Objective:To analyze the prognostic risk factors of patients with traumatic pancreatitis (TP) and establish an early combined prediction of multiple indicators model for TP.Methods:Patients admitted to the ICU of the First Affiliated Hospital of Zhengzhou University from June 2017 to June 2022 were collected retrospectively. Based on their prognosis, the patients were divided into two groups: the good prognosis group and the poor prognosis group. The general data such as sex, age, underlying diseases, Glasgow Coma Scale (GCS), acute physiology and chronic health evaluationⅡ (APACHEⅡ), injury severity score (ISS), bedside index for severity in acute pancreatitis (BISAP), and clinical test indices such as blood routine, blood coagulation, blood gas analysis, and liver and kidney function at admission were compared between the two groups. Univariate analysis and multivariate logistic regression analysis were used to screen the early independent predictors of poor prognosis of TP, and the prediction model of TP was established by combining all of the independent indicators. The receiver operating characteristic (ROC) curve of each independent predictor and prediction model was drawn, and the area under the curve (AUC), sensitivity, specificity, and optimal cut-off value were calculated to examine the diagnostic impact of each independent predictor and the combined prediction model.Results:There were statistically significant differences in the complication rate of mental disorders, GCS, APACHE II, combined craniocerebral injury, combined chest injury, activated partial thromboplastin time, fibrin(pro)degradation products, lactate, aspartate aminotransferase, glomerular filtration rate, amylase, lipase, NT-proBNP, myoglobin, procalcitonin, ISS, and BISAP between the good and poor prognosis groups (all P<0.05). Multivariate logistic regression analysis showed that lactate ( OR=1.636, 95% CI: 1.046-2.559), lipase ( OR=1.005, 95% CI: 1.001-1.008), and ISS ( OR=1.161, 95% CI: 1.064-1.266) were independent risk factors influencing the prognosis of patients with TP. Based on the risk factors listed above, a prediction model was created: Logit P=-9.260+0.492×lactate+0.005×lipase+0.149×ISS, and the ROC curve was plotted. The AUC curve of the prediction model was 0.96 (95% CI: 0.91-1.00). Conclusions:Lactate, lipase, and ISS are early independent risk factors associated with the prognosis of TP. Their combined multi-indicator prediction model has an excellent clinical prediction effect, which can provide a clinical reference for early prediction and treatment of TP.

Objective : To investigate the incidence of dental anxiety in pregnant women and its influencing factors. Methods : A total of 2 638 pregnant women in Shanghai were included in this study. Data on demographic and social factors, oral health behaviors and the number of teeth were collected. Participants completed the modified dental anxiety scale (MDAS), and an MDAS score greater than 12 was defined as a dental anxiety disorder. An electronic data capture system (EDC) was used to establish a database, and SPSS 22.0 was used to analyze the degree of anxiety and its influencing factors. Results: A total of 2 638 valid questionnaires were received. The incidence of dental anxiety in pregnant women was 34.9%. The results of a univariate analysis showed that the prevalence of dental anxiety in pregnant women (P<0.001) and MDAS score decreased (P<0.05) with increasing age, annual family income, educational level, frequency of cleaning, frequency of brushing and number of teeth. Conversely, the prevalence of dental anxiety in pregnant women (P<0.05) and MDAS score increased (P<0.05) as the number of pregnancies and gingival bleeding increased. Logistic analysis showed that education level “college” (P = 0.003) and “bachelor and above” (P<0.001), frequency of dental cleaning “semiannually or annually” (P = 0.021) and “biennial” (P<0.001), and frequency of brushing “twice a day” (P<0.001) were significantly associated with dental anxiety in pregnant women and were protective factors (OR<1). The frequency of gingival bleeding “Sometimes”(P<0.001) and the number of teeth “≤ 27” (P<0.001) were also significant risk factors for dental anxiety in pregnant women (OR>1).The results of a negative binomial regression analysis of MDAS showed that the frequency of dental cleaning and the number of teeth were significantly associated with dental anxiety. The frequency of dental cleaning "semiannually or annually" (P<0.001) was a protective factor (OR<1) for pregnant women's dental anxiety. The number of teeth ≤27 (P<0.001) was a risk factor (OR>1) for dental anxiety in pregnant women. Conclusion Educational level, teeth cleaning frequency, teeth brushing frequency, gingival bleeding, and the number of teeth influence dental anxiety in pregnant women. To effectively reduce the prevalence of dental anxiety, dentists should attach great importance to it in clinical practice and enhance pregnant women's awareness of oral hygiene maintenance by disseminating relevant health care knowledge.