• Amy Yang, Daniel H Arndt, Robert A Berg, Jessica L Carpenter, Kevin E Chapman, Dennis J Dlugos, William B Gallentine, Christopher C Giza, Joshua L Goldstein, Cecil D Hahn, Jason T Lerner, Tobias Loddenkemper, Joyce H Matsumoto, Kendall B Nash, Eric T Payne, Iván Sánchez Fernández, Justine Shults, Alexis A Topjian, Korwyn Williams, Courtney J Wusthoff, and Nicholas S Abend.
• Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at The University of Pennsylvania, United States.
• Seizure. 2015 Feb 1;25:104-11.

PurposeElectrographic 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.MethodWe 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.ResultsThe 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.ConclusionDespite 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).Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

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