Outcomes of children and adolescents 1 year after being seen in a multidisciplinary psychogenic nonepileptic seizures clinic.

OBJECTIVE: Psychogenic nonepileptic seizures (PNES) are paroxysmal events that may involve altered subjective experience and change in motor activity with a psychological cause. The aim of this work is to describe a population of pediatric patients with PNES and identify factors predictive of 12-month outcomes. METHODS: We conducted a prospective observational study of children and adolescents referred to the multidisciplinary Nationwide Children's Hospital PNES clinic between November 2017 and July 2019. Information was collected from patients during clinic visits and semistructured follow-up phone calls. Descriptive statistics and Fisher exact test were used for analysis. RESULTS: Of the 139 consecutive patients referred to the PNES clinic, 104 were seen in clinic and 63 answered 12-month follow-up calls. Patients with comorbid epilepsy had increased rates of participation at 12-month follow-up (p = .04). Complete remission was achieved by 32% (20/63) of patients at 12 months. Combined PNES remission and improvement was 89% (56/63) at 12 months. Patients and families who were linked with counseling at 1 month were more likely to achieve remission at 12 months (p = .005). Less than half (44%, 28/63) of patients reached at 12 months had their events documented on video-electroencephalogram (EEG) at diagnosis; however, those who did were not more likely to be accepting of the diagnosis at 12 months (p = 1.0), be linked with counseling at 12 months (p = .59), or be event-free at 12 months (p = .79). SIGNIFICANCE: Remission occurred in one third of patients by 12 months; however, improvement in events was seen in 89%. Connection to counseling by 1 month was associated with increased remission rates at 12 months. Capturing events on video-EEG was not associated with increased acceptance or event freedom at 12 months. Diagnosis should be followed by strong encouragement to connect with counseling quickly to achieve a goal of increasing 12-month PNES remission rates.

Clinical identification of psychogenic nonepileptic events using combinations of psychological tests in a veteran sample.

OBJECTIVE: Patients with psychogenic nonepileptic events (PNEE) exhibit heterogenous symptoms and are best diagnosed with long-term video-electroencephalogram (vEEG) data. While extensive univariate data suggest psychological tests may confirm the etiology of PNEE, the multivariate discriminant utility of psychological tests is less clear. The current study aggregated likelihood ratios of multiple psychological tests to evaluate incremental and discriminant utility for PNEE. METHODS: Veterans with vEEG-diagnosed PNEE (n = 166) or epileptic seizures (n = 92) completed self-report measures and brief neuropsychological evaluations during the 4-day vEEG hospitalization. Receiver operating characteristic (ROC) curves identified discriminating psychological tests and corresponding cut-scores (0.85 minimum specificity). Likelihood ratios from the remaining cut-scores were sequentially linked using the sample base rate of PNEE (64%) and alternative base rates (10%, 20%, 30%, 40%) to estimate posttest probabilities (PTP) of test combinations. RESULTS: The Health Attitudes Survey, Health History Checklist, and Minnesota Multiphasic Personality Inventory-2-Restructured Form scales FBS-r, RC1, MLS, and NUC were identified as discriminating indicators of PNEE. Average PTPs were ≥90% when three or more indicators out of six administered were present at the sample base rate. Regardless of PNEE base rate, PTP for PNEE was ≥98% when all discriminating indicators were present and 92-99% when five of six indicators administered were present. PTPs were largely consistent with observed positive predictive values, particularly as indicators present increased. SIGNIFICANCE: Aggregating psychological tests identified PNEE with a high degree of accuracy, regardless of PNEE base rate. Combining psychological tests may be useful for confirming the etiology of PNEE.

Effect of lunar phase on frequency of psychogenic nonepileptic events in the EMU.

INTRODUCTION: Studies of the effect of a full moon on seizures have yielded mixed results, despite a continuing prevailing belief regarding the association of lunar phase with human behavior. The potential effect of a full moon on psychogenic nonepileptic events has not been as well studied, despite what anecdotal accounts from most epilepsy monitoring unit (EMU) staff would suggest. METHODS: We obtained the dates and times of all events from patients diagnosed with psychogenic nonepileptic events discharged from our EMU over a two-year period. The events were then plotted on a 29.5-day lunar calendar. Events were also broken down into lunar quarters for statistical analysis. RESULTS: We found a statistically significant increase in psychogenic nonepileptic events during the new moon quarter in our EMU during our studied timeframe. CONCLUSION: Our results are not concordant with the results of a similarly designed past study, raising the possibility that psychogenic nonepileptic events are not influenced by lunar phase.

Prevalence and distribution of MRI abnormalities in patients with psychogenic nonepileptic events.

INTRODUCTION: Both structural and functional abnormalities have been reported in patients with psychogenic nonepileptic events (PNEEs), although no truly consistent abnormalities have been found. METHODS: We retrospectively identified patients discharged from our EMU with video-EEG diagnoses of epileptic seizures, PNEEs, epileptic seizures plus PNEEs, interictal epileptiform abnormalities only, and nondiagnostic admissions. We then collected brain MRI results for analysis. RESULTS: We found significant brain MRI abnormalities in 33.8% of patients with PNEEs, clearly higher than the rate of brain MRI abnormalities in the healthy population. In addition, we found statistically significant differences in the locations of brain MRI abnormalities in patients with epileptic seizures (more frequently temporal) versus PNEEs (more frequently multifocal). CONCLUSION: This multifocal nature of abnormalities in patients with psychogenic nonepileptic events may help to explain the underlying pathophysiology as it relates to psychiatric disorders which are so frequently comorbid with PNEEs.

Differentiation of epilepsy and psychogenic nonepileptic events based on body fluid characteristics.

OBJECTIVE: Differential diagnosis between epileptic seizures and psychogenic nonepileptic events (PNEEs) is a worldwide problem for neurologists. The present study aims to identify important characteristics from body fluid tests and develop diagnostic models based on them. METHODS: This is a register-based observational study in patients with a diagnosis of epilepsy or PNEEs at West China Hospital of Sichuan University. Data from body fluid tests between 2009 and 2019 were used as a training set. We constructed models with a random forest approach in eight training subsets divided by sex and categories of tests, including electrolyte, blood cell, metabolism, and urine tests. Then, we collected data prospectively from patients between 2020 and 2022 to validate our models and calculated the relative importance of characteristics in robust models. Selected characteristics were finally analyzed with multiple logistic regression to establish nomograms. RESULTS: A total of 388 patients, including 218 with epilepsy and 170 with PNEEs, were studied. The AUROCs of random forest models of electrolyte and urine tests in the validation phase achieved 80.0% and 79.0%, respectively. Carbon dioxide combining power, anion gap, potassium, calcium, and chlorine in electrolyte tests and specific gravity, pH, and conductivity in urine tests were selected for the logistic regression analysis. C (ROC) of the electrolyte and urine diagnostic nomograms achieved 0.79 and 0.85, respectively. SIGNIFICANCE: The application of routine indicators of serum and urine may help in the more accurate identification of epileptic and PNEEs.

Latency to First Event is Shorter in Psychogenic Non-epileptic Seizures than in Epileptic Seizures in an Epilepsy Monitoring Unit.

Objective: The objective was to study latency to first event among patients with psychogenic nonepileptic seizures compared (PNES) to epileptic seizures (ES) in an epilepsy monitoring unit (EMU). Introduction: PNES are common imitators of ES. This study investigates latency to first event in patients with PNES compared to patients with ES. Methods: We performed a retrospective chart review of patients admitted to our EMU from March 2016 to October 2017. We identified patients with PNES and ES. Patients with other nonepileptic events and mixed PNES (epilepsy plus PNES) were excluded. Patient demographics, baseline seizure frequency, length of EMU stay and time from admission to first event were recorded. Results: In total, 111 patients with PNES and 121 patients with ES were included. The mean age (in years) was 42 and 38, respectively. The average baseline seizure frequency was four times higher in the PNES group than the ES group. Greater than half (52%) of the patients with PNES and about one third (38%) of the patients with ES had an event within the first 24 hours. The average time to first event was 20.88 hours for the PNES group and 30.99 hours for the ES group (p<0.01). The median latency to first event was 14 hours for the PNES group and 23 hours for the ES group. The average length of EMU stay was significantly longer in the ES group (70.82 hours) than the PNES group (53.95 hours). Conclusion: The average time to first event is shorter for PNES than in ES. In patients with high pre-EMU clinical suspicion for PNES, relatively shorter EMU monitoring (24 to 48 hours) can confirm diagnosis. This phenomenon might improve cost-effectiveness of EMU monitoring in patients with PNES.

Epilepsy Benchmarks Area IV: Limit or Prevent Adverse Consequence of Seizures and Their Treatment Across the Life Span.

Epilepsy represents a complex spectrum disorder, with patients sharing seizures as a common symptom and manifesting a broad array of additional clinical phenotypes. To understand this disorder and treat individuals who live with epilepsy, it is important not only to identify pathogenic mechanisms underlying epilepsy but also to understand their relationships with other health-related factors. Benchmarks Area IV focuses on the impact of seizures and their treatment on quality of life, development, cognitive function, and other aspects and comorbidities that often affect individuals with epilepsy. Included in this review is a discussion on sudden unexpected death in epilepsy and other causes of mortality, a major area of research focus with still many unanswered questions. We also draw attention to special populations, such as individuals with nonepileptic seizures and pregnant women and their offspring. In this study, we review the progress made in these areas since the 2016 review of the Benchmarks Area IV and discuss challenges and opportunities for future study.