[Prevalence of sharp instrument injuries among medical staff in a tertiary general hospital].

Objective: To investigate the current situation of sharp instrument injuries among medical workers in a tertiary general hospital in Fuzhou, so as to provide basis for formulating relevant policies. Methods: In June 2019, medical personnel working in a tertiary general hospital in Fuzhou, who may have sharp instrument injuries were selected as the research object. A total of 2720 questionnaires were received, including 2688 valid questionnaires, with an effective rate of 98.8%. The age, type of work, professional title, working years, operating habits, occurrence and reporting of sharp instrument injuries among medical staff from June 2018 to May 2019 were investigated. Results: The incidence of sharp instrument injury was 37.6% (1011/2688) . Among them, 20.6% (208/1011) had multiple sharp instrument injuries. The exposure rate of contaminated sharp instruments was 15.1% (405/2688) . With the increase of age, professional title and working years, the incidence of sharp instrument injury decreased year by year (χ(2)(trend)=12.393, 33.339, 15.160, P<0.05) . The first three causes of sharp instrument injury were breaking glass ampoules by hand (39.1%, 395/1011) , extracting liquid medicine (10.4%, 105/1011) and handling sharp instruments by hand (10.3%, 104/1011) . The main sharp instruments causing sharp injury were ampoules (43.2%, 437/1011) , syringe needles (20.3%, 205/1011) and suture needles (9.6%, 97/1011) . 874 (86.4%) medical staff had missed reports after sharp instrument injuries. Conclusion: The occurrence of sharp instrument injury in this hospital is still serious, and the protection of sharp instrument injury should be strengthened.

[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.