Objective To evaluate the application effect of evidence-based care model in hemostasis by compression in patients after renal biopsy.Methods 80 patients undergoing renal biopey from December 2006 to December 2009 in our hospital were chosen as the research object.They were randomly divided into the control group and the observation group with 40 patients in each group.The control group was given routine nursing,and the observation group was treated with evidence-based care model for nusing.The re-bleeding rates,satisfaction degree and the SAS,SDS score,mastering degree of related knowledge,treatment compliance for the two groups before and after nursing were compared.Results The ineidence rate of bleeding in the observation group was higher than the control group,satisfaction degree was higher,and the SAS,SDS score,related knowledge,treatment compliance were all better than the control group,there were significant differences.Conclusions The effect of evidence-based care model in hemostasis by compression after renal biopsy is better.It can significantly reduce the incidence of adverse circumstances and improve the negative emotional state of patients.

Objective:To explore the prevalence and risk factors of exit-site infection (ESI) in elderly peritoneal dialysis (PD) patients.Methods:The status of exit-site was evaluated in elderly PD patients (≥60 years) who had catheter insertion in our center between January 1, 2009 and December 31, 2013, with follow-up for 1 year or withdrawing from peritoneal dialysis in this period. The patients were divided into ESI and non-ESI group. The data was collected including demographics, clinical features, and nursing care methods of the exit-site.Results:A total of 247 patients were recruited in this study, aged (68.6±6.2) years, among whom there were 132 male (53.4%) and 119 diabetes (48.2%). Median follow-up time was 12.0 months. Thirty-two patients had 34 episodes of ESI with a rate of 82.5 patient-months per episode (0.15 episodes per year). Coagulase-negative Staphylococcus was the main pathogen, accounting for 35.3% of the ESI. No bacterial growth was found in 8.8%. The exit-site nursing care status included that poor compliance of exit-site care 23.5%, poor catheter immobilization 62.3%, history of catheter-pulling injury 9.7%, mechanical stress on exit-site 5.3%, improper frequency of nursing care 29.6%, mupirocin usage 13.8%, patients taking exit-site care 26.7%, exit-site caregiver instability 16.6%. There were no differences in demographic (such as age, gender, primary disease, etc) and laboratory data (hemoglobin, serum albumin, blood potassium, etc) between the ESI and non-ESI groups. Poor compliance with exit-site care ( HR=2.352, 95% CI 1.008-5.488, P=0.048), poor catheter immobilization ( HR=3.074, 95% CI 1.046-9.035, P=0.041) and exit-site caregiver instability ( HR=2.423, 95% CI 1.004-5.845, P=0.049) were significantly correlated with increased risk of ESI. Conclusions:The prevalence of ESI in elderly PD patients was 0.15 episodes per year. Educating PD patients to improve the compliance with exit-site care, maintain catheter immobilization and do exit-site care by a stable and trained caregiver may reduce ESI events in elderly PD patients.

Objective:To investigate a time series deep learning model for respiratory motion prediction.Methods:Eighty pieces of respiratory motion data from lung cancer patients were used in this study. They were divided into a training set and a test set at a ratio of 8∶2. The Informer deep learning network was employed to predict the respiratory motions with a latency of about 600 ms. The model performance was evaluated based on normalized root mean square errors (nRMSEs) and relative root mean square errors (rRMSEs).Results:The Informer model outperformed the conventional multilayer perceptron (MLP) and long short-term memory (LSTM) models. The Informer model yielded an average nRMSE and rRMSE of 0.270 and 0.365, respectively, at a prediction time of 423 ms, and 0.380 and 0.379, respectively, at a prediction time of 615 ms.Conclusions:The Informer model performs well in the case of a longer prediction time and has potential application value for improving the effects of the real-time tracking technology.