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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Veröffentlicht in:Epilepsy & behavior 2020-12, Vol.113, p.107525-107525, Article 107525
Hauptverfasser: 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.
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container_issue
container_start_page 107525
container_title Epilepsy & behavior
container_volume 113
creator 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.
description •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.
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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. 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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. 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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
title Objective score from initial interview identifies patients with probable dissociative seizures
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