: Retrospective case-control study of patients with BIPDs compared to patients without periodic discharges (“No PDs”) and patients with lateralized periodic discharges (“LPDs”), matched for age, etiology and level of alertness. Results: We included 85 cases and 85 controls in each group. The most frequent etiologies of BIPDs were stroke, CNS infections, and anoxic brain injury. Acute bilateral cerebral injury was more common in the BIPDs group than in the No PDs and LPDs groups (70% vs. 37% vs. 35%). Electrographic seizures were more common with BIPDs than in the absence of PDs (45% vs. 8%), but not than with LPDs (52%). Mortality was higher in the BIPDs group (36%) than in the No PDs group (18%), with fewer patients with BIPDs achieving good outcome (moderate disability or better; 18% vs. 36%), but not than in the LPDs group (24% mortality, 26% good outcome). In multivariate analyses, BIPDs remained associated with mortality (OR: 3.0 [1.4–6.4]) and poor outcome (OR: 2.9 [1.4–6.2]). Conclusion: BIPDs are caused by bilateral acute brain injury and are associated with a high risk of electrographic seizures and of poor outcome. Significance: BIPDs are uncommon but their identification in critically ill patients has potential important implications, both in terms of clinical management and prognostication., SCOPUS: ar.j, info:eu-repo/semantics/published

Objective: To systematically examine the electro-clinical characteristics of post anoxic myoclonus (PAM) and their prognostic implications in comatose cardiac arrest (CA) survivors. Methods: Fifty-nine CA survivors who developed myoclonus within 72 h of arrest and underwent continuous EEG monitoring were included in the study. Retrospective chart review was performed for all relevant clinical variables including time of PAM onset (“early onset” when within 24 h) and semiology (multi-focal, facial/ocular, whole body and limbs only). EEG findings including background, reactivity, epileptiform patterns and EEG correlate to myoclonus were reviewed at 6, 12, 24, 48 and 72 h after the return of spontaneous circulation (ROSC). Outcome was categorized as either with recovery of consciousness (Cerebral Performance Category (CPC) 1–3) or without recovery of consciousness (CPC 4–5) at the time of discharge. Results: Seven of the 59 patients (11.9%) regained consciousness, including 6/51 (11.8%) with early onset PAM. Patients with recovery of consciousness had shorter time to ROSC, and were more likely to have preserved brainstem reflexes and normal voltage background at all times. No patient with suppression burst or low voltage background (N = 52) at any point regained consciousness. In the subset where precise electro-clinical correlation was possible, all (5/5) those with recovery of consciousness had multi-focal myoclonus and most (4/5) had midline-maximal spikes over a continuous background. No patient with any other semiology (N = 21) regained consciousness. Conclusions: Early onset PAM is not always associated with lack of recovery of consciousness. EEG can help discriminate between patients who may or may not regain consciousness by the time of hospital discharge., SCOPUS: ar.j, info:eu-repo/semantics/published

Importance: The pathophysiological mechanisms of Posterior Reversible Encephalopathy Syndrome (PRES) and related seizures remain poorly understood. The prevalence and clinical significance of nonconvulsive seizures (NCSz) and related epileptiform patterns during continuous electroencephalography monitoring (CEEG) in PRES have not been well described. Objective: To report the prevalence, characteristics and risk factors for NCSz and related highly epileptiform patterns in patients with PRES, and to determine their relation to imaging abnormalities and outcome. Design, Setting and Participants: From a prospective CEEG database, we retrospectively identified patients with PRES and reviewed their medical charts. Based on CEEG findings, we designed a retrospective cohort study comparing two groups defined based on the presence or the absence of NCSz and/or periodic discharges (PDs). Main outcomes and Measures: The prevalence and risk factors for PDs and NCSz, description of EEG and magnetic resonance imaging (MRI) abnormalities and functional outcome as measured by the Glasgow Outcome Scale (GOS) at hospital discharge. Results: Among 37 eligible patients, 23 (62%) had PDs or NCSz. The presence of NCSz was associated with the presence of PDs (15/22 vs. 1/15; p = 0.0002). NCSz and PDs were usually either lateralized or bilateral independent and predominated in the posterior regions. No clinical features were associated with the occurrence of PDs or NCSz. Cortical restricted diffusion on MRI was more frequent in the PDs/NCSz group (17/23 vs. 1/14; p < 0.001). PDs/NCSz were associated with worse outcome, with 3 deaths vs. 0 in the no PDs/NCSz group and fewer cases with low disability (4 vs. 9 cases with GOS = 5, p < 0.04). Conclusions and Relevance: Our results reveal a high prevalence of NCSz and PDs in critically ill patients with PRES and an association with restricted diffusion and worse outcome, whether treating or preventing these EEG findings can improve outcome requi, SCOPUS: ar.j, info:eu-repo/semantics/published

We convened an international group of experts to standardize definitions of New-Onset Refractory Status Epilepticus (NORSE), Febrile Infection-Related Epilepsy Syndrome (FIRES), and related conditions. This was done to enable improved communication for investigators, physicians, families, patients, and other caregivers. Consensus definitions were achieved via email messages, phone calls, an in-person consensus conference, and collaborative manuscript preparation. Panel members were from 8 countries and included adult and pediatric experts in epilepsy, electroencephalography (EEG), and neurocritical care. The proposed consensus definitions are as follows: NORSE is a clinical presentation, not a specific diagnosis, in a patient without active epilepsy or other preexisting relevant neurological disorder, with new onset of refractory status epilepticus without a clear acute or active structural, toxic or metabolic cause. FIRES is a subcategory of NORSE, applicable for all ages, that requires a prior febrile infection starting between 2 weeks and 24 hours prior to onset of refractory status epilepticus, with or without fever at onset of status epilepticus. Proposed consensus definitions are also provided for Infantile Hemiconvulsion-Hemiplegia and Epilepsy syndrome (IHHE) and for prolonged, refractory and super-refractory status epilepticus. This document has been endorsed by the Critical Care EEG Monitoring Research Consortium. We hope these consensus definitions will promote improved communication, permit multicenter research, and ultimately improve understanding and treatment of these conditions., SCOPUS: re.j, info:eu-repo/semantics/published