Wesley T. Kerr|Emily A. Janio|Chelsea T. Braesch|Justine M. Le|Jessica M. Hori|Akash B. Patel|Norma L. Gallardo|Janar Bauirjan|Shannon D’Ambrosio|Andrea M. Chau|Eric S. Hwang|Emily Davis|Albert Buchard|David Torres?Barba|Mona Al Banna|Sarah E. Barritt|Andrew Y. Cho|Jerome Engel|Mark S. Cohen|John M. Stern
Identifying psychogenic seizures through comorbidities and medication history
Eisenhower Medical Center|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|University of California, Los Angeles|California NanoSystems Institute|University of California, Los Angeles
lowcost evidencebased tools needed facilitate early identification patients possible psychogenic nonepileptic seizures pnes prior accurate diagnosis patients pnes receive interventions address cause seizures therefore incur high medical costs disability due uncontrolled seizure disorder seizures comorbidities may contribute high costbased data 1365 adult patients videoelectroencephalographyconfirmed diagnoses single center used logistic poisson regression compare total number comorbidities number medications presence specific comorbidities five mutually exclusive groups diagnoses epileptic seizures es pnes mixed pnes es physiologic nonepileptic seizurelike events inconclusive monitoring determine diagnostic utility comorbid diagnoses medication history differentiate pnes es used multivariate logistic regression controlling sex age trained using retrospective database validated using prospective databaseour model differentiated pnes es prospective accuracy 78 95 confidence interval 7284 area curve 79 exceptions number comorbidities medications predictive specific comorbidity comorbidities associated pnes asthma chronic pain migraines p 001 comorbidities associated es diabetes mellitus nonmetastatic neoplasm p 001 populationlevel analysis suggested patients mixed pnes es may population distinct patients either condition alonean accurate patientreported medical history medication history useful screening possible pnes prospectively validated objective score may assist interpretation medication medical history context seizure description history