ObjectiveTo analyze the risk factors of cognitive dysfunction in patients with epilepsy, and provide evidence for clinical prevention and treatment.MethodsDuring the period from January 1, 2018 to January 31, 2019, 101 patients with epilepsy who were admitted to the epilepsy specialist clinic of the General Hospital of Ningxia Medical University were included in this study. The cognitive function of the patients was evaluated by the Mini-mental State Examination (MMSE) scale and patients were divided into cognitive impairment group and normal cognitive function group according to the MMSE. Single factor and logistic regression analysis were used to find the differences of influencing factors between the two groups.Results① There were 27 cases of cognitive dysfunction in 101 patients with epilepsy, the incidence of cognitive impairment was 26.7%; ② Univariate analysis showed that the course of disease, frequency of seizures, seizure forms, anti-epileptic drugs (AEDs) and abnormal rate of electroencephalogram (EEG) existed significant differences between the two groups (P<0.05). ③ Logistic regression showed that course of disease, frequency of seizures and AEDs multidrug therapy were independent risk factors for cognitive dysfunction in patients with epilepsy (P<0.05).ConclusionCourse of disease, frequency of seizures and AEDs multidrug therapy are independent risk factors for cognitive dysfunction in patients with epilepsy.
Network meta-analysis (NMA) is a statistical technique that integrates data from multiple clinical studies and compares the efficacy and safety of multiple interventions, which can provide pro and con ranking results for all intervention options in the evidence network and provide direct evidence support for clinical decision-making. At present, NMA is usually based on the aggregation of the same type of data set, and there are still methodological and software difficulties in achieving cross-study design and cross-data format data set merging. The crossnma package of R programming language is based on Bayesian framework and Markov chain Monte Carlo algorithm, extending the three-level hierarchical model to the standard NMA data model to achieve differential merging of varied data types. The crossnma package fully considers the impact of risk bias caused by the combination of different types of data on the results by introducing model variables. In addition, the package provides functions such as result output and easy graphing, which makes it possible to combine NMA across study designs and evidence across data formats. In this study, the model based on crossnma package method and software operation will be demonstrated and explained through the examples of four individual participant datasets and two aggregate datasets.