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Objective: Although the populations of patients with functional seizures (FS) and epileptic seizures (ES) are extremely heterogeneous with multiple etiologies and phenotypes, patients with FS have increased somatic sensitivity and report more positive complaints on review-of-systems questionnaires (ROSQs). We evaluated if data-driven clustering and projection analysis could identify symptom phenotypes that could differentiate between patients with FS and ES., Methods: The dataset included all adult patients admitted from January 2006 to March 2020 for video-electroencephalography with available ROSQs (N = 877). Latent clusters and axes of variation in ROSQ responses were evaluated using multiple well-established methods. Leave-one-out cross-validation was used to evaluate if logistic regression using information could differentiate patients with FS from ES., Results: When evaluating individual symptom response and proportion of positive responses, the area under the receiver operating curve (AUC) was 62% (95% CI, 53%-69%) and 72% (CI, 65%-78%), respectively. The best AUC achieved by phenotyping methods was 74%. The patterns of clusters and components reflected properties of each analysis and did not correlate with assigned "system" from the ROSQ or other interpretations., Discussion: The overall proportion of positive responses was the most informative metric to differentiate patients with FS compared to ES. While both FS and ES are heterogeneous populations with multiple subgroups, these subgroups were not meaningfully identified based on ROSQ symptoms. The limited overall predictive accuracy and AUC suggests that, in absence of other supporting data, ROSQ responses in patients with FS and ES were not clinically useful for screening., (© Copyright 2022 Physicians Postgraduate Press, Inc.)

Objective: The clinical characteristics of functional seizures may vary based on age-of-onset or age-of-presentation. Description of age-related differences has focused on three categories: pediatric, young-adult, and older-adult. We evaluated how factors continuously varied based on age-of-presentation across the adult lifespan., Methods: Based on cross-sectional data from 365 adult (18 to 88 years old) patients with documented diagnoses of functional seizures, we evaluated how the quantity and prevalence of specific ictal behaviors, historical factors, and comorbidities varied based on patient age-of-presentation using sequential weighted averages., Results: Four factors changed prominently with age-of-presentation: female predominance decreased at two inflection points - ages 35 and 62; the prevalence of work disability was higher until age-of-presentation 30 then plateaued at 80%; there was greater delay to diagnosis in older patients; and comorbidities was higher with age-of-presentation, starting from early adulthood. The proportion of patients who presented with functional seizures decreased after 50. Ictal behavior did not substantially vary with age-of-presentation., Conclusion: The time from onset to diagnosis increased with age-of-presentation, which may be related to increased comorbidities and the misconception that FS do not start in older age. The female predominance decreased nonlinearly with age. By age 30, most patients' seizures already had substantial association with unemployment. These findings emphasize that patients can develop functional seizures at any age. The rapid development of disability relatively early in life, which then stays at a high prevalence rate, demonstrates the need for prompt referral for definitive diagnosis and treatment., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: While certain clinical factors suggest a diagnosis of dissociative seizures (DS), otherwise known as functional or psychogenic nonepileptic seizures (PNES), ictal video-electroencephalography monitoring (VEM) is the gold standard for diagnosis. Diagnostic delays were associated with worse quality of life and more seizures, even after treatment. To understand why diagnoses were delayed, we evaluated which factors were associated with delay to VEM., Methods: Using data from 341 consecutive patients with VEM-documented dissociative seizures, we used multivariate log-normal regression with recursive feature elimination (RFE) and multiple imputation of some missing data to evaluate which of 76 clinical factors were associated with time from first dissociative seizure to VEM., Results: The mean delay to VEM was 8.4 years (median 3 years, IQR 1-10 years). In the RFE multivariate model, the factors associated with longer delay to VEM included more past antiseizure medications (0.19 log-years/medication, standard error (SE) 0.05), more medications for other medical conditions (0.06 log-years/medication, SE 0.03), history of physical abuse (0.75 log-years, SE 0.27), and more seizure types (0.36 log-years/type, SE 0.11). Factors associated with shorter delay included active employment or student status (-1.05 log-years, SE 0.21) and higher seizure frequency (0.14 log-years/log[seizure/month], SE 0.06)., Conclusions: Patients with greater medical and seizure complexity had longer delays. Delays in multiple domains of healthcare can be common for victims of physical abuse. Unemployed and non-student patients may have had more barriers to access VEM. These results support earlier referral of complex cases to a comprehensive epilepsy center., (Copyright © 2021 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Objective: 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., Methods: 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., Results: 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%)., Significance: 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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: Differentiating psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES) can be difficult, even when expert clinicians have video recordings of seizures. Moreover, witnesses who are not trained observers may provide descriptions that differ from the expert clinicians', which often raises concern about whether the patient has both ES and PNES. As such, quantitative, evidence-based tools to help differentiate ES from PNES based on patients' and witnesses' descriptions of seizures may assist in the early, accurate diagnosis of patients., Methods: Based on patient- and observer-reported data from 1372 patients with diagnoses documented by video-elect roencephalography (vEEG), we used logistic regression (LR) to compare specific peri-ictal behaviors and seizure triggers in five mutually exclusive groups: ES, PNES, physiologic non-epileptic seizure-like events, mixed PNES plus ES, and inconclusive monitoring. To differentiate PNES-only from ES-only, we retrospectively trained multivariate LR and a forest of decision trees (DF) to predict the documented diagnoses of 246 prospective patients., Results: The areas under the receiver operating characteristic curve (AUCs) of the DF and LR were 75% and 74%, respectively (empiric 95% CI of chance 37-62%). The overall accuracy was not significantly higher than the naïve assumption that all patients have ES (accuracy DF 71%, LR 70%, naïve 68%, p > 0.05)., Conclusions: Quantitative analysis of patient- and observer-reported peri-ictal behaviors objectively changed the likelihood that a patient's seizures were psychogenic, but these reports were not reliable enough to be diagnostic in isolation. Instead, our scores may identify patients with "probable" PNES that, in the right clinical context, may warrant further diagnostic assessment., (Copyright © 2019 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Objective: Psychogenic nonepileptic seizure (PNES) is a common diagnosis after evaluation of medication resistant or atypical seizures with video-electroencephalographic monitoring (VEM), but usually follows a long delay after the development of seizures, during which patients are treated for epilepsy. Therefore, more readily available diagnostic tools are needed for earlier identification of patients at risk for PNES. A tool based on patient-reported psychosocial history would be especially beneficial because it could be implemented in the outpatient clinic., Methods: Based on the data from 1375 patients with VEM-confirmed diagnoses, we used logistic regression to compare the frequency of specific patient-reported historical events, demographic information, age of onset, and delay from first seizure until VEM in five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of this information to differentiate PNES only from ES only, we used multivariate piecewise-linear logistic regression trained using retrospective data from chart review and validated based on data from 246 prospective standardized interviews., Results: The prospective area under the curve of our weighted multivariate piecewise-linear by-sex score was 73%, with the threshold that maximized overall retrospective accuracy resulting in a prospective sensitivity of 74% (95% CI: 70-79%) and prospective specificity of 71% (95% CI: 64-82%). The linear model and piecewise linear without an interaction term for sex had very similar performance statistics. In the multivariate piecewise-linear sex-split predictive model, the significant factors positively associated with ES were history of febrile seizures, current employment or active student status, history of traumatic brain injury (TBI), and longer delay from first seizure until VEM. The significant factors associated with PNES were female sex, older age of onset, mild TBI, and significant stressful events with sexual abuse, in particular, increasing the likelihood of PNES. Delays longer than 20years, age of onset after 31years for men, and age of onset after 40years for women had no additional effect on the likelihood of PNES., Discussion: Our promising results suggest that an objective score has the potential to serve as an early outpatient screening tool to identify patients with greater likelihood of PNES when considered in combination with other factors. In addition, our analysis suggests that sexual abuse, more than other psychological stressors including physical abuse, is more associated with PNES. There was a trend of increasing frequency of PNES for women during childbearing years and plateauing outside those years that was not observed in men., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost., Methods: Based on data from 1,365 adult patients with video-electroencephalography-confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications, and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizurelike events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database., Results: Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% confidence interval =72-84%) and area under the curve of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain, and migraines (p < 0.01). Comorbidities associated with ES were diabetes mellitus and nonmetastatic neoplasm (p < 0.01). The population-level analysis suggested that patients with mixed PNES and ES may be a population distinct from patients with either condition alone., Significance: An accurate patient-reported medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history., (Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.)

Objective: Early and accurate diagnosis of patients with psychogenic nonepileptic seizures (PNES) leads to appropriate treatment and improves long-term seizure prognosis. However, this is complicated by the need to record seizures to make a definitive diagnosis. Suspicion for PNES can be raised through knowledge that patients with PNES have increased somatic sensitivity and report more positive complaints on review-of-systems questionnaires (RoSQs) than patients with epileptic seizures. If the responses on the RoSQ can differentiate PNES from other seizure types, then these forms could be an early screening tool., Methods: Our dataset included all patients admitted from January 2006 to June 2016 for video-electroencephalography at UCLA. RoSQs prior to May 2015 were acquired through retrospective chart review (n=405), whereas RoSQs from subsequent patients were acquired prospectively (n=190). Controlling for sex and number of comorbidities, we used binomial regression to compare the total number of symptoms and the frequency of specific symptoms between five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of RoSQs to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and the number of medical comorbidities., Results: On average, patients with PNES or mixed PNES and ES reported more than twice as many symptoms than patients with isolated ES or PSLE (p<0.001). The prospective accuracy to differentiate PNES from ES was not significantly higher than naïve assumption that all patients had ES (76% vs 70%, p>0.1)., Discussion: This analysis of RoSQs confirms that patients with PNES with and without comorbid ES report more symptoms on a population level than patients with epilepsy or PSLE. While these differences help describe the population of patients with PNES, the consistency of RoSQ responses was neither accurate nor specific enough to be used solely as an early screening tool for PNES. Our results suggest that the RoSQ may help differentiate PNES from ES only when, based on other information, the pre-test probability of PNES is at least 50%., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: The average delay from first seizure to diagnosis of psychogenic non-epileptic seizures (PNES) is over 7 years. The reason for this delay is not well understood. We hypothesized that a perceived decrease in seizure frequency after starting an anti-seizure medication (ASM) may contribute to longer delays, but the frequency of such a response has not been well established., Methods: Time from onset to diagnosis, medication history and associated seizure frequency was acquired from the medical records of 297 consecutive patients with PNES diagnosed using video-electroencephalographic monitoring. Exponential regression was used to model the effect of medication trials and response on diagnostic delay., Results: Mean diagnostic delay was 8.4 years (min 1 day, max 52 years). The robust average diagnostic delay was 2.8 years (95% CI: 2.2-3.5 years) based on an exponential model as 10 to the mean of log10 delay. Each ASM trial increased the robust average delay exponentially by at least one third of a year (Wald t=3.6, p=0.004). Response to ASM trials did not significantly change diagnostic delay (Wald t=-0.9, p=0.38)., Conclusion: Although a response to ASMs was observed commonly in these patients with PNES, the presence of a response was not associated with longer time until definitive diagnosis. Instead, the number of ASMs tried was associated with a longer delay until diagnosis, suggesting that ASM trials were continued despite lack of response. These data support the guideline that patients with seizures should be referred to epilepsy care centers after failure of two medication trials., (Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)