Clinical factors associated with work disability in epilepsy: A cross-sectional study at a tertiary referral hospital.

OBJECTIVE: The impact of epilepsy on work disability remains unclear. The aim of this study was to determine the percentage of patients with epilepsy who are unemployed or on temporary or permanent disability leave and to analyze associated clinical factors. METHODS: We performed an observational cross-sectional study of consecutively recruited patients with epilepsy seen at a specialized epilepsy unit or admitted to the epilepsy monitoring unit of a tertiary referral hospital. We analyzed the percentage of patients who were actively employed, unemployed, and on temporary or permanent disability leave. The groups were compared for sociodemographic data (age, sex, marital status, and type of work), clinical data (type of epilepsy, disease duration, monthly seizure frequency, and presence of anxiety or depression), treatment-related factors, and quality of life. RESULTS: We included 742 patients (53% male, mean age 44.3 ±â€¯13.7 years old): 40.5% were employed, 29.2% were unemployed, 19% were on temporary disability leave, and 11.1% had a permanent work disability. Depressive symptoms and poorer quality of life were associated with unemployment (OR 2.3, p = 0.02 and OR 1.8, p = 0.01), temporary disability leave (OR 1.4, p = 0.05 and OR 1.7, p = 0.02), and permanent work disability (OR 1.9, p = 0.01 and OR 2.2, p = 0.01). Low-skilled work was also predictive of unemployment (OR 1.9, p = 0.04), temporary disability leave (OR 2.8, p = 0.03), and permanent work disability (OR 1.7, p = 0.04). A higher monthly seizure frequency was associated with permanent work disability (OR 2.01, p = 0.02). CONCLUSION: Less than 50% of patients with epilepsy in our setting are working. Factors associated with unemployment and work disability are a higher frequency of seizures, low-skilled work, depressive symptoms, and poor quality of life.

Blood biomarkers predictive of epilepsy after an acute stroke event.

OBJECTIVE: Blood biomarkers have not been widely investigated in poststroke epilepsy. In this study, we aimed to describe clinical factors and biomarkers present during acute stroke and analyze their association with the development of epilepsy at long term. METHODS: A panel of 14 blood biomarkers was evaluated in patients with ischemic and hemorrhagic stroke. Biomarkers were normalized and standardized using Z-scores. Stroke and epilepsy-related variables were also assessed: stroke severity, determined by National Institutes of Health Stroke Scale (NIHSS) score, stroke type and cause, time from stroke to onset of late seizures, and type of seizure. Multiple Cox regression models were used to identify clinical variables and biomarkers independently associated with epilepsy. RESULTS: From a cohort of 1115 patients, 895 patients were included. Mean ± standard deviation (SD) age was 72.0 ± 13.1 years, and 57.8% of patients were men. Fifty-one patients (5.7%) developed late seizures, with a median time to onset of 232 days (interquartile range [IQR] 86-491). NIHSS score ≥8 (P < .001, hazard ratio [HR] 4.013, 95% confidence interval [CI] 2.123-7.586) and a history of early onset seizures (P < .001, HR 4.038, 95% CI 1.802-9.045) were factors independently associated with a risk of developing epilepsy. Independent blood biomarkers predictive of epilepsy were high endostatin levels >1.203 (P = .046, HR 4.300, 95% CI 1.028-17.996) and low levels of heat shock 70 kDa protein-8 (Hsc70) <2.496 (P = .006, HR 3.795, 95% CI 1.476-9.760) and S100B <1.364 (P = .001, HR 2.955, 95% CI 1.534-5.491). The risk of epilepsy when these biomarkers were combined increased to 17%. The area under the receiver-operating characteristic (ROC) curve of the predictive model was stronger when clinical variables were combined with blood biomarkers (74.3%, 95% CI 65.2%-83.3%) than when they were used alone (68.9%, 95% CI 60.3%-77.6%). SIGNIFICANCE: Downregulated S100B and Hsc70 and upregulated endostatin may assist in prediction of poststroke epilepsy and may provide additional information to clinical risk factors. In addition, these data are hypothesis-generating for the epileptogenic process.