Patients with late onset psychogenic non-epileptic seizures (PNES): How do they compare to those with younger onset?

Death of a loved one: A potential risk factor for onset of functional seizures

Functional seizures (FS) are a symptom of Functional Neurological Disorder (FND), the second most common neurological diagnosis made worldwide. Childhood trauma is associated with the development of FS, but more research is needed to truly understand the effects of trauma on FS onset. A sample of 256 responses by adults with FS to the Childhood Traumatic Events Scale were analyzed using a Cox proportional hazard model. When investigating each unique childhood traumatic exposure and its associated self-reported severity together, experiencing death of a loved one and experiencing violence were significantly associated with FS onset, suggesting reduced time from trauma exposure to first FS. Death of a loved one in childhood is often overlooked as an influential risk factor for future development of serious mental illnesses such as FS. In this study we show death of a loved one in childhood should be considered as an influential traumatic experience and recommend FND researchers examine its prevalence in patient histories and the potential effects on attachment-related processes and clinical treatment formulations. We recommend future studies incorporate loss of a loved one during childhood (before age 18) in both quantitative and qualitative assessments of persons with FND.

Illness representations of people with later-onset functional seizures

PURPOSE

Although functional seizures can start at any age, little is known about the individuals for whom onset occurs after the age of 40. It has been proposed that health-related traumatic events are more relevant causal factors for people with 'later-onset functional seizures' than for those whose functional seizures begin earlier in life, however, the illness representations of people with later-onset functional seizures have not yet been investigated. This study aimed to understand the experiences and illness representations of people with later-onset functional seizures.

METHODS

This was a mixed-methods study. People with later-onset functional seizures were recruited via a neurologist's caseload and online membership-led organisations. Semi-structured interview transcripts were analysed using Template Analysis according to the Common-Sense Model (CSM). Self-report measures of demographic and clinical details were collected to characterise the sample and verify themes.

RESULTS

Eight people with later-onset functional seizures participated in the study. Illness representations relating to all domains of the CSM as well as an additional theme of 'Triggers' were identified. Functional seizures were characterised as a mysterious brain disorder analogous to a computer malfunction and involving involuntary movements associated with alterations in consciousness. Perceptions of duration were indefinite, and triggers were unknown or at the extremes of autonomic arousal. Half of the sample identified health-related events/trauma as causal. Opinions were divided on 'cumulative life stress' as a causal factor. Most perceived themselves to have limited or no control but having 'control' over seizures was conceptualised as different to reducing their likelihood, frequency, or impact. Later-onset functional seizures were viewed as being more detrimental for caring and financial responsibilities but to have advantages for acceptance.

CONCLUSIONS

This is the first study to assess the illness representations of people with later-onset functional seizures. Many themes were similar to those identified in samples including people with earlier-onset functional seizures. Health-related trauma or events were the most strongly endorsed perceived causal factor, but with the exception of 'consequences', all representations were characterised by uncertainty. Clinicians should hold in mind the interaction between life stage and the consequences of later-onset functional seizures.

The clinical characteristics of childhood-onset and late-onset functional seizures: How do they differ?

BACKGROUND

We investigated childhood-onset functional seizures (FS) and late-onset FS and hypothesized that there are differences in their characteristics.

METHODS

In this retrospective study, we investigated all patients with confirmed FS with an age at onset of 14 years or younger and those with an age at onset of 50 years or older, who were admitted to the epilepsy monitoring units at one center in Iran (Shiraz Comprehensive Epilepsy Center, from 2008 until 2022) and one center in the USA (Vanderbilt University Medical Center, from 2011 until 2022).

RESULTS

One-hundred and forty patients were included. They included 80 patients with childhood-onset FS and 60 with late-onset FS. Those with late-onset FS were more likely to have medical comorbidities compared with the patients with childhood-onset FS (OR = 13.9). Those with late-onset FS more likely had a history of head injury compared with the patients with childhood-onset FS (OR = 5.97). Duration of illness was significantly longer in patients with childhood-onset FS compared with the patients with late-onset FS (6 years vs. 2 years).

CONCLUSION

Our study identified several similarities and differences in the clinical characteristics and predisposing factors of patients with childhood-onset and late-onset FS. In addition, we found that childhood-onset FS is more likely to remain undiagnosed and thus untreated for many years. These findings provide additional evidence that FS is a heterogenous condition and we propose that a proportion of the differences between patients may be accounted for by age-associated factors.

Functional seizures: Cluster analysis may predict the associated risk factors

OBJECTIVES

We applied the Two-Step cluster analysis on a large cohort of patients with functional seizures (FS). We studied whether the background risk factors differed between the patient clusters.

METHODS

All patients with a diagnosis of FS were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2021. The Two-Step cluster analysis was applied considering the age at onset, sex, and seizure semiology. We also studied whether the background risk factors (e.g., a history of sexual abuse, physical abuse, etc.) differed between these patient clusters.

RESULTS

Three-hundred and fifty four patients were studied. The Two-Step cluster analysis was applied to the 230 patients who reported any associated risk factors; there were three clusters of patients. The most prominent feature of cluster 1 included akinetic seizures. The most prominent features of cluster 2 included motor seizures and no ictal injury. The most prominent features of cluster 3 included motor seizures with ictal injury. Compared with patients in cluster 3, a history of sexual abuse was more often reported by patients in cluster 1 (OR: 3.26, 95%CI: 1.12-9.47; p = 0.03). Compared with patients in cluster 3, a history of physical abuse was less often reported by patients in cluster 2 (OR: 0.45, 95%CI: 0.22-0.90; p = 0.026).

CONCLUSION

The Two-Step cluster analysis could identify three distinct clusters of patients based on their demographic and clinical characteristics. These clusters had correlations with the associated risk factors in patients with FS.

Emergency department visits and readmissions in patients with psychogenic nonepileptic seizures (PNES) at a safety net hospital

INTRODUCTION

Readmissions and emergency department (ED) visits after an index admission have been become a quality measure due to associations with poor outcomes and increased healthcare costs. Readmissions and ED encounters have been studied in a variety of conditions including epilepsy but have not been examined exclusively in psychogenic nonepileptic seizures (PNES). In this study we examined the rate of readmissions and ED visits after a discharge from an Epilepsy Monitoring Unit (EMU) in a safety net hospital. We also determined patient phenotypes that are associated with readmissions.

MATERIAL AND METHODS

This was a retrospective chart review study with index admission being a discharge from an EMU between January 1 and December 31 2016 with follow-up until August 31 2020. We obtained data regarding demographics, medical and psychiatric history, and social history and treatment interventions. Our outcome variables were both all-cause and seizure-related hospital readmissions and ED visits 30 days following the index discharge and readmissions and ED visits 30 days thereafter.

RESULTS

Eleven of 122 patients (9%) had a non-seizure-related ED visit and/or hospitalization within 30 days of index discharge while 45 (37%) had re-contact with the health system thereafter for non-seizure-related issues. Seven of 122 patients (6%) had a seizure-related ED visit or hospital readmission within 30 days of discharge. Twenty-eight (23%) had a seizure-related readmission or ED visit after 30 days. Of these 28, 4 patients had been to an ER within 7 days of EMU discharge. The majority of subsequent encounters with the healthcare system were through the ED (n = 38) as compared to hospital (n = 10) and EMU readmissions (n = 9). On bivariate statistical analysis, charity or self-pay insurance status (p < 0.01), homelessness (p < 0.01), emergent EMU admission on index admission (p < 0.01), history of a psychiatric diagnosis (p < 0.02), and ED encounters 12 months prior to admission (p < 0.01) were significantly associated with readmission; however, on multivariate analysis only charity insurance status was a significant predictor.

CONCLUSIONS

In this study of readmissions and ED visits after discharge with a diagnosis of PNES at a safety net hospital, we found a seizure-related readmission rate of approximately 6% in 30 days and 23% thereafter with the majority of re-contact with the hospital being in the ED. On multi-variate analysis insurance status was a significant factor associated with readmission and ED visits. Our future research directions include examining referrals and treatment completion at the hospital's PNES clinic as well as creating a risk score to better identify patients with PNES at risk of readmission.