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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.

Although the use of antiepileptic drugs (AEDs) is routine, 30–40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients’ prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes. One hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model’s performance. Absolute weight value was used to rank the features that had impacts on classification. An integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85–1.0], sensitivity of 95% [95% CI 0.82–1.0], specificity of 93% [95% CI 0.77–1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91–1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and β band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier. We constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.

Objective We developed and validated a prediction score for predicting the probability of 6-month and 12-month seizure freedom of antiepileptic drug (AED) treatment in newly diagnosed patients with magnetic resonance imaging (MRI)-negative epilepsy. Methods The development cohort included 543 consecutive patients from the Epilepsy Center of Henan Provincial People's Hospital, while the validation cohorts included 493 consecutive patients in two independent cohorts. Univariate analysis and a forward and backward elimination of multivariate Cox regression analysis were used to select predictive factors. The performance of the score was evaluated with C-index, calibration plots, and decision curve analysis. The risk stratification was also performed. Results The score included five routinely available predictors including Circadian rhythms, Electroencephalography before AED treatment, Neuropsychiatric disorders, Perinatal brain injury, and History of central nervous system infection (CENPH score). When applied to the external validation cohort, the score showed good discrimination with C-index (development group: 0.83; validation group: 0.78), and calibration plots indicated well calibration, as well as the decision curve analysis showed good predictive accuracy and clinical values in four cohorts. The points of the score were categorized to the following three probability levels for predicting seizure freedom: high probability (0–83.11 points), medium probability (83.11–122.71 points), and low probability (> 122.71 points). And online calculator was established to make this score easily applicable in clinical practice. Conclusions We established a simple, practical, and evidence-based prediction score for predicting seizure freedom with AEDs to aid in the clinical consultation and treatment decision for the newly diagnosed patients with MRI-negative epilepsy.

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.

Backgrounds and aims Increased arterial stiffness may increase cardiovascular morbidity and mortality. Angiotensin II type 1 receptor blocker losartan is potentially useful in controlling the central blood pressure and arterial stiffness in mild to moderate essential hypertension, while the effects of losartan in aged patients with essential hypertension are not entirely investigated. Methods The carotid-femoral arterial pulse wave velocity (PWV) was measured in aged patients with essential hypertension. Results In a cross-sectional study, PWV value was significantly higher in these old patients with essential hypertension, compared with patients without essential hypertension. Logistic regression analysis indicated that age, hypertension duration, and losartan treatment are risk factors of arterial stiffness. In a perspective study, long-term administration of losartan (50 mg/d) remarkably reduced PWV in aged patients with essential hypertension. In a longitudinal study, PWV is an independent predictor of the occurrence of acute coronary syndrome (ACS) in elderly patients with essential hypertension by using multivariate analysis. Further, the ACS occurrence was reduced by long-term administration of losartan in aged patients with essential hypertension, compared with the old hypertensive patients without taking losartan. Conclusion Losartan treatment is a negative risk factor of arterial stiffness and reduces the risk of ACS in aged patients with essential hypertension.