Development of a model to predict electroencephalographic seizures in neonates with hypoxic ischemic encephalopathy treated with therapeutic hypothermia.

Objective: Electroencephalographic seizures (ES) are common in neonates with hypoxic-ischemic encephalopathy (HIE), but identification with continuous electroencephalographic (EEG) monitoring (CEEG) is resource-intensive. We aimed to develop an ES prediction model.

Methods: Using a prospective observational study of 260 neonates with HIE undergoing CEEG, we identified clinical and EEG risk factors for ES, evaluated model performance with area under the receiver operating characteristic curve (AUROC), and calculated test characteristics emphasizing high sensitivity.

Education Research: Quality and Validity Evidence for a National In-Training Examination for Epilepsy Fellows.

Background And Objectives: Epilepsy education has been transformed over the past 2 decades, leading to a need for structured formative assessment tools. The American Epilepsy Society developed the Epilepsy Fellowship In-Training Examination (EpiFITE) to provide high-quality formative assessment for fellows, to stimulate program improvement, and to guide future learning and teaching. The aim of this study was to explore validity evidence for the EpiFITE in meeting these goals.

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.

Diagnostic Yield of CSF Testing in Infants for Disorders of Biogenic Amine Neurotransmitter Metabolism.

Background And Objectives: Biochemical testing of CSF for neurotransmitter metabolites and their cofactors is often used in the diagnostic evaluation of infants with neurologic disorders but requires an invasive, labor-intensive procedure with many potential sources of error. Our aim was to determine the diagnostic yield of CSF testing for biogenic amines (serotonin, norepinephrine, epinephrine, and dopamine) and their cofactors in identifying inborn errors of neurotransmitter metabolism among infants.

Methods: We evaluated all infants aged 1 year or younger who underwent CSF biogenic amine neurotransmitter (CSFNT) testing at Children's Hospital of Philadelphia (CHOP) and Boston Children's Hospital (BCH) between 2008 and 2017 in this cross-sectional study.

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.

Association of EEG Background With Diffusion-Weighted Magnetic Resonance Neuroimaging and Short-Term Outcomes After Pediatric Cardiac Arrest.

Background And Objectives: EEG and MRI features are independently associated with pediatric cardiac arrest (CA) outcomes, but it is unclear whether their combination improves outcome prediction. We aimed to assess the association of early EEG background category with MRI ischemia after pediatric CA and determine whether addition of MRI ischemia to EEG background features and clinical variables improves short-term outcome prediction.

Methods: This was a single-center retrospective cohort study of pediatric CA with EEG initiated ≤24 hours and MRI obtained ≤7 days of return of spontaneous circulation.

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.

Periodic Discharges in Critically Ill Children: Predictors and Outcome.

Objectives: We aimed to identify clinical and EEG monitoring characteristics associated with generalized, lateralized, and bilateral-independent periodic discharges (GPDs, LPDs, and BIPDs) and to determine which patterns were associated with outcomes in critically ill children.

Methods: We performed a prospective observational study of consecutive critically ill children undergoing continuous EEG monitoring, including standardized scoring of GPDs, LPDs, and BIPDs. We identified variables associated with GPDs, LPDs, and BIPDs and assessed whether each pattern was associated with hospital discharge outcomes including the Glasgow Outcome Scale-Extended Pediatric version (GOS-E-Peds), Pediatric Cerebral Performance Category (PCPC), and mortality.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

Expanding Access to Continuous EEG Monitoring in Neonatal Intensive Care Units.

Purpose: Neonatal seizures are common and difficult to identify clinically because the majority are subclinical and correct identification of electroclinical seizures based on semiology is unreliable. Therefore, continuous EEG monitoring (CEEG) is critical for seizure identification in neonates and is recommended as the gold standard method in American Clinical Neurophysiology Society guidelines. Despite these recommendations, barriers to implementing widespread CEEG exist.

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.

Acceptability of Standardized EEG Reporting in an Electronic Health Record.

Rationale: Implementation of electronic health records may improve the quality, accuracy, timeliness, and availability of documentation. Thus, our institution developed a system that integrated EEG ordering, scheduling, standardized reporting, and billing. Given the importance of user perceptions for successful implementation, we performed a quality improvement study to evaluate electroencephalographer satisfaction with the new EEG report system.

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.

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.

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring.

Purpose: Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers.

High electroencephalographic seizure exposure is associated with unfavorable outcomes in neonates with hypoxic-ischemic encephalopathy.

Purpose: Electroencephalographic seizures (ES) are common among neonates with hypoxic-ischemic encephalopathy (HIE), and they represent a treatable complication that might improve neurodevelopmental outcomes. We aimed to establish whether higher ES exposure was predictive of unfavorable outcomes while adjusting for other important clinical and electroencephalographic parameters.

Methods: We performed a single-center, retrospective study of consecutive neonates with HIE managed with therapeutic hypothermia from June 2010 through December 2016.