Interobserver reproducibility of electroencephalogram interpretation in critically ill children

Ictal-Interictal Continuum in the Pediatric Intensive Care Unit

BACKGROUND

The ictal-interictal continuum (IIC) consists of several electroencephalogram (EEG) patterns that are common in critically ill adults. Studies focused on the IIC are limited in critically ill children and have focused primarily on associations with electrographic seizures (ESs). We report the incidence of the IIC in the pediatric intensive care unit (PICU). We then compare IIC patterns to rhythmic and periodic patterns (RPP) not meeting IIC criteria looking for associations with acute cerebral abnormalities, ES, and in-hospital mortality.

METHODS

This was a retrospective review of prospectively collected data for patients admitted to the PICU at Children's National Hospital from July 2021 to January 2023 with continuous EEG. We excluded patients with known epilepsy and cerebral injury prior to presentation. All patients were screened for RPP. The American Clinical Neurophysiology Society standardized Critical Care EEG terminology for the IIC was applied to each RPP. Associations between IIC and RPP not meeting IIC criteria, with clinical and EEG variables, were calculated using odds ratios (ORs).

RESULTS

Of 201 patients, 21% (42/201) had RPP and 12% (24/201) met IIC criteria. Among patients with an IIC pattern, the median age was 3.4 years (interquartile range (IQR) 0.6-12 years). Sixty-seven percent (16/24) of patients met a single IIC criterion, whereas the remainder met two criteria. ESs were identified in 83% (20/24) of patients and cerebral injury was identified in 96% (23/24) of patients with IIC patterns. When comparing patients with IIC patterns with those with RPP not qualifying as an IIC pattern, both patterns were associated with acute cerebral abnormalities (IIC OR 26 [95% confidence interval {CI} 3.4-197], p = 0.0016 vs. RPP OR 3.5 [95% CI 1.1-11], p = 0.03), however, only the IIC was associated with ES (OR 121 [95% CI 33-451], p < 0.0001) versus RPP (OR 1.3 [0.4-5], p = 0.7).

CONCLUSIONS

Rhythmic and periodic patterns and subsequently the IIC are commonly seen in the PICU and carry a high association with cerebral injury. Additionally, the IIC, seen in more than 10% of critically ill children, is associated with ES. The independent impact of RPP and IIC patterns on secondary brain injury and need for treatment of these patterns independent of ES requires further study.

Electrographic Seizures and Predictors of Epilepsy after Pediatric Arteriovenous Malformation Rupture

OBJECTIVES

To assess clinical and electroencephalogram (EEG) predictors of epilepsy and to describe the percentage of electrographic seizures and development of epilepsy among patients with spontaneous intracerebral hemorrhage (ICH) due to arteriovenous malformation (AVM) rupture.

STUDY DESIGN

Retrospective review of patients admitted to the pediatric intensive care unit with ICH secondary to AVM rupture over 11 years. Clinical variables were collected by review of the electronic medical record. Seizures were described as acute symptomatic (7 days after AVM rupture), subacute (7-30 days after AVM rupture) and remote (greater than 30 days after AVM rupture). Outcome metrics included mortality, and the development of epilepsy post discharge. Descriptive statistics were used.

RESULTS

Forty-three patients met inclusion criteria with a median age of 12.2 years (IQR 7.3-14.8) and 49% (21/43) were female. Sixteen percent (7/43) presented with a clinical seizure prior to EEG placement. EEG was performed in 62% (27/43) of patients; one had electrographic status epilepticus without clinical signs. Sixteen percent (7/43) of patients were diagnosed with epilepsy, with a median time to diagnosis of 1.34 years (IQR 0.55-2.07) after AVM rupture. One-year epilepsy-free survival was 84% (95% CI 70%-98%) and 2-year epilepsy-free survival was 79% (95% CI 63%-95%) Remote seizures were associated with epilepsy (P < .001), but acute symptomatic seizures were not (P = .16).

CONCLUSIONS

EEG-confirmed seizures are uncommon in patients with ICH secondary to AVM rupture; however, when identified, the seizure burden appears to be high. Patients with seizures 30 days after AVM rupture are more likely to develop epilepsy.

Seizure Characteristics and EEG Features in Intoxication Type and Energy Deficiency Neurometabolic Disorders in the Pediatric Intensive Care Unit: Single-Center Experience Over 10 Years

BACKGROUND

Acute metabolic crises in inborn errors of metabolism (such as urea cycle disorders, organic acidemia, maple syrup urine disease, and mitochondrial disorders) are neurological emergencies requiring management in the pediatric intensive care unit (PICU). There is a paucity of data pertaining to electroencephalograms (EEG) characteristics in this cohort. We hypothesized that the incidence of background abnormalities and seizures in this cohort would be high. Neuromonitoring data from our center's PICU over 10 years are presented in this article.

METHODS

Data were collected by retrospective chart review for patients with the aforementioned disorders who were admitted to the PICU at our institution because of metabolic/neurologic symptoms from 2008 to 2018. Descriptive statistics (χ2 test or Fisher's exact test) were used to study the association between EEG parameters and outcomes.

RESULTS

Our cohort included 40 unique patients (8 with urea cycle disorder, 7 with organic acidemia, 3 with maple syrup urine disease, and 22 with mitochondrial disease) with 153 admissions. Presenting symptoms included altered mentation (36%), seizures (41%), focal weakness (5%), and emesis (28%). Continuous EEG was ordered in 34% (n = 52) of admissions. Twenty-three admissions were complicated by seizures, including eight manifesting as status epilepticus (seven nonconvulsive and one convulsive). Asymmetry and focal slowing on EEG were associated with seizures. Moderate background slowing or worse was noted in 75% of EEGs. Among those patients monitored on EEG, 4 (8%) died, 3 (6%) experienced a worsening of their Pediatric Cerebral Performance Category (PCPC) score as compared to admission, and 44 (86%) had no change (or improvement) in their PCPC score during admission.

CONCLUSIONS

This study shows a high incidence of clinical and subclinical seizures during metabolic crisis in patients with inborn errors of metabolism. EEG background features were associated with risk of seizures as well as discharge outcomes. This is the largest study to date to investigate EEG features and risk of seizures in patients with neurometabolic disorders admitted to the PICU. These data may be used to inform neuromonitoring protocols to improve mortality and morbidity in inborn errors of metabolism.

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 (β = 6.28, p < 0.001) and mortality (β = 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.

Neuromonitoring after Pediatric Cardiac Arrest: Cerebral Physiology and Injury Stratification

BACKGROUND

Significant long-term neurologic disability occurs in survivors of pediatric cardiac arrest, primarily due to hypoxic-ischemic brain injury. Postresuscitation care focuses on preventing secondary injury and the pathophysiologic cascade that leads to neuronal cell death. These injury processes include reperfusion injury, perturbations in cerebral blood flow, disturbed oxygen metabolism, impaired autoregulation, cerebral edema, and hyperthermia. Postresuscitation care also focuses on early injury stratification to allow clinicians to identify patients who could benefit from neuroprotective interventions in clinical trials and enable targeted therapeutics.

METHODS

In this review, we provide an overview of postcardiac arrest pathophysiology, explore the role of neuromonitoring in understanding postcardiac arrest cerebral physiology, and summarize the evidence supporting the use of neuromonitoring devices to guide pediatric postcardiac arrest care. We provide an in-depth review of the neuromonitoring modalities that measure cerebral perfusion, oxygenation, and function, as well as neuroimaging, serum biomarkers, and the implications of targeted temperature management.

RESULTS

For each modality, we provide an in-depth review of its impact on treatment, its ability to stratify hypoxic-ischemic brain injury severity, and its role in neuroprognostication.

CONCLUSION

Potential therapeutic targets and future directions are discussed, with the hope that multimodality monitoring can shift postarrest care from a one-size-fits-all model to an individualized model that uses cerebrovascular physiology to reduce secondary brain injury, increase accuracy of neuroprognostication, and improve outcomes.

Validation of a Model for Targeted EEG Monitoring Duration in Critically Ill Children

PURPOSE

Continuous EEG monitoring (CEEG) to identify electrographic seizures (ES) in critically ill children is resource intense. Targeted strategies could enhance implementation feasibility. We aimed to validate previously published findings regarding the optimal CEEG duration to identify ES in critically ill children.

METHODS

This was a prospective observational study of 1,399 consecutive critically ill children with encephalopathy. We validated the findings of a multistate survival model generated in a published cohort ( N = 719) in a new validation cohort ( N = 680). The model aimed to determine the CEEG duration at which there was <15%, <10%, <5%, or <2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors.

RESULTS

A model aiming to determine the CEEG duration at which a patient had <10% risk of ES if CEEG were continued longer showed similar performance in the generation and validation cohorts. Patients without emergent EEG risk factors would undergo 7 hours of CEEG in both cohorts, whereas patients with emergent EEG risk factors would undergo 44 and 36 hours of CEEG in the generation and validation cohorts, respectively. The <10% risk of ES model would yield a 28% or 64% reduction in CEEG hours compared with guidelines recommending CEEG for 24 or 48 hours, respectively.

CONCLUSIONS

This model enables implementation of a data-driven strategy that targets CEEG duration based on readily available clinical and EEG variables. This approach could identify most critically ill children experiencing ES while optimizing CEEG use.

A machine learning approach to seizure detection in a rat model of post-traumatic epilepsy

Epilepsy is a common neurologic condition frequently investigated using rodent models, with seizures identified by electroencephalography (EEG). Given technological advances, large datasets of EEG are widespread and amenable to machine learning approaches for identification of seizures. While such approaches have been explored for human EEGs, machine learning approaches to identifying seizures in rodent EEG are limited. We utilized a predesigned deep convolutional neural network (DCNN), GoogLeNet, to classify images for seizure identification. Training images were generated through multiplexing spectral content (scalograms), kurtosis, and entropy for two-second EEG segments. Over 2200 h of EEG data were scored for the presence of seizures, with 95.6% of seizures identified by the DCNN and a false positive rate of 34.2% (1.52/h), as compared to visual scoring. Multiplexed images were superior to scalograms alone (scalogram-kurtosis-entropy 0.956 ± 0.010, scalogram 0.890 ± 0.028, t(7) = 3.54, p < 0.01) and a DCNN trained specifically for the individual animal was superior to using DCNNs across animals (intra-animal 0.960 ± 0.0094, inter-animal 0.811 ± 0.015, t(30) = 5.54, p < 0.01). For this dataset the DCNN approach is superior to a previously described algorithm utilizing longer local line lengths, calculated from wavelet-decomposition of EEG, to identify seizures. We demonstrate the novel use of a predesigned DCNN constructed to classify images, utilizing multiplexed images of EEG spectral content, kurtosis, and entropy, to rapidly and objectively identifies seizures in a large dataset of rat EEG with high sensitivity.

Developing a Standardized Approach to Grading the Level of Brain Dysfunction on EEG

PURPOSE

To assess variability in interpretation of electroencephalogram (EEG) background activity and qualitative grading of cerebral dysfunction based on EEG findings, including which EEG features are deemed most important in this determination.

METHODS

A web-based survey (Qualtrics) was disseminated to electroencephalographers practicing in institutions participating in the Critical Care EEG Monitoring Research Consortium between May 2017 and August 2018. Respondents answered 12 questions pertaining to their training and EEG interpretation practices and graded 40 EEG segments (15-second epochs depicting patients' most stimulated state) using a 6-grade scale. Fleiss' Kappa statistic evaluated interrater agreement.

RESULTS

Of 110 respondents, 78.2% were attending electroencephalographers with a mean of 8.3 years of experience beyond training. Despite 83% supporting the need for a standardized approach to interpreting the degree of dysfunction on EEG, only 13.6% used a previously published or an institutional grading scale. The overall interrater agreement was fair ( k = 0.35). Having Critical Care EEG Monitoring Research Consortium nomenclature certification (40.9%) or EEG board certification (70%) did not improve interrater agreement ( k = 0.26). Predominant awake frequencies and posterior dominant rhythm were ranked as the most important variables in grading background dysfunction, followed by continuity and reactivity.

CONCLUSIONS

Despite the preference for a standardized grading scale for background EEG interpretation, the lack of interrater agreement on levels of dysfunction even among experienced academic electroencephalographers unveils a barrier to the widespread use of EEG as a clinical and research neuromonitoring tool. There was reasonable agreement on the features that are most important in this determination. A standardized approach to grading cerebral dysfunction, currently used by the authors, and based on this work, is proposed.

Sporadic and Periodic Interictal Discharges in Critically Ill Children: Seizure Associations and Time to Seizure Identification

PURPOSE

We evaluated interictal discharges (IEDs) as a biomarker for the time to development of electrographic seizures (ES).

METHODS

Prospective observational study of 254 critically ill children who underwent continuous electroencephalography (cEEG) monitoring. We excluded neonates and patients with known epilepsy or the sole cEEG indication to characterize events. Interictal discharges included sporadic epileptiform discharges and periodic and rhythmic patterns. Sporadic epileptiform discharges were categorized as low frequency (rare [<1/hour] and occasional [≥1/hour but <1/minute]) and high frequency (frequent, [≥1/minute] and abundant [≥1/10 seconds]). Time variables included time from cEEG start to first IED and time between first IED and ES.

RESULTS

Interictal discharges were present in 33% (83/254) of patients. We identified ES in 20% (50/254), and 86% (43/50) had IEDs. High-frequency sporadic epileptiform discharges (odds ratio [OR], 35; 95% confidence interval [CI], 14.5-88; P < 0.0001) and lateralized periodic discharges (OR, 27; 95% CI, 7.3-100; P < 0.0001) were associated with ES. Mildly abnormal EEG background without IEDs or background asymmetry was associated with the absence of seizures (OR, 0.1; 95% CI, 0.04-0.3; P < 0.0001). Time from cEEG start to first IED was 36 minutes (interquartile range, 3-131 minutes), and time between first IED and ES was 9.6 minutes (interquartile range, 0.6-165 minutes).

CONCLUSIONS

Interictal discharges are associated with ES and are identified in the first 3 hours of cEEG. High-frequency sporadic epileptiform discharges and periodic patterns have the highest risk of ES. Our findings define a window of high seizure risk after the identification of IEDs in which to allocate resources to improve seizure identification and subsequent treatment.

Association of EEG and Blood-Based Brain Injury Biomarker Accuracy to Prognosticate Mortality After Pediatric Cardiac Arrest: An Exploratory Study

BACKGROUND

Evaluate the accuracy of brain-based blood biomarkers neuron-specific enolase (NSE) and S100b and electroencephalography (EEG) features alone and in combination with prognosticate 6-month mortality after pediatric cardiac arrest. We hypothesized that the combination of blood brain-based biomarkers and EEG features would have superior classification accuracy of outcome versus either alone.

METHODS

Children (n = 58) aged between 1 week and 17 years admitted to the ICU following cardiac arrest at a tertiary care children's hopital were eligible for this secondary study. Blood NSE and S100b were measured closest to 24 hours after return of spontaneous circulation (ROSC). EEGs closest to 24 hours (median 11, interquartile range [IQR] 6 to 16 h) post-ROSC were evaluated by two epileptologists. EEG grade was informed by background frequency, amplitude, and continuity. Sleep spindles were present or absent. Mortality was assessed at six months post-ROSC. Area under the receiver operator curve (AUC) was performed for individual and combined brain-based biomarkers and EEG features.

RESULTS

Children were aged 2.6 (IQR 0.6 to 10.4) years, and 25 (43%) died. Children who died had increased blood NSE (49.7 [28.0 to 63.1] vs 18.2 [9.8 to 31.8] ng/mL) and S100b (0.118 [0.036 to 0.296] vs 0.012 [0.003 to 0.021] ng/mL) and poor (discontinuous or isoelectric) EEG grade (76% vs 33%) more frequently than survivors (P < 0.05). AUC for NSE to predict mortality was 0.789, and was 0.841 when combined with EEG grade and spindles. S100b AUC for mortality was 0.856 and was optimal alone.

CONCLUSIONS

In this exploratory study, the combination of brain-based biomarkers and EEG features may provide more accurate prognostication than either test alone after pediatric cardiac arrest.

Alpha-power in electroencephalography as good outcome predictor for out-of-hospital cardiac arrest survivors

This study aimed to investigate the utility of quantitative EEG biomarkers for predicting good neurologic outcomes in OHCA survivors treated with targeted temperature management (TTM) using power spectral density (PSD), event-related spectral perturbation (ERSP), and spectral entropy (SE). This observational registry-based study was conducted at a tertiary care hospital in Korea using data of adult nontraumatic comatose OHCA survivors who underwent standard EEG and treated with TTM between 2010 and 2018. Good neurological outcome at 1 month (Cerebral Performance Category scores 1 and 2) was the primary outcome. The linear mixed model analysis was performed for PSD, ESRP, and SE values of all and each frequency band. Thirteen of the 54 comatose OHCA survivors with TTM and EEG were excluded due to poor EEG quality or periodic/rhythmic pattern, and EEG data of 41 patients were used for analysis. The median time to EEG was 21 h, and the rate of the good neurologic outcome at 1 month was 52.5%. The good neurologic outcome group was significantly younger and showed higher PSD and ERSP and lower SE features for each frequency than the poor outcome group. After age adjustment, only the alpha-PSD was significantly higher in the good neurologic outcome group (1.13 ± 1.11 vs. 0.09 ± 0.09, p = 0.031) and had best performance with 0.903 of the area under the curve for predicting good neurologic outcome. Alpha-PSD best predicts good neurologic outcome in OHCA survivors and is an early biomarker for prognostication. Larger studies are needed to conclusively confirm these findings.

Continuous Video Electroencephalography (EEG) for Event Characterization in Critically Ill Children

OBJECTIVE

To determine features of paroxysmal events and background electroencephalographic (EEG) abnormalities associated with electroclinical seizures in critically ill children who undergo continuous video EEG to characterize clinical events.

METHODS

This is a prospective study of critically ill children from July 2016 to October 2018. Non-neonates with continuous video EEG indication to characterize a clinical event were included. Patients with continuous video EEG to assess for subclinical seizures due to unexplained encephalopathy and those whose event of concern were not captured on continuous video EEG were excluded. The event to be characterized was taken from documented descriptions of health care providers and classified as motor, ocular, orobuccal, autonomic, and other. In patients with more than 1 component to their paroxysmal event, the events were classified as motor plus and nonmotor plus.

RESULTS

One hundred patients met inclusion and exclusion criteria, with electroclinical seizures captured in 30% (30/100). The most common event to be characterized was an autonomic event in 32% (32/100). Asymmetry and epileptiform discharges were associated with electroclinical seizures (odds ratio [OR] 2.7, 95% confidence interval [CI] 1.1-6.5, P = .03; and OR 12.5, 95% CI 4.4-35.6, P < .0001). Autonomic events alone, particularly unexplained vital sign changes, were not associated with electroclinical seizures (OR 0.3, 95% CI 0.11-0.93, P = .03).

CONCLUSIONS

Isolated autonomic events are unlikely to be electroclinical seizures. Details of the paroxysmal events in question can help decide which patient will benefit most from continuous video EEG based on institutional resources.

Quantitative Electroencephalography for Early Detection of Elevated Intracranial Pressure in Critically Ill Children: Case Series and Proposed Protocol

OBJECTIVE

To describe quantitative EEG (electroencephalography) suppression ratio in children with increased intracranial pressure comparing acute suppression ratio changes to imaging and/or examination findings.

METHODS

We retrospectively reviewed the suppression ratio from patients with neuroimaging and /or examination findings of increased intracranial pressure while on continuous EEG. The time of the first change in the suppression ratio was compared to the time of the first image and/or examination change confirming increased intracranial pressure.

RESULTS

Thirteen patients with a median age of 3.1 years(interquartile range 1.8-6.3) had a rise in the suppression ratio with median time from identification to acute neuroimaging or examination of increased intracranial pressure of 3.12 hours (interquartile range 2.2-33.5) after the first increase in the suppression ratio.

CONCLUSIONS

Acute suppression ratio increase is seen prior to imaging and/or examination findings of increased intracranial pressure. With further study, the suppression ratio can be targeted with intracranial pressure-lowering agents to prevent morbidity and mortality associated with increased intracranial pressure.

Periodic and rhythmic patterns in critically ill children: Incidence, interrater agreement, and seizures

OBJECTIVES

We aimed to determine the incidence of periodic and rhythmic patterns (PRP), assess the interrater agreement between electroencephalographers scoring PRP using standardized terminology, and analyze associations between PRP and electrographic seizures (ES) in critically ill children.

METHODS

This was a prospective observational study of consecutive critically ill children undergoing continuous electroencephalographic monitoring (CEEG). PRP were identified by one electroencephalographer, and then two pediatric electroencephalographers independently scored the first 1-h epoch that contained PRP using standardized terminology. We determined the incidence of PRPs, evaluated interrater agreement between electroencephalographers scoring PRP, and evaluated associations between PRP and ES.

RESULTS

One thousand three hundred ninety-nine patients underwent CEEG. ES occurred in 345 (25%) subjects. PRP, ES + PRP, and ictal-interictal continuum (IIC) patterns occurred in 142 (10%), 81 (6%), and 93 (7%) subjects, respectively. The most common PRP were generalized periodic discharges (GPD; 43, 30%), lateralized periodic discharges (LPD; 34, 24%), generalized rhythmic delta activity (GRDA; 34, 24%), bilateral independent periodic discharges (BIPD; 14, 10%), and lateralized rhythmic delta activity (LRDA; 11, 8%). ES risk varied by PRP type (p < .01). ES occurrence was associated with GPD (odds ratio [OR] = 6.35, p < .01), LPD (OR = 10.45, p < .01), BIPD (OR = 6.77, p < .01), and LRDA (OR = 6.58, p < .01). Some modifying features increased the risk of ES for each of those PRP. GRDA was not significantly associated with ES (OR = 1.34, p = .44). Each of the IIC patterns was associated with ES (OR = 6.83-8.81, p < .01). ES and PRP occurred within 6 h (before or after) in 45 (56%) subjects.

SIGNIFICANCE

PRP occurred in 10% of critically ill children who underwent CEEG. The most common patterns were GPD, LPD, GRDA, BIPD, and LRDA. The GPD, LPD, BIPD, LRDA, and IIC patterns were associated with ES. GRDA was not associated with ES.

Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset

PURPOSE

To compare the seizure detection performance of three expert humans and two computer algorithms in a large set of epilepsy monitoring unit EEG recordings.

METHODS

One hundred twenty prolonged EEGs, 100 containing clinically reported EEG-evident seizures, were evaluated. Seizures were marked by the experts and algorithms. Pairwise sensitivity and false-positive rates were calculated for each human-human and algorithm-human pair. Differences in human pairwise performance were calculated and compared with the range of algorithm versus human performance differences as a type of statistical modified Turing test.

RESULTS

A total of 411 individual seizure events were marked by the experts in 2,805 hours of EEG. Mean, pairwise human sensitivities and false-positive rates were 84.9%, 73.7%, and 72.5%, and 1.0, 0.4, and 1.0/day, respectively. Only the Persyst 14 algorithm was comparable with humans-78.2% and 1.0/day. Evaluation of pairwise differences in sensitivity and false-positive rate demonstrated that Persyst 14 met statistical noninferiority criteria compared with the expert humans.

CONCLUSIONS

Evaluating typical prolonged EEG recordings, human experts had a modest level of agreement in seizure marking and low false-positive rates. The Persyst 14 algorithm was statistically noninferior to the humans. For the first time, a seizure detection algorithm and human experts performed similarly.

The accuracy of quantitative EEG biomarker algorithms depends upon seizure onset dynamics

OBJECTIVE

To compare the performance of different ictal quantitative biomarkers of the seizure onset zone (SOZ) across many seizures in a cohort of consecutive patients with a variety of seizure onset patterns.

METHODS

The Epileptogenicity Index (EI, a measure of fast activity) and Slow Polarizing Shift index (SPS, a measure of infraslow activity) were calculated for 212 seizures (22 patients). After stratification by onset pattern, median index values inside and outside the SOZ were compared in aggregate and for each of the onset patterns. Receiver Operating Characteristic (ROC) curves were constructed to compare the performance of each index.

RESULTS

Median values of EI (0.056 vs 0.0087), SPS (0.27 vs 0.19), and CI (0.21 vs 0.12) were significantly higher for contacts inside the SOZ, all p < 0.0001. Analysis of AUC showed variable performance of these indices across seizure types, although AUC for EI and SPS was generally greatest for seizures with fast activity at onset.

CONCLUSIONS

All indices were significantly higher for contacts inside the SOZ; however, the performance of these indices varied depending on the pattern of seizure onset.

SIGNIFICANCE

These findings suggest that future studies of quantitative biomarkers of the SOZ should account for seizure onset pattern.

Electrographic Seizures and Outcome in Critically Ill Children

OBJECTIVE

To determine the association between electroencephalographic seizure (ES) and electroencephalographic status epilepticus (ESE) exposure and unfavorable neurobehavioral outcomes in critically ill children with acute encephalopathy.

METHODS

This was a prospective cohort study of acutely encephalopathic critically ill children undergoing continuous EEG monitoring (CEEG). ES exposure was assessed as (1) no ES/ESE, (2) ES, or (3) ESE. Outcomes assessed at discharge included the Glasgow Outcome Scale-Extended Pediatric Version (GOS-E-Peds), Pediatric Cerebral Performance Category (PCPC), and mortality. Unfavorable outcome was defined as a reduction in GOS-E-Peds or PCPC score from preadmission to discharge. Stepwise selection was used to generate multivariate logistic regression models that assessed associations between ES exposure and outcomes while adjusting for multiple other variables.

RESULTS

Among 719 consecutive critically ill patients, there was no evidence of ES in 535 patients (74.4%), ES occurred in 140 patients (19.5%), and ESE in 44 patients (6.1%). The final multivariable logistic regression analyses included ES exposure, age dichotomized at 1 year, acute encephalopathy category, initial EEG background category, comatose at CEEG initiation, and Pediatric Index of Mortality 2 score. There was an association between ESE and unfavorable GOS-E-Peds (odds ratio 2.21, 95% confidence interval 1.07-4.54) and PCPC (odds ratio 2.17, 95% confidence interval 1.05-4.51) but not mortality. There was no association between ES and unfavorable outcome or mortality.

CONCLUSIONS

Among acutely encephalopathic critically ill children, there was an association between ESE and unfavorable neurobehavioral outcomes, but no association between ESE and mortality. ES exposure was not associated with unfavorable neurobehavioral outcomes or mortality.

Machine learning models to predict electroencephalographic seizures in critically ill children

OBJECTIVE

To determine whether machine learning techniques would enhance our ability to incorporate key variables into a parsimonious model with optimized prediction performance for electroencephalographic seizure (ES) prediction in critically ill children.

METHODS

We analyzed data from a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy who underwent clinically-indicated continuous EEG monitoring (CEEG). We implemented and compared three state-of-the-art machine learning methods for ES prediction: (1) random forest; (2) Least Absolute Shrinkage and Selection Operator (LASSO); and (3) Deep Learning Important FeaTures (DeepLIFT). We developed a ranking algorithm based on the relative importance of each variable derived from the machine learning methods.

RESULTS

Based on our ranking algorithm, the top five variables for ES prediction were: (1) epileptiform discharges in the initial 30 minutes, (2) clinical seizures prior to CEEG initiation, (3) sex, (4) age dichotomized at 1 year, and (5) epileptic encephalopathy. Compared to the stepwise selection-based approach in logistic regression, the top variables selected by our ranking algorithm were more informative as models utilizing the top variables achieved better prediction performance evaluated by prediction accuracy, AUROC and F1 score. Adding additional variables did not improve and sometimes worsened model performance.

CONCLUSION

The ranking algorithm was helpful in deriving a parsimonious model for ES prediction with optimal performance. However, application of state-of-the-art machine learning models did not substantially improve model performance compared to prior logistic regression models. Thus, to further improve the ES prediction, we may need to collect more samples and variables that provide additional information.

Modelling epilepsy in the mouse: challenges and solutions

In most mouse models of disease, the outward manifestation of a disorder can be measured easily, can be assessed with a trivial test such as hind limb clasping, or can even be observed simply by comparing the gross morphological characteristics of mutant and wild-type littermates. But what if we are trying to model a disorder with a phenotype that appears only sporadically and briefly, like epileptic seizures? The purpose of this Review is to highlight the challenges of modelling epilepsy, in which the most obvious manifestation of the disorder, seizures, occurs only intermittently, possibly very rarely and often at times when the mice are not under direct observation. Over time, researchers have developed a number of ways in which to overcome these challenges, each with their own advantages and disadvantages. In this Review, we describe the genetics of epilepsy and the ways in which genetically altered mouse models have been used. We also discuss the use of induced models in which seizures are brought about by artificial stimulation to the brain of wild-type animals, and conclude with the ways these different approaches could be used to develop a wider range of anti-seizure medications that could benefit larger patient populations.

Summary: This Review discusses the challenges of modelling epilepsy in mice, a condition in which the outward manifestation of the disorder appears only sporadically, and reviews possible solutions encompassing both genetic and induced models.

Electroencephalography Technologist Inter-rater Agreement and Interpretation of Pediatric Critical Care Electroencephalography

OBJECTIVES

Electroencephalography (EEG) technologists commonly screen continuous EEG. Until now, the inter-rater agreement or sensitivity for important EEG findings has been unknown in this group.

METHODS

Twenty-nine EEG technologists and three clinical neurophysiologists interpreted 90 five-minute samples of pediatric critical care EEG. Inter-rater agreement was examined with Cohen's kappa and Fleiss' kappa for EEG findings. A gold-standard consensus agreement was developed for examining sensitivity and specificity for seizures or discontinuity. Kruskal-Wallis tests with Benjamani-Hochberg corrections for multiple comparisons were utilized to examine associations between correct scoring and certification status and years of experience.

RESULTS

Aggregate agreement was moderate for seizures and fair for EEG background continuity among EEG technologists. Individual agreement for seizures and continuity varied from slight to substantial. For individual EEG technologists, sensitivity for seizures ranged from 44 to 93% and sensitivity for continuity ranged from 81 to 100%. Raters with Certified Long Term Monitoring credentials were more likely to identify seizures correctly.

SIGNIFICANCE

This is the first study to evaluate inter-rater agreement and interpretation correctness among EEG technologists interpreting pediatric critical care EEG. EEG technologists demonstrated better aggregate agreement for seizure detection than other EEG findings, yet individual results and internal consistency varied widely. These data provide important insight into the common practice of utilizing EEG technologists for screening critical care EEG.

Viability of Preictal High-Frequency Oscillation Rates as a Biomarker for Seizure Prediction

Motivation: There is an ongoing search for definitive and reliable biomarkers to forecast or predict imminent seizure onset, but to date most research has been limited to EEG with sampling rates <1,000 Hz. High-frequency oscillations (HFOs) have gained acceptance as an indicator of epileptic tissue, but few have investigated the temporal properties of HFOs or their potential role as a predictor in seizure prediction. Here we evaluate time-varying trends in preictal HFO rates as a potential biomarker of seizure prediction. Methods: HFOs were identified for all interictal and preictal periods with a validated automated detector in 27 patients who underwent intracranial EEG monitoring. We used LASSO logistic regression with several features of the HFO rate to distinguish preictal from interictal periods in each individual. We then tested these models with held-out data and evaluated their performance with the area-under-the-curve (AUC) of their receiver-operating curve (ROC). Finally, we assessed the significance of these results using non-parametric statistical tests. Results: There was variability in the ability of HFOs to discern preictal from interictal states across our cohort. We identified a subset of 10 patients in whom the presence of the preictal state could be successfully predicted better than chance. For some of these individuals, average AUC in the held-out data reached higher than 0.80, which suggests that HFO rates can significantly differentiate preictal and interictal periods for certain patients. Significance: These findings show that temporal trends in HFO rate can predict the preictal state better than random chance in some individuals. Such promising results indicate that future prediction efforts would benefit from the inclusion of high-frequency information in their predictive models and technological architecture.

Preictal variability of high-frequency oscillation rates in refractory epilepsy

OBJECTIVE

High-frequency oscillations (HFOs) have shown promising utility in the spatial localization of the seizure onset zone for patients with focal refractory epilepsy. Comparatively few studies have addressed potential temporal variations in HFOs, or their role in the preictal period. Here, we introduce a novel evaluation of the instantaneous HFO rate through interictal and peri-ictal epochs to assess their usefulness in identifying imminent seizure onset.

METHODS

Utilizing an automated HFO detector, we analyzed intracranial electroencephalographic data from 30 patients with refractory epilepsy undergoing long-term presurgical evaluation. We evaluated HFO rates both as a 30-minute average and as a continuous function of time and used nonparametric statistical methods to compare individual and population-level differences in rate during peri-ictal and interictal periods.

RESULTS

Mean HFO rate was significantly higher for all epochs in seizure onset zone channels versus other channels. Across the 30 patients of our cohort, we found no statistically significant differences in mean HFO rate during preictal and interictal epochs. For continuous HFO rates in seizure onset zone channels, however, we found significant population-wide increases in preictal trends relative to interictal periods. Using a data-driven analysis, we identified a subset of 11 patients in whom either preictal HFO rates or their continuous trends were significantly increased relative to those of interictal baseline and the rest of the population.

SIGNIFICANCE

These results corroborate existing findings that HFO rates within epileptic tissue are higher during interictal periods. We show this finding is also present in preictal, ictal, and postictal data, and identify a novel biomarker of preictal state: an upward trend in HFO rate leading into seizures in some patients. Overall, our findings provide preliminary evidence that HFOs can function as a temporal biomarker of seizure onset.

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

OBJECTIVE

Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but identification requires extensive resources for continuous electroencephalographic monitoring (CEEG). In a previous study, we developed a clinical prediction rule using three clinical variables (age, acute encephalopathy category, clinically evident seizure[s] prior to CEEG initiation) and two electroencephalographic (EEG) variables (EEG background category and interictal discharges within the first 30 minutes of EEG) to identify patients at high risk for ESs for whom CEEG might be essential. In the current study, we aimed to validate the ES prediction model using an independent cohort.

METHODS

The prospectively acquired validation cohort consisted of 314 consecutive critically ill children treated in the Pediatric Intensive Care Unit of a quaternary care referral hospital with acute encephalopathy undergoing clinically indicated CEEG. We calculated test characteristics using the previously developed prediction model in the validation cohort. As in the generation cohort study, we selected a 0.10 cutpoint to emphasize sensitivity.

RESULTS

The incidence of ESs in the validation cohort was 22%. The generation and validation cohorts were alike in most clinical and EEG characteristics. The ES prediction model was well calibrated and well discriminating in the validation cohort. The model had a sensitivity of 90%, specificity of 37%, positive predictive value of 28%, and negative predictive value of 93%. If applied, the model would limit 31% of patients from undergoing CEEG while failing to identify 10% of patients with ESs. The model had similar performance characteristics in the generation and validation cohorts.

SIGNIFICANCE

A model employing five readily available clinical and EEG variables performed well when validated in a new consecutive cohort. Implementation would substantially reduce CEEG utilization, although some patients with ESs would not be identified. This model may serve a critical role in targeting limited CEEG resources to critically ill children at highest risk for ESs.

Interrater Agreement Between Critical Care Providers for Background Classification and Seizure Detection After Implementation of Amplitude-Integrated Electroencephalography in Neonates, Infants, and Children

PURPOSES

Amplitude-integrated EEG (aEEG) has been widely developed in neonatal intensive care unit, but few studies focused on pediatric intensive care unit. Furthermore, reliability of aEEG under real-life conditions is unknown.

METHODS

Participants were nurses from a 12-bed pediatric intensive care unit in a referral university hospital in France. Amplitude EEG was implemented after standardized training, including e-learning course, individual feedback and bedside teaching concerning monitoring installation, background classification patterns recognition, artefact analysis, and seizure detection. The primary judgment criterion was the agreement (Cohen Kappa) between nurses and aEEG experts for the detection of abnormal aEEG traces (moderately or severely altered background pattern according to Hellström-Westas classification and/or seizure activity).

RESULTS

During the study period, 196 consecutives traces from 79 patients were analyzed by 51 nurses. According to expert's classification, 53% of traces were abnormal, including 17.5% of severely abnormal traces (severely altered traces and/or seizure activity) and 14% exhibiting seizure activity. Moderate agreement between experts and nurses was found for detection of any abnormal trace (k = 0.53; 95% confidence interval [CI]: 0.39-0.67). Substantial agreement was found for severely altered traces (k = 0.71; 95% CI: 0.57-0.85). Finally, fair agreement was found for seizure detection (irrespective of background classification, k = 0.40; 95% CI: 0.25-0.54).

CONCLUSIONS

These results suggest that aEEG monitoring may be implemented in routine nursing care in pediatric intensive care unit. Further training courses are needed to enhance nurses' skill in detecting seizures activity at the bedside.

Development and Validation of a Seizure Prediction Model in Neonates After Cardiac Surgery

BACKGROUND

Electroencephalographic seizures (ESs) after neonatal cardiac surgery are often subclinical and have been associated with poor outcomes. An accurate ES prediction model could allow targeted continuous electroencephalographic monitoring (CEEG) for high-risk neonates.

METHODS

ES prediction models were developed and validated in a multicenter prospective cohort where all postoperative neonates who underwent cardiopulmonary bypass (CPB) also underwent CEEG.

RESULTS

ESs occurred in 7.4% of neonates (78 of 1053). Model predictors included gestational age, head circumference, single-ventricle defect, deep hypothermic circulatory arrest duration, cardiac arrest, nitric oxide, extracorporeal membrane oxygenation, and delayed sternal closure. The model performed well in the derivation cohort (c-statistic, 0.77; Hosmer-Lemeshow, P = .56), with a net benefit (NB) over monitoring all and none over a threshold probability of 2% in decision curve analysis (DCA). The model had good calibration in the validation cohort (Hosmer-Lemeshow, P = .60); however, discrimination was poor (c-statistic, 0.61), and in DCA there was no NB of the prediction model between the threshold probabilities of 8% and 18%. By using a cut point that emphasized negative predictive value in the derivation cohort, 32% (236 of 737) of neonates would not undergo CEEG, including 3.5% (2 of 58) of neonates with ESs (negative predictive value, 99%; sensitivity, 97%).

CONCLUSIONS

In this large prospective cohort, a prediction model of ESs in neonates after CPB had good performance in the derivation cohort, with an NB in DCA. However, performance in the validation cohort was weak, with poor discrimination, poor calibration, and no NB in DCA. These findings support CEEG of all neonates after CPB.

EEG monitoring duration to identify electroencephalographic seizures in critically ill children

OBJECTIVES

To determine the optimal duration of continuous EEG monitoring (CEEG) for electrographic seizure (ES) identification in critically ill children.

METHODS

We performed a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy. We evaluated baseline clinical risk factors (age and prior clinically evident seizures) and emergent CEEG risk factors (epileptiform discharges and ictal-interictal continuum patterns) using a multistate survival model. For each subgroup, we determined the CEEG duration for which the risk of ES was <5% and <2%.

RESULTS

ES occurred in 184 children (26%). Patients achieved <5% risk of ES after (1) 6 hours if ≥1 year without prior seizures or EEG risk factors; (2) 1 day if <1 year without prior seizures or EEG risks; (3) 1 day if ≥1 year with either prior seizures or EEG risks; (4) 2 days if ≥1 year with prior seizures and EEG risks; (5) 2 days if <1 year without prior seizures but with EEG risks; and (6) 2.5 days if <1 year with prior seizures regardless of the presence of EEG risks. Patients achieved <2% risk of ES at the same durations except patients without prior seizures or EEG risk factors would require longer CEEG (1.5 days if <1 year of age, 1 day if ≥1 year of age).

CONCLUSIONS

A model derived from 2 baseline clinical risk factors and emergent EEG risk factors would allow clinicians to implement personalized strategies that optimally target limited CEEG resources. This would enable more widespread use of CEEG-guided management as a potential neuroprotective strategy.

CLINICALTRIALSGOV IDENTIFIER

NCT03419260.

Neurological Prognostication in Children After Cardiac Arrest

Early after pediatric cardiac arrest, families and care providers struggle with the uncertainty of long-term neurological prognosis. Cardiac arrest characteristics such as location, intra-arrest factors, and postarrest events have been associated with outcome. We paid particular attention to postarrest modalities that have been shown to predict neurological outcome. These modalities include neurological examination, somatosensory evoked potentials, electroencephalography, and neuroimaging. There is no one modality that accurately predicts neurological prognosis. Thus, a multimodal approach should be undertaken by both neurologists and intensivists to present a clear and consistent message to families. Methods used for the prediction of long-term neurological prognosis need to be specific enough to identify indivuals with a poor outcome. We review the evidence evaluating children with coma, each with various etiologies of cardiac arrest, outcome measures, and timing of follow-up.

Levetiracetam Versus Phenobarbital for Neonatal Seizures: A Randomized Controlled Trial

BACKGROUND AND OBJECTIVES

There are no US Food and Drug Administration-approved therapies for neonatal seizures. Phenobarbital and phenytoin frequently fail to control seizures. There are concerns about the safety of seizure medications in the developing brain. Levetiracetam has proven efficacy and an excellent safety profile in older patients; therefore, there is great interest in its use in neonates. However, randomized studies have not been performed. Our objectives were to study the efficacy and safety of levetiracetam compared with phenobarbital as a first-line treatment of neonatal seizures.

METHODS

The study was a multicenter, randomized, blinded, controlled, phase IIb trial investigating the efficacy and safety of levetiracetam compared with phenobarbital as a first-line treatment for neonatal seizures of any cause. The primary outcome measure was complete seizure freedom for 24 hours, assessed by independent review of the EEGs by 2 neurophysiologists.

RESULTS

Eighty percent of patients (24 of 30) randomly assigned to phenobarbital remained seizure free for 24 hours, compared with 28% of patients (15 of 53) randomly assigned to levetiracetam (P < .001; relative risk 0.35 [95% confidence interval: 0.22-0.56]; modified intention-to-treat population). A 7.5% improvement in efficacy was achieved with a dose escalation of levetiracetam from 40 to 60 mg/kg. More adverse effects were seen in subjects randomly assigned to phenobarbital (not statistically significant).

CONCLUSIONS

In this phase IIb study, phenobarbital was more effective than levetiracetam for the treatment of neonatal seizures. Higher rates of adverse effects were seen with phenobarbital treatment. Higher-dose studies of levetiracetam are warranted, and definitive studies with long-term outcome measures are needed.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

SIGNIFICANCE

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

Early EEG Features for Outcome Prediction After Cardiac Arrest in Children

PURPOSE

We aimed to determine which early EEG features and feature combinations most accurately predicted short-term neurobehavioral outcomes and survival in children resuscitated after cardiac arrest.

METHODS

This was a prospective, single-center observational study of infants and children resuscitated from cardiac arrest who underwent conventional EEG monitoring with standardized EEG scoring. Logistic regression evaluated the marginal effect of each EEG variable or EEG variable combinations on the outcome. The primary outcome was neurobehavioral outcome (Pediatric Cerebral Performance Category score), and the secondary outcome was mortality. The authors identified the models with the highest areas under the receiver operating characteristic curve (AUC), evaluated the optimal models using a 5-fold cross-validation approach, and calculated test characteristics maximizing specificity.

RESULTS

Eighty-nine infants and children were evaluated. Unfavorable neurologic outcome (Pediatric Cerebral Performance Category score 4-6) occurred in 44 subjects (49%), including mortality in 30 subjects (34%). A model incorporating a four-level EEG Background Category (normal, slow-disorganized, discontinuous or burst-suppression, or attenuated-flat), stage 2 Sleep Transients (present or absent), and Reactivity-Variability (present or absent) had the highest AUC. Five-fold cross-validation for the optimal model predicting neurologic outcome indicated a mean AUC of 0.75 (range, 0.70-0.81) and for the optimal model predicting mortality indicated a mean AUC of 0.84 (range, 0.76-0.97). The specificity for unfavorable neurologic outcome and mortality were 95% and 97%, respectively. The positive predictive value for unfavorable neurologic outcome and mortality were both 86%.

CONCLUSIONS

The specificity of the optimal model using a combination of early EEG features was high for unfavorable neurologic outcome and mortality in critically ill children after cardiac arrest. However, the positive predictive value was only 86% for both outcomes. Therefore, EEG data must be considered in overall clinical context when used for neuroprognostication early after cardiac arrest.

Electrographic seizure burden and outcomes following pediatric status epilepticus

Pediatric status epilepticus carries a substantial risk for morbidity and mortality, but the relationship between seizure burden, treatment, and outcome remains incompletely understood. This review summarizes the evidence linking seizure burden and outcomes among critically ill children in the intensive care unit (ICU), a population in whom accurate quantification of seizure burden is possible using continuous electroencephalographic monitoring. Several high-quality observational studies among critically ill children have reported an association between higher seizure burden and worse outcome, even after adjusting for potential confounders such as age, etiology, and illness severity. Although these studies support the hypothesis that seizures contribute to brain injury and worsen outcome, a causal link between seizures and outcome remains to be proven. The relationship between seizures and outcome is likely complex, and dependent on factors such as etiology, preexisting neurological disability, medication exposure, and possibly individual genetic factors. Studies attempting to define this complex relationship will need to measure and account for these factors in their analyses. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Quantitative EEG Metrics Differ Between Outcome Groups and Change Over the First 72 h in Comatose Cardiac Arrest Patients

BACKGROUND

Forty to sixty-six percent of patients resuscitated from cardiac arrest remain comatose, and historic outcome predictors are unreliable. Quantitative spectral analysis of continuous electroencephalography (cEEG) may differ between patients with good and poor outcomes.

METHODS

Consecutive patients with post-cardiac arrest hypoxic-ischemic coma undergoing cEEG were enrolled. Spectral analysis was conducted on artifact-free contiguous 5-min cEEG epochs from each hour. Whole band (1-30 Hz), delta (δ, 1-4 Hz), theta (θ, 4-8 Hz), alpha (α, 8-13 Hz), beta (β, 13-30 Hz), α/δ power ratio, percent suppression, and variability were calculated and correlated with outcome. Graphical patterns of quantitative EEG (qEEG) were described and categorized as correlating with outcome. Clinical outcome was dichotomized, with good neurologic outcome being consciousness recovery.

RESULTS

Ten subjects with a mean age = 50 yrs (range = 18-65) were analyzed. There were significant differences in total power (3.50 [3.30-4.06] vs. 0.68 [0.52-1.02], p = 0.01), alpha power (1.39 [0.66-1.79] vs 0.27 [0.17-0.48], p < 0.05), delta power (2.78 [2.21-3.01] vs 0.55 [0.38-0.83], p = 0.01), percent suppression (0.66 [0.02-2.42] vs 73.4 [48.0-97.5], p = 0.01), and multiple measures of variability between good and poor outcome patients (all values median [IQR], good vs. poor). qEEG patterns with high or increasing power or large power variability were associated with good outcome (n = 6). Patterns with consistently low or decreasing power or minimal power variability were associated with poor outcome (n = 4).

CONCLUSIONS

These preliminary results suggest qEEG metrics correlate with outcome. In some patients, qEEG patterns change over the first three days post-arrest.

Electroencephalographic patterns preceding cardiac arrest in neonates following cardiac surgery

AIM

To identify EEG changes that could predict impending cardiac arrest (CA) in neonates with congenital heart disease undergoing postoperative continuous EEG monitoring.

METHODS

Single-center observational study of neonates who underwent cardiac surgery and had CA postoperatively while undergoing EEG monitoring from 2012-2018. Clinical data were extracted from the medical record. EEG backgrounds were evaluated at defined time-points using standardized terminology.

RESULTS

We assessed 22 neonates. The median gestational age was 38.7 weeks (IQR 37.6, 39), the median age at surgery was 5 days (IQR 2, 8), 12 patients (55%) underwent repair for hypoplastic left heart syndrome, and the median time from cardiac intensive care unit arrival postoperatively to CA was 9.5 h (IQR 7, 23). The initial EEG background was abnormal in 15 (68%). All 22 neonates (100%) had worsening of the EEG background prior to initiation of chest compressions for CA at a median of 3 min (IQR 1.5, 3). Eighteen neonates (82%) had an EEG change more than 1 min prior to chest compressions. The EEG backgrounds immediately prior to CA were continuous low voltage in 1 (5%), excessive discontinuity in 8 (36%), burst-suppression in 2 (9%), and low voltage suppression in 11 (50%).

CONCLUSION

EEG background was abnormal in 68% of neonates at EEG monitoring onset and worsened in all minutes before CA. EEG background changes may be an early sign of impending CA and indicative of developing cerebral dysfunction. Further study is needed to determine whether rapid identification of EEG changes could drive implementation of interventions to prevent CA.

Electroencephalographic seizures in critically ill children: Management and adverse events

OBJECTIVE

Guidelines recommend that encephalopathic critically ill children undergo continuous electroencephalographic (CEEG) monitoring for electrographic seizure (ES) identification and management. However, limited data exist on antiseizure medication (ASM) safety for ES treatment in critically ill children.

METHODS

We performed a single-center prospective observational study of encephalopathic critically ill children undergoing CEEG. Clinical and EEG features and ASM utilization patterns were evaluated. We determined the incidence, types, and risk factors for adverse events associated with ASM administration.

RESULTS

A total of 472 consecutive critically ill children undergoing CEEG were enrolled. ES occurred in 131 children (28%). Clinicians administered ASM to 108 children with ES (82%). ES terminated after the initial ASM in 38% of patients who received one ASM, after the second ASM in 35% of patients who received two ASMs, after the third ASM in 50% of patients who received three ASMs, and after the fourth ASM in 53% of patients who received four ASMs. Thirty patients (28%) received anesthetic infusions for ES management. Adverse events occurred in 18 patients (17%). Adverse effects were expected and resolved in all patients, and they were generally serious (in 15 patients) and definitely related (in 12 patients). Adverse events were rare in patients with acute symptomatic seizures requiring only one to two ASMs for treatment, but were more common in children with epilepsy, ictal-interictal continuum EEG patterns, or patients requiring more extensive ASM management.

SIGNIFICANCE

ES ceased after one ASM in only 38% of critically ill children but ceased after two ASMs in 73% of critically ill children. Thus, ES management was often accomplished with readily available medications, but optimization of multistep ES management strategies might be beneficial. Adverse events were rare and manageable in children with acute symptomatic seizures requiring only one to two ASMs for treatment. Future studies are needed to determine whether management of acute symptomatic ES improves neurobehavioral outcomes.

Pediatric Post–Cardiac Arrest Care: A Scientific Statement From the American Heart Association

Successful resuscitation from cardiac arrest results in a post–cardiac arrest syndrome, which can evolve in the days to weeks after return of sustained circulation. The components of post–cardiac arrest syndrome are brain injury, myocardial dysfunction, systemic ischemia/reperfusion response, and persistent precipitating pathophysiology. Pediatric post–cardiac arrest care focuses on anticipating, identifying, and treating this complex physiology to improve survival and neurological outcomes. This scientific statement on post–cardiac arrest care is the result of a consensus process that included pediatric and adult emergency medicine, critical care, cardiac critical care, cardiology, neurology, and nursing specialists who analyzed the past 20 years of pediatric cardiac arrest, adult cardiac arrest, and pediatric critical illness peer-reviewed published literature. The statement summarizes the epidemiology, pathophysiology, management, and prognostication after return of sustained circulation after cardiac arrest, and it provides consensus on the current evidence supporting elements of pediatric post–cardiac arrest care.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring

PURPOSE

Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns.

METHODS

Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients.

RESULTS

Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair-substantial; kappa = 0.375-0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate-substantial; kappa = 0.493-0.643), delta activity presence (fair-moderate; kappa = 0.369-0.432), theta activity presence (fair-moderate; kappa = 0.347-0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair-moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate-substantial; kappa = 0.43-0.67) and overall impression (moderate-substantial; kappa = 0.42-0.68).

CONCLUSIONS

This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.

Quantitative EEG predicts outcomes in children after cardiac arrest

OBJECTIVE

To determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.

METHODS

We performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.

RESULTS

The best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1-3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4-6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.

CONCLUSIONS

QEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.

Stability of Early EEG Background Patterns After Pediatric Cardiac Arrest

PURPOSE

We aimed to determine whether EEG background characteristics remain stable across discrete time periods during the acute period after resuscitation from pediatric cardiac arrest.

METHODS

Children resuscitated from cardiac arrest underwent continuous conventional EEG monitoring. The EEG was scored in 12-hour epochs for up to 72 hours after return of circulation by an electroencephalographer using a Background Category with 4 levels (normal, slow-disorganized, discontinuous/burst-suppression, or attenuated-featureless) or 2 levels (normal/slow-disorganized or discontinuous/burst-suppression/attenuated-featureless). Survival analyses and mixed-effects ordinal logistic regression models evaluated whether the EEG remained stable across epochs.

RESULTS

EEG monitoring was performed in 89 consecutive children. When EEG was assessed as the 4-level Background Category, 30% of subjects changed category over time. Based on initial Background Category, one quarter of the subjects changed EEG category by 24 hours if the initial EEG was attenuated-featureless, by 36 hours if the initial EEG was discontinuous or burst-suppression, by 48 hours if the initial EEG was slow-disorganized, and never if the initial EEG was normal. However, regression modeling for the 4-level Background Category indicated that the EEG did not change over time (odds ratio = 1.06, 95% confidence interval = 0.96-1.17, P = 0.26). Similarly, when EEG was assessed as the 2-level Background Category, 8% of subjects changed EEG category over time. However, regression modeling for the 2-level category indicated that the EEG did not change over time (odds ratio = 1.02, 95% confidence interval = 0.91-1.13, P = 0.75).

CONCLUSIONS

The EEG Background Category changes over time whether analyzed as 4 levels (30% of subjects) or 2 levels (8% of subjects), although regression analyses indicated that no significant changes occurred over time for the full cohort. These data indicate that the Background Category is often stable during the acute 72 hours after pediatric cardiac arrest and thus may be a useful EEG assessment metric in future studies, but that some subjects do have EEG changes over time and therefore serial EEG assessments may be informative.

Interrater Agreement of EEG Interpretation After Pediatric Cardiac Arrest Using Standardized Critical Care EEG Terminology

PURPOSE

We evaluated interrater agreement of EEG interpretation in a cohort of critically ill children resuscitated after cardiac arrest using standardized EEG terminology.

METHODS

Four pediatric electroencephalographers scored 10-minute EEG segments from 72 consecutive children obtained 24 hours after return of circulation using the American Clinical Neurophysiology Society's (ACNS) Standardized Critical Care EEG terminology. The percent of perfect agreement and the kappa coefficient were calculated for each of the standardized EEG variables and a predetermined composite EEG background category.

RESULTS

The overall background category (normal, slow-disorganized, discontinuous, or attenuated-featureless) had almost perfect agreement (kappa 0.89).The ACNS Standardized Critical Care EEG variables had agreement that was (1) almost perfect for the seizures variable (kappa 0.93), (2) substantial for the continuity (kappa 0.79), voltage (kappa 0.70), and sleep transient (kappa 0.65) variables, (3) moderate for the rhythmic or periodic patterns (kappa 0.55) and interictal epileptiform discharge (kappa 0.60) variables, and (4) fair for the predominant frequency (kappa 0.23) and symmetry (kappa 0.31) variables. Condensing variable options led to improved agreement for the continuity and voltage variables.

CONCLUSIONS

These data support the use of the standardized terminology and the composite overall background category as a basis for standardized EEG interpretation for subsequent studies assessing EEG background for neuroprognostication after pediatric cardiac arrest.

Admission EEG findings in diverse paediatric cerebral malaria populations predict outcomes

BACKGROUND

Electroencephalography at hospital presentation may offer important insights regarding prognosis that can inform understanding of cerebral malaria (CM) pathophysiology and potentially guide patient selection and risk stratification for future clinical trials. Electroencephalogram (EEG) findings in children with CM in Uganda and Malawi were compared and associations between admission EEG findings and outcome across this diverse population were assessed. Demographic, clinical and admission EEG data from Ugandan and Malawian children admitted from 2009 to 2012 with CM were gathered, and survivors assessed for neurological abnormalities at discharge.

RESULTS

281 children were enrolled (Uganda n = 122, Malawi n = 159). The Malawian population was comprised only of retinopathy positive children (versus 72.5% retinopathy positive in Uganda) and were older (4.2 versus 3.7 years; p = 0.046), had a higher HIV prevalence (9.0 versus 2.8%; p = 0.042), and worse hyperlactataemia (7.4 versus 5.2 mmol/L; p < 0.001) on admission compared to the Ugandan children. EEG findings differed between the two groups in terms of average voltage and frequencies, reactivity, asymmetry, and the presence/absence of sleep architecture. In univariate analyses pooling EEG and outcomes data for both sites, higher average and maximum voltages, faster dominant frequencies, and retained reactivity were associated with survival (all p < 0.05). Focal slowing was associated with death (OR 2.93; 95% CI 1.77-7.30) and a lower average voltage was associated with neurological morbidity in survivors (p = 0.0032).

CONCLUSIONS

Despite substantial demographic and clinical heterogeneity between subjects in Malawi and Uganda as well as different EEG readers at each site, EEG findings on admission predicted mortality and morbidity. For CM clinical trials aimed at decreasing mortality or morbidity, EEG may be valuable for risk stratification and/or subject selection.

The effect of increased intracranial EEG sampling rates in clinical practice

OBJECTIVE

Recent research suggests that high frequency intracranial EEG (iEEG) may improve localization of epileptic networks. This study aims to determine whether recording macroelectrode iEEG with higher sampling rates improves seizure localization in clinical practice.

METHODS

14 iEEG seizures from 10 patients recorded with >2000 Hz sampling rate were downsampled to four sampling rates: 100, 200, 500, 1000 Hz. In the 56 seizures, seizure onset time and location was marked by 5 independent, blinded EEG experts.

RESULTS

When reading iEEG under clinical conditions, there was no consistent difference in time or localization of seizure onset or number of electrodes involved in the seizure onset zone with sampling rates varying from 100 to 1000 Hz. Stratification of patients by outcome did not improve with higher sampling rate.

CONCLUSION

When utilizing standard clinical protocols, there was no benefit to acquiring iEEGs with sampling rate >100 Hz. Significant variability was noted in EEG marking both within and between individual expert EEG readers.

SIGNIFICANCE

Although commercial equipment is capable of sampling much faster than 100 Hz, tools allowing visualization of subtle high frequency activity such as HFOs will be required to improve patient care. Quantitative methods may decrease reader variability, and potentially improve patient outcomes.

Early Presence of Sleep Spindles on Electroencephalography Is Associated With Good Outcome After Pediatric Cardiac Arrest

OBJECTIVES

The role of sleep architecture as a biomarker for prognostication after resuscitation from cardiac arrest in children hospitalized in an ICU remains poorly defined. We sought to investigate the association between features of normal sleep architecture in children after cardiac arrest and a favorable neurologic outcome at 6 months.

DESIGN

Retrospective review of medical records and continuous electroencephalography monitoring.

SETTING

Cardiac and PICU of a tertiary children's hospital.

PATIENTS

All patients from 6 months to 18 years old resuscitated from cardiac arrest who underwent continuous electroencephalography monitoring in the first 24 hours after in- or out-of-hospital cardiac arrest from January 2010 to June 2015.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Thirty-four patients underwent continuous electroencephalography monitoring after cardiac arrest. The median age was 6.1 years (interquartile range, 1.5-12.5 yr), 20 patients were male (59%). Most cases (n = 23, 68%) suffered from in-hospital cardiac arrest. Electroencephalography monitoring was initiated a median of 9.3 hours (5.8-14.9 hr) after return of spontaneous circulation, for a median duration of 14.3 hours (6.0-16.0 hr) within the first 24-hour period after the cardiac arrest. Five patients had normal spindles, five had abnormal spindles, and 24 patients did not have any sleep architecture. The presence of spindles was associated with a favorable neurologic outcome at 6-month postcardiac arrest (p = 0.001).

CONCLUSIONS

Continuous electroencephalography monitoring can be used in children to assess spindles in the ICU. The presence of spindles on continuous electroencephalography monitoring in the first 24 hours after resuscitation from cardiac arrest is associated with a favorable neurologic outcome. Assessment of sleep architecture on continuous electroencephalography after cardiac arrest could improve outcome prediction.

Evaluation of Dry Sensors for Neonatal EEG Recordings

PURPOSE

Neonatal seizures are a common neurologic diagnosis in neonatal intensive care units, occurring in approximately 14,000 newborns annually in the United States. Although the only reliable means of detecting and treating neonatal seizures is with an electroencephalography (EEG) recording, many neonates do not receive an EEG or experience delays in getting them. Barriers to obtaining neonatal EEGs include (1) lack of skilled EEG technologists to apply conventional wet electrodes to delicate neonatal skin, (2) poor signal quality because of improper skin preparation and artifact, and (3) extensive time needed to apply electrodes. Dry sensors have the potential to overcome these obstacles but have not previously been evaluated on neonates.

METHODS

Sequential and simultaneous recordings with wet and dry sensors were performed for 1 hour on 27 neonates from 35 to 42.5 weeks postmenstrual age. Recordings were analyzed for correlation and amplitude and were reviewed by neurophysiologists. Performance of dry sensors on simulated vernix was examined.

RESULTS

Analysis of dry and wet signals showed good time-domain correlation (reaching >0.8), given the nonsuperimposed sensor positions and similar power spectral density curves. Neurophysiologist reviews showed no statistically significant difference between dry and wet data on most clinically relevant EEG background and seizure patterns. There was no skin injury after 1 hour of dry sensor recordings. In contrast to wet electrodes, impedance and electrical artifact of dry sensors were largely unaffected by simulated vernix.

CONCLUSIONS

Dry sensors evaluated in this study have the potential to provide high-quality, timely EEG recordings on neonates with less risk of skin injury.

Spectral Electroencephalogram Analysis for the Evaluation of Encephalopathy Grade in Children With Acute Liver Failure

OBJECTIVE

Spectral electroencephalogram analysis is a method for automated analysis of electroencephalogram patterns, which can be performed at the bedside. We sought to determine the utility of spectral electroencephalogram for grading hepatic encephalopathy in children with acute liver failure.

DESIGN

Retrospective cohort study.

SETTING

Tertiary care pediatric hospital.

PATIENTS

Patients between 0 and 18 years old who presented with acute liver failure and were admitted to the PICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Electroencephalograms were analyzed by spectral analysis including total power, relative δ, relative θ, relative α, relative β, θ-to-Δ ratio, and α-to-Δ ratio. Normal values and ranges were first derived using normal electroencephalograms from 70 children of 0-18 years old. Age had a significant effect on each variable measured (p < 0.03). Electroencephalograms from 33 patients with acute liver failure were available for spectral analysis. The median age was 4.3 years, 14 of 33 were male, and the majority had an indeterminate etiology of acute liver failure. Neuroimaging was performed in 26 cases and was normal in 20 cases (77%). The majority (64%) survived, and 82% had a good outcome with a score of 1-3 on the Pediatric Glasgow Outcome Scale-Extended at the time of discharge. Hepatic encephalopathy grade correlated with the qualitative visual electroencephalogram scores assigned by blinded neurophysiologists (rs = 0.493; p < 0.006). Spectral electroencephalogram characteristics varied significantly with the qualitative electroencephalogram classification (p < 0.05). Spectral electroencephalogram variables including relative Δ, relative θ, relative α, θ-to-Δ ratio, and α-to-Δ ratio all significantly varied with the qualitative electroencephalogram (p < 0.025). Moderate to severe hepatic encephalopathy was correlated with a total power of less than or equal to 50% of normal for children 0-3 years old, and with a relative θ of less than or equal to 50% normal for children more than 3 years old (p > 0.05). Spectral electroencephalogram classification correlated with outcome (p < 0.05).

CONCLUSIONS

Spectral electroencephalogram analysis can be used to evaluate even young patients for hepatic encephalopathy and correlates with outcome. Spectral electroencephalogram may allow improved quantitative and reproducible assessment of hepatic encephalopathy grade in children with acute liver failure.