Psychogenic nonepileptic seizures

Therapeutic value in the time of digital brainwaves

This article examines the value of medical technology through the case of electroencephalograms (EEGs), devices used to visualize brain activity and diagnose seizures. Drawing on ethnographic fieldwork, the article shows that EEGs are valued differently by patients and medical practitioners. While practitioners value EEGs for their clinical utility, i.e., ability to inform clinical decisions, patients value EEGs even in the absence of clinical utility. Indeed, patients derive long-lasting therapeutic effects from this diagnostic technology. These findings intervene in the utilitarian calculus of therapeutic value-a mode of reasoning that equates value with clinical utility-commonly deployed in biomedicine and engineering and call for a recognition of alternative notions such as the therapeutic value of being witnessed and cared for by medical experts via EEGs and other technologies that require time to work. Expansive notions of therapeutic value are imperative for including marginalized patients-especially low-income, disabled, and women patients-in debates on automation and the future of healthcare. Studying how multiple stakeholders value a medical technology provides insight into valuation, objectification, expertise, and other concerns central to science and technology studies.

Outcome of psychogenic non-epileptic seizures following diagnosis in the epilepsy monitoring unit

Objective

To study the outcome of patients with psychogenic non-epileptic seizures (PNES) after their diagnosis in the epilepsy monitoring unit (EMU).

Methods

Patients diagnosed in our EMU with definite PNES between January 2009 and May 2023 were contacted by phone, and those who agreed to participate were asked a set of predetermined questions. Comparative analyses were carried out on several variables before and after diagnosis: number of participants with daily PNES, number of visits to the emergency department, number of participants who consulted their general practitioner or a neurologist outside of a scheduled follow-up, number of participants who took antiseizure medications (ASMs) or psychotropic drugs, and employment status.

Results

Out of the 103 patients with a definite diagnosis of PNES, 61 patients (79% female) accepted to participate in our study. The median age at PNES onset was 35 years, and the median delay to diagnosis was 3 years. Almost two-thirds (62%) were receiving ASMs and 40% psychotropic drugs. The mean stay at the EMU was 5 days. PNES diagnosis was explained to almost all patients (97%) by the end of their EMU stay and was well-accepted by most (89%). When contacted, 46% of participants no longer had PNES; 32% mentioned that their PNES had ceased immediately upon communication of the diagnosis. The median follow-up duration was 51 months. Fewer patients had daily seizures after the diagnosis (18 vs. 38%; p < 0.0455). Similarly, the median number of emergency department visits was significantly lower (0 vs. 2; p < 0.001). Only 17 patients consulted their general practitioner (vs. 40, p < 0.001) and 20 a neurologist (vs. 55, p < 0.001) after a PNES attack outside of a scheduled follow-up. The use of ASMs was also significantly reduced from 70 to 33% (p < 0.01), with only one still taking an ASM for its antiseizure properties. Significantly more participants were working at last follow-up than at PNES diagnosis (49 vs. 25%; p < 0.001).

Conclusion

Our study revealed a relatively favorable long-term outcome of definite PNES diagnosed in the EMU that translated in significant reductions in PNES frequency, health care utilization and ASM use, as well as a significant increase in employment rate.

Health Care Utilization in Functional Neurologic Disorders: Impact of Explaining the Diagnosis of Functional Seizures on Health Care Costs

Background and Objective

The objectives of this study were to investigate health care utilization costs of patients with video-electroencephalography (VEEG)-confirmed functional seizures (FS), determine whether patients who received a satisfactory functional neurologic disorder (FND) diagnosis explanation had reduced health care utilization compared with those with a poor explanation; and to quantify the overall health care costs 2 years prediagnosis and postdiagnosis for those receiving a different explanation.

Methods

Patients with VEEG-confirmed pure FS (pFS) or mixed (functional seizure plus epileptic seizures) diagnosis between July 1, 2017, and July 1, 2019, were evaluated. Explanation of the diagnosis was determined "unsatisfactory" or "satisfactory" using self-developed criteria, and health care utilization data were collected using an itemized list. The subsequent costs 2 years post-FND diagnosis were compared with those 2 years before, and cost outcomes were compared between both groups.

Results

In patients who received a satisfactory explanation (n = 18), total health care costs were reduced from $169,803 to $117,133 USD (-31%). An increase in costs was found ($73,430 to $186,553 USD = +154%) in patients with pPNES after an unsatisfactory explanation (n = 7). On an individual level, 78% with a satisfactory explanation saw a reduction in total health care costs per year (mean $5,111 USD to $1,728 USD), and in 57%, an unsatisfactory explanation led to an increase (mean $4,425 to $20,524 USD). A similar effect was seen from explanation on patients with a dual diagnosis.

Discussion

The method of communicating an FND diagnosis has a significant impact on subsequent health care utilization. Those receiving satisfactory explanations demonstrated reduced health care utilization, whereas an unsatisfactory explanation resulted in additional expenses.

Functional (psychogenic non-epileptic/dissociative) seizures: why and how?

Functional seizures (FS) known also as psychogenic non-epileptic seizures or dissociative seizures, present with ictal semiological manifestations, along with various comorbid neurological and psychological disorders. Terminology inconsistencies and discrepancies in nomenclatures of FS may reflect limitations in understanding the neuropsychiatric intricacies of this disorder. Psychological and neurobiological processes of FS are incompletely understood. Nevertheless, important advances have been made on underlying neuropsychopathophysiological mechanisms of FS. These advances provide valuable information about the underlying mechanisms of mind-body interactions. From this perspective, this narrative review summarises recent studies about aetiopathogenesis of FS at two levels: possible risk factors (why) and different aetiopathogenic models of FS (how). We divided possible risk factors for FS into three categories, namely neurobiological, psychological and cognitive risk factors. We also presented different models of FS based on psychological and neuroanatomical understanding, multilevel models and integrative understanding of FS. This work should help professionals to better understand current views on the multifactorial mechanisms involved in the development of FS. Shedding light on the different FS profiles in terms of aetiopathogenesis will help guide how best to direct therapy, based on these different underlying mechanisms.

Managing Functional Neurological Disorders: Protocol of a Cohort Study on Psychogenic Non-Epileptic Seizures Study

BACKGROUND

Functional neurological disorders (FNDs) are neurological symptoms that cannot be explained by an underlying neurological lesion or other medical illness and that do not have clear neuropathological correlates. Psychogenic non-epileptic seizures (PNES) are a common and highly disabling form of FND, characterized by paroxysmal episodes of involuntary movements and altered consciousness that can appear clinically similar to epileptic seizures. PNES are unique among FNDs in that they are diagnosed by video electroencephalographic (VEEG), a well-established biomarker for the disorder. The course of illness and response to treatment of PNES remain controversial. This study aims to describe the epidemiology of PNES in the Department of Veterans Affairs Healthcare System (VA), evaluate outcomes of veterans offered different treatments, and compare models of care for PNES.

METHODS

This electronic health record (EHR) cohort study utilizes an informatics search tool and a natural language processing algorithm to identify cases of PNES nationally. We will use VA inpatient, outpatient, pharmacy, and chart abstraction data across all 170 medical centers to identify cases in fiscal years 2002-2018. Outcome measurements such as seizure frequency, emergency room visits, hospital admissions, suicide-related behavior, and the utilization of psychotherapy prior to and after PNES diagnosis will be used to assess the effectiveness of models of care.

DISCUSSION

This study will describe the risk factors and course of treatment of a large cohort of people with PNES. Since PNES are cared for by a variety of different modalities, treatment orientations, and models of care, effectiveness outcomes such as seizure outcomes and utilization of emergency visits for seizures will be assessed. Outcome measurements such as seizure frequency, emergency room visits, hospital admissions, suicide-related behavior, and psychotherapy prior to and after PNES diagnosis will be used to assess the effectiveness of models of care.

Semiological characteristics of patients with psychogenic nonepileptic seizures: Gender-related differences

Psychogenic nonepileptic seizures (PNES) are more prevalent among women, and diagnosis may sometimes be delayed by as much as seven years. Understanding the effect of gender on the presentation of a PNES may assist with diagnosis based on semiological details in the clinical setting. Although video-EEG monitoring (VEM) is the gold standard for diagnosing PNES, determining gender-related seizure semiology through careful history may prevent diagnostic delay while waiting for VEM. The aim of this study was to investigate gender-related differences in the semiology of PNES. Patients, all aged at least 16 years, diagnosed with PNES following VEM between December 2005 and November 2016 were included in this study. All patients' medical records and video-EEG-documented PNES were reviewed, and the presence or absence of semiological signs was recorded for each documented attack. Demographic features and semiological signs of PNES were compared between female and male patients. Forty-one patients (31 females, 10 males) aged 27.2 ± 12.2 years (range: 16-65) were included in the study. Mean age at onset of PNES was higher for female patients than males, at 24.3 ± 11.5 versus 17.5 ± 3.2 years (p = 0.005). The median duration of PNES was longer for female patients than males, at 10 min (range: 5 s-120 min) versus 2 min (range: 10 s-60 min) (p = 0.016). The most common symptom was forced eye closure in both genders. No significant gender-specific differences were observed in terms of the type or semiology of PNES. Although there are no major gender-related differences in PNES semiology, our findings highlight the importance of greater caution, especially in male patients, when diagnosing PNES, remembering that onset may also occur at young ages and that a short seizure duration does not exclude PNES.

Increased odds and predictive rates of MMPI-2-RF scale elevations in patients with psychogenic non-epileptic seizures and observed sex differences

OBJECTIVE

The Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) is a self-report instrument, previously shown to differentiate patients with epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES). At present, the odds of MMPI-2-RF scale elevations in PNES patients, as well as the diagnostic predictive value of such scale elevations, remain largely unexplored. This can be of clinical utility, particularly when a diagnosis is uncertain.

METHOD

After looking at mean group differences, we applied contingency table derived odds ratios to a sample of ES (n=92) and PNES (n=77) patients from a video EEG (vEEG) monitoring unit. We also looked at the positive and negative predictive values (PPV, NPV), as well as the false discovery rate (FDR) and false omission rate (FOR) for scales found to have increased odds of elevation in PNES patients. This was completed for the overall sample, as well as the sample stratified by sex.

RESULTS

The odds of elevations related to somatic concerns, negative mood, and suicidal ideation in the PNES sample ranged from 2 to 5 times more likely. Female PNES patients had 3-6 times greater odds of such scale elevations, while male PNES patients had odds of 5-15 times more likely. PPV rates ranged from 53.66% to 84.62%, while NPV rates ranged from 47.52% to 90.91%. FDR across scales ranged from 15.38% to 50%, while the FOR ranged from 9.09% to 52.47%.

CONCLUSIONS

Consistent with prior research, PNES patients have greater odds of MMPI-2-RF scale elevations, particularly related to somatic concerns and mood disturbance. Female PNES patients endorsed greater emotional distress, including endorsement of suicide related items. Elevations of these scales could aid in differentiating PNES from ES patients, although caution is warranted due to the possibility of both false positives and the incorrect omissions of PNES cases.

Comparative prediction of nonepileptic events using MMPI-2 clinical scales, Harris Lingoes subscales, and restructured clinical scales

The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a psychological testing tool used to measure psychological and personality constructs. The MMPI-2 has proven helpful in identifying individuals with nonepileptic events/nonepileptic seizures. However, the MMPI-2 has had some updates that enhanced its original scales. The aim of this article was to test the utility of updated MMPI-2 scales in predicting the likelihood of non-epileptic seizures in individuals admitted to an EEG video monitoring unit. We compared sensitivity, specificity, and likelihood ratios of traditional MMPI-2 Clinical Scales against more homogenous MMPI-2 Harris-Lingoes subscales and the newer Restructured Clinical (RC) scales. Our results showed that the Restructured Scales did not show significant improvement over the original Clinical scales. However, one Harris-Lingoes subscale (HL4 of Clinical Scale 3) did show improved predictive utility over the original Clinical scales as well as over the newer Restructured Clinical scales. Our study suggests that the predictive utility of the MMPI-2 can be improved using already existing scales. This is particularly useful for those practitioners who are not invested in switching over to the newly developed MMPI-2 Restructured Form (MMPI-2 RF).

Epidemiology of psychogenic nonepileptic seizures

We critically review the existing literature about the epidemiology (i.e., diagnosis, occurrence, age, gender, comorbidity with epilepsy, associated factors, prognosis, mortality, and cost) of psychogenic nonepileptic seizures (PNES) and provide suggestions for future research. Psychogenic nonepileptic seizures are commonly diagnosed at epilepsy centers. The diagnosis of PNES relies on a multidisciplinary evaluation and is usually based on different combinations of data. Recording a seizure, while under video-EEG monitoring, is the most reliable diagnostic test. However, not all patients present with seizures while under video-EEG monitoring. Furthermore, not all epileptic seizures produce visible changes in the scalp EEG. The incidence of PNES was estimated to be 1.4-4.9/100,000/year in three previous studies, and the prevalence was calculated to be between 2 to 33 per 100,000 in one study, making it a significant neuropsychiatric condition. However, there remains a scarcity of data about the epidemiology of PNES, and extant studies that assessed the epidemiological characteristics of PNES have significant limitations. For example, inconsistencies with regard to the age of patients studied and lack of standardization of the diagnostic criteria are some of the significant limitations among studies. In conclusion, PNES merit further epidemiological and pathophysiological investigation. A more precise definition and clear guidance on standards for the diagnosis might influence the direction of future research. Well-designed prospective population-based studies to clarify the epidemiology of PNES in various parts of the world, including an evaluation of the predisposing, precipitating, and perpetuating factors in cross-cultural comparisons is required.

Psychiatric and neuropsychological profiles of people with psychogenic nonepileptic seizures

OBJECTIVE

This study examined the psychiatric and neuropsychological profiles of people with psychogenic nonepileptic seizures (PNES).

METHODS

Twenty-people who had been diagnosed with psychogenic nonepileptic seizures (PNES), but not epilepsy, were recruited into this study. A healthy control group was also recruited and was matched for age and gender. All participants underwent structured psychiatric assessment and psychometric assessment. Neuropsychological assessment was carried out using the Cambridge Neuropsychological Test Battery (CANTAB) after participants passed the Medical Symptom Validity Test (MSVT) of effort.

RESULTS

One patient failed the MSVT and was excluded from the analysis. Therefore, data from 19 people with PNES and their matched healthy controls were analyzed. Compared with controls, people with PNES had significantly higher levels of depressive symptoms, anxiety symptoms, dissociative experiences, and alexithymic traits. In addition, people with PNES had impairments in spatial working memory and attention when compared with healthy controls.

CONCLUSION

To our knowledge, this is the first study to report that, compared with controls, people with PNES have abnormal cognitive functioning after controlling for effects of effort and FSIQ. People with PNES also have high levels of anxiety, depressive, and dissociative symptoms. In addition, they appear to particularly focus on health problems and show evidence of chronic emotional dysregulation. Further studies are required to replicate our results and to help clarify the pathogenic mechanisms underlying PNES.

Balancing Clinical and Pathologic Relevence in the Machine Learning Diagnosis of Epilepsy

The application of machine learning to epilepsy can be used both to develop clinically useful computer-aided diagnostic tools, and to reveal pathologically relevant insights into the disease. Such studies most frequently use neurologically normal patients as the control group to maximize the pathologic insight yielded from the model. This practice yields potentially inflated accuracy because the groups are quite dissimilar. A few manuscripts, however, opt to mimic the clinical comparison of epilepsy to non-epileptic seizures, an approach we believe to be more clinically realistic. In this manuscript, we describe the relative merits of each control group. We demonstrate that in our clinical quality FDG-PET database the performance achieved was similar using each control group. Based on these results, we find that the choice of control group likely does not hinder the reported performance. We argue that clinically applicable computer-aided diagnostic tools for epilepsy must directly address the clinical challenge of distinguishing patients with epilepsy from those with non-epileptic seizures.

Clinical profile of psychogenic non-epileptic seizures in adults: A study of 63 cases

AIMS

To evaluate clinical profile and short-term outcome of psychogenic non-epileptic seizures (PNES) in Indian adult population.

SETTING AND DESIGN

A prospective observational study, conducted at tertiary teaching institute at New Delhi.

MATERIALS AND METHODS

Sixty-three patients with confirmed PNES were enrolled. The diagnosis was based on witnessing the event during video-electroencephalography (Video-EEG) monitoring. A detailed clinical evaluation was done including evaluation for coexistent anxiety or depressive disorders. Patients were divided into two groups on the basis of excessive or paucity of movements during PNES attacks. Patients were followed-up to 12 months for their PNES frequency.

STATISTICAL ANALYSIS

Means and standard deviations were calculated for continuous variables. Chi-square and Students t-test were used to compare categorical and continuous variables respectively.

RESULTS

The mean age at onset of PNES was 25.44 years; with F:M ratio of 9.5:1. Coexistent epilepsy was present in 13 (20.63%) cases. Twenty-two patients (44%) with only PNES (n = 50) had received antiepileptic drugs. Out of 63 patients of PNES 24 (38.1%) had predominant motor phenomenon, whereas 39 (61.9%) had limp attacks. The common features observed were pre-ictal headache, ictal eye closure, jaw clenching, resistant behavior, ictal weeping, ictal vocalization, and unresponsiveness during episodes. Comorbid anxiety and depressive disorders was seen in 62.3% and 90.16% patients, respectively. Short-term (6-12 months) outcome of 45 patients was good (seizure freedom in 46.66% and >50% improvement in 24.44% cases).

CONCLUSION

PNES is common, but frequently misdiagnosed and treated as epileptic seizures. A high index of suspicion is required for an early diagnosis. Proper disclosure of diagnosis and management of the psychiatric comorbidities can improve their outcome.

LIMITATION

Limited sample size and change in seizures frequency as the only parameter for the assessment of the outcome are the two major limitations of our study.

Diagnostic utility of the Structured Inventory of Malingered Symptomatology for identifying psychogenic non-epileptic events

The Structured Interview of Malingered Symptomatology (SIMS) is a self-report instrument that asks patients whether they experience atypical or implausible symptoms. The instrument has not been evaluated in an epilepsy population, and the potential for it to accurately distinguish between patients with psychogenic non-epileptic events (PNEE) and epileptic event groups has not been established. The SIMS was administered to patients in long-term video-EEG monitoring of these patients, 91 with PNEE and 29 with epilepsy were included in this study. Structured Interview of Malingered Symptomatology total scores as well as neurological and affective subscales were found to be predictors of group membership. Sensitivity and specificity across several different base rates of PNEE as well as maximum level likelihood ratios are presented. The findings not only demonstrate the utility of marked score elevations in differentiating PNEE from epilepsy but also point to considerable caution in interpreting mild elevations. Implications for the utility of this instrument in epilepsy evaluations are discussed.

Newly diagnosed psychogenic nonepileptic seizures: health care demand prior to and following diagnosis at a first seizure clinic

Patients with psychogenic nonepileptic seizures (PNES) are heavy users of emergency and nonemergency health care. We performed a 1-year prospective audit of use of a group of PNES-related health care items in patients with newly diagnosed (mean duration: 7.3 months) PNES from PNES onset to diagnosis and from diagnosis to 6 months postdiagnosis. Twenty-eight patients (20 women, age: 34±16 years) were responsible for 14 general practitioner home visits, 31 ambulance calls, 34 emergency department visits, 21 hospital admissions (66 inpatient days), 8 MRI scans, 24 CT scans, 2 standard EEGs, 28 short video EEG recordings, and 5 ambulatory EEG recordings. In the 6 months following diagnosis, there were 2 emergency department visits (94.1% reduction), no hospital admissions (100% reduction), 2 ambulance calls, no general practitioner visits, 1 MRI scan, and no CT scans or EEGs. The immediacy of this marked health care demand reduction suggests that the relationship between presentation of diagnosis and health care demand reduction is causal.

Epilepsy, mental health disorder, or both?

Temporal lobe epilepsy (TLE), a subset of the seizure disorder family, represents a complex neuropsychiatric illness, where the neurological presentation may be complemented by varying severity of affective, behavioral, psychotic, or personality abnormalities, which, in turn, may not only lead to misdiagnosis, but also affect the management. This paper outlines a spectrum of mental health presentations, including psychosis, mood, anxiety, panic, and dissociative states, associated with epilepsy that make the correct diagnosis a challenge.

Using patient history to distinguish between patients with non-epileptic and patients with epileptic events

Information obtained during psychological evaluations of 93 patients with epileptic events (EEs) and 63 with nonepileptic events (NEEs) was used to test the relative contributions of multiple risk factors to prediction of NEEs during video/EEG monitoring. The best group of independent predictors of NEEs comprised: (1) age at first spell, (2) symptoms of a psychiatric diagnosis other than anxiety or depression, (3) marital instability, (4) symptoms of an anxiety disorder other than panic disorder, and (5) years of education. Report of childhood abuse or neglect and taking psychotropic medication correlated with most of the other risk factors for NEEs. It may not be necessary to gather data on all of the variables shown to be associated with NEEs. Although there is a high prevalence of risk factors for psychopathology in patients with EEs, it is lower compared with that of patients with NEEs, and patients with EEs are less likely to report multiple risk factors.

Medical management of refractory status epilepticus

This review discusses the medical management of pediatric refractory status epilepticus, defined here as persistent seizures of any type after the administration of 2 appropriate anticonvulsants. The use of both nonanesthetic and anesthetic anticonvulsants is discussed along with the relative strengths and weaknesses of each agent. Appropriate treatment goals and the use of electroencephalographic monitoring are described as are reasonable treatment algorithms. Finally, ethical considerations are briefly discussed in the context of available outcome data.

The Minnesota Multiphasic Personality Inventory-2-Restructured Form in the epilepsy monitoring unit

The Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) is a restructuring of the MMPI-2 that has improved the psychometric characteristics of the test. The primary aim of this study was to provide diagnostic utility data on the MMPI-2-RF in an epilepsy monitoring unit population (N=429). Mean comparisons revealed group differences on Validity Scales Fs and FBS-r; Restructured Clinical Scales RC1 and RC3; and Somatic Scales MLS, GIC, HPC, and NUC. Diagnostic utility data are provided for those scales with the largest effect sizes: RC1, FBS-r, and NUC. On RC1, sensitivity was 76% and specificity was 60%, similar to values found when applying published decision rules to the MMPI-2. RC1 explains unique variance in diagnosis beyond that explained by demographic or medical history risk factors. We provide likelihood ratios for scores on RC1, FBS-r, and NUC that can be used by the clinician to calculate posttest odds and probability of nonepileptic seizures using the base rate of nonepileptic seizures in his/her population.

MMPI-2 profiles: fibromyalgia patients compared to epileptic and non-epileptic seizure patients

We compared MMPI-2 profiles of Gulf War veterans with fibromyalgia (FM) to epileptic seizure (ES) patients, psychogenic non-epileptic seizure (PNES) patients, and Gulf War veteran healthy controls. Both PNES and FM are medically unexplained conditions. In previous MMPI-2 research PNES patients were shown to have significantly higher Hs and Hy clinical scales than ES patients. In the present research the FM group had significantly higher Hs and Hy scale scores than both the ES group and the healthy control group. There was no significant difference between the FM and PNES Hs scale scores; however, the FM Hy scale score was significantly lower than the PNES Hy scale score. Present findings indicate a high level of psychological distress in the FM group.

Conversion disorder and coexisting nonepileptic seizures in patients with refractory seizures

Nonepileptic seizures (NES) are commonly observed in patients with seizures resistant to antiepileptic drugs (AEDs). However, NES may be symptomatic of different diagnoses, in particular, conversion disorder (CD) and coexisting NES and epileptic seizures (CENES). We compared the clinical characteristics of these disorders in 219 patients with refractory seizures. The prevalence of NES was similar in children (11%) and adults (16%). In both groups, CENES represented the most frequent cause of NES (75%). In adults, CD was associated with a shorter duration of illness and normal neuroimaging and interictal EEG compared with the other groups. Patients with CD represented one-quarter of all patients with AED-resistant seizures with normal presentation during interictal investigations. In both children and adults with AED-resistant seizures, NES are frequently observed and are three times more likely to be CENES than CD.