Identifying psychogenic seizures through comorbidities and medication history

Pain and functional neurological disorder: a systematic review and meta-analysis

BACKGROUND

Functional neurological disorder (FND) is characterised by neurological symptoms, such as seizures and abnormal movements. Despite its significance to patients, the clinical features of chronic pain in people with FND, and of FND in people with chronic pain, have not been comprehensively studied.

METHODS

We systematically reviewed PubMed, Embase and PsycINFO for studies of chronic pain in adults with FND and FND in patients with chronic pain. We described the proportions of patients reporting pain, pain rating and timing, pain-related diagnoses and responsiveness to treatment. We performed random effects meta-analyses of the proportions of patients with FND who reported pain or were diagnosed with pain-related disorders.

RESULTS

Seven hundred and fifteen articles were screened and 64 were included in the analysis. Eight case-control studies of 3476 patients described pain symptoms in a higher proportion of patients with FND than controls with other neurological disorders. A random effects model of 30 cohorts found that an estimated 55% (95% CI 46% to 64%) of 4272 patients with FND reported pain. Random effects models estimated diagnoses of complex regional pain syndrome in 22% (95% CI 6% to 39%) of patients, irritable bowel syndrome in 16% (95% CI 9% to 24%) and fibromyalgia in 10% (95% CI 8% to 13%). Five studies of FND diagnoses among 361 patients with chronic pain were identified. Most interventions for FND did not ameliorate pain, even when other symptoms improved.

CONCLUSIONS

Pain symptoms and pain-related diagnoses are common in FND. Classification systems and treatments should routinely consider pain as a comorbidity in patients with FND.

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.

Functional neurological disorder, physical activity and exercise: What we know and what we can learn from comorbid disorders

Functional neurological disorder (FND) is a common neurologic disorder associated with many comorbid symptoms including fatigue, pain, headache, and orthostasis. These concurrent symptoms lead patients to accumulate multiple diagnoses comorbid with FND, including fibromyalgia, chronic fatigue syndrome, postural orthostatic tachycardia syndrome, persistent post-concussive symptoms, and chronic pain. The role of physical activity and exercise has not been evaluated in FND populations, though has been studied in certain comorbid conditions. In this traditional narrative literature review, we highlight some existing literature on physical activity in FND, then look to comorbid disorders to highlight the therapeutic potential of physical activity. We then consider abnormalities in the autonomic nervous system (ANS) as a potential pathophysiological explanation for symptoms in FND and comorbid disorders and postulate how physical activity and exercise may provide benefit via autonomic regulation.

Medication use in patients with functional seizures from a public and a private hospital

PURPOSE

Currently, we have limited knowledge of any potential differences among patients with functional seizures (FS), otherwise known as psychogenic non-epileptic seizures (PNES), from different socioeconomic backgrounds. Investigating medication use among these patients may provide insight into the quality and intensity of medical care they receive. Thus, we aimed to assess and compare the frequency and quantity of antiseizure medications (ASMs), and psychiatric and other medications used among patients with FS from a private and public epilepsy monitoring units (EMUs) in Cape Town, South Africa.

METHODS

Only video-electroencephalographically (video-EEG) confirmed patients with FS with no comorbid epilepsy were eligible for the study. For this retrospective case-control study we collected data on patients' medication-taking histories using digital patient records, starting with the earliest available digital patient record for each hospital.

RESULTS

A total of 305 patients from a private hospital and 67 patients from a public hospital were included in the study (N = 372). Patients with FS attending the public hospital had lower odds of taking any ASMs at presentation (aOR=0.39, 95% CI [0.20, 0.75]) and ever taking psychiatric medications (aOR=0.41, 95% CI [0.22, 0.78]) compared to FS patients from the private hospital. They did, however, have higher odds of being discharged with an ASM (aOR=6.60, 95% CI [3.27, 13.35]) and ever taking cardiovascular medication (aOR=2.69, 95% CI [1.22, 5.90]) when compared to the private hospital patients. With every additional presenting ASM (aOR=0.63, 95% CI [0.45, 0.89]) and psychiatric medication (aOR=0.58, 95% CI [0.40, 0.84]) the odds of being from the public hospital decreased. However, they increased with every additional discharge ASM (aOR=3.63, 95% CI [2.30, 5.72]) and cardiovascular medication (aOR=1.26, 95% CI [1.02, 1.55]).

CONCLUSION

Standard approaches to pharmacological treatment for patients with FS differed between the public and private hospitals and may indicate a gap in quality of care.

Elevated Mortality Rate in Patients With Functional Seizures After Diagnosis and Referral

Background and Objectives

To evaluate the standardized mortality ratio (SMR) of patients in the United States referred to a multidisciplinary clinic for treatment of functional seizures.

Methods

We identified patients who had or had not died based on automated retrospective review of electronic health records from a registry of patients referred to a single-center multidisciplinary functional seizures treatment clinic. We calculated an SMR by comparing the number of observed deaths with the expected number of deaths in an age-matched, sex-matched, and race-matched population within the same state, and year records were available.

Results

A total of 700 patients with functional seizures (mean age 37 years, 78% female) were followed up for 1,329 patient-years for a median of 15 months per patient (interquartile range 6-37 months). We observed 11 deaths, corresponding to a mortality rate of 8.2 per 1,000 patient-years and an SMR of 2.4 (95% confidence interval: 1.17-4.22). Five of 9 patients with identified circumstances around their death were in hospice care when they passed. None of the identified causes of death were related to seizures directly.

Discussion

These data provide further evidence of elevated mortality in functional seizures soon after diagnosis and referral to treatment. These data from the decentralized health care system of the United States build on the findings from other countries with large-scale health registries.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population

BACKGROUND

Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions.

OBJECTIVE

The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults.

METHODS

A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board.

RESULTS

7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP-Hip Circumference (cm) (Gain = 7.2%), BMXHT-standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%).

CONCLUSION

Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.

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.

Functional neurological seizures and migraine: A systematic review and case series

BACKGROUND

The seizure subtype of functional neurological disorder (FND-seizures) is a common neuropsychiatric condition manifesting with episodic epilepsy-like events. Despite the common belief that FND-seizures are precipitated by psychological stressors, neurological disorders may also be triggers. In 1890, Babinski described four cases of FND symptoms associated with migraine attacks. Despite the passing of more than 130 years since this first clinical observation, the relationship between FND-seizures and migraine is not fully elucidated.

OBJECTIVES

(1) To complete a systematic review of the literature that investigated potential associations between FND-seizures and migraine and the response of FND seizures to treatment with migraine prophylactic medications (2). To undertake a retrospective study of patients with FND-seizures and migraine, including response to migraine prophylaxis.

METHODS

(1) Using PRISMA methods, we completed a systematic review of EMBASE and Scopus databases from inception to March 31, 2021, for literature on FND-seizures and migraine. (2) Our multi-disciplinary team, including subspecialists in psychosomatic medicine, epilepsy, and headache disorders, reviewed consecutive patients diagnosed with FND-seizures and migraine to assess potential causal associations and responses to standard migraine prophylactic medications.

RESULTS

(1) The search yielded seven studies from 126 screened manuscripts (N = 1,186 patients with FND-seizures; mean age 38.7 years; 72.6% female). They varied substantially in design, population, diagnostic measures, and outcomes. Nevertheless, all studies found associations between FND-seizures and migraine, which were stronger than those between epileptic seizures and migraine in comparative investigations, but provided limited information on treatment response. (2) In our case series, investigators reached unanimous consensus that migraine attacks triggered FND-seizures in 28/43 (65.1%) patients reviewed (mean age, 38.8 years; 74% female). In 19/26 (73%) patients with adequate follow-up data, treatment with migraine prophylactic medications alone (no behavioral interventions) concomitantly reduced FND-seizure and headache frequency by >50%.

CONCLUSION

Our systematic review and case series indicate that migraine attacks may trigger FND-seizures, perhaps more often that currently understood, and suggest that migraine prophylaxis may reduce FND-seizure frequency in such cases. To validate these observations, fully powered prospective investigations are required.

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.

Overall comorbidities in functional neurological disorder: A narrative review

INTRODUCTION

The importance to assess and include the frequent comorbidities in the personalised care management plan of patients with functional neurological disorders (FND) has arisen through the years. FND patients are not only complaining from motor and/or sensory symptoms. They also report some non-specific symptoms that participate to the burden of FND. In this narrative review, we aim to better describe these comorbidities in terms of prevalence, clinical characteristics and their variability depending on the subtype of FND.

METHODS

The literature was searched for on Medline and PubMed. The search was narrowed to articles between 2000 and 2022.

RESULTS

Fatigue is the most common symptom reported in relation to FND (from 47 to 93%), followed by cognitive symptoms (from 80 to 85%). Psychiatric disorders are reported in 40 to 100% FND patients, depending on the FND subtype (functional motor disorder [FMD], functional dissociative seizures [FDS]…) but also on the type of psychiatric disorder (anxiety disorders being the most frequent, followed by mood disorders and neurodevelopmental disorders). Stress factors such as childhood trauma exposure (emotional neglect and physical abuse predominantly) have also been identified in up to 75% of FND patients, along with maladaptive coping strategies. Organic disorders are commonly reported in FND, such as neurological disorders (including epilepsy in FDS [20%] and FMD in Parkinson's Disease [7%]). Somatic symptom disorders including chronic pain syndromes are frequently associated to FND (about 50%). To be noted, recent data also suggest a high comorbidity between FND and hypermobile Ehlers Danlos Syndrome (about 55%).

CONCLUSION

Put together, this narrative review highlights the high burden of FND patients, not only due to somatosensory alterations but also by considering the frequent comorbidities reported. Thus, such comorbidities must be taken into consideration when defining the FND personalised care management strategy for the patients.

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.

Frequency and economic burden of psychogenic non-epileptic seizures in patients applying for disability benefits due to epilepsy

BACKGROUND

Psychogenic non-epileptic seizures (PNES) resemble epileptic seizures and are often misdiagnosed as epilepsy.

OBJECTIVE

To investigate the frequency of PNES and to calculate the economic burden of the patients who admitted to video-electroencephalographicmonitoring (VEM) to obtain a diagnosis of epilepsy in order to apply for disability retirement.

METHODS

The present retrospective study included 134 patients who required disability reports between 2013 and 2019 and had their definite diagnoses after VEM. Following VEM, the patients were divided into three groups: epilepsy, PNES, and epilepsy + PNES.

RESULTS

In total, 22.4% (n = 30) of the patients were diagnosed with PNES, 21.6% (n = 29) with PNES and epilepsy, and 56% (n = 75), with epilepsy. The frequency of PNES among all patients was of 44% (n = 59). In patients with PNES alone, the annual cost of using anti-seizure medication was of 160.67 ± 94.04 dollars; for psychostimulant drugs, it was of 148.3 ± 72.48 dollars a year; and the mean direct cost for diagnostic procedures was of 582.9 ± 330.0 (range: 103.52-1601.3) dollars.

CONCLUSIONS

Although it is challenging to determine the qualitative and quantitative total cost in these patient groups, early diagnosis and sociopsychological support will reduce the additional financial burden on the health system and increase the quality of life of the patients.

Multidimensional psychopathological profile differences between patients with psychogenic nonepileptic seizures and epileptic seizure disorders

OBJECTIVE

Early differential diagnosis of psychogenic nonepileptic seizures (PNES) and epileptic seizures (ES) remains difficult. Self-reported psychopathology is often elevated in patients with PNES, although relatively few studies have examined multiple measures of psychopathology simultaneously. This study aimed to identify differences in multidimensional psychopathology profiles between PNES and ES patient groups.

METHOD

This was a retrospective case-control study involving patients admitted for video-EEG monitoring (VEM) over a two-year period. Clinicodemographic variables and psychometric measures of depression, anxiety, dissociation, childhood trauma, maladaptive personality traits, and cognition were recorded. Diagnosis of PNES or ES was determined by multidisciplinary assessment and consensus opinion. General linear mixed models (GLMMs) were used to investigate profile differences between diagnostic groups across psychometric measures. A general psychopathology factor was then computed using principal components analysis (PCA) and differences between groups in this 'p' factor were investigated.

RESULTS

261 patients (77 % with ES and 23 % with PNES) were included in the study. The PNES group endorsed greater symptomatology with GLMM demonstrating a significant main effect of group (η²_p = 0.05) and group by measure interaction (η²_p = 0.03). Simple effects analysis indicated that the PNES group had particularly elevated scores for childhood trauma (β = 0.78), dissociation (β = 0.70), and depression (β = 0.60). There was a high correlation between psychopathology measures, with a single p factor generated to explain 60 % variance in the psychometric scores. The p factor was elevated in the PNES group (β = 0.61). ROC curve analysis indicated that these psychometric measures had limited usefulness when considered individually (AUC range = 0.63-0.69).

CONCLUSION

Multidimensional psychopathological profile differences exist between patients with PNES and ES. Patients with PNES report more psychopathology overall, with particular elevations in childhood trauma, dissociation, and depression. Although not suitable to be used as a standalone screening tool to differentiate PNES and ES, understanding of these profiles at a construct level might help triage patients and guide further psychiatric examination and enquiry.

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.

Assessing the hidden burden of psychiatric disease in patients with nonepileptic seizures

Nonepileptic seizures are commonly associated with psychiatric comorbidities, and specifically PTSD. Despite increased prevalence of psychiatric disease noted on referral of patients to our dedicated clinic for nonepileptic seizures, we found even higher rates of comorbid psychiatric disease or significant symptomatology after our initial clinic intakes, whereby patients are formally evaluated by a behavioral health provider, in addition to an epileptologist. After intake, an additional 21% of patients were identified as having PTSD or significant trauma-related symptoms, an additional 7% of patients were identified with significant anxiety or panic-related symptoms, and an additional 11% of patients were identified with significant depressive symptoms. While highly effective treatment of nonepileptic seizures remains elusive, well-developed treatment paradigms with proven efficacy exist for depression, anxiety, and PTSD. Eliciting these psychiatric comorbidities and pursuing targeted treatments, especially for those patients that do not have easy access to providers with dedicated expertise in the management of nonepileptic seizures, may be a more easily scalable and implementable treatment modality for these patients.

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.

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.

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.

Differentiating psychogenic nonepileptic seizures from epileptic seizures: An observational study of patients undergoing video-electroencephalography (VEEG) in Australia

Video-electroencephalography (VEEG) is an invaluable tool for the differentiation of psychogenic nonepileptic seizures (PNES) from epileptic seizures (ES). This paper (i) evaluates the demographics of patients undergoing VEEGs at the Princess Alexandra Hospital from July 1 2017 to July 1 2019 and identifies those subsequently diagnosed with PNES, (ii) determines the delay in PNES diagnosis and proportion of PNES patients on antiepileptic drugs (AEDs) prior to a VEEG, and (iii) determines the proportion of PNES patients who subsequently ceased their AEDs. Of the 94 patients in the study group, 51 were female and 43 were male. In total, 22 (23%) of VEEGs were consistent with epilepsy (ES), 25 (26%), with PNES, 5 (5%) mixed (PNES and ES), 5 (5%) with other types of events, and 37 were nondiagnostic. Two patients with non-diagnostic VEEGs were subsequently treated as PNES at the treating clinician's discretion. Regarding AEDs, 62 of the patients were on at least one agent at the time of VEEG recording. The mean durations of AED therapy and diagnostic delay for patients subsequently diagnosed with PNES were six years and seven years, respectively. Of these, the majority had their AEDs subsequently weaned/ceased. This study highlights the value of VEEG in diagnostic differentiation of epileptic from nonepileptic events, thereby preventing unnecessary AED therapy for patients with PNES. The results are consistent with previous studies in the literature evaluating VEEG clinical outcomes.

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.

Suggestive seizure induction for inpatients with suspected psychogenic nonepileptic seizures

OBJECTIVE

To determine the utility of suggestive seizure induction for inpatient work-up of suspected psychogenic nonepileptic seizures (PNES).

METHODS

Prospective study of epilepsy center inpatient admissions with suspected PNES. Patients were randomized to undergo suggestive induction first (group A) and then, if necessary, long-term video-electroencephalography (EEG) monitoring, or vice versa (group B). Diagnostic pathways were compared. Potential clinical predictors for diagnostic success were evaluated.

RESULTS

Length of in-hospital stay did not significantly differ between groups. Suspicion of PNES was confirmed in 43 of 77 (56%) patients, evenly distributed between group A (22 of 39) and group B (21 of 38). In nine patients, recorded habitual seizures were epileptic and in 25 cases, no diagnostic event could be recorded. Diagnosis of PNES was ascertained primarily by recording a typical seizure through suggestive induction in 24 patients and through long-term monitoring in 19 patients. In group A (induction first), monitoring was not deemed necessary in 21% of cases. In group B (monitoring first), 13% would have remained inconclusive without suggestive induction. Patients who reported triggers to their habitual seizures were not more likely to have spontaneous or provoked PNES during monitoring or suggestive inducion, respectively. Patients with subjective seizure prodromes (auras) were significantly more likely to have a PNES during suggestive induction than those without (odds ratio [OR] 3.4, 95% confidence interval [CI] 1.1-10.4). There was no significant difference in seizure frequency between patients with spontaneous PNES during long-term monitoring and those with nondiagnostic monitoring sessions.

SIGNIFICANCE

Our results support the notion that suggestive seizure induction can reduce the number of inconclusive inpatient workups, and can obviate resource-intensive long-term monitoring in one fifth of cases. Patients who are aware of prodromes might have a higher chance of having seizures induced through suggestion.

Mortality in patients with psychogenic nonepileptic seizures

Objective

To investigate the hypothesis that patients diagnosed with psychogenic nonepileptic seizures (PNES) on video-EEG monitoring (VEM) have increased mortality by comparison to the general population.

Methods

This retrospective cohort study included patients evaluated in VEM units of 3 tertiary hospitals in Melbourne, Australia, between January 1, 1995, and December 31, 2015. Diagnosis was based on consensus opinion of experienced epileptologists and neuropsychiatrists at each hospital. Mortality was determined in patients diagnosed with PNES, epilepsy, or both conditions by linkage to the Australian National Death Index. Lifetime history of psychiatric disorders in PNES was determined from formal neuropsychiatric reports.

Results

A total of 5,508 patients underwent VEM. A total of 674 (12.2%) were diagnosed with PNES, 3064 (55.6%) with epilepsy, 175 (3.2%) with both conditions, and 1,595 (29.0%) received other diagnoses or had no diagnosis made. The standardized mortality ratio (SMR) of patients diagnosed with PNES was 2.5 (95% confidence interval [CI] 2.0–3.3). Those younger than 30 had an 8-fold higher risk of death (95% CI 3.4–19.8). Direct comparison revealed no significant difference in mortality rate between diagnostic groups. Among deaths in patients diagnosed with PNES (n = 55), external causes contributed 18%, with 20% of deaths in those younger than 50 years attributed to suicide, and “epilepsy” was recorded as the cause of death in 24%.

Conclusions

Patients diagnosed with PNES have a SMR 2.5 times above the general population, dying at a rate comparable to those with drug-resistant epilepsy. This emphasizes the importance of prompt diagnosis, identification of risk factors, and implementation of appropriate strategies to prevent potential avoidable deaths.

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.

Welfare consequences for people diagnosed with nonepileptic seizures: A matched nationwide study in Denmark

OBJECTIVE

We aimed to evaluate the excess direct and indirect costs associated with nonepileptic seizures.

METHODS

From the Danish National Patient Registry (2011-2016), we identified 1057 people of any age with a diagnosis of psychogenic nonepileptic seizures (PNESs) and matched them with 2113 control individuals. Additionally, 239 partners of patients with PNES aged ≥18 years were identified and compared with 471 control partners. Direct costs included frequencies and costs of hospitalizations and outpatient use weighted by diagnosis-related group, and specific outpatient costs based on data from the Danish Ministry of Health. The use and costs of drugs were based on data from the Danish Medicines Agency. The frequencies of visits and hospitalizations and costs of general practice were derived from National Health Security data. Indirect costs included labor supply-based income data, and all social transfer payments were obtained from Coherent Social Statistics.

RESULTS

A higher percentage of people with PNES and their partners compared with respective control subjects received welfare benefits (sick pay, disability pension, home care). Those with PNES had a lower employment rate than did controls for equivalent periods up to three years before the diagnosis was made. The additional direct and indirect annual costs for those aged ≥18 years, including transfers to patients with PNES, compared with controls, were €33,697 for people with PNES and €15,121 for their partners.

SIGNIFICANCE

Psychogenic nonepileptic seizures have substantial socioeconomic consequences for individual patients, their partners, and society.

Morbidity and mortality of nonepileptic seizures (NES): A controlled national study

Nonepileptic seizures (NES, psychogenic NES-PNES) are associated with significant morbidities. We evaluated the morbidities and mortality in a national group of children, adolescent, and adult patients before and after a first diagnosis of PNES.

METHODS

From the Danish National Patient Registry (1998-2013), we identified 1057 people of all ages with a diagnosis of NES and matched them with 2113 control individuals by age, gender, and geography. Comorbidities were calculated three years before and after diagnoses.

RESULTS

Patients with PNES showed increased comorbidities 3 years before and after diagnosis in almost all the diagnostic domains. The strongest associations were identified with other neurological diseases (after diagnosis, Hazard Ratio (HR): 38.63; 95% Confidence Interval (CI): 21.58-69.13; P < 0.001), abnormal clinical and laboratory findings (HR: 46.59; 95 CI: 27.30-79.52; P < 0.001), other health-related factors (HR: 12.83; 95%CI: 8.45-19.46; P < 0.001), and psychiatric comorbidities (HR: 15.45; 95% CI: 9.81-24.33). Epilepsy was identified in 8% of the patients with PNES. We found especially frequent comorbidity involving overweight, depression, anxiety, dissociative somatoform condition, other convulsions, lipothymias, reports of pain and other symptoms in several organ systems, and several reports of minimal traumas to the head, trunk, and extremities. Mortality was higher in patients with NES than in controls (HR: 3.21; 95% CI: 1.92-5.34; P < 0.001).

CONCLUSION

Morbidity is more frequent in several domains, including neurological, psychiatric, and other diseases, before and after a diagnosis of NES. Mortality is significantly higher in patients with PNES as compared to controls.

Atypical Prodromal Symptoms Help to Distinguish Patients With Psychogenic Nonsyncopal Collapse Among Youth Referred for Fainting

BACKGROUND

Distinguishing patients with psychogenic nonsyncopal collapse (PNSC), a conversion disorder that resembles syncope, can pose a difficult clinical challenge. Using the open-ended question "what does it feel like to faint?," the present study aimed to characterize how patients with PNSC perceive and communicate the prodromal symptoms associated with their attacks by comparing narratives between patients with PNSC and those with syncope.

METHODS

During a 42-month database-type study of tilt-table diagnoses, all patients with a history of fainting were asked the open-ended question. Symptom descriptions were compared, qualitatively and quantitatively, between patients with PNSC and those with tilt-induced syncope (n = 121 in both cohorts).

RESULTS

Twenty-nine patients (24%) diagnosed with PNSC and eight (6.6%) diagnosed with syncope either denied having any prodromal symptoms or could not recall any symptoms (P < 0.001). Among patients who reported prodromal symptoms, patient narratives led to the formation of 26 symptom categories. Symptom frequencies differed between cohorts in 19 of the symptom categories (each P value <0.05). Qualitative differences in the descriptions of symptoms were often present, even when symptom frequencies did not differ. More patients with PNSC described atypical symptoms than patients with syncope, 54 of 92 (58.7%) versus eight of 113 (7.1%), P < 0.001.

CONCLUSIONS

Symptom narratives can help to distinguish patients with PNSC from those with syncope. The use of a single, open-ended question as a screening tool for conversion disorder has immediate clinical relevance because it can be instituted easily in a busy clinical setting.

Functional non-epileptic attacks: essential information for psychiatrists

Functional non-epileptic attacks (FNEA) are seizure-like events occurring in the absence of epilepsy. Having had many different names over the years including dissociative convulsions and pseudo-seizures, they now fall in the borderland between neurology and psychiatry, often not accepted by either specialty. However, there is evidence that there is a high rate of psychiatric comorbidity in these patients and therefore it is likely that psychiatrists will come across patients with FNEA and they should know the broad principles of assessment and management.

We have provided a clinically based overview of the evidence regarding epidemiology, risk factors, clinical features, differentiation from epilepsy, prognosis, assessment and treatment.

By the end of this article, readers should be able to understand the difference between epileptic seizures and FNEA, know how to manage acute FNEA, and understand the principles of neuropsychiatric assessment and management of these patients, based on knowledge of the evidence base.

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.

Potential use of leukocytosis and anion gap elevation in differentiating psychogenic nonepileptic seizures from epileptic seizures

Epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) can be difficult to differentiate from each other in the emergency department (ED) setting. We have previously shown that the anion gap (AG) can help differentiate between ES and PNES in the ED. In this study, we explored whether additionally considering leukocytosis can help better differentiate between ES and PNES. We screened a total of 1354 subjects seen in the ED of a tertiary care medical center; 27 PNES and 27 ES patients were identified based on clinical description and subsequent electroencephalography (EEG). Multivariable logistic regression analysis was used to model the association between ES, leukocytosis, and AG. Our results indicated that within 9 hours after the index event, serum AG (adjusted odds ratio [aOR] 2.07) and white blood cell (WBC) count (aOR 1.61) were both independently associated with ES. We derived an equation to help differentiate between ES and PNES: 1.5*AG+WBC. A score >24.8 indicated a >90% likelihood of ES. A score <15.5 indicated a <10% likelihood of ES (ie, the alternate diagnosis of PNES should be considered). This study for the first time provides evidence to help differentiate PNES and ES utilizing acidosis and leukocytosis.

Self-reporting versus clinical scrutiny: the value of adding questionnaires to the routine evaluation of seizure disorders. An exploratory study on the differential diagnosis between epilepsy and psychogenic nonepileptic seizures

Questionnaires or symptom lists have proved effective for differentiating epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES). However, monitoring the events, corroborated by medical history gathered by experts, remains the gold standard. We directly compared symptoms and characteristic of the events self-reported by patients/eyewitnesses (Questionnaire A/B) with the information contained in the clinical charts of 50 patients with undefined diagnosis undergoing long-term monitoring. Data extracted from medical records were reformatted to fit the questionnaires' templates (A1/B1) for comparison. Quantitatively, self-reported information was considerably greater and more complete. Calculating sensitivity (SE) and specificity (SP) of all variables in the group with confirmed diagnosis, we identified those above the preset thresholds with the potential to discriminate between ES and PNES. Eight predictive variables were common to both methods: head injury, physical/emotional abuse, chronic fatigue (A); talked out of seizures, eyes closed, apnea, and collapsing (B). Eleven predictive variables were specific to direct questioning: preictal headache, bright light, feeling overwhelmed, heart racing, tingling and numbness, postictal trouble speaking, physical pain, history of gastro-esophageal reflux disease (GERD), self-inflicted injuries (A); on/off shaking, and side-to-side head movements (B). Thirteen predictive variables were generated by chart review: sleep deprivation, strong emotions/anxiety, preictal headache (warning), nausea/vomiting, history of PNES, cholecystectomy, depression, medications for behavioral problems (A1), sudden start/sudden stop of shaking, both sides shaking, falling during the seizure, feeling confused and disoriented postictally (B1). CONCLUSION: Self-reporting and clinical scrutiny are complementary. Structured questionnaires increase the range of predictive variables and should be utilized routinely to facilitate clinicians' quest for the correct diagnosis.

Diagnostic criteria to aid the differential diagnosis of patients presenting with transient loss of consciousness: A systematic review

BACKGROUND

Transient loss of consciousness (TLOC) is a common presentation in primary care. Over 90% of these are due to epileptic seizures (ES), syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnosis rates are as high as 30%.

METHODS

Systematic review of inter-ictal clinical criteria to aid differential diagnosis of TLOC. We searched Medline, EMBASE, CINAHL and PsycInfo databases, as well as relevant grey literature depositories and citations of relevant reviews and guidelines for studies giving sensitivity and specificity of inter-ictal clinical characteristics used to differentiate between causes of TLOC. Two independent reviewers selected studies for inclusion and performed critical appraisal of included articles. We performed a narrative synthesis of included studies.

RESULTS

Of 1023 results, 16 papers were included. Two compared syncope, ES, and PNES; all others compared ES and PNES. All were at significant risk of bias in at least one domain. 6 studied patient symptoms, 6 medical and social history, 3 witness reports and 1 examination findings. No individual criterion differentiated between diagnoses with high sensitivity and specificity.

CONCLUSIONS

There is a lack of validated diagnostic criteria to help clinicians assessing patients in primary or emergency care settings to discriminate between common causes of TLOC. Performance may be improved by combining sets of criteria in a clinical decision rule, but no such rule has been validated prospectively against gold-standard diagnostic criteria.

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

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.