Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Biospecimens and Biomarkers.

BACKGROUND: In patients with disorders of consciousness (DoC), laboratory and molecular biomarkers may help define endotypes, identify therapeutic targets, prognosticate outcomes, and guide patient selection in clinical trials. We performed a systematic review to identify common data elements (CDEs) and key design elements (KDEs) for future coma and DoC research. METHODS: The Curing Coma Campaign Biospecimens and Biomarkers work group, composed of seven invited members, reviewed existing biomarker and biospecimens CDEs and conducted a systematic literature review for laboratory and molecular biomarkers using predetermined search words and standardized methodology. Identified CDEs and KDEs were adjudicated into core, basic, supplemental, or experimental CDEs per National Institutes of Health classification based on level of evidence, reproducibility, and generalizability across different diseases through a consensus process. RESULTS: Among existing National Institutes of Health CDEs, those developed for ischemic stroke, traumatic brain injury, and subarachnoid hemorrhage were most relevant to DoC and included. KDEs were common to all disease states and included biospecimen collection time points, baseline indicator, biological source, anatomical location of collection, collection method, and processing and storage methodology. Additionally, two disease core, nine basic, 24 supplemental, and 59 exploratory biomarker CDEs were identified. Results were summarized and generated into a Laboratory Data and Biospecimens Case Report Form (CRF) and underwent public review. A final CRF version 1.0 is reported here. CONCLUSIONS: Exponential growth in biomarkers development has generated a growing number of potential experimental biomarkers associated with DoC, but few meet the quality, reproducibility, and generalizability criteria to be classified as core and basic biomarker and biospecimen CDEs. Identification and adaptation of KDEs, however, contribute to standardizing methodology to promote harmonization of future biomarker and biospecimens studies in DoC. Development of this CRF serves as a basic building block for future DoC studies.

Treatment of seizures in the neonate: Guidelines and consensus-based recommendations-Special report from the ILAE Task Force on Neonatal Seizures.

Seizures are common in neonates, but there is substantial management variability. The Neonatal Task Force of the International League Against Epilepsy (ILAE) developed evidence-based recommendations about antiseizure medication (ASM) management in neonates in accordance with ILAE standards. Six priority questions were formulated, a systematic literature review and meta-analysis were performed, and results were reported following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 standards. Bias was evaluated using the Cochrane tool and risk of Bias in non-randomised studies - of interventions (ROBINS-I), and quality of evidence was evaluated using grading of recommendations, assessment, development and evaluation (GRADE). If insufficient evidence was available, then expert opinion was sought using Delphi consensus methodology. The strength of recommendations was defined according to the ILAE Clinical Practice Guidelines development tool. There were six main recommendations. First, phenobarbital should be the first-line ASM (evidence-based recommendation) regardless of etiology (expert agreement), unless channelopathy is likely the cause for seizures (e.g., due to family history), in which case phenytoin or carbamazepine should be used. Second, among neonates with seizures not responding to first-line ASM, phenytoin, levetiracetam, midazolam, or lidocaine may be used as a second-line ASM (expert agreement). In neonates with cardiac disorders, levetiracetam may be the preferred second-line ASM (expert agreement). Third, following cessation of acute provoked seizures without evidence for neonatal-onset epilepsy, ASMs should be discontinued before discharge home, regardless of magnetic resonance imaging or electroencephalographic findings (expert agreement). Fourth, therapeutic hypothermia may reduce seizure burden in neonates with hypoxic-ischemic encephalopathy (evidence-based recommendation). Fifth, treating neonatal seizures (including electrographic-only seizures) to achieve a lower seizure burden may be associated with improved outcome (expert agreement). Sixth, a trial of pyridoxine may be attempted in neonates presenting with clinical features of vitamin B6-dependent epilepsy and seizures unresponsive to second-line ASM (expert agreement). Additional considerations include a standardized pathway for the management of neonatal seizures in each neonatal unit and informing parents/guardians about the diagnosis of seizures and initial treatment options.

The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit.

Objective.The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.Approach.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device. Vectors of inter-beat intervals were matched between both datasets and robust linear regression was applied to measure the relative time offset between the two datasets as a function of time.Main Results.The timing error between the two unsynchronized datasets ranged between -84 s and +33 s (mean 0.77 s, median 4.31 s, IQR25-4.79 s, IQR75 11.38s). Application of our method improved the relative alignment to within ± 5ms for more than 61% of the dataset. The mean clock drift between the two datasets was 418.3 parts per million (ppm) (median 414.6 ppm, IQR25 411.0 ppm, IQR75 425.6 ppm). A signal quality index was generated that described the quality of alignment for each cEEG study as a function of time.Significance.We developed and tested a method to retrospectively time-align two clinical waveform datasets acquired from different devices using a common signal. The method was applied to 33,911h of signals collected in a paediatric critical care unit over six years, demonstrating that the method can be applied to long-term recordings collected under clinical conditions. The method can account for unknown clock drift rates and the presence of discontinuities caused by clock resynchronization events.

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence.

Importance: Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed. Objective: To develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG-Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse. Design, Setting, and Participants: In this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded. Main Outcomes and Measures: Diagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients' habitual clinical episodes obtained during video-EEG recording. Results: The characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models (P < .001) and similar to human experts. Conclusions and Relevance: In this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.

Brain network properties of clinical versus subclinical seizures among critically ill children.

OBJECTIVE: Electrographic seizures are common among critically ill children, and have been associated with worse outcomes. Despite their often-widespread cortical representation, most of these seizures remain subclinical, a phenomenon which remains poorly understood. We compared the brain network properties of clinical versus subclinical seizures to gain insight into their relative potential deleterious effects. METHODS: Functional connectivity (phase lag index) and graph measures (global efficiency and clustering coefficients) were computed for 2178 electrographic seizures recorded during 48-hours of 19-channel continuous EEG monitoring obtained in 20 comatose children. Frequency-specific group differences in clinical versus subclinical seizures were analyzed using a non-parametric ANCOVA, adjusting for age, sex, medication exposure, treatment intensity and seizures per subject. RESULTS: Clinical seizures demonstrated greater functional connectivity than subclinical seizures at alpha frequencies, but less connectivity than subclinical seizures at delta frequencies. Clinical seizures also demonstrated significantly higher median global efficiency than subclinical seizures (p < 0.01), and significantly higher median clustering coefficients across all electrodes at alpha frequencies. CONCLUSIONS: Clinical expression of seizures correlates with greater alpha synchronization of distributed brain networks. SIGNIFICANCE: The stronger global and local alpha-mediated functional connectivity observed during clinical seizures may indicate greater pathological network recruitment. These observations motivate further studies to investigate whether the clinical expression of seizures may influence their potential to cause secondary brain injury.

Survey of Pediatric ICU EEG Monitoring-Reassessment After a Decade.

PURPOSE: In 2011, the authors conducted a survey regarding continuous EEG (CEEG) utilization in critically ill children. In the interim decade, the literature has expanded, and guidelines and consensus statements have addressed CEEG utilization. Thus, the authors aimed to characterize current practice related to CEEG utilization in critically ill children. METHODS: The authors conducted an online survey of pediatric neurologists from 50 US and 12 Canadian institutions in 2022. RESULTS: The authors assessed responses from 48 of 62 (77%) surveyed institutions. Reported CEEG indications were consistent with consensus statement recommendations and included altered mental status after a seizure or status epilepticus, altered mental status of unknown etiology, or altered mental status with an acute primary neurological condition. Since the prior survey, there was a 3- to 4-fold increase in the number of patients undergoing CEEG per month and greater use of written pathways for ICU CEEG. However, variability in resources and workflow persisted, particularly regarding technologist availability, frequency of CEEG screening, communication approaches, and electrographic seizure management approaches. CONCLUSIONS: Among the surveyed institutions, which included primarily large academic centers, CEEG use in pediatric intensive care units has increased with some practice standardization, but variability in resources and workflow were persistent.

Seizure Burden and Neurologic Outcomes After Neonatal Encephalopathy.

BACKGROUND AND OBJECTIVES: Seizures are common during neonatal encephalopathy (NE), but the contribution of seizure burden (SB) to outcomes remains controversial. This study aims to examine the relationship between electrographic SB and neurologic outcomes after NE. METHODS: This prospective cohort study recruited newborns ≥36 weeks postmenstrual age around 6 hours of life between August 2014 and November 2019 from a neonatal intensive care unit (NICU). Participants underwent continuous electroencephalography for at least 48 hours, brain MRI within 3-5 days of life, and structured follow-up at 18 months. Electrographic seizures were identified by board-certified neurophysiologists and quantified as total SB and maximum hourly SB. A medication exposure score was calculated based on all antiseizure medications given during NICU admission. Brain MRI injury severity was classified based on basal ganglia and watershed scores. Developmental outcomes were measured using the Bayley Scales of Infant Development, Third Edition. Multivariable regression analyses were performed, adjusting for significant potential confounders. RESULTS: Of 108 enrolled infants, 98 had continuous EEG (cEEG) and MRI data collected, of which 5 were lost to follow-up, and 6 died before age 18 months. All infants with moderate-severe encephalopathy completed therapeutic hypothermia. cEEG-confirmed neonatal seizures occurred in 21 (24%) newborns, with a total SB mean of 12.5 ± 36.4 minutes and a maximum hourly SB mean of 4 ± 10 min/h. After adjusting for MRI brain injury severity and medication exposure, total SB was significantly associated with lower cognitive (-0.21, 95% CI -0.33 to -0.08, p = 0.002) and language (-0.25, 95% CI -0.39 to -0.11, p = 0.001) scores at 18 months. Total SB of 60 minutes was associated with 15-point decline in language scores and 70 minutes for cognitive scores. However, SB was not significantly associated with epilepsy, neuromotor score, or cerebral palsy (p > 0.1). DISCUSSION: Higher SB during NE was independently associated with worse cognitive and language scores at 18 months, even after adjusting for exposure to antiseizure medications and severity of brain injury. These observations support the hypothesis that neonatal seizures occurring during NE independently contribute to long-term outcomes.

Machine learning to support triage of children at risk for epileptic seizures in the pediatric intensive care unit.

Objective.Epileptic seizures are relatively common in critically-ill children admitted to the pediatric intensive care unit (PICU) and thus serve as an important target for identification and treatment. Most of these seizures have no discernible clinical manifestation but still have a significant impact on morbidity and mortality. Children that are deemed at risk for seizures within the PICU are monitored using continuous-electroencephalogram (cEEG). cEEG monitoring cost is considerable and as the number of available machines is always limited, clinicians need to resort to triaging patients according to perceived risk in order to allocate resources. This research aims to develop a computer aided tool to improve seizures risk assessment in critically-ill children, using an ubiquitously recorded signal in the PICU, namely the electrocardiogram (ECG).Approach.A novel data-driven model was developed at a patient-level approach, based on features extracted from the first hour of ECG recording and the clinical data of the patient.Main results.The most predictive features were the age of the patient, the brain injury as coma etiology and the QRS area. For patients without any prior clinical data, using one hour of ECG recording, the classification performance of the random forest classifier reached an area under the receiver operating characteristic curve (AUROC) score of 0.84. When combining ECG features with the patients clinical history, the AUROC reached 0.87.Significance.Taking a real clinical scenario, we estimated that our clinical decision support triage tool can improve the positive predictive value by more than 59% over the clinical standard.

A phase 2, randomized, double-blind, placebo-controlled study to evaluate the efficacy and safety of soticlestat as adjunctive therapy in pediatric patients with Dravet syndrome or Lennox-Gastaut syndrome (ELEKTRA).

OBJECTIVE: Dravet syndrome (DS) and Lennox-Gastaut syndrome (LGS) are rare treatment-resistant childhood epilepsies classed as developmental and epileptic encephalopathies. ELEKTRA investigated the efficacy and safety of soticlestat (TAK-935) as adjunctive therapy in children with DS or LGS (NCT03650452). METHODS: ELEKTRA was a phase 2, randomized, double-blind, placebo-controlled study of soticlestat (≤300 mg twice daily, weight-adjusted) in children (aged 2-17 years) with DS, demonstrating three or more convulsive seizures/month, or with LGS, demonstrating four or more drop seizures/month at baseline. The 20-week treatment period comprised an 8-week dose-optimization period and a 12-week maintenance period. Efficacy endpoints included change from baseline in seizure frequency versus placebo. Safety assessments included incidence of treatment-emergent adverse events (TEAEs). RESULTS: ELEKTRA enrolled 141 participants; 126 (89%) completed the study. The modified intent-to-treat population included 139 participants who received one or more doses of study drug and had one or more efficacy assessments (DS, n = 51; LGS, n = 88). ELEKTRA achieved its primary endpoint: the combined soticlestat-treated population demonstrated a placebo-adjusted median reduction in seizure frequency of 30.21% during the maintenance period (p = .0008, n = 139). During this period, placebo-adjusted median reductions in convulsive and drop seizure frequencies of 50.00% (p = .0002; patients with DS) and 17.08% (p = .1160; patients with LGS), respectively, were observed. TEAE incidences were similar between the soticlestat (80.3%) and placebo (74.3%) groups and were mostly mild or moderate in severity. Serious TEAEs were reported by 15.5% and 18.6% of participants receiving soticlestat and placebo, respectively. TEAEs reported in soticlestat-treated patients with ≥5% difference from placebo were lethargy and constipation. No deaths were reported. SIGNIFICANCE: Soticlestat treatment resulted in statistically significant, clinically meaningful reductions from baseline in median seizure frequency (combined patient population) and in convulsive seizure frequency (DS cohort). Drop seizure frequency showed a nonstatistically significant numerical reduction in children with LGS. Soticlestat had a safety profile consistent with previous studies.

The brain in pediatric critical care: unique aspects of assessment, monitoring, investigations, and follow-up.

As survival after pediatric intensive care unit (PICU) admission has improved over recent years, a key focus now is the reduction of morbidities and optimization of quality of life for survivors. Neurologic disorders and direct brain injuries are the reason for 11-16% of admissions to PICU. In addition, many critically ill children are at heightened risk of brain injury and neurodevelopmental difficulties affecting later life, e.g., complex heart disease and premature birth. Hence, assessment, monitoring and protection of the brain, using fundamental principles of neurocritical care, are crucial to the practice of pediatric intensive care medicine. The assessment of brain function, necessary to direct appropriate care, is uniquely challenging amongst children admitted to the PICU. Challenges in assessment arise in children who are unstable, or pharmacologically sedated and muscle relaxed, or who have premorbid abnormality in development. Moreover, the heterogeneity of diseases and ages in PICU patients, means that high caliber evidence is harder to accrue than in adult practice, nonetheless, great progress has been made over recent years. In this 'state of the art' paper about critically ill children, we discuss (1) patient types at risk of brain injury, (2) new standardized clinical assessment tools for age-appropriate, clinical evaluation of brain function, (3) latest evidence related to cranial imaging, non-invasive and invasive monitoring of the brain, (4) the concept of childhood 'post intensive are syndrome' and approaches for neurodevelopmental follow-up. Better understanding of these concepts is vital for taking PICU survivorship to the next level.

Prevalence, Risk Factors, and Impact of Preoperative Seizures in Neonates With Congenital Heart Disease.

PURPOSE: The purpose of this study was to assess the prevalence, risk factors, and impact of electrographic seizures in neonates with complex congenital heart disease before cardiac surgery. METHODS: A cohort of 31 neonates with congenital heart disease monitored preoperatively with continuous video-EEG (cEEG) was first reviewed for electrographic seizure burden and EEG background abnormalities. Second, cEEG findings were correlated with brain MRI and 18-month outcomes. RESULTS: Continuous video-EEG was recorded preoperatively for a median duration of 20.5 hours (range, 2.5-93.5 hours). The five neonates (16%; 95% confidence interval, 5.5% to 34%) with seizures detected on cEEG in the preoperative period had a diagnosis of transposition of the great arteries or similar physiology, detected in four of five postnatally. None of the 157 recorded electrographic seizures had a clinical correlate. The median time to first seizure was 65 minutes (range, 6-300 minutes) after cEEG hookup. The median maximum hourly seizure burden was 12.4 minutes (range, 7-23 minutes). Before the first electrographic seizure, a prolonged interburst interval (>10 seconds) was not associated with seizures (coefficient 1.2; 95% confidence interval, -1.1 to 3.6). MRI brain lesions were three times more common in neonates with seizures. Sharp wave transients on cEEG were associated with delayed opercular development. CONCLUSIONS: In this cohort, preoperative electrographic seizures were common, were all subclinical, and were associated with MRI brain injury and postnatal diagnosis of transposition of the great arteries. The findings motivate further study of the mechanisms of preoperative brain injury, particularly among neonates with a postnatal diagnosis of transposition of the great arteries.

Spectrograms for Seizure Detection in Critically Ill Children.

SUMMARY: Electrographic seizures are common in critically ill children and a significant proportion of these seizures are nonconvulsive. There is an association between electrographic seizures and neurophysiological disturbances, worse short- and long-term neurologic outcomes, and mortality in critically ill patients. In this context, timely diagnosis and treatment of electrographic seizures in critically ill children becomes important. However, most institutions lack the resources to support round-the-clock or frequent review of continuous EEG recordings causing significant delays in seizure diagnosis. Given the current gaps in review of continuous EEG across institutions globally, use of visually simplified, time-compressed quantitative EEG trends such as spectrograms has the potential to enhance timeliness of seizure diagnosis and treatment in critically ill children.

Alterations in coordinated EEG activity precede the development of seizures in comatose children.

OBJECTIVE: We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children. METHODS: In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring. RESULTS: Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results. CONCLUSIONS: These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures. SIGNIFICANCE: If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.

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

PURPOSE: We systematically reviewed existing critical care electroencephalography (EEG) educational programs for non-neurologists, with the primary goal of reporting the content covered, methods of instruction, overall duration, and participant experience. Our secondary goals were to assess the impact of EEG programs on participants' core knowledge, and the agreement between non-experts and experts for seizure identification. SOURCE: Major databases were searched from inception to 30 August 2020. Randomized controlled trials, cohort studies, and descriptive studies were all considered if they reported an EEG curriculum for non-neurologists in a critical care setting. Data were presented thematically for the qualitative primary outcome and a meta-analysis using a random effects model was performed for the quantitative secondary outcomes. PRINCIPAL FINDINGS: Twenty-nine studies were included after reviewing 7,486 citations. Twenty-two studies were single centre, 17 were from North America, and 16 were published after 2016. Most EEG studies were targeted to critical care nurses (17 studies), focused on processed forms of EEG with amplitude-integrated EEG being the most common (15 studies), and were shorter than one day in duration (24 studies). In pre-post studies, EEG programs significantly improved participants' knowledge of tested material (standardized mean change, 1.79; 95% confidence interval [CI], 0.86 to 2.73). Agreement for seizure identification between non-experts and experts was moderate (Cohen's kappa = 0.44; 95% CI, 0.27 to 0.60). CONCLUSIONS: It is feasible to teach basic EEG to participants in critical care settings from different clinical backgrounds, including physicians and nurses. Brief training programs can enable bedside providers to recognize high-yield abnormalities such as non-convulsive seizures.

RéSUMé: OBJECTIF: Nous avons réalisé une revue systématique des programmes éducatifs d'électroencéphalographie (EEG) en soins intensifs s'adressant aux non-neurologues, avec pour but principal de rapporter le contenu couvert, les méthodes d'enseignement, la durée globale et l'expérience des participants. Nos objectifs secondaires étaient d'évaluer l'impact des programmes d'EEG sur les connaissances de base des participants, et l'accord entre non-experts et experts pour l'identification des convulsions. MéTHODE: Les principales bases de données ont été consultées depuis leur création jusqu'au 30 août 2020. Les études randomisées contrôlées, les études de cohorte et les études descriptives ont toutes été prises en compte si elles décrivaient un programme de formation en EEG pour les non-neurologues en milieu de soins intensifs. Les données ont été présentées thématiquement en ce qui touchait notre critère d'évaluation principal qualitatif, et une méta-analyse utilisant un modèle à effets aléatoires a été exécutée pour les critères secondaires quantitatifs. CONSTATATIONS PRINCIPALES: Vingt-neuf études ont été incluses après avoir examiné 7486 citations. Vingt-deux études étaient monocentriques, 17 provenaient d'Amérique du Nord et 16 avaient été publiées après 2016. La plupart des études sur l'EEG visaient le personnel infirmier en soins intensifs (17 études); elles se concentraient sur les formes analysées d'EEG; l'EEG à amplitude intégrée était le thème le plus fréquemment abordé (15 études), et la plupart duraient moins d'un jour (24 études). Dans les études avant-après, les programmes d'EEG ont considérablement amélioré les connaissances des participants du matériel testé (changement moyen normalisé, 1,79; intervalle de confiance [IC] à 95 %, 0,86 à 2,73). L'accord en matière d''identification des convulsions entre non-experts et experts était modéré (kappa de Cohen = 0,44; IC 95 %, 0,27 à 0,60). CONCLUSION: Il est possible d'enseigner l'EEG de base dans des milieux de soins intensifs à des participants provenant de différents milieux cliniques, y compris les médecins et le personnel infirmier. De brefs programmes de formation peuvent permettre aux fournisseurs de soins au chevet de reconnaître les anomalies à haut impact comme par exemple des crises épileptiques non convulsives.

Emergency management of the paediatric patient with convulsive status epilepticus.

This guideline addresses the emergency management of convulsive status epilepticus (CSE) in children and infants older than 1 month of age. It replaces a previous position statement from 2011, and includes a new treatment algorithm and table of recommended medications based on new evidence and reflecting the evolution of clinical practice over the past several years. This statement emphasizes the importance of timely pharmacological management of CSE, and includes some guidance for diagnostic approach and supportive care.

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

BACKGROUND: Use of electroencephalography (EEG) is currently recommended by the American Clinical Neurophysiology Society for a wide range of indications, including diagnosis of nonconvulsive status epilepticus and evaluation of unexplained disorders of consciousness. Data interpretation usually occurs by expert personnel (e.g., epileptologists, neurophysiologists), with information relayed to the primary care team. However, data cannot always be read in time-sensitive fashion, leading to potential delays in EEG interpretation and patient management. Multiple training programs have recently been described to enable non-experts to rapidly interpret EEG at the bedside. A comprehensive review of these training programs, including the tools used, outcomes obtained, and potential pitfalls, is currently lacking. Therefore, the optimum training program and implementation strategy remain unknown. METHODS: We will conduct a systematic review of descriptive studies, case series, cohort studies, and randomized controlled trials assessing training programs for EEG interpretation by non-experts. Our primary objective is to comprehensively review educational programs in this domain and report their structure, patterns of implementation, limitations, and trainee feedback. Our secondary objective will be to compare the performance of non-experts for EEG interpretation with a gold standard (e.g., interpretation by a certified electroencephalographers). Studies will be limited to those performed in acute care settings in both adult and pediatric populations (intensive care unit, emergency department, or post-anesthesia care units). Comprehensive search strategies will be developed for MEDLINE, EMBASE, WoS, CINAHL, and CENTRAL to identify studies for review. The gray literature will be scanned for further eligible studies. Two reviewers will independently screen the search results to identify studies for inclusion. A standardized data extraction form will be used to collect important data from each study. If possible, we will attempt to meta-analyze the quantitative data. If heterogeneity between studies is too high, we will present meaningful quantitative comparisons of secondary outcomes as per the synthesis without meta-analysis (SWiM) reporting guidelines. DISCUSSION: We will aim to summarize the current literature in this domain to understand the structure, patterns, and pitfalls of EEG training programs for non-experts. This review is undertaken with a view to inform future education designs, potentially enabling rapid detection of EEG abnormalities, and timely intervention by the treating physician. PROSPERO REGISTRATION: Submitted and undergoing review. Registration ID: CRD42020171208 .

Epilepsy and Electroencephalographic Abnormalities in SATB2-Associated Syndrome.

BACKGROUND: Seizures are an under-reported feature of the SATB2-associated syndrome phenotype. We describe the electroencephalographic findings and seizure semiology and treatment in a population of individuals with SATB2-associated syndrome. METHODS: We performed a retrospective review of 101 individuals with SATB2-associated syndrome who were reported to have had a previous electroencephalographic study to identify those who had at least one reported abnormal result. For completeness, a supplemental survey was distributed to the caregivers and input from the treating neurologist was obtained whenever possible. RESULTS: Forty-one subjects were identified as having at least one prior abnormal electroencephalography. Thirty-eight individuals (93%) had epileptiform discharges, 28 (74%) with central localization. Sleep stages were included as part of the electroencephalographies performed in 31 individuals (76%), and epileptiform activity was recorded during sleep in all instances (100%). Definite clinical seizures were diagnosed in 17 individuals (42%) with a mean age of onset of 3.2 years (four months to six years), and focal seizures were the most common type of seizure observed (42%). Six subjects with definite clinical seizures needed polytherapy (35%). Delayed myelination and/or abnormal white matter hyperintensities were seen on neuroimaging in 19 individuals (61%). CONCLUSIONS: Epileptiform abnormalities are commonly seen in individuals with SATB2-associated syndrome. A baseline electroencephalography that preferably includes sleep stages is recommended during the initial evaluation of all individuals with SATB2-associated syndrome, regardless of clinical suspicion of epilepsy.

Seizure Detection Algorithms in Critically Ill Children: A Comparative Evaluation.

OBJECTIVES: To evaluate the performance of commercially available seizure detection algorithms in critically ill children. DESIGN: Diagnostic accuracy comparison between commercially available seizure detection algorithms referenced to electroencephalography experts using quantitative electroencephalography trends. SETTING: Multispecialty quaternary children's hospital in Canada. SUBJECTS: Critically ill children undergoing electroencephalography monitoring. INTERVENTIONS: Continuous raw electroencephalography recordings (n = 19) were analyzed by a neurophysiologist to identify seizures. Those recordings were then converted to quantitative electroencephalography displays (amplitude-integrated electroencephalography and color density spectral array) and evaluated by six independent electroencephalography experts to determine the sensitivity and specificity of the amplitude-integrated electroencephalography and color density spectral array displays for seizure identification in comparison to expert interpretation of raw electroencephalography data. Those evaluations were then compared with four commercial seizure detection algorithms: ICTA-S (Stellate Harmonie Version 7; Natus Medical, San Carlos, CA), NB (Stellate Harmonie Version 7; Natus Medical), Persyst 11 (Persyst Development, Prescott, AZ), and Persyst 13 (Persyst Development) to determine sensitivity and specificity in comparison to amplitude-integrated electroencephalography and color density spectral array. MEASUREMENTS AND MAIN RESULTS: Of the 379 seizures identified on raw electroencephalography, ICTA-S detected 36.9%, NB detected 92.3%, Persyst 11 detected 75.9%, and Persyst 13 detected 74.4%, whereas electroencephalography experts identified 76.5% of seizures using color density spectral array and 73.7% using amplitude-integrated electroencephalography. Daily false-positive rates averaged across all recordings were 4.7 with ICTA-S, 126.3 with NB, 5.1 with Persyst 11, 15.5 with Persyst 13, 1.7 with color density spectral array, and 1.5 with amplitude-integrated electroencephalography. Both Persyst 11 and Persyst 13 had sensitivity comparable to that of electroencephalography experts using amplitude-integrated electroencephalography and color density spectral array. Although Persyst 13 displayed the highest sensitivity for seizure count and seizure burden detected, Persyst 11 exhibited the best trade-off between sensitivity and false-positive rate among all seizure detection algorithms. CONCLUSIONS: Some commercially available seizure detection algorithms demonstrate performance for seizure detection that is comparable to that of electroencephalography experts using quantitative electroencephalography displays. These algorithms may have utility as early warning systems that prompt review of quantitative electroencephalography or raw electroencephalography tracings, potentially leading to more timely seizure identification in critically ill patients.