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.

Comparison of CT black hole sign and other CT features in predicting hematoma expansion in patients with ICH.

BACKGROUND AND PURPOSE: The hematoma expansion (HE) is an important risk factor for early neurological deterioration and poor prognosis. In this study, we aimed to compare the black hole sign with other computed tomography (CT) features to predict the HE and the outcome in patients with intracerebral hemorrhage (ICH). METHODS: Patients were enrolled within 12 h after stroke attack in the emergency department of Henan Provincial People's Hospital between January 2012 and June 2016. The clinical characters and CT features including the initial CT and the follow-up CT within 48 h were recorded. The outcome was assessed by using the modified Rankin Scale on discharge. Logistic regression analyses were used to investigate whether the factors were the independent predictor of HE and the outcome in patients with ICH. The sensitivity, specificity, positive predictive value, and negative predictive of CT features in predicting HE were calculated. RESULTS: A total of 185 ICH patients were enrolled, including 70 (37.8%) patients in HE group and 115 (62.2%) patients in non-HE group. There were significant difference in the initial hematoma volume, irregular shape, and CT black hole sign (P = 0.013, 0.006 and P < 0.001) between the two groups. While irregular shape and CT black hole sign were independent predictors for HE, the sensitivity and specificity were 71.45 and 54.78, 51.4 and 81.7%, respectively. Multivariable analysis identified CT black hole sign (P = 0.108) and initial intraventricular hemorrhage expansion (P = 0.214) were not the independent predictors of poor outcome. CONCLUSION: CT black hole sign presented the best predictive accuracy of predicting HE in patients with ICH compared to other CT features. However, it was not an independent predictor of poor outcome.