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.

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.

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.

Video Ambulatory EEG in Children: A Quality Improvement Study.

Purpose: We implemented a video ambulatory EEG (VA-EEG) Program as an alternative to inpatient video EEG monitoring for some patients given potential benefits related to quicker access, greater convenience, and lower cost. To evaluate the newly initiated program, we performed a quality improvement study to assess whether VA-EEG yielded studies with interpretable EEG and video quality that generated clinically beneficial data.

Methods: This was a single-center prospective quality improvement study.

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.

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.

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.

Collodion Remover Can Degrade Plastic-Containing Medical Devices Commonly Used in the Intensive Care Unit.

Collodion remover, a solvent blend used to remove collodion glue after long-term video EEG monitoring, was implicated as a potential causative factor in patient safety events at our institution during which damage to plastic components of medical devices was noted in the intensive care unit. We sought to determine experimentally whether collodion remover could lead to degradation of multiple plastic-containing medical devices commonly used in the intensive care unit to determine whether workflow changes were needed during electrode removal. We exposed devices to collodion remover for brief, intermediate, and prolonged durations.