ObjectiveTo analyze the clinical features of psychogenic non-epileptic seizures (Psychogenic nonepileptic seizures, PNES) in Tibetan population in Tibet, so as to help clinicians identify the disease.MethodsRetrospective analyzed the clinical data of patients with PNES in the Department of Neurology, People's Hospital of Tibet Autonomous Region from June 2016 to December 2018.ResultsIn general clinical data, there were significant differences between male and female patients in the results of video electroencephalogram (EEG) monitoring the non-epileptic seizures (P< 0.05). There were no significant differences in mean age, mean onset time, family history of epilepsy, head injury and marital status between male and female patients (P> 0.05). There was no significant difference in symptoms between male and female, but there were differences among different age groups (P> 0.05). In the onset age, the main manifestation was young women, but there was no significant difference in the onset of PNES among different age groups.ConclusionsThere was significant differences between male and female PNES petients, but no significant differences in onset time, marriage and family history of epilepsy between the male and female patients with PNES in Tibet. The clinical manifestations of PNES were different in different ages of patients in Tibet.
ObjectivesTo explore the construction method of prediction model of absolute risk for breast cancer and provide personalized breast cancer management strategies based on the results.MethodsA case-control design was conducted with 2 747 individuals diagnosed as primary breast cancer by pathology in West China Hospital of Sichuan University from 2000 to 2017 and 6 307 healthy controls from Breast Cancer Screening Cohort in Sichuan Women and Children Center and Chengdu Shuangliu District Maternal and Child Health Hospital. Standardized questionnaires and information management systems in hospital were used to collect information. Decision trees, logistic regression, the formula in Gail model and registration data in China were used to estimate the probability of 5-year risk of breast cancer. Eventually a ROC (receiver operating characteristics) curve was drawn to identify optimal cut-off value, and the power was evaluated.ResultsThe decision tree exported 4 variables, which were urban or rural sources, number of live birth, age and age at menarche. The median 5-year risk and interquartile range of the controls was 0.027% and 0.137%, while the median 5-year risk and interquartile range of the cases was 0.219% and 0.256%. The ROC curve showed the cut-off value was 0.100%. Through verification, the sensitivity was 0.79, the specificity was 0.73, the accuracy was 0.75, and the AUC (area under the curve) was 0.79.ConclusionsThe methods used in our study based on 9 054 female individuals in Sichuan province could be used to predict the 5-year risk for breast cancer. Predictor variables include urban or rural sources, number of live birth, age, and age at menarche. If the 5-year risk is more than 0.100%, the person will be judged as a high risk individual.