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Validation of a Model for Targeted EEG Monitoring Duration in Critically Ill Children
France W Fung1,2, Jiaxin Fan3, Darshana S Parikh1
1Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Insights
A new model optimizes continuous electroencephalography (cEEG) duration for critically ill children, reducing monitoring time while effectively identifying electrographic seizures (ES). This data-driven approach enhances resource allocation in pediatric intensive care units.
Area of Science:
- Pediatric Neurology
- Critical Care Medicine
- Neurophysiology
Background:
- Continuous electroencephalography monitoring (CEEG) is crucial for detecting electrographic seizures (ES) in critically ill children.
- However, CEEG is resource-intensive, necessitating optimized implementation strategies.
Purpose of the Study:
- To validate a predictive model for optimal CEEG duration in critically ill children with encephalopathy.
- To determine CEEG durations associated with a low risk (<10%) of undetected ES.
Main Methods:
- Prospective observational study of 1,399 critically ill children with encephalopathy.
- Validation of a multistate survival model using clinical and emergent EEG risk factors.
- Assessed CEEG duration thresholds for <15%, <10%, <5%, and <2% risk of ES.
Main Results:
- The model accurately predicted ES risk in both generation (N=719) and validation (N=680) cohorts.
- For patients without emergent EEG risk factors, 7 hours of CEEG was sufficient.
- For patients with emergent EEG risk factors, CEEG duration varied (44 and 36 hours).
- The model offered significant reductions in CEEG hours compared to standard guidelines (28-64%).
Conclusions:
- A data-driven model can personalize CEEG duration based on clinical and EEG variables.
- This strategy effectively identifies ES in critically ill children.
- Optimized CEEG duration improves resource utilization in pediatric intensive care.
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.
