Prevalence and risk factors for depression and anxiety in adult patients with epilepsy: Caregivers' anxiety and place of residence do mater.

OBJECTIVES: This study was aimed to assess the prevalence of anxiety and depression and the possible contributions of the caregiver's anxiety and depression, disease status, and socio-demographic characteristics to psychopathological comorbidities among adult patients with epilepsy. METHODS: A total of 262 participants (131 adult patient-caregiver pairs) were enrolled in this study. The Hamilton Rating Scale for Depression (HAM-D) and the Hamilton Rating Scale for Anxiety (HAM-A) were applied to evaluate the depression and anxiety status among adult patients with epilepsy and their caregivers, respectively. We collected caregivers' anxiety and depression, patients' sociodemographic characteristic data, and disease status as independent variables using stepwise multiple linear regression analysis that were correlated to the degree of anxiety and depression among these adult patients with epilepsy. RESULTS: Among adult patients with epilepsy, 46 (35.11%) subjects showed anxiety symptoms (HAM-A scores > 6), and 48 (36.64%) had depression symptoms (HAM-D scores > 6). Caregivers' anxiety levels and place of residence were significant independent predictors of both anxiety and depression levels among adult patients with epilepsy. CONCLUSIONS: Adult patients with epilepsy are at a high risk of suffering from anxiety and depression. Caregivers' anxiety and place of residence are definite independent predictors for anxiety and depression severity among adult patients with epilepsy. Therefore, clinicians should be careful in closely monitoring the psychological status of adult patients with epilepsy and their caregivers. Furthermore, the government and medical institutions should increase educational awareness about epilepsy and its cure, especially among adult patients with epilepsy who live in rural areas and consider offering a multidisciplinary management program to improve these patients' psychological status.

A scale for prediction of response to AEDs in patients with MRI-negative epilepsy.

OBJECTIVES: Antiepileptic drugs (AEDs) are the first choice in magnetic resonance imaging (MRI)-negative patients with epilepsy, although the responses to AEDs are diverse. Preoperative evaluation and postoperative prognosis in MRI-negative epilepsy have been reported. However, there are few tools for predicting the response to AEDs. Herein, we developed an AED response scale based on clinical factors and video-electroencephalography (VEEG) in MRI-negative patients with epilepsy. METHODS: A total of 132 consecutive patients with MRI-negative epilepsy at the Epilepsy Center of Henan Provincial People's Hospital between August 2016 and August 2018 were included. Patients were further divided into drug-responsive epilepsy ([DSE-MRI (-)]; n = 101) and drug-resistant epilepsy ([DRE-MRI (-)]; n = 31) groups. The clinical and VEEG factors were evaluated in univariate analyses and multivariate logistic regression analyses. A scale was derived and the scores categorized into 3 risk levels of DRE-MRI (-). RESULTS: A scale was established based on 4 independent risk factors for DRE-MRI (-). The scale had a sensitivity of 83.87%, specificity of 80.20%, positive likelihood ratio of 4.24, negative likelihood ratio of 0.20, and showed good discrimination with the area under the curve (AUC) of 0.886 (0.826-0.946). The categorization of the risk score based on this scale was: low risk (0-3 points), medium risk (3-5 points), and high risk (>5 points). CONCLUSION: We established a DRE-MRI (-) scale with a good sensitivity and specificity, which may be useful for clinicians when making medical decisions in patients with MRI-negative epilepsy.

High risk of anxiety and depression in caregivers of adult patients with epilepsy and its negative impact on patients' quality of life.

OBJECTIVE: The objective of this study was to assess the anxiety and depression of caregivers of adult patients with epilepsy (PWE) and evaluate its effect on patient quality of life (QOL). METHOD: One hundred sixty pairs of adult PWE and their caregivers were enrolled in our study. Quality of life in adult PWE was evaluated with the Quality of Life in Epilepsy Inventory-31 scale (QOLIE-31). Symptoms of anxiety and depression in caregivers were assessed with the Hamilton Anxiety Rating Scale (HAM-A) and the Hamilton Depression Rating Scale (HAM-D) respectively. Correlation and stepwise multiple liner regression analyses were used as statistical analysis. RESULTS: Of the caregivers, 41 (31.30%) had anxiety symptoms (HAM-A scores > 6) and 44 (33.59%) had depression symptoms (HAM-D scores > 6). Caregiver anxiety was significantly associated with poorer adult PWE QOL scores in four of the seven subscales and the QOLIE-31 total score. Caregiver depression was significantly associated with poorer adult PWE QOL in all seven subscales as well as the QOLIE-31 total score. Caregiver depression was an independent predictor of the QOLIE-31 total score and five subscales: seizure worry, emotional wellbeing, energy/fatigue, cognitive, and medication effects. CONCLUSION: Caregivers of adult PWE are at high risk of experiencing anxiety and depression. Caregiver psychological status, especially depression, was an independent predictor of poorer QOL for adult PWE.

Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine.

AIMS: To predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the clinical and electroencephalographic (EEG) factors that affect outcomes. METHODS: Retrospective analysis of PWEs treated with levetiracetam for 3 years identified 22 patients who were SF and 24 who were not. Before starting levetiracetam, 11 clinical factors and four EEG features (sample entropy of α, ß, θ, δ) were identified. Overall, 80% of each the two groups were chosen to establish a support vector machine (SVM) model with 5-fold cross-validation, hold-out validation and jack-knife validation. The other 20% were used to predict the efficacy of levetiracetam. The mean impact value (MIV) algorithm was used to rank the relativity between factors and outcomes. RESULTS: Compared with SF patients, not SF patients displayed a specific decrease in EEG sample entropy in α band from the F4 channel, ß band from Fp2 and F8 channels, θ band from C3 channel (P < 0.05). The SVM model based on the clinical and EEG features yielded 72.2% accuracy of 5-fold cross-validation, 75.0% accuracy of jack-knife validation, 67.7% accuracy of hold-out validation in the training set and had a high prediction accuracy of 90% in test set (sensitivity was 100%, area under the receiver operating characteristic curve was 0.96). The feature of ß band from Fp2 weighs heavily in the prediction model according to the mean impact value algorithm. CONCLUSIONS: The efficacy of levetiracetam on newly diagnosed PWEs could be predicted using an SVM model, which could guide antiepileptic drug selection.