Development and validation of a seizure prediction model in critically ill children | Lurie Children's

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Development and validation of a seizure prediction model in critically ill children

Yang, A.; Arndt, D. H.; Berg, R. A.; Carpenter, J. L.; Chapman, K. E.; Dlugos, D. J.; Gallentine, W. B.; Giza, C. C.; Goldstein, J. L.; Hahn, C. D.; Lerner, J. T.; Loddenkemper, T.; Matsumoto, J. H.; Nash, K. B.; Payne, E. T.; Sanchez Fernandez, I.; Shults, J.; Topjian, A. A.; Williams, K.; Wusthoff, C. J.; Abend, N. S.

Seizure. 2014 Dec 3; 25:104-11


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).

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