Current Evidence: Seizures in Extremely Low Gestational Age Newborns (ELGANs).

Extremely low gestational age newborns (ELGANs) are born at or below 28 weeks of gestational age. Despite improved obstetric care, the incidence of preterm birth continues to rise in advanced countries. Preterm birth remains a major cause of infant mortality, and for infants who survive, neonatal seizures are a significant predictor of later neurologic morbidity. However, little is known about risk factors for neonatal seizures in ELGANs. Understanding the association between neonatal seizures and the development of other neurologic disorders is important given the increasing prevalence of ELGANs. Identifying risk factors that contribute to the development of neonatal seizures in ELGANs may offer insights into novel mechanisms of epileptogenesis in the developing brain and improvements in the prevention or treatment of seizures in preterm infants, including ELGANs. In this literature review, we outline the limitations of epidemiologic studies of neonatal seizures in ELGANs and discuss risk factors for neonatal seizures.

Corrigendum: Building an open source classifier for the neonatal EEG background: a systematic feature-based approach from expert scoring to clinical visualization.

[This corrects the article DOI: 10.3389/fnhum.2021.675154.].

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.

Neonatal Hypoxic-Ischemic Encephalopathy Spectrum: Severity-Stratified Analysis of Neuroimaging Modalities and Association with Neurodevelopmental Outcomes.

OBJECTIVE: To compare hypoxic-ischemic injury on early cranial ultrasonography (cUS) and post-rewarming brain magnetic resonance imaging (MRI) in newborn infants with hypoxic-ischemic encephalopathy (HIE) and to correlate that neuroimaging with neurodevelopmental outcomes. STUDY DESIGN: This was a retrospective cohort study of infants with mild, moderate, and severe HIE treated with therapeutic hypothermia and evaluated with early cUS and postrewarming MRI. Validated scoring systems were used to compare the severity of brain injury on cUS and MRI. Neurodevelopmental outcomes were assessed at 18 months of age. RESULTS: Among the 149 included infants, abnormal white matter (WM) and deep gray matter (DGM) hyperechogenicity on cUS in the first 48 hours after birth were more common in the severe HIE group than the mild HIE group (81% vs 39% and 50% vs 0%, respectively; P < .001). In infants with a normal cUS, 95% had normal or mildly abnormal brain MRIs. In infants with severely abnormal cUS, none had normal and 83% had severely abnormal brain MRIs. Total abnormality scores on cUS were higher in neonates with near-total brain injury on MRI than in neonates with normal MRI or WM-predominant injury pattern (adjusted P < .001 for both). In the multivariable model, a severely abnormal MRI was the only independent risk factor for adverse outcomes (OR: 19.9, 95% CI: 4.0-98.1; P < .001). CONCLUSION: The present study shows the complementary utility of cUS in the first 48 hours after birth as a predictive tool for the presence of hypoxic-ischemic injury on brain MRI.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group in the Pediatric Population.

BACKGROUND: The fundamental gap obstructing forward progress of evidenced-based care in pediatric and neonatal disorders of consciousness (DoC) is the lack of defining consensus-based terminology to perform comparative research. This lack of shared nomenclature in pediatric DoC stems from the inherently recursive dilemma of the inability to reliably measure consciousness in the very young. However, recent advancements in validated clinical examinations and technologically sophisticated biomarkers of brain activity linked to future abilities are unlocking this previously formidable challenge to understanding the DoC in the developing brain. METHODS: To address this need, the first of its kind international convergence of an interdisciplinary team of pediatric DoC experts was organized by the Neurocritical Care Society's Curing Coma Campaign. The multidisciplinary panel of pediatric DoC experts proposed pediatric-tailored common data elements (CDEs) covering each of the CDE working groups including behavioral phenotyping, biospecimens, electrophysiology, family and goals of care, neuroimaging, outcome and endpoints, physiology and big Data, therapies, and pediatrics. RESULTS: We report the working groups' pediatric-focused DoC CDE recommendations and disseminate CDEs to be used in studies of pediatric patients with DoC. CONCLUSIONS: The CDEs recommended support the vision of progressing collaborative and successful internationally collaborative pediatric coma research.

Relationship between cerebral oxygenation, cardiac output, and blood pressure during transitional period in extremely low gestational age neonates.

Objective: To describe the relationship between cerebral oxygenation, cardiac output, arterial blood pressure (BP), and cerebral blood flow velocity in extremely low gestational age neonates (ELGANs) during transition. Methods: This study comprises secondary analyses from a prospective observational study conducted at a tertiary Neonatal Intensive Care Unit. Recruited ELGANs underwent cerebral saturation (CrSO2) monitoring and serial echocardiography during 72 h from birth. Correlative analyses of CrSO2 and cerebral fractional tissue oxygen extraction (CFTOE) with left (LVO) and right ventricular output (RVO), superior vena cava (SVC) flow, middle cerebral artery blood flow mean velocity (MCA.MV), systolic (SBP), diastolic (DBP), and mean (MBP) BP were conducted. Results: Fifty ELGANs with median (range) gestational age of 25.9 (23.1-27.9) weeks were recruited. Echocardiography was performed sequentially at a median (range) age 5.0 (3.8-6.6), 17.3 (15.4-19.4), 31.0 (27.0-34.1), and 53.7 (49.3-58.3) hours. RVO, LVO, CrSO2, and SBP increased over time but no changes in MBP, DBP, CFTOE, MCA.MV or SVC flow were noted. A weak correlation was identified between CrSO2 and SBP (r2 = 0.11, p = 0.047) and MBP (r2 = 0.12, p = 0.04) at 17.3 (15.4-19.4) hours. No correlation of either CrSO2 or CFTOE with any measures of blood flow was identified. Conclusion: There is a weak correlation between measures of cardiac output, BP, and MCA.MV with both CrSO2 and CFTOE in ELGANs during transition. Whether this finding suggests intact cerebral autoregulation requires prospective evaluation in a cohort of sick ELGANs.

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.

Trends in cyclicity and amplitudes on amplitude-integrated electroencephalography during transition in extremely low gestational age infants.

AIM: The aim of the study was to describe amplitude integrated electroencephalography (aEEG) cyclicity, background pattern, voltage margins and maturation scores in extremely low gestational age neonates (ELGANs) in the first 72 h. METHODS: Fifty infants with gestational age (GA) 23+0-27+6 weeks were prospectively studied. Infants with intraventricular haemorrhage ≦ Grade I and no disorders of transition (persistent pulmonary hypertension, hypotension, pulmonary haemorrhage) belonged to the 'Uncomplicated' group and those with intraventricular haemorrhage > Grade I and/or disorders of transition, to the 'Complicated' group. RESULTS: Thirty-six infants without opioid exposure were included: 23 with GA 25.9 (23.1-27.7) weeks in the 'Uncomplicated' group and 13 with GA 24.6 (23.3-27.4) weeks in the 'Complicated' group. Cyclicity was more common in the 'Uncomplicated' group [20/23 (87%) vs. 7/13 (54%), p = 0.045] with more cycles/hour [0.2 (0-0.78) vs. 0.03 (0-67), p = 0.036]. Age at appearance of cyclicity was similar [20 (7.7-40.7) hours in 'Uncomplicated' vs. 23.7 (5.4-60) hours in 'Complicated' group, p = 0.8]. In the 'Uncomplicated' group, maturation scores (p = 0.02), high (p < 0.0001) and low (p = 0.03) base voltage increased over time. CONCLUSION: During the first 72 h, clinically stable ELGANs without neurological injury demonstrate increased cyclicity compared to those with a complicated course. Maturation score, high and low base voltage increase over time.

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.

A practical approach toward interpretation of amplitude integrated electroencephalography in preterm infants.

The developing preterm brain is vulnerable to injury, especially during periods of clinical instability; therefore, monitoring the brain may provide important information on brain health. Over the last 2 decades, a growing body of literature has been reported on preterm amplitude integrated electroencephalography (aEEG) with regards to normative data and associations with adverse outcomes. Despite this, the use of aEEG for preterm infants remains mostly a research tool with limited clinical applicability. In this article, we review the literature on normal and abnormal aEEG patterns in preterm infants and propose a stepwise clinical algorithm for aEEG assessment at the bedside that takes into account assessment of maturation and identification of pathological patterns. CONCLUSION: This algorithm may be used by clinicians at the bedside for interpretation to integrate it in clinical practice for neurological surveillance of preterm infants. WHAT IS KNOWN: • Studies have reported normative data on aEEG in preterm infants for different gestational ages. • Burst suppression pattern and absent sleep-wake cycling have been described to be associated with brain pathology and adverse outcomes in preterm infants. WHAT IS NEW: • We have synthesized aEEG characteristics in preterm infants across the spectrum of prematurity reported in the literature. • We present a stepwise approach for clinically applicable interpretation of aEEG in preterm infants.

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.