Related Experiment Video
Updated: Aug 7, 2025
Author Spotlight: Assessing the Feasibility of Using Amplitude-Integrated EEG During Neonatal Transport
Published on: June 21, 2024
EEG Monitoring in Critically Ill Children: Establishing High-Yield Subgroups
France W Fung1,2, Darshana S Parikh1, Maureen Donnelly3
1Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A.
Insights
Stratifying critically ill children by electrographic seizure (ES) risk factors can optimize continuous EEG monitoring (CEEG) use. This approach identifies high-yield patient groups, potentially reducing resource intensity.
Area of Science:
- Pediatric Neurology
- Critical Care Medicine
- Neurophysiology
Background:
- Continuous EEG monitoring (CEEG) is vital for detecting electrographic seizures (ES) in critically ill children.
- However, CEEG is resource-intensive, necessitating efficient utilization strategies.
Purpose of the Study:
- To evaluate the impact of stratifying patients by known ES risk factors on CEEG utilization.
- To determine if risk factor stratification can optimize resource allocation for CEEG.
Main Methods:
- A prospective observational study involving critically ill children with encephalopathy undergoing CEEG.
- Calculation of average CEEG duration needed to identify ES in the overall cohort and stratified subgroups based on risk factors.
Main Results:
- ES occurred in 25% of 1,399 patients. The full cohort required 90 hours of CEEG to identify 90% of patients with ES.
- Stratification by age, pre-CEEG clinical seizures, and early EEG risk factors revealed significant variations in CEEG duration needed (20 to 1,046 hours).
- Patients with clinical seizures and early EEG risk factors required substantially less CEEG (20-22 hours) compared to those without (405-1,046 hours).
Conclusions:
- Patient stratification by clinical and EEG risk factors effectively identifies high- and low-yield subgroups for CEEG.
- This approach aids in optimizing CEEG resource allocation by considering ES incidence, duration, and subgroup size.
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.
