Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures.
Publication/Presentation Date
4-1-2024
Abstract
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. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed.
RESULTS: When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model.
CONCLUSIONS: Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.
Volume
117
First Page
244
Last Page
252
ISSN
1532-2688
Published In/Presented At
Coleman, K., Fung, F. W., Topjian, A., Abend, N. S., & Xiao, R. (2024). Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures. Seizure, 117, 244–252. https://doi.org/10.1016/j.seizure.2024.03.008
Disciplines
Medicine and Health Sciences | Pediatrics
PubMedID
38522169
Department(s)
Department of Pediatrics
Document Type
Article