ABSTRACT
Objective:To investigate and analyze the psychological stress status and influencing factors of medical staff in comprehensive grade A hospitals.Methods:This was a cross-sectional survey using a convenient sampling method. A questionnaire survey was conducted among 2 048 staff members of the Affiliated Hospital of Qingdao University from February 25 to June 16, 2022. The questionnaires included the Self-rating Stress Questionnaire (SSQ-53), the Self-rating Anxiety Scale, and the Self-rating Depression Scale. A total of 2 048 questionnaires were distributed and 2 048 were collected. Among them, 2 006 (97.95%) were valid questionnaires, and 2 006 medical staff were ultimately included in the analysis. According to the results of the scale, they were divided into the increased psychological stress group and the non increased psychological stress group. Descriptive analysis, independent sample t-test/chi-square test, correlation analysis, and other methods were used to analyze the psychological stress status of medical staff and the distribution of related factors, and multivariate logistic regression was used to analyze their influencing factors. Results:A total of 310 (15.5%) out of 2 006 study subjects showed increased psychological stress. Women had a statistically significant increase in physical (14.7% vs 8.0%, χ2=12.40, P<0.001) and emotional dimensions (18.2% vs 13.5%, χ2=5.04, P=0.025) of stress compared to men. The level of psychological stress was positively correlated with anxiety and depression ( r=0.810, 0.749, respectively, P<0.001). Univariate analysis showed that those who were women ( χ2=6.76, P=0.009), with low education backgrounds ( Z=-2.50, P=0.012), nurses ( χ2=15.72, P<0.001), or working in emergency departments ( χ2=13.64, P=0.009) had a higher rate of increased psychological stress, while the serum calcium level in the increased psychological stress group was lower than that in the non increased psychological stress group ( t=2.82, P=0.005). Multivariate analysis showed that low educational backgrounds ( OR=2.238, 95% CI: 1.090-4.597, P=0.028) and working in emergency department ( OR=1.589, 95% CI: 1.012-2.493, P=0.044) were independent risk factors for increased psychological stress of medical staff. Working in administrative and logistics departments ( OR=0.466, 95% CI: 0.229-0.950, P=0.036) and serum calcium level ( OR=0.213, 95% CI: 0.059-0.760, P=0.017) were negatively correlated with increased psychological stress. Conclusions:In comprehensive grade A hospitals, medical staff working in the emergency department or with lower educational backgrounds face greater psychological stress. Serum calcium level is negatively correlated with increased psychological stress, but the causal relationship needs further study.
ABSTRACT
Objective:To develop and validate a MRI-based radiomics nomogram combining with radiomics signature and clinical factors for the preoperative differentiation of benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT).Methods:From January 2015 to May 2020, 86 patients with parotid tumors confirmed by surgical pathology in the Affiliated Hospital of Qingdao University were enrolled as training sets, and 35 patients in the University of Hong Kong-Shenzhen Hospital from January 2013 to January 2020 were enrolled as independent external validation sets. The logistic regression was used to establish a clinical-factors model based on demographics and MRI findings. Radiomics features were extracted from preoperative T 1WI and fat-saturated T 2WI (fs-T 2WI), a radiomics signature model was constructed, and a radiomics score (Rad-Score) was calculated. A combined diagnostic model and nomogram combining with the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of each model and DeLong test was used for comparison of area under the ROC curve (AUC). Results:The logistic regression results showed that deep lobe involvement (OR=3.285, P=0.040) and surrounding tissue invasion (OR=15.919, P=0.013) were independent factors for MPGT and constructed the clinical-factors model. A total of 19 features were extracted from the joint T 1WI and fs-T 2WI to build the radiomics signature model. The combined diagnostic model and nomogram incorporating deep lobe involvement, surrounding tissue invasion and Rad-score were established. The AUCs of the clinical-factors model, radiomics signature model and combined diagnostic model for differentiating BPGT from MPGT for the training and validation sets were 0.758, 0.951, 0.953 and 0.752, 0.941 and 0.964 respectively. The AUCs of the radiomics signature model and the combined diagnostic model were significantly higher than those of the clinical-factors model for both training and validation sets (training set: Z=3.95, 4.31, both P<0.001; validation set: Z=2.16, 2.67, P=0.031, 0.008). There was no statistical difference in AUCs between the radiomics signature model and combined diagnostic model (training set: Z=0.39, P=0.697; validation set: Z=1.10, P=0.273). Conclusions:The MRI-based radiomics signature model and radiomics nomogram incorporating deep lobe involvement, surrounding tissue invasion, and Rad-score showed favorable predictive efficacy for differentiating BPGT from MPGT.