학술논문
Development and validation of a seizure prediction model in critically ill children.
Document Type
Academic Journal
Author
Yang, Amy; Arndt, Daniel H; Berg, Robert A; Carpenter, Jessica L; Chapman, Kevin E; Dlugos, Dennis J; Gallentine, William B; Giza, Christopher C; Goldstein, Joshua L; Hahn, Cecil D; Lerner, Jason T; Loddenkemper, Tobias; Matsumoto, Joyce H; Nash, Kendall B; Payne, Eric T; Sánchez Fernández, Iván; Shults, Justine; Topjian, Alexis A; Williams, Korwyn; Wusthoff, Courtney J; et al
Source
Subject
Language
English
ISSN
1059-1311
Abstract
Purpose: Electrographic seizures are common in encephalopathic critically ill children, but identification requires continuous EEG monitoring (CEEG). Development of a seizure prediction model would enable more efficient use of limited CEEG resources. We aimed to develop and validate a seizure prediction model for use among encephalopathic critically ill children.Method: We developed a seizure prediction model using a retrospectively acquired multi-center database of children with acute encephalopathy without an epilepsy diagnosis, who underwent clinically indicated CEEG. We performed model validation using a separate prospectively acquired single center database. Predictor variables were chosen to be readily available to clinicians prior to the onset of CEEG and included: age, etiology category, clinical seizures prior to CEEG, initial EEG background category, and inter-ictal discharge category.Results: The model has fair to good discrimination ability and overall performance. At the optimal cut-off point in the validation dataset, the model has a sensitivity of 59% and a specificity of 81%. Varied cut-off points could be chosen to optimize sensitivity or specificity depending on available CEEG resources.Conclusion: Despite inherent variability between centers, a model developed using multi-center CEEG data and few readily available variables could guide the use of limited CEEG resources when applied at a single center. Depending on CEEG resources, centers could choose lower cut-off points to maximize identification of all patients with seizures (but with more patients monitored) or higher cut-off points to reduce resource utilization by reducing monitoring of lower risk patients (but with failure to identify some patients with seizures).