[Etiological analysis and establishment of a discriminant model for lower respiratory tract infections in hospitalized patients].

Objective: To analyze the pathogens of lower respiratory tract infection(LRTI) including bacterial, viral and mixed infection, and to establish a discriminant model based on clinical features in order to predict the pathogens. Methods: A total of 243 hospitalized patients with lower respiratory tract infections were enrolled in Fujian Provincial Hospital from April 2012 to September 2015. The clinical data and airway (sputum and/or bronchoalveolar lavage) samples were collected. Microbes were identified by traditional culture (for bacteria), loop-mediated isothermal amplification(LAMP) and gene sequencing (for bacteria and atypical pathogen), or Real-time quantitative polymerase chain reaction (Real-time PCR)for viruses. Finally, a discriminant model was established by using the discriminant analysis methods to help to predict bacterial, viral and mixed infections. Results: Pathogens were detected in 53.9% (131/243) of the 243 cases.Bacteria accounted for 23.5%(57/243, of which 17 cases with the virus, 1 case with Mycoplasma pneumoniae and virus), mainly Pseudomonas Aeruginosa and Klebsiella Pneumonia. Atypical pathogens for 4.9% (12/243, of which 3 cases with the virus, 1 case of bacteria and viruses), all were mycoplasma pneumonia. Viruses for 34.6% (84/243, of which 17 cases of bacteria, 3 cases with Mycoplasma pneumoniae, 1 case with Mycoplasma pneumoniae and bacteria) of the cases, mainly Influenza A virus and Human Cytomegalovirus, and other virus like adenovirus, human parainfluenza virus, respiratory syncytial virus, human metapneumovirus, human boca virus were also detected fewly. Seven parameters including mental status, using antibiotics prior to admission, complications, abnormal breath sounds, neutrophil alkaline phosphatase (NAP) score, pneumonia severity index (PSI) score and CRUB-65 score were enrolled after univariate analysis, and discriminant analysis was used to establish the discriminant model by applying the identified pathogens as the dependent variable. The total positive predictive value was 64.7%(77/119), with 66.7% for bacterial infection, 78.0% for viral infection and 33.3% for the mixed infection. Conclusions: The mostly detected pathogens were Pseudomonas aeruginosa, atypitcal pathogens, Klebsiella pneumoniae, influenza A virus and human cytomegalovirus in hospitalized patients with LRTI in this hospital. The discriminant diagnostic model established by clinical features may contribute to predict the pathogens of LRTI.

A clinical nomogram for predicting renal calculus of university teachers.

OBJECTIVE: To establish a prediction model of renal calculus for university teachers to help them prevent renal calculus scientifically. This study involves a specific group of university teachers. We collected the physical examination index of 1043 university teachers in the Hubei University of Chinese Medicine in 2018 to build the model. We also used the physical examination data of 968 teachers in 2019 to verify the model. MATERIALS AND METHODS: We used Lasso regression to screen the factors and logistic regression analysis to establish the model. RESULTS: The models of this study included sex, age, DBP, TC, HDL. C, CEA, UA, ALT, GGT, HB, pH, RBC, RDW, and CLYMPH. Among these, sex, TC, ALT, HB, and LYMPH present high risks in the model. The result is of great significance related to the research of university teachers suffering from renal calculus. The C-index is 0.715, and the AUC is 0.7064. CONCLUSIONS: Based on the results of this study, we suggest that physical examination indicators can predict the risk of renal calculus and the individual probability of prevalence in specific groups. According to the risk of each physical examination index, it is possible to effectively prevent the occurrence of renal calculus in certain high-risk groups through lifestyle changes.