ABSTRACT
Objective To introduce the construction and application of clinical microbiology laboratory data management expert system.Methods Firstly, the process management was introduced to clinical microbiology laboratory. Then the characteristics of data on each node of work process were analyzed, and SQL Server data table was created as the knowledge base of the expert system.Finally, VB6.0 was used to compile the knowledge acquisition module, reasoning desktop module and input/output interface procedures to finally construct the expert system.Rates of defect report, errors in bacterial identification and drug sensitivity test, delay in culture results reporting and average delayed days were compared before and after the application of the expert system.Results The expert system could be used for data management in process nodes like sample reception, information collection and input, bacteria culture medium selection, bacterial identification and drug sensitive test, interpretation of drug sensitivity results, comprehensive evaluation in bacterial identification and drug sensitivity results, report of negative result, report of positive result, blood culture, Mycoplasma culture, time limit of detection, and nosocomial infection indicators.No defect report was found after the application of expert system; rate of errors in selection of drug sensitivity test medium was reduced from 0.81% ( 31/3 836 ) in 2012 to 0.02%(1/5 433) in 2014;rate of delay in culture results reporting was reduced from 1.78% (320/17 983) to 1.18%(232/19 692), and the average delayed days was also reduced (3.8 d vs.3.2 d).Conclusion Clinical microbiology laboratory data management expert system can improve work efficiency and reduce errors, which can enhance the overall management of laboratory and the quality of clinical service.
ABSTRACT
Objective To investigate the clinical isolation situation and drug resistance features of enterococcal bacteria in order to provide reference for the clinical rational use of antibacterial agents and infection control. Methods A total of 1220 strains of enterococcal bacteria that induced hospital infection were analyzed retrospectively. Walk Away 96 automated microbial analyzer was used for strain identification and drug sensitive test. MIC was used for screening high-level aminoglycoside resistant strains. WHONET 5.6 was used for data analysis. Results A total of 1220 strains of enterococci were detected, including 675 strains of enterococci faecalis, accounting for 55.3%, and 445 strains of ente-rococci faecium, accounting for 36.5%. Enterococcal bacteria mainly distributed in clinical urine specimens, accounting for 57.5%. The total drug resistance rate of enterococci faecalis was high and the drug resistance rates to penicillin, ampicillin, ciprofloxacin and levofloxacin were all higher than 90%, which were significantly higher than those of the enterococci faecium (<17%). The drug resistance rate of enterococci faecalis to quinupristin/dalfopristin was 100.0%and that of enterococci faecium was 12.6%. For both types of bacteria, no strain resistant to vancomycin was found, but 3 strains of enterococci faecalis were resistant to linezolid. The screening rates of enterococci faecalis for high-level gentamicin drug resistant strains and high-level streptomycin resistant strains were 54.1% and 27.3% respectively while those of enterococci faecium were 58.2% and 56.9% respectively. Conclusion The drug resistance situation of enterococcal bacteria to common antibacterial drugs is not optimistic, and the monitoring of clinical distribution and drug resistance situation of enterococcal bacteria is of important guiding significance to the clinical treatment of entero-coccal bacterial infection.
