Abstracts

Rationale: Psychogenic non-epileptic seizures (PNES) are challenging to differentiate from epileptic seizures (ES) based on the information available at an outpatient clinical visit because, to patients and untrained observers, the seizures themselves are similar. We evaluated the potential diagnostic value of over 100 factors to develop a comprehensive, evidence-based tool to identify patients that may benefit from early triage to more extensive diagnostic assessment. Early diagnosis of PNES may improve long-term seizure control, quality of life and healthcare utilization, thereby substantially reducing both indirect and direct costs. Methods: Based on data from 1,118 patients with video-electroencephalography confirmed diagnoses, we used information that would be available at a single outpatient neurology visit to compare patients with ES and PNES. We used data-driven methods to determine the minimum set of information that yielded the maximum diagnostic accuracy in identifying patients more likely to have psychogenic nonepileptic seizures. We validated the evidence-based tool using prospective standardized interviews with over 500 patients. Results: A piecewise-linear logistic regression model built with recursive feature elimination suggested 21 factors provided conditionally independent diagnostic information. Factors associated with PNES were: ictal eye closure, ictal hip thrusting, sexual abuse history, concussion, ictal hallucinations, asthma, psychological stressors, migraines, female sex, more seizure types, longer seizure duration, higher seizure frequency, number of medical comorbidities and medications for those comorbidities. Factors associated with epilepsy were: simple automatisms, head injury, trigger of sleep deprivation, tonic-clonic movements, number of current and past antiseizure medications. This algorithm had an overall AUC of 82%, with a PNES-predictive value of 63% and epilepsy-predictive value of 88%, as well a sensitivity for epilepsy of 82% and specificity for PNES of 73%. Conclusions: Objective combination of factors reported in the clinical history can identify patients with probable psychogenic nonepileptic seizures the likely would benefit from early video-EEG monitoring. Due to the heterogeneity of patients with PNES, all aspects of the history contributed including comorbidities, historical events and the ictal behavior. Funding: UCLA-California Institute of Technology Medical Scientist Training Program (NIH T32 GM08042), the Neuroimaging Training Program (NIH T90 DA022768, R90 DA022768 & R90 DA023422 to MSC), the William M. Keck Foundation, research grants to JE (NS03310 & NS080181)