Abstracts

Rationale: Differentiating between patients suffering epileptic seizures (ES) and those suffering psychogenic nonepileptic seizures (PNES) is difficult and represents an unmet medical need. Given the shortcoming of diagnosing ES, Cognizance Biomarkers has developed a novel diagnostic technology that allows physicians to differentiate ES and PNES events. The test is based on determining protein concentrations derived from patient blood samples, collected within 24 hours of an event, and using a diagnostic algorithm that generates a seizure probability score. Here we demonstrate that co-occurring disease or NSAID use does not influence the classification efficiency of this test. Methods: Plasma was isolated from centrifuged whole blood samples collected from 31 ES patients and 25 PNES patients admitted to an epilepsy monitoring unit (EMU) for either surgical evaluation or definitive diagnosis. All patients had a video EEG confirmed diagnosis that correlated the presence or absence of inter-ictal epileptiform discharges with clinical events. All EMU patients gave a sample (up to 10ml) of blood each morning during their EMU stay and an additional blood sample within 24 hours of a clinical event captured by video-EEG. Fifty-one different proteins were assayed across six multiplex ELISA panels and quantitated using Meso Scale Discovery Platform. Patient medical records were thoroughly reviewed to identify co-occurring disease and NSAID use. The co-occurring indications evaluated include Crohn’s Disease, Encephalitis, Sturge-Weber Syndrome, Celiac Disease, Type 2 Diabetes, Migraine, Rheumatoid Arthritis and unspecified autoimmune disease. NSAIDs considered include diclofenac, celecoxib, ketorolac, acetylsalicylic acid, naproxen, ibuprofen and meloxicam. Descriptive statistics and logistical regression techniques were applied to evaluate the influence of the co-occurring indications and NSAIDs. Results: The statistical analysis was designed to evaluate the influence of co-occurring inflammation associated disease on the performance of a diagnostic test that differentiates ES from PNES. Fifty-six patients were evaluated that suffered from either ES or PNES as diagnosed in the EMU. Of the 56 patients, 21 patients either suffered from a co-occurring inflammation disease or were currently taking NSAIDs, where 5 patients both had a co-occurring indication and were taking NSAIDS, 8 patients had a co-occurring indication and 8 patients were currently taking NSAIDs. Across all 56 cases, the algorithm correctly classified clinical events as ES or PNES 85% of the time; accuracy did not differ between the 21 patients who were suffering from co-occurring inflammation diseases and/or were taking NSAIDs, and the 35 patients who were not. Further, incorporating the parameters into a logistical regression with the protein concentrations did not contribute to the performance of the algorithm. Conclusions: This discovery of novel combinations of peripherally circulating proteins, TRAIL, ICAM-1, MCP-2, TNF-R1 provide a diagnostic tool with significant clinical utility. This diagnostic algorithm can positively differentiate ES and PNES post hoc with 84% specificity and 92% sensitivity. Co-occurring, inflammation associated disease and NSAID use was assessed for confounding effects on the diagnostic accuracy of test and did not influence the diagnostics performance. These data provide strong preliminary evidence that the diagnostic is specifically detects epileptic seizure and may have generally applicable to all patients independent of immune response status. Funding: This work was supported by grant 1R43NS079029-01A1, 1R43NS110275-01and Evogen, Inc