Electroencephalographic monitoring during hypothermia after pediatric cardiac arrest

Postcardiac Arrest Care Delivery in Pediatric Intensive Care Units: A Plan and Call to Action

Background

Despite national pediatric postcardiac arrest care (PCAC) guidelines to improve neurological outcomes and survival, there are limited studies describing PCAC delivery in pediatric institutions. This study aimed to describe PCAC delivery in centers belonging to a resuscitation quality collaborative.

Methods

An institutional review board-approved REDCap survey was distributed electronically to the lead resuscitation investigator at each institution in the international Pediatric Resuscitation Quality Improvement Collaborative. Data were summarized using descriptive statistics. A chi-square test was used to compare categorical data.

Results

Twenty-four of 47 centers (51%) completed the survey. Most respondents (58%) belonged to large centers (>1,000 annual pediatric intensive care unit admissions). Sixty-seven percent of centers reported no specific process to initiate PCAC with the other third employing order sets, paper forms, or institutional guidelines. Common PCAC targets included temperature (96%), age-based blood pressure (88%), and glucose (75%). Most PCAC included electroencephalogram (75%), but neuroimaging was only included at 46% of centers. Duration of PCAC was either tailored to clinical improvement and neurological examination (54%) or time-based (45%). Only 25% of centers reported having a mechanism for evaluating PCAC adherence. Common barriers to effective PCAC implementation included lack of time and limited training opportunities.

Conclusions

There is wide variation in PCAC delivery among surveyed pediatric institutions despite national guidelines to standardize and implement PCAC.

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.

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.

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.

The neurophysiological effect of mild hypothermia in gyrencephalic brains submitted to ischemic stroke and spreading depolarizations

Objective

Characterize the neurophysiological effects of mild hypothermia on stroke and spreading depolarizations (SDs) in gyrencephalic brains.

Methods

Left middle cerebral arteries (MCAs) of six hypothermic and six normothermic pigs were permanently occluded (MCAo). Hypothermia began 1 h after MCAo and continued throughout the experiment. ECoG signals from both frontoparietal cortices were recorded. Five-minute ECoG epochs were collected 5 min before, at 5 min, 4, 8, 12, and 16 h after MCAo, and before, during, and after SDs. Power spectra were decomposed into fast (alpha, beta, and gamma) and slow (delta and theta) frequency bands.

Results

In the vascular insulted hemisphere under normothermia, electrodes near the ischemic core exhibited power decay across all frequency bands at 5 min and the 4th hour after MCAo. The same pattern was registered in the two furthest electrodes at the 12th and 16th hour. When mild hypothermia was applied in the vascular insulted hemispheres, the power decay was generalized and seen even in electrodes with uncompromised blood flow. During SD analysis, hypothermia maintained increased delta and beta power during the three phases of SDs in the furthest electrode from the ischemic core, followed by the second furthest and third electrode in the beta band during preSD and postSD segments. However, in hypothermic conditions, the third electrode showed lower delta, theta, and alpha power.

Conclusion

Mild hypothermia attenuates all frequency bands in the vascularly compromised hemisphere, irrespective of the cortical location. During SD formation, it preserves power spectra more significantly in electrodes further from the ischemic core.

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.

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.

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.

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.

Neuromonitoring in Children with Cerebrovascular Disorders

BACKGROUND

Cerebrovascular disorders are an important cause of morbidity and mortality in children. The acute care of a child with an ischemic or hemorrhagic stroke or cerebral sinus venous thrombosis focuses on stabilizing the patient, determining the cause of the insult, and preventing secondary injury. Here, we review the use of both invasive and noninvasive neuromonitoring modalities in the care of pediatric patients with arterial ischemic stroke, nontraumatic intracranial hemorrhage, and cerebral sinus venous thrombosis.

METHODS

Narrative review of the literature on neuromonitoring in children with cerebrovascular disorders.

RESULTS

Neuroimaging, near-infrared spectroscopy, transcranial Doppler ultrasonography, continuous and quantitative electroencephalography, invasive intracranial pressure monitoring, and multimodal neuromonitoring may augment the acute care of children with cerebrovascular disorders. Neuromonitoring can play an essential role in the early identification of evolving injury in the aftermath of arterial ischemic stroke, intracranial hemorrhage, or sinus venous thrombosis, including recurrent infarction or infarct expansion, new or recurrent hemorrhage, vasospasm and delayed cerebral ischemia, status epilepticus, and intracranial hypertension, among others, and this, is turn, can facilitate real-time adjustments to treatment plans.

CONCLUSIONS

Our understanding of pediatric cerebrovascular disorders has increased dramatically over the past several years, in part due to advances in the neuromonitoring modalities that allow us to better understand these conditions. We are now poised, as a field, to take advantage of advances in neuromonitoring capabilities to determine how best to manage and treat acute cerebrovascular disorders in children.

To Treat or Not to Treat: Ethics of Management of Refractory Status Myoclonus Following Pediatric Anoxic Brain Injury

The development of status myoclonus (SM) in a postcardiac arrest patient has historically been thought of as indicative of not only a poor neurologic outcome but of neurologic devastation. In many instances, this may lead clinicians to initiate conversations about withdrawal of life sustaining therapies (WLST) regardless of the time from return of spontaneous circulation (ROSC). Recent studies showing a percentage of patients may make a good recovery has called into question whether a self-fulfilling prophecy has developed where the concern for a poor neurologic outcome leads clinicians to prematurely discuss WLST. The issue is only further complicated by changing terminology, lack of neuro-axis localization, and limited data regarding association with electroencephalogram (EEG) characteristics, all of which could aid in the understanding of the severity of neurologic injury associated with SM. Here we review the initial literature reporting SM as indicative of poor neurologic outcome, the studies that call this into question, the various definitions of SM and related terms as well as data regarding association with EEG backgrounds. We propose that improved prognostication on outcomes results from combining the presence of SM with other clinical variables (eg EEG patterns, MRI findings, and clinical exam). We discuss the ethical implications of using SM as a prognostic tool and its impact on decisions about life-sustaining care in children following cardiac arrest. We advocate for prognostication efforts to be delayed for at least 72 hours following ROSC and thus to treat SM in those early hours and days.

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.

Multimodal Neurologic Monitoring in Children With Acute Brain Injury

Children with acute neurologic illness are at high risk of mortality and long-term neurologic disability. Severe traumatic brain injury, cardiac arrest, stroke, and central nervous system infection are often complicated by cerebral hypoxia, hypoperfusion, and edema, leading to secondary neurologic injury and worse outcome. Owing to the paucity of targeted neuroprotective therapies for these conditions, management emphasizes close physiologic monitoring and supportive care. In this review, we will discuss advanced neurologic monitoring strategies in pediatric acute neurologic illness, emphasizing the physiologic concepts underlying each tool. We will also highlight recent innovations including novel monitoring modalities, and the application of neurologic monitoring in critically ill patients at risk of developing neurologic sequelae.

Multimodal monitoring including early EEG improves stratification of brain injury severity after pediatric cardiac arrest

AIMS

Assessment of brain injury severity early after cardiac arrest (CA) may guide therapeutic interventions and help clinicians counsel families regarding neurologic prognosis. We aimed to determine whether adding EEG features to predictive models including clinical variables and examination signs increased the accuracy of short-term neurobehavioral outcome prediction.

METHODS

This was a prospective, observational, single-center study of consecutive infants and children resuscitated from CA. Standardized EEG scoring was performed by an electroencephalographer for the initial EEG timepoint after return of spontaneous circulation (ROSC) and each 12-h segment from the time of ROSC up to 48 h. EEG Background Category was scored as: (1) normal; (2) slow-disorganized; (3) discontinuous or burst-suppression; or (4) attenuated-featureless. The primary outcome was neurobehavioral outcome at discharge from the Pediatric Intensive Care Unit. To develop the final predictive model, we compared areas under the receiver operating characteristic curves (AUROC) from models with varying combinations of Demographic/Arrest Variables, Examination Signs, and EEG Features.

RESULTS

We evaluated 89 infants and children. Initial EEG Background Category was normal in 9 subjects (10%), slow-disorganized in 44 (49%), discontinuous or burst suppression in 22 (25%), and attenuated-featureless in 14 (16%). The final model included Demographic/Arrest Variables (witnessed status, doses of epinephrine, initial lactate after ROSC) and EEG Background Category which achieved AUROC of 0.9 for unfavorable neurobehavioral outcome and 0.83 for mortality.

CONCLUSIONS

The addition of standardized EEG Background Categories to readily available CA variables significantly improved early stratification of brain injury severity after pediatric CA.

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.

aEEG as a useful tool for neuromonitoring in critically ill children - Current evidence and knowledge gaps

AIM

Amplitude-integrated electroencephalography (aEEG) is used in children beyond neonatal age, but systematic investigations have been lacking. This mini-review summarised aEEG studies on children aged one month to 18 years, evaluated the usefulness of aEEG and identified knowledge gaps or limitations.

METHODS

We searched the PubMed database for articles published in English up to September 2020, and 23 papers were identified.

RESULTS

aEEG was frequently used to compensate for the absence of continuous full-channel EEG monitoring, particularly for detecting seizures. Interpreting background patterns was based on neonatal classifications, as reference values for older infants and children are lacking. It is possible that aEEG could predict outcomes after paediatric cardiac arrests and other conditions. Gaps in our knowledge exist with regard to normal values in healthy children and the effects of sedation on aEEG background patterns in children.

CONCLUSION

The main application of aEEG was detecting and treating paediatric seizures. Further research should determine reference values and investigate the potential to predict outcome after critical events or in acute neurological disease. It is likely that aEEG will play a role in paediatric critical care in the future.

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.

Pediatric In-Hospital Cardiac Arrest and Cardiopulmonary Resuscitation in the United States: A Review

IMPORTANCE

Pediatric in-hospital cardiac arrest (IHCA) occurs frequently and is associated with high morbidity and mortality. The objective of this narrative review is to summarize the current knowledge and recommendations regarding pediatric IHCA and cardiopulmonary resuscitation (CPR).

OBSERVATIONS

Each year, more than 15 000 children receive CPR for cardiac arrest during hospitalization in the United States. As many as 80% to 90% survive the event, but most patients do not survive to hospital discharge. Most IHCAs occur in intensive care units and other monitored settings and are associated with respiratory failure or shock. Bradycardia with poor perfusion is the initial rhythm in half of CPR events, and only about 10% of events have an initial shockable rhythm. Pre-cardiac arrest systems focus on identifying at-risk patients and ensuring that they are in monitored settings. Important components of CPR include high-quality chest compressions, timely defibrillation when indicated, appropriate ventilation and airway management, administration of epinephrine to increase coronary perfusion pressure, and treatment of the underlying cause of cardiac arrest. Extracorporeal CPR and measurement of physiological parameters are evolving areas in improving outcomes. Structured post-cardiac arrest care focused on targeted temperature management, optimization of hemodynamics, and careful intensive care unit management is associated with improved survival and neurological outcomes.

CONCLUSIONS AND RELEVANCE

Pediatric IHCA occurs frequently and has a high mortality rate. Early identification of risk, prevention, delivery of high-quality CPR, and post-cardiac arrest care can maximize the chances of achieving favorable outcomes. More research in this field is warranted.

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.

A Systematic Review of Neuromonitoring Modalities in Children Beyond Neonatal Period After Cardiac Arrest

OBJECTIVES

Postresuscitation care in children focuses on preventing secondary neurologic injury and attempts to provide (precise) prognostication for both caregivers and the medical team. This systematic review provides an overview of neuromonitoring modalities and their potential role in neuroprognostication in postcardiac arrest children.

DATA RESOURCES

Databases EMBASE, Web of Science, Cochrane, MEDLINE Ovid, Google Scholar, and PsycINFO Ovid were searched in February 2019.

STUDY SELECTION

Enrollment of children after in- and out-of-hospital cardiac arrest between 1 month and 18 years and presence of a neuromonitoring method obtained within the first 2 weeks post cardiac arrest. Two reviewers independently selected appropriate studies based on the citations.

DATA EXTRACTION

Data collected included study characteristics and methodologic quality, populations enrolled, neuromonitoring modalities, outcome, and limitations. Evidence tables per neuromonitoring method were constructed using a standardized data extraction form. Each included study was graded according to the Oxford Evidence-Based Medicine scoring system.

DATA SYNTHESIS

Of 1,195 citations, 27 studies met the inclusion criteria. There were 16 retrospective studies, nine observational prospective studies, one observational exploratory study, and one pilot randomized controlled trial. Neuromonitoring methods included neurologic examination, routine electroencephalography and continuous electroencephalography, transcranial Doppler, MRI, head CT, plasma biomarkers, somatosensory evoked potentials, and brainstem auditory evoked potential. All evidence was graded 2B-2C.

CONCLUSIONS

The appropriate application and precise interpretation of available modalities still need to be determined in relation to the individual patient. International collaboration in standardized data collection during the (acute) clinical course together with detailed long-term outcome measurements (including functional outcome, neuropsychologic assessment, and health-related quality of life) are the first steps toward more precise, patient-specific neuroprognostication after pediatric cardiac arrest.

Pediatric Cardiac Arrest Resuscitation

Pediatric cardiac arrest is a relatively rare but devastating presentation in infants and children. In contrast to adult patients, in whom a primary cardiac dysrhythmia is the most likely cause of cardiac arrest, pediatric patients experience cardiovascular collapse most frequently after an initial respiratory arrest. Aggressive treatment in the precardiac arrest state should be initiated to prevent deterioration and should focus on support of oxygenation, ventilation, and hemodynamics, regardless of the presumed cause. Unfortunately, outcomes for pediatric cardiac arrest, whether in hospital or out of hospital, continue to be poor.

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.

Epileptiform Discharge and Electrographic Seizures during the Hypothermia Phase as Predictors of Rewarming Seizures in Children after Resuscitation

The aim of this study was to determine the frequency, timing, and predictors of rewarming seizures in a cohort of children undergoing therapeutic hypothermia after resuscitation. We retrospectively reviewed consecutive pediatric patients undergoing therapeutic hypothermia after resuscitation admitted to our pediatric intensive care unit between January 2000 and December 2019. Continuous electroencephalographic monitoring was performed during hypothermia (24 h for cardiac aetiologies and 72 h for asphyxial aetiologies), rewarming (72 h), and then an additional 12 h of normothermia. Thirty comatose children undergoing therapeutic hypothermia after resuscitation were enrolled, of whom 10 (33.3%) had rewarming seizures. Two (20%) of these patients had their first seizure during the rewarming phase. Four (40%) patients had electroclinical seizures, and six (60%) had nonconvulsive seizures. The median time from starting rewarming to the onset of rewarming seizures was 37.3 h (range 6 to 65 h). The patients with interictal epileptiform activity and electrographic seizures during the hypothermia phase were more likely to have rewarming seizures compared to those without interictal epileptiform activity or electrographic seizures (p = 0.019 and 0.019, respectively). Therefore, in high-risk patients, continuous electroencephalographic monitoring for a longer duration may help to detect rewarming seizures and guide clinical management.

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.

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.

aEEG Use in Pediatric Critical Care-An Online Survey

Background: Evidence supporting continuous EEG monitoring in pediatric intensive care is increasing, but continuous full-channel EEG is a scarce resource. Amplitude-integrated EEG (aEEG) monitors are broadly available in children's hospitals due to their use in neonatology and can easily be applied to older patients. Objective: The aim of this survey was to evaluate the use of amplitude-integrated EEG in German and Swiss pediatric intensive care units (PICUs). Design: An online survey was sent to German and Swiss PICUs that were identified via databases provided by the German Pediatric Association (DGKJ) and the Swiss Society of Intensive Care (SGI). The questionnaire contained 18 multiple choice questions including the PICU size and specialization, indications for aEEG use, perceived benefits from aEEG, and data storage. Main results: Forty-three (26%) PICUs filled out the questionnaire. Two thirds of all interviewed PICUs use aEEG in non-neonates. Main indications were neurological complications or disease and altered mental state. Features assessed were mostly seizures and side differences, less frequently height of amplitude and background pattern. Interpretation of raw EEG also played an important role. All interviewees would appreciate the establishment of reference values for toddlers and children. Conclusions: aEEG is used in a large proportion of the interviewed PICUs. The wide-spread use without validation of data generates the need for further evaluation of this technique and the establishment of reference values for non-neonates.

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.

Electroencephalographic Reporting for Refractory Status Epilepticus

PURPOSE

We aimed to determine whether clinical EEG reports obtained from children in the intensive care unit with refractory status epilepticus could provide data for comparative effectiveness research studies.

METHODS

We conducted a retrospective descriptive study to assess the documentation of key variables within clinical continuous EEG monitoring reports based on the American Clinical Neurophysiology Society's standardized EEG terminology for children with refractory status epilepticus from 10 academic centers. Two pediatric electroencephalographers reviewed the EEG reports. We compared reports generated using free text or templates.

RESULTS

We reviewed 191 EEG reports. Agreement between the electroencephalographers regarding whether a variable was described in the report ranged from fair to very good. The presence of electrographic seizures (ES) was documented in 46% (87/191) of reports, and these reports documented the time of first ES in 64% (56/87), ES duration in 72% (63/85), and ES frequency in 68% (59/87). Reactivity was documented in 16% (31/191) of reports, and it was more often documented in template than in free-text reports (40% vs. 14%, P = 0.006). Other variables were not differentially reported in template versus free-text reports.

CONCLUSIONS

Many key EEG features are not documented consistently in clinical continuous EEG monitoring reports, including ES characteristics and reactivity assessment. Standardization may be needed for clinical EEG reports to provide informative data for large multicenter observational studies.

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.

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".

Seizure Prediction Models in the Neonatal Intensive Care Unit

PURPOSE

Conventional video-EEG monitoring is required to diagnose seizures accurately in neonates. This tool is resource-intense and has limited availability in many centers. Seizure prediction models could help allocate resources by improving efficiency in which conventional video-EEG monitoring is used to detect subclinical seizures. The aim of this retrospective study was to create a neonate-specific seizure prediction model using clinical characteristics and EEG background findings.

METHODS

We conducted a 3-year retrospective study of all consecutive neonates who underwent conventional video-EEG monitoring at a tertiary care pediatric hospital. Variables including age, EEG indication, high-risk clinical characteristics, and EEG background informed seizure prediction models based on a multivariable logistic regression model. A Cox proportional hazard regression model was used to construct time to first EEG seizure.

RESULTS

Prediction models with clinical variables or background EEG features alone versus combined clinical and background EEG features were created from 210 neonates who met inclusion criteria. The combined clinical and EEG model had a higher area under the curve for combined sensitivity and specificity to 83.0% when compared to the clinical model (76.4%) or EEG model (66.2%). The same trend of higher sensitivity of the combined model was found for time to seizure outcome.

CONCLUSIONS

While both clinical and EEG background features were predictive of neonatal seizures, the combination improved overall prediction of seizure occurrence and prediction of time to first seizure as compared with prediction models based solely on clinical or EEG features alone. With prospective validation, this model may improve efficiency of patient-oriented EEG 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.

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.

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.

Amplitude-Integrated EEG for Neurological Assessment and Seizure Detection in a German Pediatric Intensive Care Unit

Objective: The aim of our study was to assess the use of aEEG in our pediatric intensive care unit (PICU), indications for neuromonitoring and its findings, utility for seizure detection, and associations with outcome. Design: We retrospectively analyzed non-neonates who were treated in our PICU and received amplitude-integrated EEG (aEEG). Patients: 27 patients aged between 29 days and 10 0/12 years (median 7.3 months) were included, who received a total of 35 aEEGS. Measurements: aEEG tracings were assessed for background (BG) pattern and its evolution, seizures, and side differences using a visual classification (Hellström-Westas). Clinical data were collected from patients' histories and analyzed for correlation with aEEG findings. Main results: While rare in early years, there was an increase in use over time. Most aEEGs were conducted because of (suspected) seizures or for management of antiepileptic treatment. aEEG had low sensitivity but high specificity for recognition of pathological BG pattern with reference to conventional EEG. Worsening of BG pattern or failure to improve was associated with death. Seizure detection rates by aEEG were higher than by clinical observation, especially for identification of non-convulsive epileptic state (ES). Side differences in aEEG were rare, but if present, they were associated with unilateral brain injury. Conclusions: aEEG is useful for the detection of seizures and ES in pediatric intensive care patients. Abnormal BG pattern and poor evolution of BG are negatively associated with survival. aEEG is a potential supplement to conventional EEG, facilitating long-term surveillance of cerebral function when continuous full-channel EEG is not available. Further investigation is needed.

Prognostic value of continuous electroencephalography in children undergoing therapeutic hypothermia after cardiac arrest: A pilot study

OBJECTIVE

To determine the prognostic value of continuous electroencephalography (EEG) in children undergoing therapeutic hypothermia after cardiac arrest.

METHOD

We retrospectively reviewed medical records and continuous EEG of all patients undergoing therapeutic hypothermia after cardiac arrest from November 2013 to September 2016. Demographic, clinical data and immediate complications were collected. Characteristics of continuous EEG including EEG background, time to normal trace (TTNT) and electrographic seizures were reviewed by investigators. Cerebral performance category scales at 6 months' follow up were evaluated and divided into good (grade 1-2) and poor (grade 3-5) outcome groups.

RESULT

Six patients were included (two boys and four girls) with median age of 19.5 months (range13-128 months). Five patients (83.3%) presented with cardiac arrest from near-drowning and one patient with underlying acute lymphocytic leukemia presented an in-hospital cardiac arrest. Initial EKG rhythm was asystole in 3 patients (50%), pulseless activity in 1 patient (16.7%) and initially unknown in 2 patients (33.3%). Two patients (33.3%) who had EEG reactivity and TTNT within 5minutes and 2.5hours had good neurological outcome (CPC1). Four patients (66.7%) with absent EEG reactivity had poor neurological outcome (CPC4, 5 in 3 and 1 children respectively). Three patients from the poor outcome group had electrographic seizures, of whom 2/3 progressed to status epilepticus. Three out of four patients in the poor outcome group had the following complications: pneumonia, bleeding and pancreatitis.

CONCLUSION

Early TTNT and EEG reactivity help to predict good neurological outcome in children undergoing therapeutic hypothermia after cardiac arrest. Seizures and status epilepticus may predict poor neurological outcome.

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.

EEG Factors After Pediatric Cardiac Arrest

PURPOSE

We aimed to determine whether conventional standardized EEG features could be consolidated into a more limited number of factors and whether the derived factor scores changed during the acute period after pediatric cardiac arrest.

METHODS

Children resuscitated after cardiac arrest underwent conventional continuous EEG monitoring. The EEG was scored in 12-hour epochs for up to 72-hours after return of circulation by an electroencephalographer using standardized critical care EEG terminology. We performed a polychoric factor analysis to determine whether numerous observed EEG features could be represented by a smaller number of derived factors. Linear mixed-effects regression models and heat maps evaluated whether the factor scores remained stable across epochs.

RESULTS

We performed EEG monitoring in 89 consecutive children, which yielded 453 EEG segments. We identified two factors, which were not correlated. The background features were factor loaded with the features continuity, voltage, and frequency. The intermittent features were factor loaded with the features of seizures, periodic patterns, and interictal discharges. Factor scores were calculated for each EEG segment. Linear, mixed-effect, regression results indicated that the factor scores did not change over time for the background features factor (coefficient, 0.18; 95% confidence interval, 0.04-0.07; P = 0.52) or the intermittent features factor (coefficient, -0.003; 95% confidence interval, -0.02 to 0.01; P = 0.70). However, heat maps showed that some individual subjects did experience factor score changes over time, particularly if they had medium initial factor scores.

CONCLUSIONS

Subsequent studies assessing whether EEG is informative for neurobehavioral outcomes after pediatric cardiac arrest could combine numerous EEG features into two factors, each reflecting multiple background and intermittent features. Furthermore, the factor scores would be expected to remain stable during the acute period for most subjects.

Continuous EEG in Pediatric Critical Care: Yield and Efficiency of Seizure Detection

PURPOSE

Our goal was to define the duration of continuous EEG (cEEG) monitoring needed to adequately capture electrographic seizures and EEG status epilepticus in the pediatric intensive care unit using clinical and background EEG features.

METHODS

Retrospective study of patients aged 1 month to 21 years admitted to a tertiary pediatric intensive care unit and undergoing cEEG (>3 hours). Clinical data collected included admission diagnosis, EEG background features, and time variables including time to first seizure after initiation of cEEG.

RESULTS

Four hundred fourteen patients aged 4.2 (0.75-11.3) years (median, interquartile range) were included. With a median duration of 21 (16-42.2) hours of cEEG monitoring, we identified electrographic seizure or EEG status epilepticus in 25% of subjects. We identified three features that could improve the efficiency of cEEG resources and provide a decision-making framework: (1) clinical history of acute encephalopathy is not predictive of detecting electrographic seizure or EEG status epilepticus, whereas a history of status epilepticus or seizures is; (2) normal EEG background or absence of epileptiform discharges in the initial 24 hours of recording informs the decision to discontinue cEEG; (3) failure to record electrographic ictal events within the first 4 to 6 hours of monitoring may be sufficient to predict the absence of subsequent ictal events.

CONCLUSIONS

Individualized monitoring plans are necessary to increase seizure detection yield while improving resource utilization. A strategy using information from the clinical history, initial EEG background, and the first 4 to 6 hours of recording may be effective in determining the necessary duration of cEEG monitoring in the pediatric intensive care unit.

Successful Therapeutic Hypothermia in a Propofol-Related Cardiac Arrest Case: A Case Report and Literature Review

Targeted temperature management (therapeutic hypothermia) is a treatment method used to prevent potential complications that can develop in relation to the increased temperature in the brain as a result of cardiac arrest. Due to costs and various health policies there is no comprehensive study in the world that has been able to guide the relevant literature on therapeutic hypothermia. We have presented a 25-year-old female patient in our study who developed cardiac arrest after the administration of propofol for sedation before undergoing a diagnostic upper gastroscopy procedure and received a successful therapeutic hypothermia therapy following a resuscitation of 19 minutes.