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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.

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.

Purpose Video-electroencephalographic monitoring (VEM) is a core component to the diagnosis and evaluation of epilepsy and dissociative seizures (DS)—also known as functional or psychogenic seizures—but VEM evaluation often occurs later than recommended. To understand why delays occur, we compared how patient-reported clinical factors were associated with time from first seizure to VEM (TVEM) in patients with epilepsy, DS or mixed. Methods We acquired data from 1245 consecutive patients with epilepsy, VEM-documented DS or mixed epilepsy and DS. We used multivariate log-normal regression with recursive feature elimination (RFE) to evaluate which of 76 clinical factors interacting with patients’ diagnoses were associated with TVEM. Results The mean and median TVEM were 14.6 years and 10 years, respectively (IQR 3–23 years). In the multivariate RFE model, the factors associated with longer TVEM in all patients included unemployment and not student status, more antiseizure medications (current and past), concussion, and ictal behavior suggestive of temporal lobe epilepsy. Average TVEM was shorter for DS than epilepsy, particularly for patients with depression, anxiety, migraines, and eye closure. Average TVEM was longer specifically for patients with DS taking more medications, more seizure types, non-metastatic cancer, and with other psychiatric comorbidities. Conclusions In all patients with seizures, trials of numerous antiseizure medications, unemployment and non-student status was associated with longer TVEM. These associations highlight a disconnect between International League Against Epilepsy practice parameters and observed referral patterns in epilepsy. In patients with dissociative seizures, some but not all factors classically associated with DS reduced TVEM.

Alcohol use disorder (AUD) and anxiety disorders are frequently comorbid and share mechanisms that could be therapeutic targets. To facilitate mechanistic studies, we adapted an inhibitory avoidance-based "2-hit" rat model of posttraumatic stress disorder (PTSD) and identified predictors and biomarkers of comorbid alcohol (ethanol)/PTSD-like symptoms in these animals. Stressed Wistar rats received a single footshock on two occasions. The first footshock occurred when rats crossed into the dark chamber of a shuttle box. Forty-eight hours later, rats received the second footshock in a familiar (FAM) or novel (NOV) context. Rats then received 4 weeks of two-bottle choice (2BC) ethanol access. During subsequent abstinence, PTSD-like behavior responses, GABAergic synaptic transmission in the central amygdala (CeA), and circulating cytokine levels were measured. FAM and NOV stress more effectively increased 2BC drinking in males and females, respectively. Stressed male rats, especially drinking-vulnerable individuals (≥0.8 g/kg average 2-h ethanol intake with >50% ethanol preference), showed higher fear overgeneralization in novel contexts, increased GABAergic transmission in the CeA, and a profile of increased G-CSF, GM-CSF, IL-13, IL-6, IL-17a, leptin, and IL-4 that discriminated between stress context (NOV > FAM > Control). However, drinking-resilient males showed the highest G-CSF, IL-13, and leptin levels. Stressed females showed increased acoustic startle and decreased sleep maintenance, indicative of hyperarousal, with increased CeA GABAergic transmission in NOV females. This paradigm promotes key features of PTSD, including hyperarousal, fear generalization, avoidance, and sleep disturbance, with comorbid ethanol intake, in a sex-specific fashion that approximates clinical comorbidities better than existing models, and identifies increased CeA GABAergic signaling and a distinct pro-hematopoietic, proinflammatory, and pro-atopic cytokine profile that may aid in treatment.

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 naive assumption that all patients have ES (accuracy DF 71%, LR 70%, naive 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.

Purpose Descriptions of seizure manifestations (SM), or semiology, can help localize the symptomatogenic zone and subsequently included brain regions involved in epileptic seizures, as well as identify patients with dissociative seizures (DS). Patients and witnesses are not trained observers, so these descriptions may vary from expert review of seizure video recordings of seizures. To better understand how reported factors can help identify patients with DS or epileptic seizures (ES), we evaluated the associations between more than 30 SMs and diagnosis using standardized interviews. Methods Based on patient- and observer-reported data from 490 patients with diagnoses documented by video-electoencephalography, we compared the rate of each SM in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic seizure-like events (PSLE), mixed DS and ES, and inconclusive testing. Results In addition to SMs that we described in a prior manuscript, the following were associated with DS: light triggers, emotional stress trigger, pre-ictal and post-ictal headache, post-ictal muscle soreness, and ictal sensory symptoms. The following were associated with ES: triggered by missing medication, aura of deja vu, and leftward eye deviation. There were numerous manifestations separately associated with mixed ES and DS. Conclusions Reported SM can help identify patients with DS, but no manifestation is pathognomonic for either ES or DS. Patients with mixed ES and DS reported factors divergent from both ES-alone and DS-alone.

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.

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 log 10 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.

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 20 years, age of onset after 31 years for men, and age of onset after 40 years 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.

SummaryObjective 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.