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.

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.

Purpose: The aim of this study was to verify the potential risk factors of ventilator-associated pneumonia (VAP) in elderly Chinese patients receiving mechanical ventilation (MV). The secondary aim of this study was to present logistical regression prediction models of VAP occurrence in elderly Chinese patients receiving MV. Methods: Patients (aged 80 years or above) receiving MV for ≥48 h were enrolled from the Chinese People’s Liberation Army (PLA) General Hospital from January 2011 to December 2015. A chi-squared test and Mann–Whitney U-test were used to compare the data between participants with VAP and without VAP. Univariate logistic regression models were performed to explore the relationship between risk factors and VAP. Results: A total of 901 patients were included in the study, of which 156 were diagnosed as VAP (17.3%). The incidence density of VAP was 4.25/1,000 ventilator days. Logistic regression analysis showed that the independent risk factors for elderly patients with VAP were COPD (OR =1.526, P 0.05), intensive care unit (ICU) admission (OR=1.947, P 0.01), the MV methods ( P 0.023), the number of antibiotics administered (OR=4.947, P 0.01), the number of central venous catheters (OR=1.809, P 0.05), the duration of indwelling urinary catheter (OR=1.805, P 0.01) and the use of corticosteroids prior to MV (OR=1.618, P 0.05). Logistic regression prediction model of VAP occurrence in the Chinese elderly patients with mechanical ventilation: Conclusion: VAP occurrence is associated with a variety of controllable factors including the MV methods and the number of antibiotics administered. A model was established to predict VAP occurrence so that high-risk patients could be identified as early as possible.

Purpose. The aim of this study was to investigate the risk factors and the efficacy of the preventive measurements for the in-hospital complications of fall-related fractures. Methods. The data on older Chinese patients with fall-related fractures were collected, including information on the patients, diseases, and preventive measurements. The potential risk factors for the in-hospital complications included health status on admission, comorbidity, fractures, preventive measures of the complications, and drugs use for the comorbidity. After univariate analyses, multivariate logistic regression analyses were applied to investigate the impact of the potential risk factors on the number of the complications and each individual complication, respectively, and the efficacy of the preventive measurements. Results. A total of 525 male and 1367 female were included in this study. After univariate analyses, multiple logistic regression showed that dementia, pneumonia, antidepressant, postural hypotension, and cerebral infarction could increase the incidence and number of comorbidities. Meanwhile, dementia has shown the strongest association with each individual complication. Conclusions. Different combinations of comorbidity, medication use, and preventive measurements were related to the in-hospital complications of fall-related fractures. Dementia emerged as the most important risk factor for these complications, while most of the preventive measurements could not reduce their incidences.

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