Objective To analyze the changes and characteristics of respiratory tract bacteria in Hebei 3A Hospital, and to provide new rationale for clinical diagnosis and treatment.Methods A single-center retrospective analysis was conducted. 7497 patients with respiratory tract infection admitted to Hebei Chest Hospital from January 2013 to December 2016 were enrolled. Deep sputum was collected, and the bacterial cultures and susceptibility analysis was conducted in sputum and upper respiratory secretions were collected by fiberoptic bronchoscopy.Results A total of 7497 patients with respiratory tract infection were enrolled in the study, and 11909 strains of 13 kinds of dominant pathogens were isolated. The dominant pathogens for respiratory tract infection wereMonilia albican (23.7%),Klebsiella pneumoniae (12.9%),Pseudomonas aeruginosa (11.6%),Escherichia coli (9.5%),Candida glabrata (9.1%),Acinetobacter baumanii (7.9%),Aspergillus (6.7%),Stenotrophomonas maltophilia (4.5%), coagulase negativeStaphylococcus(3.7%) and some species ofPseudomonas (3.7%),Staphylococcus aureus (3.0%),Aerobacter cloacae (1.9%), andCandida tropicalis (1.8%). A total of 6198 strains of 7 kinds of Gram negative (G-) bacilli infection dominant pathogens accounts for 52.0% of all infections,Klebsiella pneumonia (24.8%),Pseudomonas aeruginosa (22.3%),Escherichia coli (18.2%) andAcinetobacter baumanii (15.3%) were the main pathogens, and increased year by year. Susceptibility analysis showed that the preferred antibiotics for G- bacteria were carbapenems, followed by risperidone, sulbactam, cefepime, amikacin, and the third generation of cephalosporins. A total of 798 strains of 2 kinds of Gram positive (G+) bacilli infection dominant pathogens accounted for 6.7% of all infections, were coagulase negativeStaphylococcus(54.8%) and Staphylococcus aureus (45.2%), each had changed little by year. Susceptibility analysis showed that G+ bacteria were sensitive to glycopeptides, followed by cefoxitin, cotrimoxazole, the tetracyclines, quinolones, azithromycin, erythromycin and so on. The advantages of 4 species of fungi were 4913 strains, accounted for all of the 41.3% strains, with 57.5% of Candida albicans, and the trend was increasing year by year. Susceptibility analysis results showed that the antifungal susceptibility of dominant fungi were higher.Conclusions G- bacilli is still the main source of infection, and showed an upward trend year by year. Fungal infection rate cannot be ignored, and we must pay attention to fungal infection incentives. We should strengthen the rational use of antibiotics.

Objective:To analyze risk factors for diabetes mellitus in patients with vitiligo, and to construct and validate a prediction model.Methods:A total of 110 vitiligo patients with diabetes mellitus (comorbidity group) and 4 505 vitiligo patients without diabetes mellitus (control group) were collected from the medical record database in Xijing Hospital, the Fourth Military Medical University from January 2010 to October 2021, and matched for gender and age at a ratio of 1∶4 by using a propensity score method. After matching, the matched pairs were randomly divided into a training set and a test set at a ratio of 4∶1. Univariate and multivariate logistic regression analyses were used to assess demographic and clinical characteristics of patients in the training set, screen differential factors, and construct a prediction model. A five-fold cross-validation method was used for internal validation after construction of the prediction model. The discrimination (area under the curve [AUC]) , calibration (Hosmer-Lemeshow test) and accuracy (sensitivity, specificity, positive predictive value, and negative predictive value) of the prediction model were evaluated in the test set.Results:A total of 107 cases in the comorbidity group and 428 cases in the control group were successfully matched. The training set included 430 cases, and the test set included 105 cases. Based on multivariate logistic regression results, a total of 6 factors were included in the prediction model, including course of vitiligo (odds ratio [ OR] = 1.04, 95% confidence interval [ CI]: 1.02 - 1.07, P<0.001) , high-sugar/high-fat/high-salt diet ( OR = 3.19, 95% CI: 1.38 - 7.38, P = 0.007) , family history of diabetes ( OR = 23.23, 95% CI: 9.72 - 55.50, P<0.001) , metabolic comorbidities ( OR = 12.53, 95% CI: 5.60 - 28.07, P<0.001) , autoimmune comorbidities ( OR = 5.89, 95% CI: 2.52 - 13.76, P<0.001) , and acral vitiligo ( OR = 3.84, 95% CI: 1.45 - 10.19, P = 0.007) . Five-fold cross-validation results showed a good predictive performance of the prediction model, with the AUC being 0.902 (95% CI: 0.864 - 0.940) in the training set and 0.895 (95% CI: 0.815 - 0.974) in the test set. The prediction model also showed favourable discrimination (AUC =0.814, 95% CI: 0.715 - 0.913) , calibration (Hosmer-Lemeshow test, P = 0.068) , and accuracy (sensitivity = 0.810, 95% CI: 0.574 - 0.937; specificity = 0.786, 95% CI: 0.680 - 0.865; positive predictive value = 0.486, 95% CI: 0.317 - 0.657; negative predictive value = 0.943, 95% CI: 0.853 - 0.982) in the test set. Conclusion:A risk prediction model was constructed for diabetes mellitus in patients with vitiligo based on 6 factors (course of vitiligo, high-sugar/high-fat/high-salt diet, family history of diabetes, metabolic comorbidities, autoimmune comorbidities, and acral vitiligo) , which showed favourable discrimination, calibration and accuracy, and might provide a reference for screening the high-risk diabetic population in vitiligo patients.