Immediate and long-term management practices of acute symptomatic seizures and epileptiform abnormalities: A cross-sectional international survey.

OBJECTIVES: Acute symptomatic seizures (ASyS) and epileptiform abnormalities (EAs) on electroencephalography (EEG) are commonly encountered following acute brain injury. Their immediate and long-term management remains poorly investigated. We conducted an international survey to understand their current management. METHODS: The cross-sectional web-based survey of 21 fixed-response questions was based on a common clinical encounter: convulsive or suspected ASyS following an acute brain injury. Respondents selected the option that best matched their real-world practice. Respondents completing the survey were compared with those who accessed but did not complete it. RESULTS: A total of 783 individuals (44 countries) accessed the survey; 502 completed it. Almost everyone used anti-seizure medications (ASMs) for secondary prophylaxis after convulsive or electrographic ASyS (95.4% and 97.2%, respectively). ASM dose escalation after convulsive ASyS depends on continuous EEG (cEEG) findings: most often increased after electrographic seizures (78% of respondents), followed by lateralized periodic discharges (LPDs; 41%) and sporadic epileptiform discharges (sEDs; 17.5%). If cEEG is unrevealing, one in five respondents discontinue ASMs after a week. In the absence of convulsive and electrographic ASyS, a large proportion of respondents start ASMs due to LPD (66.7%) and sED (44%) on cEEG. At hospital discharge, most respondents (85%) continue ASM without dose change. The recommended duration of outpatient ASM use is as follows: 1-3 months (36%), 3-6 months (30%), 6-12 months (13%), >12 months (11%). Nearly one-third of respondents utilized ancillary testing before outpatient ASM taper, most commonly (79%) a <2 h EEG. Approximately half of respondents had driving restrictions recommended for 6 months after discharge. SIGNIFICANCE: ASM use for secondary prophylaxis after convulsive and electrographic ASyS is a universal practice and is continued upon discharge. Outpatient care, particularly the ASM duration, varies significantly. Wide practice heterogeneity in managing acute EAs reflects uncertainty about their significance and management. These results highlight the need for a structured outpatient follow-up and optimized care pathway for patients with ASyS.

Background EEG Suppression Ratio for Early Detection of Cerebral Injury in Pediatric Cardiac Arrest.

BACKGROUND: Our objective was to assess the utility of the 1-h suppression ratio (SR) as a biomarker of cerebral injury and neurologic prognosis after cardiac arrest (CA) in the pediatric hospital setting. METHODS: Prospectively, we reviewed data from children presenting after CA and monitored by continuous electroencephalography (cEEG). Patients aged 1 month to 21 years were included. The SR, a quantitative measure of low-voltage cEEG (≤ 3 µV) content, was dichotomized as present or absent if there was > 0% suppression for one continuous hour. A multivariate logistic regression analysis was performed including age, sex, type of CA (i.e., in-hospital or out-of-hospital), and the presence of SR as a predictor of global anoxic cerebral injury as confirmed by magnetic resonance imaging (MRI). RESULTS: We included 84 patients with a median age of 4 years (interquartile range 0.9-13), 64% were male, and 49% (41/84) had in-hospital CA. Cerebral injury was seen in 50% of patients, of whom 65% had global injury. One-hour SR presence, independent of amount, predicted cerebral injury with 81% sensitivity (95% confidence interval (CI) (66-91%) and 98% specificity (95% CI 88-100%). Multivariate logistic regression analyses indicated that SR was a significant predictor of both cerebral injury (ß = 6.28, p < 0.001) and mortality (ß = 3.56, p < 0.001). CONCLUSIONS: The SR a sensitive and specific marker of anoxic brain injury and post-CA mortality in the pediatric population. Once detected in the post-CA setting, the 1-h SR may be a useful threshold finding for deployment of early neuroprotective strategies prior or for prompting diagnostic neuroimaging.

Prognostic value of early electrographic biomarkers of epileptogenesis in high-risk ischaemic stroke patients.

BACKGROUND AND PURPOSE: Post-stroke epilepsy (PSE) is frequent. Better prediction of PSE would enable individualized management and improve trial design for epilepsy prevention. The aim was to assess the complementary value of continuous electroencephalography (EEG) data during the acute phase compared with clinical risk factors currently used to predict PSE. METHODS: A prospective cohort of 81 patients with ischaemic stroke who received early continuous EEG monitoring was studied to assess the association of early EEG seizures, other highly epileptogenic rhythmic and periodic patterns, and regional attenuation without delta (RAWOD, an EEG pattern of stroke severity) with PSE. Clinical risk factors were investigated using the SeLECT (stroke severity; large-artery atherosclerosis; early clinical seizures; cortical involvement; territory of middle cerebral artery) scores. RESULTS: Twelve (15%) patients developed PSE. The presence of any of the investigated patterns was associated with a risk of epilepsy of 46%, with a sensitivity and specificity of 83% and 78%. The association remained significant after adjusting for the SeLECT score (odds ratio 18.8, interquartile range 3.8-72.7). CONCLUSIONS: It was found that highly epileptogenic rhythmic and periodic patterns and RAWOD were associated with the development of PSE and complemented clinical risk factors. These findings indicate that continuous EEG provides useful information to determine patients at higher risk of developing PSE and could help individualize care.

Case report: Invasive neuromonitoring in status epilepticus induced hypoxic ischemic brain injury.

Objectives: Literature on invasive neuromonitoring and bilateral decompressive craniectomies (BDC) in patients with refractory status epilepticus (RSE)-mediated hypoxic-ischemic brain injury (HIBI) is limited. Neuromonitoring can guide decision making and treatment escalation. Methods and results: We report a case of a 17 years-old male who was admitted to our hospital's intensive care unit for RSE. HIBI was detected on neuroimaging on this patient's second day of admission after he developed central diabetes insipidus (DI). Invasive neuromonitoring revealed raised intracranial pressure (ICP) and brain hypoxia as measured by reduced brain tissue oxygen tension (PbtO2). Treatments were escalated in a tiered fashion, including administration of hyperosmolar agents, analgesics, sedatives, and a neuromuscular blocking drug. Eventually, BDC was performed as a salvage therapy as a means of controlling refractory ICP crisis in the setting of diffuse cerebral edema (DCE) following HIBI. Discussion: SE-mediated HIBI can result in refractory ICP crisis. Neuromonitoring can help identify secondary brain injury (SBI), guide treatment strategies, including surgical interventions, and may lead to better outcomes.

Crisis neonatales: evaluacion, diagnostico y tratamiento / Neonatal seizures: evaluation, diagnosis, and treatment

Resumen Las crisis convulsivas tienen una alta incidencia en la etapa neonatal, representando la principal manifes tación de disfunción neurológica. Ciertas condiciones fisiológicas del cerebro neonatal facilitan su aparición. Su diagnóstico puede ser un reto debido a que su semio logía no es tan clara comparado con niños mayores, y además, es necesario la confirmación por medio de EEG continuo o aEEG. Su reconocimiento oportuno es muy importante para un adecuado tratamiento y así evitar un impacto negative en el pronóstico a largo plazo. En la siguiente revisión, recapitulamos la fisiopatología, las causas y la clasificación de las crisis convulsivas neo natales, además de su correcto abordaje y las mejores opciones terapéuticas para su tratamiento dependiendo de la causa.

Abstract Seizures have a high incidence in the neonatal stage, being the main manifestation of neurological dysfunc tion. Certain physiological conditions of the neonatal brain facilitate its appearance. Its diagnosis can be a challenging because its semiology is not as clear as in older children, furthermore, confirmation by either EEG or aEEG is necessary. Its timely recognition is very im portant for adequate treatment and thus avoid a nega tive impact on the long-term outcome. In the following review, we recapitulate the pathophysiology, causes, and classification of neonatal seizures, as well as their correct approach and the best therapeutic options for their treatment depending on the cause.

[Neonatal seizures: evaluation, diagnosis, and treatment]. / Crisis neonatales: evaluación, diagnóstico y tratamiento.

Seizures have a high incidence in the neonatal stage, being the main manifestation of neurological dysfunction. Certain physiological conditions of the neonatal brain facilitate its appearance. Its diagnosis can be a challenging because its semiology is not as clear as in older children, furthermore, confirmation by either EEG or aEEG is necessary. Its timely recognition is very important for adequate treatment and thus avoid a negative impact on the long-term outcome. In the following review, we recapitulate the pathophysiology, causes, and classification of neonatal seizures, as well as their correct approach and the best therapeutic options for their treatment depending on the cause.

Las crisis convulsivas tienen una alta incidencia en la etapa neonatal, representando la principal manifestación de disfunción neurológica. Ciertas condiciones fisiológicas del cerebro neonatal facilitan su aparición. Su diagnóstico puede ser un reto debido a que su semiología no es tan clara comparado con niños mayores, y además, es necesario la confirmación por medio de EEG continuo o aEEG. Su reconocimiento oportuno es muy importante para un adecuado tratamiento y así evitar un impacto negative en el pronóstico a largo plazo. En la siguiente revisión, recapitulamos la fisiopatología, las causas y la clasificación de las crisis convulsivas neonatales, además de su correcto abordaje y las mejores opciones terapéuticas para su tratamiento dependiendo de la causa.

Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning.

Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.

Ellen R. Grass Lecture: Disparities in Access to Neurodiagnostic Testing.

Limited access to EEG services in rural areas creates health disparities in neurological care, including unnecessary transfers and delays in diagnosis and treatment. Rural facilities face several challenges to expanding EEG resources, including a lack of neurologists, technologists, EEG equipment, and adequate IT infrastructure. Potential solutions include investment in innovative technology, expansion of the workforce, and development of hub-and-spoke EEG networks. Bridging the EEG gap requires collaboration between academic and community practices to advance practical technologies, train competent personnel and develop cost-effective resource-sharing strategies.

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.

The Spectrum of Quantitative EEG Utilization Across North America: A Cross-Sectional Survey.

BACKGROUND: Continuous electroencephalography (cEEG) is commonly used for neuromonitoring in pediatric intensive care units (PICU); however, there are barriers to real-time interpretation of EEG data. Quantitative EEG (qEEG) transforms the EEG signal into time-compressed graphs, which can be displayed at the bedside. A survey was designed to understand current PICU qEEG use. METHODS: An electronic survey was sent to the Pediatric Neurocritical Care Research Group and Pediatric Status Epilepticus Research Group, and intensivists in 16 Canadian PICUs. Questions addressed demographics, qEEG acquisition and storage, clinical use, and education. RESULTS: Fifty respondents from 39 institutions completed the survey (response rate 53% [39 of 74 institutions]), 76% (37 of 50) from the United States and 24% (12 of 50) from Canada. Over half of the institutions (22 of 39 [56%]) utilize qEEG in their ICUs. qEEG use was associated with having a neurocritical care (NCC) service, ≥200 NCC consults/year, ≥1500 ICU admissions/year, and ≥4 ICU EEGs/day (P < 0.05 for all). Nearly all users (92% [24 of 26]) endorsed that qEEG enhanced care of children with acute neurological injury. Lack of training in qEEG was identified as a common barrier [85% (22 of 26)]. Reviewing and reporting of qEEG was not standard at most institutions. Training was required by 14% (three of 22) of institutions, and 32% (seven of 22) had established curricula. CONCLUSIONS: ICU qEEG was used at more than half of the institutions surveyed, but review, reporting, and application of this tool remained highly variable. Although providers identify qEEG as a useful tool in patient management, further studies are needed to define clinically meaningful pediatric trends, standardize reporting, and enhance educate bedside providers.

Power spectrum analysis and outcomes of non-convulsive status epilepticus: a single-center study.

OBJECTIVES: Diagnosis of non-convulsive status epilepticus (NCSE) is challenging and outcomes during follow-up are not clear. This study aimed to conduct power spectrum analysis in NCSE and measure outcomes of patients. METHODS: We searched continuous EEG monitoring (cEEG) recordings to identify patients of NCSE. An artifact-free cEEG epoch of continuous 60 s was chosen for spectral power analysis. We also collected electronic medical records of the patients for extracting clinical information. Patients recruited were followed up at least every half a year. RESULTS: There were 48 patients with 64 independent NCSE episodes during different course of disease recruited in the study, with a mean age of 40.3 ± 19.1 years (range, 12-72 years), including 24 males (50%) and 24 females (50%). When the spectral power of 60 s equaled to 11.30 µV2 for predicting impairment of consciousness, (sensitivity, specificity) = (0.979, 0.625). When the spectral power of 60 s equaled to 52.70 µV2 for predicting myoclonic jerks, (sensitivity, specificity) = (0.783, 0.756). There were 27 patients (56.3%) followed up with a duration over 12 months. Nineteen patients (70.4%) continued to have seizures. Eleven (40.7%) resisted to at least two kinds of appropriate anti-seizure medication at maximum tolerated levels. Five patients with prolonged NCSE suffered from loss of brain parenchymal volume on follow-up MRI scans. CONCLUSION: Spectral power analysis can be used to detect mental status and limb jerks. Early diagnosis and treatment of NCSE are important, which can influence outcomes of the patients during follow-up.

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.

Ride the Wave: Continuous Electroencephalography is Indicated in the Management of Traumatic Brain Injury.

Introduction: Patients with traumatic brain injury (TBI) are at risk for seizures and other abnormalities that can have permanent adverse effects on the brain. We aimed to report the incidence of seizures and continuous EEG (cEEG) abnormalities after TBI and identify any risk factors associated with the development of these abnormalities. Materials and Methods: This retrospective study identified 245 adult patients with mild to severe TBI who had a cEEG performed within one week of admission to a Midwest Level 1 Trauma Center between July 2014 and July 2019. Trauma registry and electronic medical record (EPIC) data were extracted. Results: Twelve percent of patients with TBI developed seizures and an additional 23% demonstrated electrographic patterns that are correlated with risk for seizures (such as lateralized periodic patterns and sporadic epileptiform discharges). Fifty three percent of seizures would have been missed unless a cEEG was performed. Age, history of epilepsy or prior TBI, hypertension, bleeding disorder, and dementia were associated with an increased risk of developing seizures or higher risk patterns. Conclusions: Thirty-five percent of patients who presented with TBI were noted to have seizures or electrographic patterns associated with a higher risk of seizures. The incidence of cEEG abnormalities in this study is higher than previously reported and these patients are at risk for permanent neurological injury. We recommend the routine use of cEEG for all critically ill patients with TBI as over half of the seizures would have been missed if cEEG was not employed.

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.

Simulation-based assessment of trainee's performance in post-cardiac arrest resuscitation.

Objectives: To assess trainees' performance in managing a patient with post-cardiac arrest complicated by status epilepticus. Methods: In this prospective, observational, single-center simulation-based study, trainees ranging from sub interns to critical care fellows evaluated and managed a post cardiac arrest patient, complicated by status epilepticus. Critical action items were developed by a modified Delphi approach based on American Heart Association guidelines and the Neurocritical Care Society's Emergency Neurological Life Support protocols. The primary outcome measure was the critical action item sum score. We sought validity evidence to support our findings by including attending neurocritical care physicians and comparing performance across four levels of training. Results: Forty-nine participants completed the simulation. The mean sum of critical actions completed by trainees was 10/21 (49%). Eleven (22%) trainees verbalized a differential diagnosis for the arrest. Thirty-two (65%) reviewed the electrocardiogram, recognized it as abnormal, and consulted cardiology. Forty trainees (81%) independently decided to start temperature management, but only 20 (41%) insisted on it when asked to reconsider. There was an effect of level of training on critical action checklist sum scores (novice mean score [standard deviation (SD)] = 4.8(1.8) vs. intermediate mean score (SD) = 10.4(2.1) vs. advanced mean score (D) = 11.6(3.0) vs. expert mean score (SD) = 14.7(2.2)). Conclusions: High-fidelity manikin-based simulation holds promise as an assessment tool in the performance of post-cardiac arrest care.

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.

Serum neuron-specific enolase: a new tool for seizure risk monitoring after status epilepticus.

BACKGROUND AND PURPOSE: There is a need for accurate biomarkers to monitor electroencephalography (EEG) activity and assess seizure risk in patients with acute brain injury. Seizure recurrence may lead to cellular alterations and subsequent neurological sequelae. Whether neuron-specific enolase (NSE) and S100-beta (S100B), brain injury biomarkers, can reflect EEG activity and help to evaluate the seizure risk was investigated. METHODS: Eleven patients, admitted to an intensive care unit for refractory status epilepticus, who underwent a minimum of 3 days of continuous EEG concomitantly with daily serum NSE and S100B assays were included. At 103 days the relationships between serum NSE and S100B levels and two EEG scores able to monitor the seizure risk were investigated. Biochemical biomarker thresholds able to predict seizure recurrence were sought. RESULTS: Only NSE levels positively correlated with EEG scores. Similar temporal dynamics were observed for the time courses of EEG scores and NSE levels. NSE levels above 17 ng/ml were associated with seizure in 71% of patients. An increase of more than 15% of NSE levels was associated with seizure recurrence in 80% of patients. CONCLUSIONS: Our study highlights the potential of NSE as a biomarker of EEG activity and to assess the risk of seizure recurrence.

Etiology and prognostic significance of ictal EEG patterns in patients with non-convulsive seizures.

We aimed to study the ictal EEG patterns in patients with non-convulsive seizures (NCS) and their relationship with underlying etiology and patient outcome. We conducted a retrospective review of EEG studies from patients undergoing continuous EEG (cEEG) monitoring for indication of altered mental status with a suspicion of NCS. Ictal EEG findings of NCS were categorized as three patterns: focal or generalized epileptiform discharges (EDs) at frequencies >2.5 Hz (Pattern 1); EDs at frequencies of ≤2.5 Hz or rhythmic activity >0.5 Hz with spatiotemporal evolution (Pattern 2); and EDs with ≤2.5 Hz with subtle clinical correlate during the ictal EEG or clinical and EEG improvement after a trial of IV anti-seizure drugs (Pattern 3). Patients with anoxic brain injury were excluded from the study. Associations between ictal EEG patterns and underlying etiology and their impact on in-hospital mortality was measured. Of 487 patients included in the study, NCS was recorded on cEEG monitoring in 57 (12%). The ictal EEG Pattern 2 was the most commonly seen ictal EEG finding in our cohort of patients with NCS (70%, n=40/57), followed by Pattern 3 (15%, n=9/57) and Pattern 1(14%, n=8/57). In patients with acute brain injury, Pattern 2 (67%, n=27/40) was a commonly seen ictal EEG finding, whereas Pattern 1 (62% n=5/8) was seen in patients with underlying acute medical illness. No statistically significant difference was found between ictal EEG patterns and underlying neurological versus medical etiologies (p=0.27) or in-hospital mortality (p=0.5). Spatiotemporal evolution of epileptiform discharges at a lower frequency was the most commonly recorded ictal EEG pattern in our cohort. Further prospective studies with a larger sample size of patients with NCS may provide valuable clinical data that could be used to evaluate the etiologic correlate of the various ictal EEG patterns and their effect on outcome.