An objective score to identify psychogenic seizures based on age of onset and history

Medical and psychiatric comorbidities, somatic and cognitive symptoms, injuries and medical procedure history in patients with functional seizures from a public and a private hospital

PURPOSE

Patients with functional seizures (FS), otherwise known as psychogenic non-epileptic seizures (PNES), from different socioeconomic backgrounds may differ, however, this remains a gap in current literature. Comorbidities can play both a precipitating and a perpetuating role in FS and are important in the planning of individual treatment for this condition. With this study, we aimed to describe and compare the reported medical and psychiatric comorbidities, injuries, somatic and cognitive symptoms, and medical procedures among patients with FS from a private and a public epilepsy monitoring unit (EMU) in Cape Town, South Africa.

METHODS

This is a retrospective case-control study. We collected data on the comorbidity and medical procedure histories, as well as symptoms and clinical signs reported by patients with video-electroencephalographically (video-EEG) confirmed FS without comorbid epilepsy. We used digital patient records starting with the earliest available digital record for each hospital until the year 2022.

RESULTS

A total of 305 patients from a private hospital and 67 patients from a public hospital were included in the study (N = 372). Public hospital patients had higher odds of reporting intellectual disability (aOR=15.58, 95% CI [1.80, 134.95]), circulatory system disease (aOR=2.63, 95% CI [1.02, 6.78]) and gait disturbance (aOR=8.52, 95% CI [1.96, 37.08]) compared to patients with FS attending the private hospital. They did, however, have fewer odds of reporting a history of an infectious or parasitic disease (aOR=0.31, 95% CI [0.11, 0.87]), respiratory system disease (aOR=0.23, 95% CI [0.06, 0.82]), or medical procedures in the past (aOR=0.32, 95% CI [0.16, 0.63]).

CONCLUSION

The study presents prevalence and comparative data on the medical profiles of patients with FS from different socioeconomic backgrounds which may inform future considerations in FS diagnosis and treatment.

EEG and MRI Abnormalities in Patients With Psychogenic Nonepileptic Seizures

OBJECTIVE

To compare the rate of EEG and MRI abnormalities in psychogenic nonepileptic seizures (PNES) patients with and without suspected epilepsy. Patients were also compared in terms of their demographic and clinical profiles.

METHODS

A retrospective analysis of 271 newly diagnosed PNES patients admitted to the epilepsy monitoring unit between May 2000 and April 2008, with follow-up clinical data collected until September 2015.

RESULTS

One hundred ninety-four patients were determined to have PNES alone, 16 PNES plus possible epilepsy, 14 PNES plus probable epilepsy, and 47 PNES plus confirmed epilepsy. Fifty-seven of the 77 patients (74.0%) with possible, probable, or definite epilepsy exhibited epileptiform activity on EEG, versus only 16 of the 194 patients (8.2%) in whom epilepsy was excluded. Twenty-four of these 194 patients (12.4%) had MRI abnormalities. Three of 38 patients (7.9%) with both EEG and MRI abnormalities were confirmed not to have epilepsy. In PNES patients with EEG or MRI abnormalities compared with those without, patients with abnormalities were more likely to have epilepsy risk factors, such as central nervous system structural abnormalities, and less likely to report minor head trauma. The presence of EEG abnormalities in PNES-only patients did not influence antiseizure medication reduction, whereas those with MRI abnormalities were less likely to have their antiseizure medications reduced.

CONCLUSIONS

Psychogenic nonepileptic seizure patients without MRI or EEG abnormalities are less likely to have associated epilepsy, risk factors for epilepsy, and had different demographic profiles. There is a higher-than-expected level of EEG and MRI abnormalities in PNES patients without epilepsy.

Using Verbally-Reported and Video-Observed Semiology to Identify Functional Seizures

Diagnosis of functional seizures, also known as psychogenic nonepileptic seizures, starts with a clinical interview and description of the seizures. A targeted approach to this evaluation can provide valuable information to gauge the likelihood of functional seizures as compared with other similar conditions including but not limited to epileptic seizures. This review focuses on the use of patient and witness descriptions and seizure videos to identify patients with probable functional seizures. Particular emphasis is given to recognizing the limitations of the available data and the influence of health-care provider expertise on diagnostic accuracy.

Amygdala subfield and prefrontal cortex abnormalities in patients with functional seizures

BACKGROUND

Functional seizures (FS) are paroxysmal episodes, resembling epileptic seizures, but without underlying epileptic abnormality. The aetiology and neuroanatomic associations are incompletely understood. Recent brain imaging data indicate cerebral changes, however, without clarifying possible pathophysiology. In the present study, we specifically investigated the neuroanatomic changes in subregions of the amygdala and hippocampus in FS.

METHODS

T1 MRI scans of 37 female patients with FS and 37 age-matched female seizure naïve controls (SNC) were analyzed retrospectively in FreeSurfer version 7.1. Seizure naïve controls included patients with depression and anxiety disorders. The analysis included whole-brain cortical thickness, subcortical volumes, and subfields of the amygdala and hippocampus. Group comparisons were carried out using multivariable linear models.

RESULTS

The FS and SNC groups did not differ in the whole hippocampus and amygdala volumes. However, patients had a significant reduction of the right lateral amygdala volume (p = 0.00041), an increase of the right central amygdala, (p = 0.037), and thinning of the left superior frontal gyrus (p = 0.024). Additional findings in patients were increased volumes of the right medial amygdala (p = 0.031), left anterior amygdala (p = 0.017), and left dentate gyrus of the hippocampus (p = 0.035).

CONCLUSIONS

The observations from the amygdala and hippocampus segmentation affirm that there are neuroanatomic associations of FS. The pattern of these changes aligned with some of the cerebral changes described in chronic stress conditions and depression. The pattern of detected changes further study, and may, after validation, provide biomarkers for diagnosis and treatment.

Prospective multicenter cohort study of possible psychogenic nonepileptic seizure cases-Results at 1-year follow-up examinations

OBJECTIVE

The primary purpose of this prospective multicenter study was to examine clinical and demographic feature differences according to the diagnostic level of psychogenic nonepileptic seizures (PNES) and then clarify whether prognosis may also differ accordingly.

METHODS

Two hundred forty-two consecutive patients strongly suspected of having PNES attacks were invited to participate, of whom 52 did not consent or contact was lost. At the 1-year follow-up examination, PNES diagnosis was reconsidered in nine patients. In 96 patients, the diagnostic level remained the same (P-group), with that in 43 considered to be clinically established (CE-group) and in 42 documented (D-group). The Qolie-10 and NDDI-E questionnaires were examined at both the study entry and the follow-up examination.

RESULTS

Multiple regression analysis of quality of life (QoL) score (n = 173; R²  = 0.374; F = 7.349; P < 0.001) revealed NDDI-E score (t = -6.402; P < 0.001), age of PNES onset (t = -3.026; P = 0.003), and ethnic minority status (t = 3.068; P = 0.003) as significant contributors. At entry, the P-group showed the lowest PNES attack frequency (P < 0.000), the lowest rate of antiseizure, antidepressant, and antipsychotic medication (P < 0.000; P = 0.031; P = 0.013, respectively), and the lowest proportion of psychosis (P = 0.046). At follow-up, PNES attack frequency (P < 0.000), number of admittances to emergency room (P < 0.000), and scores for QoL (P < 0.000) as well as depression (P = 0.004) were found to be significantly improved together with other collateral indicators, such as rate of antiseizure medication prescription (P = 0.001) and psychiatric symptoms (P = 0.03). Multiple regression analysis of a sample limited to patients with intellectual disability (ID) (n = 44; R²  = 0.366; F = 4.493; P = 0.002) revealed continued psychotherapy at follow-up (t = 2.610, P = 0.013) and successful reduction in antiseizure medication (t = 2.868; P = 0.007) as positively related with improved QoL.

SIGNIFICANCE

Clinical and the socio-psychological constellation of possible, clinically established, and documented PNES were found to differ greatly. Unexpectedly, significant effects of the continuous psychotherapeutic intervention were confirmed in PNES patients with ID.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls

PURPOSE

Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations.

METHODS

Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners.

RESULTS

After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm³, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS.

CONCLUSIONS

These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

Factors associated with comorbid epilepsy in patients with psychogenic nonepileptic seizures: A large cohort study

OBJECTIVE

Comorbid epilepsy and psychogenic nonepileptic seizures (PNES) occur in 12-22% of cases and the diagnosis of both simultaneous disorders is challenging. We aimed to identify baseline characteristics that may help distinguish patients with PNES-only from those with comorbid epilepsy.

METHODS

We performed a longitudinal cohort study on those patients diagnosed with PNES in our epilepsy monitoring unit (EMU) between May 2001 and February 2011, prospectively followed up until September 2016. Patients were classified into PNES-only, PNES + possible or probable epilepsy, and PNES + definite epilepsy based on the clinical, vEEG, and neuroimaging data. Demographic and basal clinical data were obtained from chart review. Multiple regression models were performed to identify significant predictors of PNES + definite epilepsy, excluding patients with only possible or probable epilepsy for this specific analysis.

RESULTS

One-hundred and ninety four patients with PNES-only, 30 with PNES + possible or probable epilepsy and 47 with PNES + definite epilepsy were included. 73.8% were female and the mean age at EMU admission was 37.4 ± standard deviation 13.5 years. Patients with PNES + definite epilepsy most likely had never worked, had history of febrile seizures, structural brain lesions, developmental disabilities, and maximum reported seizure duration between 0.5 and 2 min. Patients with PNES-only were on fewer anti-seizure medications (ASM), reported more frequently an initial minor head trauma, seizures longer than 10 min, and a higher number of neurological and medical illnesses - being migraine (18.1%), other types of headaches (18.5%), and asthma (15.5%) the most prevalent ones. All p < 0.05. On the hierarchical regression analysis, history of febrile seizures, developmental disabilities, brain lesions, longest reported seizure duration between 0.5 and 2 min, and lack of neurological comorbidity, remained as significant predictors of PNES + epilepsy. The model's performance of a 5-fold cross-validation analysis showed an overall accuracy of 84.7% to classify patients correctly.

CONCLUSIONS

Some demographic and clinical characteristics may support the presence of comorbid epilepsy in patients with PNES, being unemployment, the presence of brain lesions, developmental disabilities, history of febrile seizures, seizure duration and lack of comorbid headaches the most relevant ones.

Machine learning applications to differentiate comorbid functional seizures and epilepsy from pure functional seizures

PURPOSE

We have utilized different methods in machine learning (ML) to develop the best algorithm to differentiate comorbid functional seizures (FS) and epilepsy from those who have pure FS.

METHODS

This was a retrospective study of an electronic database of patients with seizures. All patients with a diagnosis of FS (with or without comorbid epilepsy) were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2021. We arbitrarily selected 14 features that are important in making the diagnosis of patients with seizures and also are easily obtainable during history taking. Pytorch and Scikit-learn packages were used to construct various models including random forest classifier, decision tree classifier, support vector classifier, k-nearest neighbor, and TabNet classifier.

RESULTS

Three hundred and two patients had FS (82.5%), while 64 patients had FS and comorbid epilepsy (17.5%). The "TabNet classifier" could provide the best sensitivity (90%) and specificity (74%) measures (accuracy of 76%) to help differentiate patients with FS from those with FS and comorbid epilepsy.

CONCLUSION

These satisfactory differentiating measures suggest that the current algorithm could be used in clinical practice to help with the difficult task of distinguishing patients with FS from those with FS and comorbid epilepsy. Based on the results of the current study, we have developed an Application (SeiDx). This App is freely accessible at the following address: https://drive.google.com/file/d/1rAgBXKNPW9bmUCDioaGHHzLBQgzZ-HZ2/view. This App should be validated in a prospective assessment.

Title: Functional seizures across the adult lifespan: female sex, delay to diagnosis and disability

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.

A minority of patients with functional seizures have abnormalities on neuroimaging

OBJECTIVE

Functional seizures often are managed incorrectly as a diagnosis of exclusion. However, a significant minority of patients with functional seizures may have abnormalities on neuroimaging that typically are associated with epilepsy, leading to diagnostic confusion. We evaluated the rate of epilepsy-associated findings on MRI, FDG-PET, and CT in patients with functional seizures.

METHODS

We studied radiologists' reports from neuroimages at our comprehensive epilepsy center from a consecutive series of patients diagnosed with functional seizures without comorbid epilepsy from 2006 to 2019. We summarized the MRI, FDG-PET, and CT results as follows: within normal limits, incidental findings, unrelated findings, non-specific abnormalities, post-operative study, epilepsy risk factors (ERF), borderline epilepsy-associated findings (EAF), and definitive EAF.

RESULTS

Of the 256 MRIs, 23% demonstrated ERF (5%), borderline EAF (8%), or definitive EAF (10%). The most common EAF was hippocampal sclerosis, with the majority of borderline EAF comprising hippocampal atrophy without T2 hyperintensity or vice versa. Of the 87 FDG-PETs, 26% demonstrated borderline EAF (17%) or definitive EAF (8%). Epilepsy-associated findings primarily included focal hypometabolism, especially of the temporal lobes, with borderline findings including subtle or questionable hypometabolism. Of the 51 CTs, only 2% had definitive EAF.

SIGNIFICANCE

This large case series provides further evidence that, while uncommon, EAF are seen in patients with functional seizures. A significant portion of these abnormal findings are borderline. The moderately high rate of these abnormalities may represent framing bias from the indication of the study being "seizures," the relative subtlety of EAF, or effects of antiseizure medications.

A novel scale for suspicion of psychogenic nonepileptic seizures: development and accuracy

OBJECTIVE

The differential diagnosis between epileptic and psychogenic nonepileptic seizures (PNES) is challenging, yet suspicion of PNES is crucial to rethink treatment strategies and select patients for diagnostic confirmation through video EEG (VEEG). We developed a novel scale to prospectively suspect PNES.

METHODS

First, we developed a 51-item scale in two steps, based upon literature review and panel expert opinion. A pilot study verified the applicability of the instrument, followed by a prospective evaluation of 158 patients (66.5% women, mean age 33 years) who were diagnosed for prolonged VEEG. Only epileptic seizures were recorded in 103 patients, and the other 55 had either isolated PNES or both types of seizures. Statistical procedures identified 15 items scored between 0 and 3 that best discriminated patients with and without PNES, with a high degree of consistency.

RESULTS

Internal consistency reliability of the scale for suspicion of PNES was 0.77 with Cronbach's Alpha Coefficient and 0.95 with Rasch Item Reliability Index, and performance did not differ according to the patient's gender. For a cut-off score of 20 (of 45) points, area under the curve was 0.92 (95% IC: 0.87-0.96), with an accuracy of 87%, sensitivity of 89%, specificity of 85%, positive predictive value of 77%, and negative predictive value of 94% (95% IC) for a diagnosis of PNES.

CONCLUSIONS

The scale for suspicion of PNES (SS-PNES) has high accuracy to a reliable suspicion of PNES, helping with the interpretation of apparent seizure refractoriness, reframing treatment strategies, and streamlining referral for prolonged VEEG.

Distinguishing psychogenic nonepileptic, mixed, and epileptic seizures using systemic measures and reported experiences

OBJECTIVES

Our primary objective was to better discern features that can differentiate people with 'mixed' symptomatology from those who experience epileptic seizures (ES) or functional/psychogenic nonepileptic seizures (PNES) alone, in a population of patients referred for video-telemetry. We wished to see if we could establish the prevalence of PNES in this population of interest as well as compare both objective (e.g. videotelemetry reports and heart rate measurements) and subjective, patient-centered measures (reported symptoms and experiences).

METHODS

Data were sourced from a database of all video-telemetry patients admitted to the John Radcliffe Hospital (Oxford, UK) between 1st Jan 2014 and 31st Jan 2016; video-electroencephalogram (vEEG) reports for the above patients; neurology clinic letters; multidisciplinary Team (MDT) reports; psychology assessments and patient notes for all vEEG patients referred for surgical work up. Mixed cases with a dual ES/PNES diagnosis were carefully evaluated again by the Consultant Neurologist under whose care each respective patient was, through case-by-case evaluation of EEG and telemetry reports. We compared mean heart rate during attacks captured on vEEG, number of physical symptoms reported, episode length, and postictal confusion between the three groups (ES; PNES; ES and PNES (mixed)). We evaluated the groups in terms of demographic and psychological parameters as well as prescription of anti-seizure medication. Pearson correlation significance was examined at 95% level of significance for p-values corrected for multiple comparisons.

RESULTS

Overall, mixed cases reported experiencing a significantly lower number of physical symptoms compared to PNES cases (p = 0.018). The heart rate of PNES cases was significantly lower than that of mixed cases during the attacks (p = 0.003). ES patients exhibited the highest heart rate of all three groups and a greater degree of postictal confusion (adjusted p = 0.003 and p < 0.001, respectively) compared to those with PNES. There was no statistically significant difference in episode length between mixed and ES cases, while PNES patients had significantly longer episode duration (p = 0.021) compared to the mixed group. We noted that 81.6% of PNES patients were taking at least one anti-seizure medication.

CONCLUSION

Patients with mixed seizures seem to be part of a spectrum between ES and PNES cases. Mixed cases are more similar to the ES group with regard to episode length and number of symptoms reported. In the PNES cohort, we found an over-reporting of ictal symptoms (e.g. palpitations, diaphoresis) disproportionate to recorded heart rate, which is lower in PNES than in epileptic attacks. This seems consistent with PNES cases experiencing a degree of impaired interoceptive processing, as part of a functional disorder spectrum. We noted that there was tendency for overmedication in the PNES group. The need for 'de-prescribing' should be addressed with measures that include better liaison with the community care team. With regard to potential autonomic dysregulation in the mixed cases, it might be interesting to see if vagus nerve stimulation could be accompanied by normalization of cardiovascular physiology parameters for people with both epileptic and psychogenic nonepileptic seizures.

Can Big Data guide prognosis and clinical decisions in epilepsy?

Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of "-omics" domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence-based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision-making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.

Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG

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.

Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures

Dissociative seizures (also known as psychogenic nonepileptic seizures) are a common functional neurological disorder that can be difficult to distinguish from epileptic seizures. Patients with dissociative seizures provide diagnostic challenges, leading to delays in care, inappropriate care, and significant healthcare utilization and associated costs. The dissociative seizure likelihood score (DSLS) was developed by Kerr and colleagues at UCLA to distinguish between patients with epileptic seizures and dissociative seizures based on clinical and medication history as well as features of seizure semiology. We validated this calculator at the University of Colorado, which is a Level 4 National Association of Epilepsy Center. The DSLS accurately predicted the diagnosis in 81% of patients, despite local variability in the factors associated with epileptic versus dissociative seizures between the two populations. The DSLS can be a useful tool to assist with history taking and may have important utility for clinical decision making with these difficult to distinguish patient populations.

Factors associated with delay to video-EEG in dissociative seizures

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.

Reliability of additional reported seizure manifestations to identify dissociative seizures

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 déjà 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.

Adverse events related to antiepileptic drugs

OBJECTIVE

Adverse events (AEs) related to antiepileptic drugs (AEDs) may interfere with adequate dosing and patient adherence, leading to suboptimal seizure control, and relatedly, increased injuries, hospitalizations, and mortality. This study investigated the clinicodemographic factors associated with AEs related to AEDs as reported by the Liverpool Adverse Events Profile (LAEP), and explored the ability of LAEP to discriminate between epilepsy and psychogenic nonepileptic seizures (PNES). We hypothesized that female sex, mood disorders, AED-polytherapy, duration, and severity of epilepsy are associated with increased endorsement of AEs related to AEDs, and that endorsement of AEs related to AEDs would significantly differ between epilepsy and PNES patients.

METHODS

We prospectively enrolled adult patients admitted to two inpatient video-electroencephalogram monitoring units. Clinicodemographic variables and psychometric measures of depression, anxiety, and cognitive function were recorded. Patient-reported AE endorsement was obtained using the LAEP, which was reduced to four latent domains using exploratory structural equation modeling. General linear models identified variables associated with each domain. Logistic regression determined the ability of LAEP scores to differentiate between epilepsy and PNES.

RESULTS

311 patients met inclusion criteria. Mean age was 38 years and 56% of patients were female. Network analysis demonstrated strong relationships between depression and anxiety with physical, sleep, psychiatric, and dermatological AE endorsement. Depression, female sex, and AED polytherapy were associated with greater AE endorsement. Epilepsy, compared to PNES, was associated with lower AE endorsement. Fewer prescribed AEDs and greater reported physical AE endorsement were associated with PNES diagnosis.

SIGNIFICANCE

There is a strong relationship between patient-reported AEs and psychiatric symptomatology. Those with PNES paradoxically endorse greater physical AEs despite receiving fewer AEDs. Patients who endorse AEs in clinical practice should be screened for comorbid depression or anxiety and treated accordingly.

Association of Epileptic and Nonepileptic Seizures and Changes in Circulating Plasma Proteins Linked to Neuroinflammation

OBJECTIVE

To develop a diagnostic test that stratifies epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES) by developing a multimodal algorithm that integrates plasma concentrations of selected immune response-associated proteins and patient clinical risk factors for seizure.

METHODS

Daily blood samples were collected from patients evaluated in the epilepsy monitoring unit within 24 hours after EEG confirmed ES or PNES and plasma was isolated. Levels of 51 candidate plasma proteins were quantified using an automated, multiplexed, sandwich ELISA and then integrated and analyzed using our diagnostic algorithm.

RESULTS

A 51-protein multiplexed ELISA panel was used to determine the plasma concentrations of patients with ES, patients with PNES, and healthy controls. A combination of protein concentrations, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), intercellular adhesion molecule 1 (ICAM-1), monocyte chemoattractant protein-2 (MCP-2), and tumor necrosis factor-receptor 1 (TNF-R1) indicated a probability that a patient recently experienced a seizure, with TRAIL and ICAM-1 levels higher in PNES than ES and MCP-2 and TNF-R1 levels higher in ES than PNES. The diagnostic algorithm yielded an area under the receiver operating characteristic curve (AUC) of 0.94 ± 0.07, sensitivity of 82.6% (95% confidence interval [CI] 62.9-93.0), and specificity of 91.6% (95% CI 74.2-97.7). Expanding the diagnostic algorithm to include previously identified PNES risk factors enhanced diagnostic performance, with AUC of 0.97 ± 0.05, sensitivity of 91.3% (95% CI 73.2-97.6), and specificity of 95.8% (95% CI 79.8-99.3).

CONCLUSIONS

These 4 plasma proteins could provide a rapid, cost-effective, and accurate blood-based diagnostic test to confirm recent ES or PNES.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that variable levels of 4 plasma proteins, when analyzed by a diagnostic algorithm, can distinguish PNES from ES with sensitivity of 82.6% and specificity of 91.6%.

DDESVSFS: A simple, rapid and comprehensive screening tool for the Differential Diagnosis of Epileptic Seizures VS Functional Seizures

OBJECTIVE

Functional seizures (FS) are often misclassified as epileptic seizures (ES). This study aimed to create an easy to use but comprehensive screening tool to guide further evaluation of patients presenting with this diagnostic dilemma.

MATERIALS AND METHODS

Demographic, clinical and diagnostic data were collected on patients admitted for video-EEG monitoring for clarification of their diagnosis. Upon discharge, patients were classified as having ES vs FS. Using the collected characteristics and video-EEG diagnosis, we created a multivariable logistic regression model to identify predictors of ES. Then, we trained an integer-coefficient model with the most frequently selected predictors, creating a pointing system coined DDESVSFS, with scores ranging from -17 to +8 points.

RESULTS

43 patients with FS and 165 patients with ES were recruited. In the final integer-coefficient model, 8 predictors were identified as significant in differentiating ES from FS: normal electroencephalogram (-3 points), predisposing factors for FS (-3 points), increased number of comorbidities (-3 points), semiology suggestive of FS (-4 points), increased seizure frequency (-4 points), longer disease duration (+3 points), antiepileptic polypharmacy (+2 points) and compliance with antiepileptic drugs (+3 points). Cumulative scores of ≤ -9 points carried <5% predictive value for ES, while cumulative scores of ≥ -1 points carried >95% predictive value. The model performed well (AUC: 0.923, sensitivity: 0.945, specificity: 0.698).

CONCLUSIONS

We propose DDESVSFS as a simple, rapid and comprehensive prediction score for the Differential Diagnosis of Epileptic Seizures VS Functional Seizures. Large prospective studies are needed to evaluate its utility in clinical practice.

A South African review of routinely-collected health data of psychogenic nonepileptic seizure patients referred to psychiatrists in Johannesburg

Patients with psychogenic nonepileptic seizures (PNES) are often referred to psychiatrists for treatment of functional neurological symptom disorder (FNSD). However, not all patients with FNSD have an identified psychiatric comorbidity [1]. The aim of this observational study was to characterize the clinical and psychiatric features of patients with PNES from Johannesburg, South Africa, where a high frequency of PNES has been reported [2], and compare these findings to other reports. We hypothesized that patient outcomes regarding treatment adherence and episode frequency would improve when treated within a closed multidisciplinary team. The data included a retrospective record review of patients diagnosed with PNES from an epilepsy monitoring unit and referred for psychiatric assessment and treatment between November 2013 and July 2017. Fifty-nine cases met the criteria for the study. There were 7 male and 52 female participants, aged between 14 and 72 years (M = 33.76, SD = 13.88). The most frequently reported comorbid symptoms were anxiety (90%); dissociative symptoms (51%); headaches (76%) and gastrointestinal symptoms (36%). Important patient characteristics included past substance abuse (76%); impaired attachment (86%); past trauma (69%) and sexual trauma (29%). Generalized anxiety disorder (76%), major depressive disorder (64%) and PTSD (22%) were the most prevalent psychiatric diagnoses. After receiving psychiatric treatment, 47% of patients experienced a decrease in the frequency of episodes, while 86% became aware of the precipitants of their episodes. Psychiatric data can valuably inform current theories of PNES management. This study contributes to the understanding of comorbid, aetiological, and prognostic factors that are crucial to refining coherent models that will guide practice.

Objective score from initial interview identifies patients with probable dissociative seizures

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.

Sex Effects on Coping, Dissociation, and PTSD in Patients With Non-epileptic Seizures

PURPOSE OF REVIEW

Sex differences in non-epileptic seizures (NES) are of interest, as the diagnosis is more frequent in women than men (3:1 ratio). This paper reviews clinical findings regarding sex differences in NES through selective literature review and compares coping measures between women and men in our NES clinic.

RECENT FINDINGS

Some distinguishing clinical features of NES in women and men are reported in the literature. However, we found few sex differences in demographics and coping. In our population, avoidance and dissociation were strongly related to one another and significantly related to co-occurring PTSD diagnosis, which was seen in over 50% in both sexes. Our findings confirm a high prevalence of PTSD in patients with NES, suggesting that comorbid PTSD may override sex differences in accounting for use of avoidant and dissociative coping. These findings raise the possibility that NES may, at times, represent an extreme variant in dysfunctional coping in patients with PTSD.

The first-line management of psychogenic non-epileptic seizures (PNES) in adults in the emergency: a practical approach

Distinguishing non-epileptic events, especially psychogenic non-epileptic seizures (PNES), from epileptic seizures (ES) constitutes a diagnostic challenge. Misdiagnoses are frequent, especially when video-EEG recording, the gold-standard for PNES confirmation, cannot be completed. The issue is further complicated in cases of combined PNES with ES. In emergency units, a misdiagnosis can lead to extreme antiepileptic drug escalade, unnecessary resuscitation measures (intubation, catheterization, etc.), as well as needless biologic and imaging investigations. Outside of the acute window, an incorrect diagnosis can lead to prolonged hospitalization or increase of unhelpful antiepileptic drug therapy. Early recognition is thus desirable to initiate adequate treatment and improve prognosis. Considering experience-based strategies and a thorough review of the literature, we aimed to present the main clinical clues for physicians facing PNES in non-specialized units, before management is transferred to epileptologists and neuropsychiatrists. In such conditions, patient recall or witness-report provide the first orientation for the diagnosis, recognizing that collected information may be inaccurate. Thorough analysis of an event (live or based on home-video) may lead to a clinical diagnosis of PNES with a high confidence level. Indeed, a fluctuating course, crying with gestures of frustration, pelvic thrusting, eye closure during the episode, and the absence of postictal confusion and/or amnesia are highly suggestive of PNES. Moreover, induction and/or inhibition tests of PNES have a good diagnostic value when positive. Prolactinemia may also be a useful biomarker to distinguish PNES from epileptic seizures, especially following bilateral tonic-clonic seizures. Finally, regardless the level of certainty in the diagnosis of the PNES, it is important to subsequently refer the patient for epileptological and neuropsychiatric follow-up.

Personality traits, illness behaviors, and psychiatric comorbidity in individuals with psychogenic nonepileptic seizures (PNES), epilepsy, and other nonepileptic seizures (oNES): Differentiating between the conditions

OBJECTIVES

The study aimed to investigate if South African individuals with psychogenic nonepileptic seizures (PNES) differ from individuals with epileptic seizures (ES) and other nonepileptic seizures (oNES) in terms of demographic and seizure characteristics, personality traits, illness behaviors, and depression, anxiety, and posttraumatic stress disorder (PTSD) comorbidity in statistically significant ways; and if so, to test if these differences can be utilized in raising suspicion of PNES as the differential diagnosis to epilepsy and oNES in practice.

METHODS

Data were analyzed from 29 adults with seizure complaints recruited using convenience sampling from a private and a government hospital with video-electroencephalography (vEEG) technology. A quantitative double-blind convenient sampling comparative design was used. A demographic and seizure questionnaire, the NEO Five Factor Inventory-3 (NEO-FFI-3), an abbreviated version of Illness Behavior Questionnaire (IBQ), and the Beck Anxiety Inventory - Primary Care (BAI-PC) were administered. Cronbach's alphas, analysis of variance (ANOVA), cross-tabulation, Fisher exact test, and receiver operating characteristic (ROC) analyses were performed on the dataset.

RESULTS

The total sample consisted of 29 participants, of which 5 had PNES (17%), 21 ES (73%), and 3 oNES (10%). The final sample was comprised of 24 participants from the private hospital and 5 from the government hospital. The group with PNES was found to be significantly more male, to experience significantly more monthly seizures, and chronic pain when comparing the PNES with the ES group, and the PNES with the combined ES and oNES group in both private only sample, as well as the private and government hospital combined sample. Patients with PNES also had a higher level of education compared with the group with ES in the combined private and government hospital sample, something that was not evident in the private hospital only sample. No significant differences between groups were found in either sample in terms of age, population group, language, age at first seizure, and the NEO-FFI-3 subscales. All three groups scored above the cutoff point of 5 exhibiting depression, anxiety, and PTSD symptoms on the BAI-PC in both samples. However, the group with PNES tended to score significantly higher than the group with ES and the combined ES and oNES group in the private hospital sample. A cutoff point of 12 on the BAI-PC was found to predict PNES in this seizure population with 80% sensitivity and 89% specificity. However, once the analysis was repeated on the combined private and government hospital sample, significance in BAI-PC scores between groups was lost. All scales showed good reliability in our study, with the exception of the "Openness to Experience" subscale of the NEO-FFI-3 once reliability analysis was carried out on the combined private and government hospital group.

CONCLUSIONS

This study provides an important stepping stone in the understanding of demographic and seizure factors, personality domains, abnormal illness behaviors, and psychiatric comorbidity in the South African population with PNES. The study also reported on a cutoff score of 12 on the BAI-PC predicting PNES with 80% sensitivity and 89% specificity in a private hospital sample.

The feasibility of a multidisciplinary group therapy clinic for the treatment of nonepileptic seizures

A high percentage of patients presenting to epilepsy centers have a functional neurological disorder with apparent seizures, ultimately diagnosed as nonepileptic seizures (NES). Meta-analyses suggest that psychological treatment is required, but this treatment is not reliably available, resulting in reentry of these patients to neurology clinics and urgent care settings, reducing access for these services to patients with epilepsy and resulting in inadequate psychological care for patients with NES. A sustainable, group therapy-focused treatment clinic for patients with NES was developed as a combined effort between the departments of neurology and psychiatry at the University of Colorado Hospital, consisting of a full psychiatric evaluation, a five-week psychoeducational group, a 12-week psychodynamic therapy group, individual therapy, medication management, and family assessment. One hundred and six patients were treated in this clinic between July 2016 and October 2018. Patient retention after referral for treatment was 89/136 (65.4%), and group therapy adherence was 89/106 (84.0%). Healthcare utilization, used as a proxy to demonstrate worth, decreased during and after treatment. Analysis of the 106 treated patients elucidates other clinical characteristics of this population, including psychiatric comorbidities and specific medication classes at time of NES diagnosis. We conclude that this clinic model is feasible for recruiting, retaining, and engaging patients in appropriate treatment for their NES.

Nonepileptic Episodic Events

PURPOSE OF REVIEW

This review addresses the scope, evaluation, treatments, and outcomes of patients with nonepileptic episodic events with a focus on psychogenic nonepileptic seizures. Differentiation of the types of events, including a review of terminology, is included, as well as a brief review of special patient populations with these disorders.

RECENT FINDINGS

There are continued efforts to develop tools to improve the diagnosis of these disorders. A thorough evaluation with trained personnel and physicians knowledgeable in the assessment and treatment of these disorders is important. Although inpatient video-EEG monitoring in an epilepsy monitoring unit remains the gold standard for diagnosis, the assessment of clinical and historical factors is critical and can be useful in expediting the process and improving diagnostic certainty. International efforts have recently assisted in providing guidelines for the evaluation of the psychogenic disorders and may help target educational and other resources to underserved areas.

SUMMARY

The prompt and accurate diagnosis of nonepileptic episodic events and psychogenic nonepileptic seizures is possible with current technology, and the appropriate and targeted use of evidence-based treatments may help improve patient quality of life and avoid unnecessary disability in patients with these disorders.

Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures

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.

Secure Attachment and Depression Predict 6-Month Outcome in Motor Functional Neurological Disorders: A Prospective Pilot Study

BACKGROUND

The relationships between baseline neuropsychiatric factors and clinical outcome in patients with functional neurological disorder (FND)/conversion disorder remain poorly understood.

OBJECTIVE

This prospective, naturalistic pilot study investigated links between predisposing vulnerabilities (risk factors) and clinical outcome in patients with motor FND engaged in usual care within a subspecialty FND clinic.

METHODS

Thirty-four patients with motor FND were enrolled and completed baseline and 6-month follow-up psychometric questionnaires. Univariate screening tests followed by multivariate linear regression analyses were used to investigate neuropsychiatric predictors of 6-month clinical outcome in patients with motor FND.

RESULTS

In univariate analyses, baseline secure attachment traits and depression as measured by the Relationship Scales Questionnaire and Beck Depression Inventory-II positively correlated with improved Patient Health Questionnaire-15 scores. In a multivariate linear regression analysis adjusting for the interval time between baseline and follow-up data collection, baseline secure attachment and depression scores independently predicted improvements in Patient Health Questionnaire-15 scores. In additional analyses, patients with a diagnosis of psychogenic nonepileptic seizures compared to individuals with other motor FND subtypes showed a trend toward worse 6-month physical health outcomes as measured by the Short Form Health Survey-36.

CONCLUSION

Future large-scale, multi-site longitudinal studies are needed to comprehensively investigate neuropsychiatric predictors of clinical outcome in patients with motor FND, including functional weakness, functional movement disorders, and psychogenic nonepileptic seizures.