Development and validation of a seizure prediction model in critically ill children. (February 2015)

• Record Type: Journal Article
• Authors: 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.; Abend, Nicholas S.
• Is Part Of: Seizure. Volume 25(2015)
• Page Start: 104
• Page End: 111
• Publication Date: 2015-02
• Subjects: EEG monitoring -- Seizure -- Status epilepticus -- Pediatric -- Non-convulsive seizure -- Prediction model; Epilepsy -- Periodicals; Epilepsy -- Periodicals; Seizures -- Periodicals; Épilepsie -- Périodiques; Electronic journals; Electronic journals; 616.853
• DOI: 10.1016/j.seizure.2014.09.013
• Languages: English
• ISSNs: 1059-1311

Abstract

Highlights: Aimed to predict electrographic seizures in critically ill children. We developed and validated an electrographic seizure prediction model. Model has fair to good discrimination ability and overall performance. At the optimal cut-off point, sensitivity of 59% and a specificity of 81%. Varied cut-off points could optimize sensitivity or specificity. 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).