Psychiatric symptoms are the strongest predictors of quality of life in patients with drug-resistant epilepsy or psychogenic nonepileptic seizures.

OBJECTIVE: This cross-sectional study aimed to determine the effect of psychiatric comorbidity and neurocognitive deficits on the quality of life in a cohort of patients admitted for Video-EEG Monitoring (VEM) for investigation into a presumed seizure disorder. METHODS: Patients were recruited from an inpatient VEM unit between January 2009 and December 2016. All patients had formal neuropsychiatric assessment. All patients completed questionnaires assessing psychiatric symptomatology (SCL-90-R), Anxiety and Depression (HADS), quality of life (QOLIE-89), and cognition (NUCOG). RESULTS: A total of 451 patients were enrolled. Upon discharge, 204 patients were diagnosed to have epilepsy, 118 psychogenic nonepileptic seizures (PNES), and 29 both epilepsy and PNES, while the diagnosis was uncertain diagnosis in 100. Diagnosis (p = .002), HADS Depression score (p < .001), SCL-90-R positive symptoms total (p < .001), and NUCOG total score (p < .001) were found to be significant predictors of QOLIE-89 total scores, together explaining 65.4% of variance in quality of life. Seizure frequency was not a significant predictor of quality of life (p = .082). Patients with PNES had significantly worse quality of life, and scored higher on measures of psychiatricsymptomatology, compared to patients with epilepsy alone. The prevalence of psychiatric comorbidity was significantly higher in patients with PNES (70.3%) or both PNES and epilepsy (62.1%) compared to patients with epilepsy alone (41.2%) (p < .001). SIGNIFICANCE: Psychiatric symptomatology, depression, and cognition were stronger determinants of quality of life than seizure frequency in this study population of patients with drug-resistant epilepsy and PNES. Patients with PNES with or without comorbid epilepsy had similar neuropsychiatric profiles.

Factors associated with subjective cognitive function in epilepsy and psychogenic non-epileptic seizures.

OBJECTIVE: To identify factors associated with subjective cognitive complaints in people with presumed seizure disorders referred for video electroencephalogram monitoring (VEM). METHODS: Adult patients admitted for inpatient VEM were recruited. Objective cognitive function was measured with the Neuropsychiatry Unit Cognitive Screening Tool, subjective cognitive function with the Quality of Life in Epilepsy Inventory-89 subscales, and anxiety and depressive symptoms with the Hospital Anxiety and Depression Scale. Multivariate Bayesian general linear models were used to identify predictors of subjective cognitive function. RESULTS: 331 patients met inclusion criteria. Mean age was 39.3 years and 61.9 % patients were female. Diagnoses included epilepsy, psychogenic non-epileptic seizures (PNES) or both conditions. Depression, anxiety and objective cognitive function were predictors of subjective cognitive function across all domains. Depression was the strongest predictor of subjective memory and attention, whilst objective cognition was the strongest predictor of subjective language function. Mood also mediated the relationship between objective function and subjective function across all domains to varying extents; depression exerted the strongest effect of 22 % for the memory domain; conversely, language domain was least influenced by mood, with depression mediating 11 % and anxiety mediating only 9% of the subjective-objective relationship. SIGNIFICANCE: Mood and objective cognitive function are both important contributors to subjective cognitive function for patients undergoing VEM. Clinicians should consider referring patients with cognitive complaints for both neurocognitive workup and neuropsychiatric evaluation. Future work may examine the effects of treating concomitant mood disorders on subjective cognitive function.

Antiepileptic drugs are not independently associated with cognitive dysfunction.

OBJECTIVE: To test the hypothesis that individual antiepileptic drugs (AEDs) are not associated with cognitive impairment beyond other clinically relevant factors, we performed a cross-sectional study of patients admitted to an inpatient video-EEG monitoring unit. METHODS: We prospectively enrolled patients admitted to an inpatient specialist epilepsy program between 2009 and 2016. Assessments included objective cognitive function, quality of life subscales for subjective cognitive function, and questionnaires for anxiety and depressive symptoms. Bayesian model averaging identified predictors of cognitive function. Bayesian model selection approach investigated effect of individual AEDs on cognition. Conventional frequentist analyses were also performed. RESULTS: A total of 331 patients met inclusion criteria. Mean age was 39.3 years and 61.9% of patients were women. A total of 45.0% of patients were prescribed AED polypharmacy, 25.1% AED monotherapy, and 29.9% no AED. Age, seizure frequency, and a diagnosis of concomitant epilepsy and psychogenic nonepileptic seizure were predictors of objective cognitive function. Depression, anxiety, and seizure frequency were predictors of subjective cognitive function. Individual AEDs were not independently associated with impaired cognitive function beyond other clinically relevant variables. CONCLUSIONS: This study found that no AED was independently associated with cognitive dysfunction. Significant determinants of objective and subjective cognitive dysfunction included seizure frequency and depression, respectively. These findings suggest that optimizing therapy to prevent seizures is not likely to occur at the expense of cognitive function.

Psychometric properties of the Hospital Anxiety and Depression Scale in an inpatient video-monitoring epilepsy cohort.

BACKGROUND: People with epilepsy (PWE) have high rates of comorbid anxiety disorders and depressive disorders, from 25% in general population cohorts to rates of 55% in people with treatment resistant epilepsy. High rates are also seen in patients with psychogenic nonepileptic seizures (PNES). Depressive disorders and anxiety disorders in PWE are associated with decreased quality of life measures and are the strongest risk factors for increased suicidality, rates of which are markedly elevated in PWE, at 12%, compared to the general Australian population (1.8%). The Hospital Anxiety and Depression Scale (HADS) is one of the more commonly used screening tools in medical populations. Past studies of the HADS in general outpatient populations with epilepsy have demonstrated promising validity for detecting depression. OBJECTIVES: The following were the objectives of the study: 1. To examine the validity of HADS in detecting depressive disorders and anxiety disorders in an inpatient population of patients admitted for video monitoring. 2. To investigate the measurement structure of the HADS across the diagnosis groups of epilepsy subtypes and PNES. METHODS: This was a retrospective cohort study of 485 patients admitted to a tertiary epilepsy video electroencephalography (EEG) monitoring unit. All patients received clinical neurological, neuropsychiatric, and neuroimaging assessments to arrive at consensus epilepsy and psychiatric diagnoses. Clinical psychiatric diagnosis of depressive disorders and anxiety disorders, based on the assessment of a neuropsychiatrist, were compared to accepted HADS cutoff scores for these conditions. FINDINGS: Of the 485 patients, 229 patients were with epilepsy, 28 had both epilepsy and PNES, and 121 had PNES. In 107 cases, no definite diagnosis could be made. At a cutoff score of 7 HADS was able to significantly classify patients with depression (area under the curve [AUC] = 0.79, 95% confidence interval [CI] = 0.72-0.82) with a sensitivity of 70% and a specificity of 83%. A similar result was observed for anxiety disorders; a cutoff score of 7 (AUC = 0.75, 95% CI = 0.72-0.81) was able to significantly classify anxiety disorders in patients with a sensitivity of 88% and specificity of 54%. CONCLUSIONS: This study has found that HADS measures two separate, yet correlated, constructs of anxiety disorders and depressive disorders. Our results indicate that while the HADS is sensitive to distress in this population, relatively low cutoff scores would be required to achieve highly sensitive screening. This sample includes patients with a diagnosis of epilepsy and/or PNES, and thus, the findings have clinical applicability to screening in tertiary epilepsy video-EEG monitoring units where both these conditions frequently co-occur.

Distinct psychopathology profiles in patients with epileptic seizures compared to non-epileptic psychogenic seizures.

OBJECTIVE: Similarities in clinical presentations between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) produces a risk of misdiagnosis. Video-EEG monitoring (VEM) is the diagnostic gold standard, but involves significant cost and time commitment, suggesting a need for efficient screening tools. METHODS: 628 patients were recruited from an inpatient VEM unit; 293 patients with ES, 158 with PNES, 31 both ES and PNES, and 146 non-diagnostic. Patients completed the SCL-90-R, a standardised 90-item psychopathology instrument. Bayesian linear models were computed to investigate whether SCL-90-R domain scores or the overall psychopathology factor p differed between groups. Receiver operating characteristic (ROC) curves were computed to investigate the PNES classification accuracy of each domain score and p. A machine learning algorithm was also used to determine which subset of SCL-90-R items produced the greatest classification accuracy. RESULTS: Evidence was found for elevated scores in PNES compared to ES groups in the symptom domains of anxiety (b = 0.47, 95%HDI = [0.10, 0.80]), phobic anxiety (b = 1.32, 95%HDI = [0.98, 1.69]), somatisation (b = 0.84, 95%HDI = [0.49, 1.20]), and the general psychopathology factor p (b = 1.35, 95%HDI = [0.86, 1.82]). Of the SCL-90-R domain scores, somatisation produced the highest classification accuracy (AUC = 0.74, 95%CI = [0.69, 0.79]). The genetic algorithm produced a 6-item subset from the SCL-90-R, which produced comparable classification accuracy to the somatisation scores (AUC = 0.73, 95%CI = [0.64, 0.82]). SIGNIFICANCE: Compared to patients with ES, patients with PNES report greater symptoms of somatisation, general anxiety, and phobic anxiety against a background of generally elevated psychopathology. While self-reported psychopathology scores are not accurate enough for diagnosis in isolation, elevated psychopathology in these domains should raise the suspicion of PNES in clinical settings.

Abbreviated assessment of psychopathology in patients with suspected seizure disorders.

PURPOSE: Psychopathology is common in patients undergoing investigation for seizure-related disorders. Psychometric examination using self-report instruments, such as the Symptom Checklist 90 - Revised (SCL-90-R), can assist diagnosis. The SCL-90-R, however, is a lengthy instrument and might not be tolerated by all patients. We assessed several abbreviated forms of the SCL-90-R in patients undergoing video encephalographic monitoring (VEM). METHOD: Six hundred eighty-seven patients completed the SCL-90-R, and scores were computed for the full SCL-90-R and five abbreviated forms. Correlations and mean differences were computed between different forms. Classification accuracy was assessed via receiver operating characteristic (ROC) curves, and measurements models were examined using confirmatory factor analysis (CFA). RESULTS: All abbreviated forms were strongly correlated with the SCL-90-R for general psychopathology (r = 0.93-0.99), depression (r = 0.89-0.95), anxiety (r = 0.97-0.98), psychosis (r = 0.95-0.99), and obsessive-compulsive symptoms (r = 0.97). Classification performance was similar across forms for depression and anxiety, with high negative predictive values (0.90-0.94) and lower positive predictive values (0.34-0.38). Classification performance for psychotic and obsessive-compulsive disorders was poor. Differences were observed between the full SCL-90-R and its abbreviated forms across most domains (d = 0.00-0.65). The published measurement model was most strongly validated for the SCL-27, SCL-14, and the SCL-K-9. CONCLUSIONS: These five SCL-90-R abbreviated forms show high convergent validity with the full version. In patients undergoing investigation for seizure-related disorders, the Brief Symptom Inventory full form (BSI) or short form (BSI-18) is most appropriate where screening for both depression and anxiety is required. The SCL-K-9 is appropriate when only a single measure of global psychological distress is required. None of the instruments were able to detect psychotic or obsessive-compulsive symptoms with great accuracy. Caution should be exercised when making direct comparisons across the different forms.

The Association Between Atrial Fibrillation and Poststroke Seizures is Influenced by Ethnicity and Environmental Factors.

GOAL: Epilepsy is a major complication of stroke. There have been suggestions that patients with cardioembolic stroke are at a greater risk of developing seizures than other stroke subtypes. However, the incidence of atrial fibrillation (AF) and cardioembolic stroke varies considerably across countries, generally higher in Western populations than in Asian populations. This study assessed whether ethnicity affects the association between AF and poststroke seizure (PSS) development. We hypothesized that Royal Melbourne Hospital ([RMH] Melbourne) patients will have significantly higher incidence of AF-related PSS than in the Jinling Hospital (Nanjing) population. MATERIALS AND METHODS: This was a retrospective, multicenter cohort study including patients with anterior circulation ischemic stroke admitted between 2008 and 2015. Occurrences of PSS were ascertained by reviewing medical records or telephone follow-up. To test the hypothesis of an interaction between ethnicity and AF for PSS occurrence, a logistic regression model with AF and ethnicity together with an ethnicity-by-AF interaction term was used. FINDINGS: Of 782 patients followed-up for seizure development at RMH, 247 (31.6%) patients had AF, of whom 10 (4%) developed PSS. Of 1185 patients followed-up and included at JH, 54 (4.8%) patients with AF, of whom 4 (7.4%) developed PSS. At RMH, no significant association was found between AF and PSS; odds ratio .75, 95% confidence interval .4-1.6, (P = .4). At JH, there was a significant association between AF and increased PSS: OR 4.0, 95% CI 1.3-12.1, (P = .01), P for interaction = .03. CONCLUSION: Further understanding of genetic risks and environmental differences across ethnic populations and the role in PSS is required.

Early childhood trauma and hippocampal volumes in patients with epileptic and psychogenic seizures.

OBJECTIVE: Exposure to early life childhood trauma has been implicated as resulting in a vulnerability to epileptic and psychogenic nonepileptic seizures (PNES), hippocampal atrophy, and psychiatric disorders. This study aimed to explore the relationships between childhood trauma, epilepsy, PNES, and hippocampal volume in patients admitted to a video-electroencephalogram monitoring (VEM) unit. METHODS: One hundred thirty-one patients were recruited from the Royal Melbourne Hospital VEM unit. The diagnostic breakdown of this group was: temporal lobe epilepsy (TLE) (32), other epilepsy syndromes (35), PNES (47), other nonepileptic syndromes (5), both epilepsy and PNES (6), and uncertain diagnosis (6). All patients completed a questionnaire assessing exposure to childhood trauma, the Childhood Trauma Questionnaire (CTQ), as well as questionnaires assessing psychiatric symptomatology (SCL-90-R), Anxiety and Depression (HADS), quality of life (QOLIE-98) and cognition (NUCOG). Volumetric coronal T1 MRI scans were available for 84 patients. Hippocampal volumes were manually traced by a blinded operator. RESULTS: The prevalence of childhood trauma in patients with PNES was higher than in patients with other diagnoses (p=0.005), and the group with PNES overall scored significantly higher on the CTQ (p=0.002). No association was found between CTQ scores and hippocampal volumes; however, patients with a history of sexual abuse were found to have smaller left hippocampal volumes than patients who had not (p=0.043). Patients reporting having experienced childhood trauma scored lower on measures of quality of life and higher on measures of psychiatric symptomatology. SIGNIFICANCE: Patients with PNES report having experienced significantly more childhood trauma than those with epileptic seizures, and in both groups there was a relationship between a history of having experienced sexual abuse and reduced left hippocampal volume. Patients with PNES and those with epilepsy who have a history of childhood trauma have overall worse quality of life and more psychiatric symptomatology.