Annual Meeting Abstracts: View

  • (Abst. 1.182), 2014
  • Authors: Amy Yang, Daniel Arndt, Robert Berg, Jessica Carpenter, Kevin Chapman, Dennis Dlugos, William Gallentine, Christopher Giza, Joshua Goldstein, Cecil Hahn, Jason Lerner, Tobias Loddenkemper, Joyce Matsumoto, Kendall Nash, Eric Payne, Iván Sánchez Fernández, Justine Shults, Alexis Topjian, Korwyn Williams, Courtney Wusthoff and Nicholas Abend
  • Content:

    Rationale: Electrographic seizures are common in encephalopathic critically ill children, even without a pre-existing diagnosis of epilepsy, but identification requires resource intense 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. Methods: We developed a seizure prediction model using a retrospective multicenter database of children with acute encephalopathy without an epilepsy diagnosis, who underwent clinically indicated CEEG.  We performed model validation using a separate single center prospective 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 specificity of 81%.  Varied cut-off points are described which could be chosen to optimize sensitivity or specificity depending on available CEEG resources. Conclusions: A model developed from multi-center CEEG data can guide the use of limited EEG resources when applied at a single center.  Depending on CEEG resources, centers could choose lower cut-off points to maximize identification of all patients who will experience electrographic seizures (but with lowered CEEG resource efficiency by monitoring more patients) or higher cut-off points to reduce resource utilization by reducing monitoring of lower risk patients (but sacrifice identification of some patients experiencing seizures).  
  • Tables:
  • Table 1
  • Figures:
  • Figure 1