Background The incidence of postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) is high. We sought to develop a POPF prediction model based on a decision tree (DT) and random forest (RF) algorithm after PD and to explore its clinical value. Methods The case data of 257 patients who underwent PD in a tertiary general hospital from 2013 to 2021 were retrospectively collected in China. The RF model was used to select features by ranking the importance of variables, and both algorithms were used to build the prediction model after automatic adjustment of parameters by setting the respective hyperparameter intervals and resampling as a 10-fold cross-validation method, etc. The prediction model’s performance was assessed by the receiver operating characteristic curve (ROC) and the area under curve (AUC). Results Postoperative pancreatic fistula occurred in 56 cases (56/257, 21.8%). The DT model had an AUC of .743 and an accuracy of .840, while the RF model had an AUC of .977 and an accuracy of .883. The DT plot visualized the process of inferring the risk of pancreatic fistula from the DT model on independent individuals. The top 10 important variables were selected for ranking in the RF variable importance ranking. Conclusion This study successfully developed a DT and RF algorithm for the POPF prediction model, which can be used as a reference for clinical health care professionals to optimize treatment strategies to reduce the incidence of POPF.
Background
Fear of future workplace violence (FFWV) has a negative impact on individuals’ health. However, no study has investigated the association between FFWV and depressive symptoms. Nurses with different experiences of workplace violence may have different levels of FFWV and differences in mental health. This study explored the association between FFWV and depressive symptoms among Chinese nurses with different experiences of workplace violence.
Methods
A cross-sectional study was conducted involving 1888 Chinese nurses from 12 tertiary hospitals in Shandong Province. The Fear of Future Violence at Work scale was used to measure FFWV. Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression scale. Multiple logistic regression analysis was used to examine the association between FFWV and depressive symptoms.
Results
The prevalence of depressive symptoms was 45.9% (no aggression group: 24.3%; non-physical violence group: 46.1%; physical violence group: 63.7%), and 72.8% of nurses had high levels of fear of future workplace violence (no aggression group: 60.2%; non-physical violence group: 75.6%; physical violence group: 70.8%). Compared with low levels of FFWV, high levels of FFWV were associated with more depressive symptoms among nurses in the no aggression group (odds ratio [OR] = 3.269, 95% confidence interval [CI]: 1.102–9.695) and in the non-physical violence group (OR = 2.338, 95% CI: 1.385–3.945).
Conclusion
Nurses who had experienced physical violence had the most depressive symptoms and nurses with experience of non-physical violence had the greatest FFWV. Our findings suggested that there was a significant association between FFWV and depressive symptoms among Chinese nurses in the no aggression and non-physical violence groups. Hospital administrators need to address FFWV needs when developing strategies to reduce depressive symptoms among nurses.
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