A Prediction Model to Determine Childhood Epilepsy After 1 or More Paroxysmal Events

Abstract

OBJECTIVES: The clinical profile of children who had possible seizures is heterogeneous, and accuracy of diagnostic testing is limited. We aimed to develop and validate a prediction model that determines the risk of childhood epilepsy by combining available information at first consultation. METHODS: We retrospectively collected data of 451 children who visited our outpatient department for diagnostic workup related to 1 or more paroxysmal event(s). At least 1 year of follow-up was available for all children who were diagnosed with epilepsy or in whom diagnosis remained inconclusive. Clinical characteristics (sex, age of first seizure, event description, medical history) and EEG report were used as predictor variables for building a multivariate logistic regression model. Performance was validated in an external cohort (n = 187). RESULTS: Model discrimination was excellent, with an area under the receiver operating characteristic curve of 0.86 (95% confidence interval [CI]; 0.80–0.92), a positive predictive value of 0.93 (95% CI 0.83–0.97) and a negative predictive value of 0.76 (95% CI 0.70–0.80). Model discrimination in a selective subpopulation of children with uncertain diagnosis after initial clinical workup was good, with an area under the receiver operating characteristic curve of 0.73 (95% CI 0.58–0.87). CONCLUSIONS: This model may prove to be valuable because predictor variables together with a first interictal EEG can be available at first consultation. A Web application is provided (http://epilepsypredictiontools.info/first-consultation) to facilitate the diagnostic process for clinicians who are confronted with children with paroxysmal events, suspected of having an epileptic origin.