Objective score from initial interview identifies patients with probable dissociative seizures

•20 of 76 factors contributed to the dissociative seizures likelihood score (DSLS).•DSLS correctly identified 77% of patients with ES or DS.•DSLS was noninferior to neurologists’ impression on a subset of patients.•The kappa of 21% between DSLS and neurologists’ suggests a unique perspective.•Combin...

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Published in:	Epilepsy & behavior Vol. 113; p. 107525
Main Authors:	Kerr, Wesley T., Janio, Emily A., Chau, Andrea M., Braesch, Chelsea T., Le, Justine M., Hori, Jessica M., Patel, Akash B., Gallardo, Norma L., Allas, Corinne H., Karimi, Amir H., Dubey, Ishita, Sreenivasan, Siddhika S., Bauirjan, Janar, Hwang, Eric S., Davis, Emily C., D'Ambrosio, Shannon R., Al Banna, Mona, Mazumder, Rajarshi, Wu, Ting, DeCant, Zachary A., Gibbs, Michael G., Chang, Edward, Zhang, Xingruo, Cho, Andrew Y., Beimer, Nicholas J., Engel, Jerome, Cohen, Mark S., Stern, John M.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01-12-2020
Subjects:	Artificial intelligence Clinical decision support tool Conversion Disorder Dissociative Disorders Electroencephalography Functional seizures Humans Machine learning Prospective Studies Psychogenic nonepileptic seizures Retrospective Studies Seizures - diagnosis Psychogenic nonepileptic seizures Functional seizures Clinical decision support tool Artificial intelligence Machine learning
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Summary:	•20 of 76 factors contributed to the dissociative seizures likelihood score (DSLS).•DSLS correctly identified 77% of patients with ES or DS.•DSLS was noninferior to neurologists’ impression on a subset of patients.•The kappa of 21% between DSLS and neurologists’ suggests a unique perspective.•Combination of the DSLS and clinical impression missed only 3% of patients. To develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with “probable” dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment. Based on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews. The DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74–80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists’ impression (84%, 95% CI: 80–88%) and the kappa between neurologists’ and the DSLS was 21% (95% CI: 1–41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0–11%). The evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1525-5050 1525-5069
DOI:	10.1016/j.yebeh.2020.107525