Epidemiology and treatment trends for acute encephalopathy under the impact of SARS-CoV-2 pandemic based on a prospective multicenter consecutive case registry

BACKGROUND

Acute encephalopathy is a severe condition predominantly affecting children with viral infections. The purpose of this study was to elucidate the epidemiology, treatment, and management of acute encephalopathy. The study also aimed to understand how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has affected epidemiological trends.

METHODS

This retrospective study used the database of the Febrile Acute Convulsion and Encephalopathy registry, a prospective multicenter consecutive case registry for acute encephalopathy and febrile convulsive status epilepticus. Pediatric patients aged 0-18 years hospitalized and diagnosed with acute encephalopathy between January 2020 and August 2023 were included in this study.

RESULTS

Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) was the most common syndrome (36 cases, 27.5 %). SARS-CoV-2 was the most common pathogen (19 cases, 14.5 %), followed by influenza virus type A (15 cases, 11.5 %). Targeted temperature management was performed for 25 (69.4 %) of 36 patients with AESD; 5 (50.0 %) of 10 patients with hemorrhagic shock and encephalopathy; and only 1 (5.9 %) of 17 patients with mild encephalitis or encephalopathy with a reversible splenial lesion (MERS). High-dose corticosteroids were administered to 9 (90.0 %) of 10 patients with hemorrhagic shock and encephalopathy and 11 (30.6 %) of 36 patients with AESD.

CONCLUSIONS

The primary causative pathogen of acute encephalopathy has changed to SARS-CoV-2. AESD remains the most common syndrome. Targeted temperature management is more, whereas high-dose corticosteroid therapy is less, frequently used. No specific treatment for mild encephalitis or encephalopathy with a reversible splenial lesion has been established.

Late-Onset Findings During Extended EEG Monitoring Are Rare in Critically Ill Children

PURPOSE

Electrographic seizures (ES) are common in critically ill children undergoing continuous EEG (CEEG) monitoring, and previous studies have aimed to target limited CEEG resources to children at highest risk of ES. However, previous studies have relied on observational data in which the duration of CEEG was clinically determined. Thus, the incidence of late occurring ES is unknown. The authors aimed to assess the incidence of ES for 24 hours after discontinuation of clinically indicated CEEG.

METHODS

This was a single-center prospective study of nonconsecutive children with acute encephalopathy in the pediatric intensive care unit who underwent 24 hours of extended research EEG after the end of clinical CEEG. The authors assessed whether there were new findings that affected clinical management during the extended research EEG, including new-onset ES.

RESULTS

Sixty-three subjects underwent extended research EEG. The median duration of the extended research EEG was 24.3 hours (interquartile range 24.0-25.3). Three subjects (5%) had an EEG change during the extended research EEG that resulted in a change in clinical management, including an increase in ES frequency, differential diagnosis of an event, and new interictal epileptiform discharges. No subjects had new-onset ES during the extended research EEG.

CONCLUSIONS

No subjects experienced new-onset ES during the 24-hour extended research EEG period. This finding supports observational data that patients with late-onset ES are rare and suggests that ES prediction models derived from observational data are likely not substantially underrepresenting the incidence of late-onset ES after discontinuation of clinically indicated CEEG.

Electrographic Seizure Characteristics and Electrographic Status Epilepticus Prediction

PURPOSE

We aimed to characterize electrographic seizures (ES) and electrographic status epilepticus (ESE) and determine whether a model predicting ESE exclusively could effectively guide continuous EEG monitoring (CEEG) utilization in critically ill children.

METHODS

This was a prospective observational study of consecutive critically ill children with encephalopathy who underwent CEEG. We used descriptive statistics to characterize ES and ESE, and we developed a model for ESE prediction.

RESULTS

ES occurred in 25% of 1,399 subjects. Among subjects with ES, 23% had ESE, including 37% with continuous seizures lasting >30 minutes and 63% with recurrent seizures totaling 30 minutes within a 1-hour epoch. The median onset of ES and ESE occurred 1.8 and 0.18 hours after CEEG initiation, respectively. The optimal model for ESE prediction yielded an area under the receiver operating characteristic curves of 0.81. A cutoff selected to emphasize sensitivity (91%) yielded specificity of 56%. Given the 6% ESE incidence, positive predictive value was 11% and negative predictive value was 99%. If the model were applied to our cohort, then 53% of patients would not undergo CEEG and 8% of patients experiencing ESE would not be identified.

CONCLUSIONS

ESE was common, but most patients with ESE had recurrent brief seizures rather than long individual seizures. A model predicting ESE might only slightly improve CEEG utilization over models aiming to identify patients at risk for ES but would fail to identify some patients with ESE. Models identifying ES might be more advantageous for preventing ES from evolving into ESE.

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.

EEG Monitoring in Critically Ill Children: Establishing High-Yield Subgroups

PURPOSE

Continuous EEG monitoring (CEEG) is increasingly used to identify electrographic seizures (ES) in critically ill children, but it is resource intense. We aimed to assess how patient stratification by known ES risk factors would impact CEEG utilization.

METHODS

This was a prospective observational study of critically ill children with encephalopathy who underwent CEEG. We calculated the average CEEG duration required to identify a patient with ES for the full cohort and subgroups stratified by known ES risk factors.

RESULTS

ES occurred in 345 of 1,399 patients (25%). For the full cohort, an average of 90 hours of CEEG would be required to identify 90% of patients with ES. If subgroups of patients were stratified by age, clinically evident seizures before CEEG initiation, and early EEG risk factors, then 20 to 1,046 hours of CEEG would be required to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation and EEG risk factors present in the initial hour of CEEG required only 20 (<1 year) or 22 (≥1 year) hours of CEEG to identify a patient with ES. Conversely, patients with no clinically evident seizures before CEEG initiation and no EEG risk factors in the initial hour of CEEG required 405 (<1 year) or 1,046 (≥1 year) hours of CEEG to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation or EEG risk factors in the initial hour of CEEG required 29 to 120 hours of CEEG to identify a patient with ES.

CONCLUSIONS

Stratifying patients by clinical and EEG risk factors could identify high- and low-yield subgroups for CEEG by considering ES incidence, the duration of CEEG required to identify ES, and subgroup size. This approach may be critical for optimizing CEEG resource allocation.

Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures

OBJECTIVE

Strategies are needed to optimally deploy continuous EEG monitoring (CEEG) for electroencephalographic seizure (ES) identification and management due to resource limitations. We aimed to construct an efficient multi-stage prediction model guiding CEEG utilization to identify ES in critically ill children using clinical and EEG covariates.

METHODS

The largest prospective single-center cohort of 1399 consecutive children undergoing CEEG was analyzed. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed.

RESULTS

When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model.

CONCLUSIONS

Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.

Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG

Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.

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.

Changes in the treatment of pediatric acute encephalopathy in Japan between 2015 and 2021: A national questionnaire-based survey

BACKGROUND

Although acute encephalopathy (AE) is the most serious disorder associated with a viral infection in childhood and often causes death or neurological sequelae, standard treatments have not been established. In 2016, the Japanese Society of Child Neurology published the "Guidelines for the Diagnosis and Treatment of Acute Encephalopathy in Childhood 2016" (AE GL 2016). We conducted a questionnaire survey to evaluate the status of the treatment of pediatric AE in 2021 and the changes in treatment before and after the publication of the AE GL 2016.

METHODS

In October 2021, questionnaires were mailed via the web to members of two mailing lists who were involved in the practice of pediatric neurological disorders.

RESULTS

Most Japanese physicians (98 %) engaged in the treatment of pediatric AE used the AE GL 2016 as a clinical reference. From 2015 to 2021, the number of institutions that implemented targeted temperature management (TTM), vitamin administration, and continuous electroencephalographic monitoring increased significantly. Regarding the targeted temperature for TTM, the proportion of patients who were treated with normothermia (36.0-37.0 °C) increased from 2015 (55 %) to 2021 (79 %). The use of corticosteroids in patients with AE caused by a cytokine storm, which is recommended in the AE GL 2016, had already been implemented in most institutions by 2015.

CONCLUSION

The AE GL 2016 could be used to disseminate the knowledge accumulated to date. Evidence of the efficacy and proper indication criteria for the treatment of AE is insufficient and must be further accumulated.

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.

Implementation of a Pediatric Neurocritical Care Program for Children With Status Epilepticus: Adherence to Continuous Electroencephalogram Monitoring

OBJECTIVES

To describe adherence to continuous electroencephalogram (cEEG) monitoring as part of a pediatric neurocritical care (PNCC) program for status epilepticus (SE).

DESIGN

Retrospective review of pre- and postintervention cohorts.

SETTING

A pediatric referral hospital.

PATIENTS

Children admitted to the PICU for SE.

INTERVENTIONS

We restructured the care delivery model to include a pediatric neurointensive care unit (neuro-ICU) and expanded the cEEG capacity. We created a criteria-based cEEG pathway. We provided education to all providers including the nursing staff.

MEASUREMENTS AND MAIN RESULTS

The main outcomes were: 1) the percentages of children meeting American Clinical Neurophysiology Society (ACNS) criteria who underwent cEEG monitoring and 2) the time interval between PICU arrival and cEEG initiation. PICU admissions with the diagnosis of SE from May 2017 to December 2017 served as the baseline, which was compared with the same periods in 2018 to 2020 (PNCC era).There were 60 admissions in the pre-PNCC period (2017), 111 in 2018, 118 in 2019, and 108 in 2020. The percentages of admissions from each period that met ACNS criteria for cEEG monitoring were between 84% and 97%. In the pre-PNCC era, 22 of 52 (42%) admissions meeting ACNS criteria underwent cEEG monitoring. In the PNCC era, greater than or equal to 80% of the qualified admissions underwent cEEG monitoring (74/93 [80%] in 2018, 94/115 [82%] in 2019, and 87/101 [86%] in 2020). Compared with the pre-PNCC era, the neuro-ICU had a shorter interval between PICU arrival and cEEG initiation (216 min [141-1,444 min] vs 138 min [103-211 min]).

CONCLUSIONS

The implementation of a PNCC program with initiatives in care delivery, allocation of resources, and education was associated with increased adherence to best care practices for the management of SE.

VEEG monitoring and electrographic seizures in 232 pediatric patients in ICU at a tertiary hospital in China

Objectives

To investigate neonatal electroencephalography (EEG) background activity and electrographic seizures in patients in the pediatric intensive care unit (PICU) who underwent bedside video-electroencephalography (vEEG) monitoring.

Methods

A total of 232 pediatric patients admitted or transferred to PICU that underwent vEEG monitoring were retrospectively enrolled in this study, and electrographic status epilepticus was observed after vEEG monitoring.

Results

The median age was 1.56 years [95% confidence interval (CI) = 1.12-2.44]. Electrographic seizures occurred in 88 patients (37.9%), out of which 36 cases (40.9%) had electrographic status epilepticus. Prior epileptic encephalopathy diagnosis [odds ratio (OR) = 6.57, 95% CI = 1.91-22.59, p = 0.003], interictal epileptiform discharges (OR = 46.82, 95%CI = 5.31-412.86, p = 0.0005), slow disorganized EEG background (OR = 11.92, 95%CI = 1.31-108.71, p = 0.028), and burst-suppression EEG background (OR = 23.64, 95%CI = 1.71-327.57, p = 0.018) were the risk factors for electrographic seizures' occurrence. Of the 232 patients, the condition of 179 (77.2%) patients improved and they were discharged, 34 cases (14.7%) were withdrawn, and 18 cases (7.8%) died. The in-hospital death rate was 47.6% (10 in 21 cases) in patients with attenuated/featureless, compared to 0/23 with normal EEG background.

Conclusions

Electrographic status epilepticus occurs in more than one-third of patients with electrographic seizures. vEEG is an efficient method to determine electrographic seizures in children. Abnormal EEG background activity is associated with both electrographic seizures' occurrence and unfavorable in-hospital outcomes.

The Role of Electroencephalography in the Prognostication of Clinical Outcomes in Critically Ill Children: A Review

Electroencephalography (EEG) is a neurologic monitoring modality that allows for the identification of seizures and the understanding of cerebral function. Not only can EEG data provide real-time information about a patient’s clinical status, but providers are increasingly using these results to understand short and long-term prognosis in critical illnesses. Adult studies have explored these associations for many years, and now the focus has turned to applying these concepts to the pediatric literature. The aim of this review is to characterize how EEG can be utilized clinically in pediatric intensive care settings and to highlight the current data available to understand EEG features in association with functional outcomes in children after critical illness. In the evaluation of seizures and seizure burden in children, there is abundant data to suggest that the presence of status epilepticus during illness is associated with poorer outcomes and a higher risk of mortality. There is also emerging evidence indicating that poorly organized EEG backgrounds, lack of normal sleep features and lack of electrographic reactivity to clinical exams portend worse outcomes in this population. Prognostication in pediatric critical illness must be informed by the comprehensive evaluation of a patient’s clinical status but the utilization of EEG may help contribute to this assessment in a meaningful way.

Use of Continuous EEG Monitoring and Short-Term Outcomes in Critically Ill Children

This study aimed to compare short-term outcomes at pediatric intensive care unit (PICU) discharge in critically ill children with and without continuous electroencephalography (cEEG) monitoring. We retrospectively compared 234 patients who underwent cEEG with 2294 patients without cEEG. Propensity score matching was used to compare patients with seizures and status epilepticus between cEEG and historical cohorts. The EEG cohort had higher in-hospital mortality, worse Pediatric Cerebral Performance Category (PCPC) scores, and greater PCPC decline at discharge. In patients with status epilepticus, the PCPC decline was higher in the cEEG cohort. PCPC decline at PICU discharge was associated with cEEG monitoring in patients with status epilepticus.

Real-time seizure detection in paediatric intensive care patients: the RESET child brain protocol

INTRODUCTION

Approximately 20%-40% of comatose children with risk factors in intensive care have electrographic-only seizures; these go unrecognised due to the absence of continuous electroencephalography (EEG) monitoring (cEEG). Utility of cEEG with high-quality assessment is currently limited due to high-resource requirements. New software analysis tools are available to facilitate bedside cEEG assessment using quantitative EEG (QEEG) trends. The primary aim of this study is to describe accuracy of interpretation of QEEG trends by paediatric intensive care unit (PICU) nurses compared with cEEG assessment by neurologist (standard clinical care) in children at risk of seizures and status epilepticus utilising diagnostic test statistics. The secondary aims are to determine time to seizure detection for QEEG users compared with standard clinical care and describe impact of confounders on accuracy of seizure detection.

METHODS AND ANALYSIS

This will be a single-centre, prospective observational cohort study evaluating a paediatric QEEG programme utilising the full 19 electrode set. The setting will be a 36-bed quaternary PICU with medical, cardiac and general surgical cases. cEEG studies in PICU patients identified as 'at risk of seizures' will be analysed. Trained bedside clinical nurses will interpret the QEEG. Seizure events will be marked as seizures if >3 QEEG criteria occur. Post-hoc dedicated neurologists, who remain blinded to the QEEG analysis, will interpret the cEEG. Determination of standard test characteristics will assess the primary hypothesis. To calculate 95% (CIs) around the sensitivity and specificity estimates with a CI width of 10%, the sample size needed for sensitivity is 80 patients assuming each EEG will have approximately 9 to 18 1-hour epochs.

ETHICS AND DISSEMINATION

The study has received approval by the Children's Health Queensland Human Research Ethics Committee (HREC/19/QCHQ/58145). Results will be made available to the funders, critical care survivors and their caregivers, the relevant societies, and other researchers.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clinical Trials Registry (ANZCTR) 12621001471875.

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.

Role of Video-EEG in Children

Routine electroencephalogram (EEG) has many limitations, especially the inability to capture reported habitual events in question. A prolonged EEG with synchronized video (video-EEG) overcomes some of these limitations by improving the sensitivity, specificity and the diagnostic yield by attempting to record the habitual events when they are frequent and when indicated. Video-EEG is employed commonly for the diagnosis and classification of epilepsy/epilepsy syndromes, to distinguish between seizures and seizures mimickers, for pre-surgical evaluation and in the management of critically ill children. The duration of recording would vary depending on the indication and frequency of events. Ambulatory EEG is another cost effective and convenient alternative in certain circumstances. However, availability of the machines and expertise, accessibility, affordability and labor intensive nature of the procedure limit widespread use in India. This review explores the role of video-EEG in the management of children with epileptic and non-epileptic paroxysmal events with respect to routine clinical practice in India.

Knowledge and attitudes of critical care providers towards neurophysiological monitoring, seizure diagnosis, and treatment

AIM

To explore the attitudes of paediatric intensive care unit (PICU) health care professionals towards diagnosis and neurophysiological monitoring of seizures.

METHOD

This study used an explanatory sequential mixed-methods approach, interconnecting quantitative and qualitative features, comprising questionnaires and interviews, with equal weighting between stages, of health care professionals working in UK PICUs. Interview data were analysed using thematic analysis and triangulated with questionnaire data.

RESULTS

Seventy-two questionnaires were returned: 49 out of 60 (71.0%) of respondents reported that seizures were extremely hard or somewhat hard to diagnose in a critically ill child, and 81.2% had seen misdiagnosis occur. Thematic analysis revealed two main themes: (1) feeling out of control when faced with 'grey areas'; and (2) regaining control, which compromised three subthemes: aggressive intervention, accurate diagnosis, and eschewing diagnosis.

INTERPRETATION

Health care professionals find accurate diagnosis of seizures difficult, particularly in sedated/paralysed children and those with chronic neurological disorders. They report they would like better educational opportunities on discriminating between epileptic and non-epileptic events to improve their confidence. Professionals want routine neurophysiological monitoring that can be applied and interpreted at the bedside throughout the day to regain a sense of control over their patient, direct treatment appropriately, and, potentially, improve outcomes, but report appropriate training and peer review are essential if it is to be introduced into routine care. What this study adds Paediatric intensive care unit (PICU) staff feel out of control when faced with diagnosing seizures. Neurophysiological monitoring is wanted to help diagnosis and treatment. Amplitude-integrated electroencephalography is the preferred, pragmatic tool by PICU staff.

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.

EEG in the Pediatric Intensive Care Unit: An Irish Experience

INTRODUCTION

Evidence for continuous EEG monitoring in the pediatric intensive care unit (PICU) is increasing. However, 24/7 access to EEG is not routinely available in most centers, and clinical management is often informed by more limited EEG resources. The experience of EEG was reviewed in a tertiary PICU where 24/7 EEG cover is unavailable.

METHODS

Retrospective EEG and clinical review of 108 PICU patients. Correlations were carried out between EEG and clinical variables including mortality. The role of EEG in clinical decision making was documented.

RESULTS

One hundred ninety-six EEGs were carried out in 108 PICU patients over 2.5 years (434 hours of recording). After exclusion of 1 outlying patient with epileptic encephalopathy, 136 EEGs (median duration, 65 minutes; range, 20 minutes to 4 hours 40 minutes) were included. Sixty-two patients (57%) were less than 12 months old. Seizures were detected in 18 of 107 patients (17%); 74% of seizures were subclinical; 72% occurred within the first 30 minutes of recording. Adverse EEG findings were associated with high mortality. Antiepileptic drug use was high in the studied population irrespective of EEG seizure detection. Prevalence of epileptiform discharges and EEG seizures diminished with increasing levels of sedation.

CONCLUSIONS

EEG provides important diagnostic information in a large proportion of PICU patients. In the absence of 24/7 EEG availability, empirical antiepileptic drug utilization is high.

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.

Does the first hour of continuous electroencephalography predict neonatal seizures?

OBJECTIVE

Prolonged continuous video-electroencephalography (cEEG) is recommended for neonates at risk of seizures. The cost and expertise required to provide a real-time response to detected seizures often limits its utility. We hypothesised that the first hour of cEEG could predict subsequent seizures.

DESIGN AND SETTING

Retrospective multicentre diagnostic accuracy study.

PATIENTS

266 term neonates at risk of seizure or with suspected seizures.

INTERVENTION

The first hour of cEEG was graded by expert and novice interpreters as normal, mildly, moderately or severely abnormal; seizures were identified.

MAIN OUTCOME MEASURES

Association between abnormalities in the first hour of cEEG and the presence of seizures during total cEEG monitoring.

RESULTS

50/98 (51%) of neonates who developed seizures had their first seizure in the first hour of cEEG monitoring. The 'time-to-event' risk of seizure from 0 to 96 hours was 0.38 (95% CI 0.32 to 0.44) while the risk in the first hour was 0.19 (95% CI 0.15 to 0.24). cEEG background was normal in 48% of neonates, mildly abnormal in 30%, moderately abnormal in 13% and severely abnormal in 9%. Inter-rater agreement for determination of background was very good (weighted kappa=0.81, 95% CI 0.72 to 0.91). When neonates with seizures during the first hour were excluded, an abnormal background resulted in 2.4 times increased risk of seizures during the subsequent monitoring period (95% CI 1.3 to 4.4, p<0.003) while a severely abnormal background resulted in a sevenfold increased risk (95% CI 3.4 to 14.3, p<0.0001).

CONCLUSIONS

The first hour of cEEG in at-risk neonates is useful in identifying and predicting whether seizures occur during cEEG monitoring up to 96 hours. This finding enables identification of high-risk neonates who require closer observation.

Non-neurophysiologist Physicians and Nurses Can Detect Subclinical Seizures in Children Using a Panel of Quantitative EEG Trends and a Seizure Detection Algorithm

PURPOSE

This study evaluated the sensitivity of nonconvulsive seizure detection by non-neurophysiologist physicians and nurses using a panel of quantitative EEG (QEEG) trends in the setting of a pediatric intensive care unit.

METHODS

Forty-five 1-hour QEEG epochs were obtained retrospectively from 10 patients admitted to the McMaster Children's Hospital pediatric intensive care unit, which included 184 electrographic seizures. Each epoch constituted 4 QEEG trends, a seizure probability marker, automated seizure detector, rhythmicity spectrograms, and amplitude-integrated EEG. Six pediatric residents and 5 pediatric intensive care unit nurses analyzed the epochs for possible seizures after a 15-minute power point presentation. This was compared with the gold standard of a board-certified epileptologist interpreting the conventional EEG data for seizures.

RESULTS

Sensitivity of seizure detection for pediatric residents and intensive care unit nurses were 0.90. The specificity was 0.87 and 0.89, respectively. The interrater agreement among the pediatric residents was moderate with a kappa (κ) value of 0.45 (confidence interval: 0.41-0.49), and among the nurses were moderate with a κ value of 0.59 (confidence interval: 0.54-0.63). A post hoc analysis involving 2 neurophysiologists demonstrated a sensitivity of 0.90 and a specificity of 0.93 (confidence interval: 0.90-0.96) for seizure detection and a substantial interrater agreement of κ = 0.76 (confidence interval: 0.61-0.91).

CONCLUSIONS

A panel of QEEG trends can be used by non-neurophysiologists in a pediatric critical care setting to detect nonconvulsive seizures with a reasonable accuracy, which may expedite subclinical seizure identification and timely intervention.

EEG Monitoring After Convulsive Status Epilepticus

After convulsive status epilepticus, patients of all ages may have ongoing EEG seizures identified by continuous EEG monitoring. Furthermore, high EEG seizure exposure has been associated with unfavorable neurobehavioral outcomes. Thus, recent guidelines and consensus statements recommend many patients with persisting altered mental status after convulsive status epilepticus undergo continuous EEG monitoring. This review summarizes the available epidemiologic data and related recommendations provided by recent guidelines and consensus statements.

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.

Continuous EEG in a Pediatric Intensive Care Unit: Adherence to Monitoring Criteria and Barriers to Adequate Implementation

BACKGROUND

Subclinical seizures are common in critically ill children and are best detected by continuous EEG (cEEG) monitoring. Timely detection of seizures requires pediatric intensive care unit (PICU) physicians to identify patients at risk of seizures and request cEEG monitoring. A recent consensus statement from the American Clinical Neurophysiology Society (ACNS) outlines the indications for cEEG monitoring in critically ill patients. However, adherence to these cEEG monitoring criteria among PICU physicians is unknown. Our project had two goals: 1. To assess adherence to cEEG monitoring indications and barriers toward their implementation; 2. To improve compliance with the ACNS cEEG monitoring criteria in our PICU.

METHODS

This is a single-institution study. A total of 234 PICU admissions (183 unique patients) were studied. A 6-month retrospective chart review identified PICU patients meeting ACNS criteria for cEEG monitoring, and patients for whom monitoring was requested. This was followed by an 8-week quality improvement project. During this mentorship period, a didactic 15-min lecture and summary handouts regarding the ACNS indications for cEEG monitoring were provided to all PICU physicians. Requests for cEEG monitoring during the mentorship period were compared to baseline adherence to cEEG monitoring recommendations, and barriers toward timely cEEG monitoring were assessed.

RESULTS

Nearly every fifth PICU patient met cEEG monitoring indications, and prevalences of patients meeting those indications were similar in the retrospective and the prospective mentorship period (18% vs. 19%). Almost all patients (98%) requiring cEEG as per ACNS criteria met the indication for monitoring already at the time of their PICU admission. During the retrospective period, 23% of patients meeting ACNS criteria had a request for cEEG monitoring, which increased to 83% during the mentorship period. The median delay to cEEG initiation was 16.7 h during the mentorship period, largely due to limited hours of EEG technician availability. Electrographic seizures were identified in 36% of patients monitored, all within the first 120 min of cEEG recording. The majority (79%) of cEEGs informed clinical management.

CONCLUSIONS

A brief teaching intervention supplemented by pictographic handouts significantly increased adherence to cEEG monitoring recommendations, and cEEGs guided clinical management. However, there were long delays to cEEG initiation. In order to promptly recognize subclinical seizures in critically ill children, we strongly advocate for a routine screening for cEEG monitoring indications as part of the PICU admission process, and a care model allowing for cEEG initiation around-the-clock.

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.

Continuous Electroencephalography Monitoring in Critically Ill Infants and Children

Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.

Weak supervision as an efficient approach for automated seizure detection in electroencephalography

Automated seizure detection from electroencephalography (EEG) would improve the quality of patient care while reducing medical costs, but achieving reliably high performance across patients has proven difficult. Convolutional Neural Networks (CNNs) show promise in addressing this problem, but they are limited by a lack of large labeled training datasets. We propose using imperfect but plentiful archived annotations to train CNNs for automated, real-time EEG seizure detection across patients. While these weak annotations indicate possible seizures with precision scores as low as 0.37, they are commonly produced in large volumes within existing clinical workflows by a mixed group of technicians, fellows, students, and board-certified epileptologists. We find that CNNs trained using such weak annotations achieve Area Under the Receiver Operating Characteristic curve (AUROC) values of 0.93 and 0.94 for pediatric and adult seizure onset detection, respectively. Compared to currently deployed clinical software, our model provides a 31% increase (18 points) in F1-score for pediatric patients and a 17% increase (11 points) for adult patients. These results demonstrate that weak annotations, which are sustainably collected via existing clinical workflows, can be leveraged to produce clinically useful seizure detection models.

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.

Current Status of Continuous Electroencephalographic Monitoring in Critically Ill Children

The utilization of continuous electroencephalographic monitoring in critical care units has increased significantly, and several consensus statements and guidelines have been published. The use of critical care electroencephalographic monitoring has become a standard of care in many centers in the United States and other countries. The most common indication is to detect electrographic seizures and status epilepticus. Other indications include monitoring treatment efficacy in patients with electrographic seizures and status epilepticus, evaluating the degree of disturbance of function in patients with encephalopathy, monitoring brain function in patients treated with sedation and neuromuscular blocking agents, and event characterization. The urgent initiation of critical care electroencephalographic monitoring is recommended in certain clinical populations, but varies among institutions. The consensus among neurologists is to start treatment after identifying electrographic seizures or electrographic status epilepticus with or without clinical signs. However, the optimal treatment of nonconvulsive and electrographic-only seizures remains controversial. Critical care electroencephalographic monitoring has significant impact on clinical management, but there is lack of clear evidence that treatment guided by critical care electroencephalographic monitoring leads to improvement of clinical and neurodevelopmental outcome. There are substantial discrepancies among institutions on personnel and technical support used for critical care electroencephalographic monitoring. The optimal critical care electroencephalographic monitoring team should include electroencephalographers with experience in critical care electroencephalographic monitoring interpretation and appropriately trained technologists certified in electroencephalography by the American Board of Registration of Electroencephalographic and Evoked Potential Technologists specializing in critical care electroencephalographic monitoring or long-term monitoring.

The probability of seizures during continuous EEG monitoring in high-risk neonates

OBJECTIVE

We evaluated the impact of monitoring indication, early electroencephalography (EEG), and clinical features on seizure risk in all neonates undergoing continuous EEG (cEEG) monitoring following a standardized monitoring protocol.

METHODS

All cEEGs from unique neonates 34-48 weeks postmenstrual age monitored from 1/2011-10/2017 (n = 291) were included. We evaluated the impact of cEEG monitoring indication (acute neonatal encephalopathy [ANE], suspicious clinical events [SCEs], or other high-risk conditions [OHRs]), age, medication status, and early EEG abnormalities (including the presence of epileptiform discharges and abnormal background continuity, amplitude, asymmetry, asynchrony, excessive sharp transients, and burst suppression) on time to first seizure and overall seizure risk using Kaplan-Meier survival curves and multivariable Cox proportional hazards models.

RESULTS

Seizures occurred in 28% of high-risk neonates. Discontinuation of monitoring after 24 hours of seizure-freedom would have missed 8.5% of neonates with seizures. Overall seizure risk was lower in neonates monitored for ANE compared to OHR (P = .004) and trended lower compared to SCE (P = .097). The time course of seizure presentation varied by group, where the probability of future seizure was less than 1% after 17 hours of seizure-free monitoring in the SCE group, but required 42 hours in the OHR group, and 73 hours in the ANE group. The presence of early epileptiform discharges increased seizure risk in each group (ANE: adjusted hazard ratio [aHR] 4.32, 95% confidence interval [CI] 1.23-15.13, P = .022; SCE: aHR 10.95, 95% CI 4.77-25.14, P < 1e-07; OHR: aHR 56.90, 95% CI 10.32-313.72, P < 1e-05).

SIGNIFICANCE

Neonates who undergo cEEG are at high risk for seizures, and risk varies by monitoring indication and early EEG findings. Seizures are captured in nearly all neonates undergoing monitoring for SCE within 24 hours of cEEG monitoring. Neonates monitored for OHR and ANE can present with delayed seizures and require longer durations of monitoring. Early epileptiform discharges are the best early EEG feature to predict seizure risk.

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.

Targeted Temperature Management in Pediatric Neurocritical Care

Targeted temperature management encompasses a range of clinical interventions to regulate systemic temperature, and includes both induction of varying degrees of hypothermia and fever prevention ("targeted normothermia"). Targeted temperature management plays a key role in the contemporary management of critically ill neonates and children with acute brain injury. Yet, many unanswered questions remain regarding optimal temperature management in pediatric neurocritical care. The introduction highlights experimental studies that have evaluated the neuroprotective efficacy of therapeutic hypothermia and explored possible mechanisms of action in several brain injury models. The next section focuses on three major clinical conditions in which therapeutic hypothermia has been evaluated in randomized controlled trials in pediatric populations: neonatal hypoxic-ischemic encephalopathy, postcardiac arrest encephalopathy, and traumatic brain injury. Clinical implications of targeted temperature management in pediatric neurocritical care are also discussed. The final section examines some of the factors that may underlie the limited neuroprotective efficacy of hypothermia that has been observed in several major pediatric clinical trials, and outlines important directions for future research.

Sedation and Analgesia Influence Electroencephalography Monitoring in Pediatric Neurocritical Care

OBJECTIVES

We assessed neuroactive medication use in critically ill children who require neurological consultation and evaluated the associations between administration of these medications and continuous electroencephalography (cEEG) utilization and seizure frequency.

METHODS

We evaluated exposure to sedatives, analgesics, anesthetics, and paralytics in consecutive patients (0 days to 18 years) for whom neurological consultation was requested in three intensive care units (ICUs) [neonatal (NICU), pediatric (PICU), and cardiothoracic (PCTU)]) at one children's hospital. We assessed cEEG usage and seizure incidence in relation to drug exposure.

RESULTS

From November 2015 to November 2016, 300 consecutive patients were evaluated (93 NICU, 139 PICU, and 68 PCTU). Ninety-seven (32%) were receiving ≥1 sedative infusion at the time of consultation [NICU 7 (8%), PICU 50(36%), PCTU 40 (58%%]; 91 (30%) received ≥1 paralytic agent within the preceding 24 hours. Continuous electroencephalography was performed more often for patients treated with sedative infusions (81 of 97 versus 133 of 203, P = 0.001) and paralytic medications (80 of 91 versus 134 of 209, P < 0.001) within 24 hours preceding consultation than those who were not. Sixty-eight of 214 (32%) had electrographic seizures (65 of 68 within initial 24 hours of monitoring); seizures were less common among patients who had received sedative infusions (18 of 81 versus 51 of 133, P = 0.014). In multivariable analysis of seizure likelihood, only younger age was associated with increased risk (P = 0.037).

CONCLUSIONS

Critically ill infants and children are frequently treated with sedatives, anesthetics, analgesics, and paralytics. Neuroactive medications limit bedside neurological assessments and, in this cohort, were associated with increased cEEG usage. Our data underscore the need to study the effect of these medications on clinical care and long-term outcomes.

Introduction of Continuous Video EEG Monitoring into 2 Different NICU Models by Training Neonatal Nurses

BACKGROUND

Continuous video electroencephalographic (EEG) (cvEEG) monitoring is emerging as the standard of care for diagnosis and management of neonatal seizures. However, cvEEG is labor-intensive and the need to initiate and interpret studies on a 24-hour basis is a major limitation.

PURPOSE

This study aims at establishing consistency in monitoring of newborns admitted to 2 different neonatal intensive care units (NICUs) managed by the same neurocritical care team.

METHODS

Neonatal nurses were trained to apply scalp electrodes, troubleshoot technical issues, and identify amplitude-integrated EEG abnormalities. Guidelines, checklists, and visual training modules were developed. A central network system allowed remote access to the cvEEGs by the epileptologist for timely interpretation and feedback. A cohort of 100 infants with moderate to severe hypoxic-ischemic encephalopathy before and after the training program was compared.

RESULTS

During the study period, 192 cvEEGs were obtained. The time to initiate brain monitoring decreased by 31.5 hours posttraining; this, in turn, led to an increase in electrographic seizure detection (20% before vs 34% after), decrease in seizure clinical misdiagnosis (65% before and 36% after), and reduction in antiseizure medication burden.

IMPLICATIONS FOR PRACTICE

Training experienced NICU nurses to set up, start, and monitor cvEEGs can decrease the time to initiate cvEEGs, which may lead to better seizure diagnosis and management.

IMPLICATIONS FOR RESEARCH

Further understanding of practice bundles for best supporting infants at risk and being treated for seizures needs to be evaluated for integration into practice.Video Abstract Available at https://journals.lww.com/advancesinneonatalcare/Pages/videogallery.aspx.

Cardiopulmonary Resuscitation in Infants and Children With Cardiac Disease: A Scientific Statement From the American Heart Association

Cardiac arrest occurs at a higher rate in children with heart disease than in healthy children. Pediatric basic life support and advanced life support guidelines focus on delivering high-quality resuscitation in children with normal hearts. The complexity and variability in pediatric heart disease pose unique challenges during resuscitation. A writing group appointed by the American Heart Association reviewed the literature addressing resuscitation in children with heart disease. MEDLINE and Google Scholar databases were searched from 1966 to 2015, cross-referencing pediatric heart disease with pertinent resuscitation search terms. The American College of Cardiology/American Heart Association classification of recommendations and levels of evidence for practice guidelines were used. The recommendations in this statement concur with the critical components of the 2015 American Heart Association pediatric basic life support and pediatric advanced life support guidelines and are meant to serve as a resuscitation supplement. This statement is meant for caregivers of children with heart disease in the prehospital and in-hospital settings. Understanding the anatomy and physiology of the high-risk pediatric cardiac population will promote early recognition and treatment of decompensation to prevent cardiac arrest, increase survival from cardiac arrest by providing high-quality resuscitations, and improve outcomes with postresuscitation care.

Risk Factors for Seizures Among Young Children Monitored With Continuous Electroencephalography in Intensive Care Unit: A Retrospective Study

Objective: cEEG is an emerging technology for which there are no clear guidelines for patient selection or length of monitoring. The purpose of this study was to identify subgroups of pediatric patients with high incidence of seizures. Study Design: We conducted a retrospective study on 517 children monitored by cEEG in the intensive care unit (ICU) of a children's hospital. The children were stratified using an age threshold selection method. Using regression modeling, we analyzed significant risk factors for increased seizure risk in younger and older children. Using two alternative correction procedures, we also considered a relevant comparison group to mitigate selection bias and to provide a perspective for our findings. Results: We discovered an approximate risk threshold of 14 months: below this threshold, the seizure risk increases dramatically. The older children had an overall seizure rate of 18%, and previous seizures were the only significant risk factor. In contrast, the younger children had an overall seizure rate of 45%, and the seizures were significantly associated with hypoxic-ischemic encephalopathy (HIE; p = 0.007), intracranial hemorrhage (ICH; p = 0.005), and central nervous system (CNS) infection (p = 0.02). Children with HIE, ICH, or CNS infection accounted for 61% of all seizure patients diagnosed through cEEG under 14 months. Conclusions: An extremely high incidence of seizures prevails among critically ill children under 14 months, particularly those with HIE, ICH, or CNS infection.

Clinical course and long-term outcome in children with alteration of consciousness underwent continuous EEG monitoring: A prospective observational study in Thailand

The study aims to explore the clinical course and long-term outcome in children with altered consciousness who underwent cEEG monitoring. A prospective observational study was conducted in neonatal and pediatric intensive care units from 1 September 2014 through 31 March 2017. Standard 10-20 cEEG monitoring was applied. Twenty children were included in this study. Their ages ranged from 1 day to 142.7 months (median age 40.6 months). Continuous EEG was commenced from 5 h to 5 days after the onset of alteration of consciousness (median 40.2 h). The range of EEG monitoring duration was 6.7-256.3 h (mean 50.4 h). Four patients (20%) had preexisting neurological diseases, which were 2 epilepsy, adrenoleukodystrophy and unknown cause of brain atrophy. Eleven patients (55%) had principle neurological diagnosis and the others 9 (45%) had systemic illnesses. Sixteen patients (80%) had clinical seizures prior to the commencement of cEEG monitoring. Fifteen patients (75%) received antiepileptic drugs before cEEG monitoring. NCSE was diagnosed in 25%. Comparison of patients' characteristics and long-term outcome between the NCSE and non NCSE groups, there was statistical significance between the two groups only with respect to epileptiform discharges. The patients are being follow up for a period of 24 months after the end of cEEG monitoring. The outcome of patients divided into those with a favorable outcome and those with poor outcome according to modified Rankin scale. The patients with isoelectric EEG background had relatively poorer outcomes than those with normal or slow background activities. The overall mortality rate for the entire population was 15%.

Use of EEG in critically ill children and neonates in the United States of America

The objective of the study was to estimate the proportion of patients who receive an electroencephalogram (EEG) among five common indications for EEG monitoring in the intensive care unit: traumatic brain injury (TBI), extracorporeal membrane oxygenation (ECMO), cardiac arrest, cardiac surgery and hypoxic-ischemic encephalopathy (HIE). We performed a retrospective cross-sectional descriptive study utilizing the Kids' Inpatient Database (KID) for the years 2010-2012. The KID is the largest pediatric inpatient database in the USA and it is based on discharge reports created by hospitals for billing purposes. We evaluated the use of electroencephalogram (EEG) or video-electroencephalogram in critically ill children who were mechanically ventilated. The KID database had a population of approximately 6,000,000 pediatric admissions. Among 22,127 admissions of critically ill children who had mechanical ventilation, 1504 (6.8%) admissions had ECMO, 9201 (41.6%) TBI, 4068 (18.4%) HIE, 2774 (12.5%) cardiac arrest, and 4580 (20.7%) cardiac surgery. All five conditions had a higher proportion of males, with the highest (69.8%) in the TBI group. The mortality rates ranged from 7.02 to 39.9% (lowest in cardiac surgery and highest in ECMO). The estimated use of EEG was 1.6% in cardiac surgery, 4.1% in TBI, 7.2% in ECMO, 8.2% in cardiac arrest, and 12.1% in HIE, with an overall use of 5.8%. Among common indications for EEG monitoring in critically ill children and neonates, the estimated proportion of patients actually having an EEG is low.

Seizure Detection by Critical Care Providers Using Amplitude-Integrated Electroencephalography and Color Density Spectral Array in Pediatric Cardiac Arrest Patients

OBJECTIVES

Determine the accuracy and confidence of critical care medicine providers to identify seizures using amplitude-integrated electroencephalography versus amplitude-integrated electroencephalography combined with color density spectral array electroencephalography (aEEG + CDSA).

DESIGN

Tutorial and questionnaire.

SETTING

PICU.

SUBJECTS

Pediatric critical care providers (attendings, fellows, and nurses).

INTERVENTIONS

A standardized powerpoint tutorial on amplitude-integrated electroencephalography and color density spectral array followed by classification of 100 amplitude-integrated electroencephalography images and 100 amplitude-integrated electroencephalography combined with color density spectral array as displaying seizures or not displaying seizures.

MEASUREMENTS AND MAIN RESULTS

Electroencephalography tracings were obtained from children monitored with continuous electroencephalography after cardiac arrest. The gold standard for seizure identification was continuous electroencephalography interpretation by a pediatric electroencephalographer. The same electroencephalography tracings were used to generate images containing only amplitude-integrated electroencephalography or aEEG + CDSA. Twenty-three critical care medicine providers underwent a 30-minute tutorial on amplitude-integrated electroencephalography and color density spectral array interpretation. They were then asked to determine if there were seizures on 100 amplitude-integrated electroencephalography images and 100 aEEG + CDSA. Amplitude-integrated electroencephalography seizure detection sensitivity was 77% (95% CI, 73%-80%), specificity of 65% (95% CI, 62%-67%), negative predictive value of 88% (95% CI, 86%-90%), and positive predictive value of 46% (95% CI, 43%-49%). For aEEG + CDSA, sensitivity was 77% (95% CI, 74%-81%), specificity of 68% (95% CI, 66%-71%), negative predictive value of 89% (95% CI, 87%-90%), and positive predictive value of 49% (95% CI, 46%-52%). Sensitivity for status epilepticus detection was 77% (95% CI, 71%-82%) with amplitude-integrated electroencephalography and 75% (95% CI, 69%-81%) with aEEG + CDSA. The addition of color density spectral array to amplitude-integrated electroencephalography did not improve seizure detection. However, 87% of critical care medicine providers qualitatively felt that combining both modalities increased their ability to detect seizures.

CONCLUSIONS

Amplitude-integrated electroencephalography and aEEG + CDSA offer reasonable sensitivity and negative predictive value for seizure detection by critical care medicine providers. aEEG + CDSA did not improve seizure detection over amplitude-integrated electroencephalography alone although critical care medicine providers felt more confident using both tools combined. Amplitude-integrated electroencephalography and color density spectral array require further evaluation as a tool for screening for seizures and should only be used in conjunction with professional continuous electroencephalography review.

Could EEG Monitoring in Critically Ill Children Be a Cost-effective Neuroprotective Strategy?

PURPOSE

Electrographic status epilepticus (ESE) in critically ill children is associated with unfavorable functional outcomes, but identifying candidates for ESE management requires resource-intense EEG monitoring. A cost-effectiveness analysis was performed to estimate how much ESE identification and management would need to improve patient outcomes to make EEG monitoring strategies a good value.

METHODS

A decision tree was created to examine the relationships among variables important to deciding whether to perform EEG monitoring. Variable costs were estimated from their component parts, outcomes were estimated in quality-adjusted life-years, and incremental cost-effectiveness ratios were calculated to compare the relative values using four alternative EEG monitoring strategies that varied by monitoring duration.

RESULTS

Forty-eight hours of EEG monitoring would be worth its cost if ESE identification and management improved patient outcomes by ≥7%. If ESE identification and management improved patient outcomes by 3% to 6%, then 24 or 48 hours of EEG monitoring would be worth the cost depending on how much decision makers were willing to pay per quality-adjusted life-year gained. If ESE identification and management improved outcomes by as little as 3%, then 24 hours of EEG monitoring would be worth the cost.

CONCLUSIONS

EEG monitoring has the potential to be cost-effective if ESE identification and management improves patient outcomes by as little as 3%.

Impact of an ICU EEG monitoring pathway on timeliness of therapeutic intervention and electrographic seizure termination

OBJECTIVES

We aimed to determine whether implementation of a structured multidisciplinary electroencephalography (EEG) monitoring pathway improved the timeliness of administration of antiseizure medication in response to electrographic seizures in encephalopathic critically ill children.

METHODS

A multidisciplinary team developed a pathway to standardize EEG monitoring and seizure management in encephalopathic critically ill children, aiming to decrease the time from electrographic seizure onset to antiseizure medication administration. Data were collected to inform the team of improvement opportunities, which were then provided by an institutional pathway, staff education, and streamlined communication. Measurements were obtained before and after pathway implementation to assess for improvement.

RESULTS

We collected data on 41 patients before and 21 after pathway implementation. There were no differences between the baseline and pathway groups in demographic characteristics, acute encephalopathy etiologies, or antiseizure medications utilized. The median duration [interquartile range, IQR] from seizure onset to antiseizure medication administration was shorter for patients treated with the pathway (64 min [50, 101]) compared to patients treated prior to pathway implementation (139 min [71, 189]; p = 0.0006). The median [IQR] interval from seizure onset to antiseizure medication order was shorter for the pathway group (31 min [20, 49]) than the baseline group (71 min [33, 131]; p = 0.003). The median [IQR] interval from antiseizure medication order to administration was shorter for the pathway group (30 min [19, 40]) than the baseline group (40 min [17, 68]) (p = 0.047). Seizure termination was more likely to occur following initial antiseizure medication administration in the pathway than baseline group (67% vs. 27%, p = 0.002).

SIGNIFICANCE

Implementation of the pathway resulted in a significant reduction in the duration between electrographic seizure onset and antiseizure medication administration, and a significant increase in the rate of electrographic seizure termination following an initial antiseizure medication. Further study is needed to determine whether these changes are associated with improved outcomes.

Consensus statement on continuous EEG in critically ill adults and children, part I: indications

INTRODUCTION

Critical Care Continuous EEG (CCEEG) is a common procedure to monitor brain function in patients with altered mental status in intensive care units. There is significant variability in patient populations undergoing CCEEG and in technical specifications for CCEEG performance.

METHODS

The Critical Care Continuous EEG Task Force of the American Clinical Neurophysiology Society developed expert consensus recommendations on the use of CCEEG in critically ill adults and children.

RECOMMENDATIONS

The consensus panel recommends CCEEG for diagnosis of nonconvulsive seizures, nonconvulsive status epilepticus, and other paroxysmal events, and for assessment of the efficacy of therapy for seizures and status epilepticus. The consensus panel suggests CCEEG for identification of ischemia in patients at high risk for cerebral ischemia; for assessment of level of consciousness in patients receiving intravenous sedation or pharmacologically induced coma; and for prognostication in patients after cardiac arrest. For each indication, the consensus panel describes the patient populations for which CCEEG is indicated, evidence supporting use of CCEEG, utility of video and quantitative EEG trends, suggested timing and duration of CCEEG, and suggested frequency of review and interpretation.

CONCLUSION

CCEEG has an important role in detection of secondary injuries such as seizures and ischemia in critically ill adults and children with altered mental status.

How much does it cost to identify a critically ill child experiencing electrographic seizures?

OBJECTIVES

Electrographic seizures in critically ill children may be identified by continuous EEG monitoring. We evaluated the cost effectiveness of 4 electrographic seizure identification strategies (no EEG monitoring and EEG monitoring for 1 hour, 24 hours, or 48 hours).

METHODS

We created a decision tree to model the relationships among variables from a societal perspective. To provide input for the model, we estimated variable costs directly related to EEG monitoring from their component parts, and we reviewed the literature to estimate the probabilities of outcomes. We calculated incremental cost-effectiveness ratios to identify the trade-off between cost and effectiveness at different willingness-to-pay values.

RESULTS

Our analysis found that the preferred strategy was EEG monitoring for 1 hour, 24 hours, and 48 hours if the decision maker was willing to pay <$1,666, $1,666-$22,648, and >$22,648 per critically ill child identified with electrographic seizures, respectively. The 48-hour strategy only identified 4% more children with electrographic seizures at substantially higher cost. Sensitivity analyses found that all 3 strategies were acceptable at lower willingness-to-pay values when children with higher electrographic seizure risk were monitored.

CONCLUSIONS

The results of this study support monitoring of critically ill children for 24 hours because the cost to identify a critically ill child with electrographic seizures is modest. Further study is needed to predict better which children may benefit from 48 hours of EEG monitoring because the costs are much higher.

Continuous electroencephalogram detection of non-convulsive seizures in the pediatric intensive care unit: review of the utility and impact on management and outcomes

Non-convulsive seizures (NCS) are common among critically ill children with acute encephalopathy. Continuous electroencephalogram (CEEG) monitoring is an indispensable tool to detect NCS, which is essential to guiding management and assessing prognosis. Risk factors for NCS are highest in pediatric intensive care unit (PICU) patients with altered mental status (AMS) and a recently witnessed clinical seizure, acute changes on neuroimaging, and/or interictal abnormalities on CEEG. Screening for at least 24 hours in at risk pediatric populations is ideal, but around half of NCS may be detected within the first hour. Rapid treatment of prolonged seizures or status epilepticus is critical, as higher seizure burdens have been associated with poorer outcomes in critically ill children. This review integrates current information on critically ill children with AMS and the use of CEEGs, risk factors for NCS, duration of CEEG monitoring, and how the detection of NCS impacts management and outcomes.

Electrographic status epilepticus and neurobehavioral outcomes in critically ill children

PURPOSE

Electrographic seizures (ESs) and electrographic status epilepticus (ESE) are common in children with acute neurologic conditions in pediatric intensive care units (PICUs), and ESE is associated with worse functional and quality-of-life outcomes. As an exploratory study, we aimed to determine if ESE was associated with worse outcomes using more detailed neurobehavioral measures.

METHODS

Three hundred children with an acute neurologic condition and altered mental status underwent clinically indicated EEG monitoring and were enrolled in a prospective observational study. We obtained follow-up data from subjects who were neurodevelopmentally normal prior to PICU admission. We evaluated for associations between ESE and adaptive behavior (Adaptive Behavior Assessment System-II, ABAS-II), behavioral and emotional problems (Child Behavior Checklist, CBCL), and executive function (Behavior Rating Inventory of Executive Function, BRIEF) using linear regression analyses. A p-value of <0.05 was considered significant.

RESULTS

One hundred thirty-seven of 300 subjects were neurodevelopmentally normal prior to PICU admission. We obtained follow-up data from 36 subjects for the CBCL, 32 subjects for the ABAS-II, and 20 subjects for the BRIEF. The median duration from admission to follow-up was 2.6 years (IQR: 1.2-3.8). There were no differences in the acute care variables (age, sex, mental status category, intubation status, paralysis status, acute neurologic diagnosis category, seizure category, EEG background category, or short-term outcome) between subjects with and without follow-up data for any of the outcome measures. On univariate analysis, significant differences were not identified for CBCL total problem (ES coefficient: -4.1, p = 0.48; ESE coefficient: 8.9, p = 0.13) or BRIEF global executive function (ES coefficient: 2.1, p = 0.78; ESE coefficient: 14.1, p = 0.06) scores, although there were trends toward worse scores in subjects with ESE. On univariate analysis, ESs were not associated with worse scores (coefficient: -21.5, p = 0.051), while ESE (coefficient: -29.7, p = 0.013) was associated with worse ABAS-II adaptive behavioral global composite scores. On multivariate analysis, when compared to subjects with no seizures, both ESs (coefficient: -28, p=0.014) and ESE (coefficient: -36, p = 0.003) were associated with worse adaptive behavioral global composite scores.

DISCUSSION

Among previously neurodevelopmentally normal children with acute neurologic disorders, ESs and ESE were associated with worse adaptive behavior and trends toward worse behavioral-emotional and executive function problems. This was a small exploratory study, and the impact of ESs and ESE on these neurobehavioral measures may be clarified by subsequent larger studies. This article is part of a Special Issue entitled "Status Epilepticus".