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Aim This study aimed to assess the overall status of burnout in nurses in China on a national scale and investigate the demographic characteristics related to burnout and the relationships between demographics, job satisfaction and burnout. Methods This was a national cross‐sectional study conducted by the Chinese Nursing Association between July 2016 and July 2017. Data were collected using a structured, self‐administered questionnaire. Results A total of 51 406 registered nurses in 311 Chinese cities completed the questionnaire. Fifty per cent of the participants suffered burnout, and 33.8% of nurses had high scores on emotional exhaustion, 66.6% had high scores on depersonalization and 93.5% had low scores on personal accomplishment; 16.2% reported a high level of job satisfaction, only 0.4% was satisfied with their jobs and 70.7% intended to leave their jobs. Marital status, educational level, income and years of working experience affected job burnout. Nurses with a high level of burnout were more likely to have a high degree of job dissatisfaction and intend to leave their jobs. Conclusion We found a high prevalence of burnout among nurses in China. Nursing managers need to pay more attention to job burnout and its influencing factors. Interventions to reduce nurse burnout should be implemented.

PurposeMinimally invasive esophagectomy is increasingly performed for esophageal or gastroesophageal junctional cancer, with advantages of improved perioperative outcomes in comparison with open esophagectomy. McKeown and Ivor Lewis are widely used procedures of minimally invasive esophagectomy, and there have been controversies on which one is preferred for patients with resectable esophageal or junctional cancer.Patients and methodsThis review was registered at the International Prospective Register of Systematic Reviews (number CRD42017075989). Studies in PubMed, Embase, Web of Science, the Cochrane Library, and ClinicalTrials.gov were thoroughly investigated. Eligible studies included prospective and retrospective studies evaluating short-term outcomes of minimally invasive McKeown esophagectomy (MIME) vs minimally invasive Ivor Lewis esophagectomy (MILE) in patients with resectable esophageal or junctional tumors. Main parameters included anastomotic leak and 30-day/in-hospital mortality. Overall incidence rates (ORs)/weighted mean difference (WMD) with 95% confidence intervals (CIs) were calculated by employing random-effects models.ResultsFourteen studies containing 3,468 cases were included in this meta-analysis. Age, male sex, and American Joint Committee on Cancer (AJCC) stage between the 2 groups were not statistically different. MIME led to more blood loss, longer operating time, and longer hospital stay than MILE. MIME was associated with higher incidence of pulmonary complications (OR =1.96, 95% CI =1.28–3.00) as well as total anastomotic leak (OR =2.55, 95% CI =1.40–4.63), stricture (OR =2.07, 95% CI =1.05–4.07), and vocal cord injury/palsy (OR =5.62, 95% CI =3.46–9.14). In addition, the differences of R0 resection rate, number of lymph modes retrieved, blood transfusion rate, length of intensive care unit stay, incidence of cardiac arrhythmia, and Chyle leak between MIME and MILE were not statistically significant. Notably, incidence of severe anastomotic leak (OR =1.28, 95% CI =0.73–2.24) and 30-day/in-hospital mortality (OR =1.76, 95% CI =0.92–3.36) as well as 90-day mortality (OR =2.22, 95% CI =0.71–6.98) between the 2 procedures were also not significantly different.ConclusionThis study suggests that MIME and MILE are comparable with respect to clinical safety. MILE may be a better option when oncologically and clinically suitable. MIME is still a safe alternative procedure when clinically indicated. However, this evidence is at risk for bias; randomized controlled trials are needed to validate or correct our results.

Purpose Build machine learning models for predicting pressure ulcer nursing adverse event, and find an optimal model that predicts the occurrence of pressure ulcer accurately. Patients and Methods Retrospectively enrolled 5814 patients, of which 1673 suffer from pressure ulcer events. Support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN) models were used to construct the pressure ulcer prediction models, respectively. A total of 19 variables are included, and the importance of screening variables is evaluated. Meanwhile, the performance of the prediction models is evaluated and compared. Results The experimental results show that the four pressure ulcer prediction models all achieve good performance. Also, the AUC values of the four models are all greater than 0.95. Besides, the comparison of the four models indicates that RF model achieves a higher accuracy for the prediction of pressure ulcer. Conclusion This research verifies the feasibility of developing a management system for predicting nursing adverse event based on big data technology and machine learning technology. The random forest and decision tree model are more suitable for constructing a pressure ulcer prediction model. This study provides a reference for future pressure ulcer risk warning based on big data.

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