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(Abst. 3.148), 2017

Usage of EpiFinder Clinical Decision Support in the Assessment of Epilepsy
Authors: Erin Okazaki, Mayo Clinic Arizona; Joseph Sirven, Mayo Clinic Arizona; Amy Crepeau, Mayo Clinic Arizona; Katherine Noe, Mayo Clinic Arizona; Joseph Drazkowski, Mayo Clinic Arizona; Matthew Hoerth, Mayo Clinic Arizona; Robert Yao, EpiFinder Inc; Edgar Salinas, EpiFinder Inc; Lidia Csernak, EpiFinder Inc; and Neel Mehta, EpiFinder Inc
Content: Rationale: The diagnosis of epilepsy is at times elusive for both neurologists and non-neurologists, often resulting in delays of diagnosis and therapy. To address this diagnostic gap, we tested a novel clinical decision support tool, EpiFinder.  This application is designed specifically to take key words from a patient’s history and incorporate them into a heuristic algorithm that dynamically produces a list of differential diagnoses.  We evaluated the utility and accuracy of EpiFinder in providing an accurate diagnosis in an adult cohort. Methods: Patients were prospectively identified upon admission to the epilepsy monitoring unit (EMU) at Mayo Clinic Arizona. A series of 50 consecutive adults admitted for spell classification consented participation and were asked to describe their events in detail.  The terms and phrases used by the patients were input into the EpiFinder application. If the algorithm predicted epilepsy, it generated a relevant differential of epilepsy syndromes.  If the algorithm did not identify an epilepsy syndrome based on the entered descriptors, it would indicate that the information provided was insufficient.  The EpiFinder-generated results were then compared to the diagnosis that resulted from the patient’s EMU stay.  Results were considered positive if the EpiFinder application produced a differential diagnosis consistent with a focal or generalized epilepsy syndrome. Results: A total of 50 patients admitted to the EMU for scalp EEG monitoring over a five month period from January through May 2017 were included. During that time, 26 patients were diagnosed with a seizure disorder, 22 patients were given a diagnosis of non-epileptic spells, and two patients were found to have a parasomnia.  The algorithm correctly predicted a diagnosis of epilepsy versus non-seizure etiology in 43 patients or 86% of the time with sensitivity of 89.5% (95% CI 77.3-96.5) and specificity of 87.5% (95% CI 67.6-97.3).   Conclusions: EpiFinder is a clinical decision support tool that can be used by providers to help identify epilepsy in the adult population. Though further testing by non-epilepsy trained providers will be required, this promising resource may accurately streamline an epilepsy diagnosis.   Funding: No funding was received in support of this abstract.