用PowerBuilder建立新生儿感染病原菌及抗菌药物药敏变化动态监测系统

Building a dynamic surveillance system for the diversity of causative pathogens and antimicrobial resistance from neonatal bacterial infections by using PowerBuilder

目的建立动态实时监测系统,了解本地区新生儿感染致病菌谱和抗菌药物耐药模式变迁情况,为长期跟踪动态监测和预测提供基础和平台。方法用PowerBuilder软件根据2247条原始数据建立数据库,编写程序建立监测系统,并用该系统进行监测及预测分析。结果2000—2004年5年间,我院新生儿最常见的病原菌为肺炎克雷伯菌(20.7%)、大肠埃希菌(18.0%)和凝固酶阴性葡萄球菌(16.2%)。机会致病菌的比例逐年增高。院内感染和院外感染致病菌谱差异越来越小(P>0.05)。抗菌药物耐药越来越严重,尤其是头孢类抗生素。产超广谱β-内酰胺酶细菌(70.9%)和甲氧西林耐药葡萄球菌(58.0%)是最突出的耐药问题。5年间大部分抗菌药物敏感率变化无显著性(P>0.05),呈轻微下降趋势,但近两年部分抗菌药物敏感率轻微回升。监测抗菌药物敏感率时序变化能被估计和预测,精确度较好(MAPE<50)。结论该监测系统能直观地从时间轴上监测新生儿细菌感染致病菌及抗菌药物敏感率的动态变化趋势,较好预测该变化,通过以上信息能及时评价经验用药、对异常发现提出新的研究假设,初步成功建立监测系统。

Objective To create a dynamic surveillance system for implying a real time surveillance of the change of causative pathogens and antimicrobial resistance in neonatal bacterial infections, find out their current patterns and the transition, forecast their short-term trends, build up a foundation for tracking and forecasting in a long-term and grasp the epidemiological features of the patterns and the transition, and provide the information for empirical administration of antimicrobial agents. Methods A total of 2 strains of bacteria were isolated from hospitalized neonates and detected for antimicrobial susceptibility. All data were built up 12 tables of a database with full functions by structured query language （ SQL ）. The surveillance system was programmed by using PowerScript and embedding Statistical Package for the Social Science（SPSS） by object linking and embedding（OLE）. Time series data were produced by our system and analyzed by embedded SPSS. Results Between 2000 to 2004, Klebsiella pneumoniae（20. 7% ）, E coli（ 18.0% ） and coagulase-negatire staphylococci（CNS） （ 16. 2% ） were predominant causative pathogens in neonatal infections in our unit. The proportion of opportunistic pathogens was increasing in total causative pathogens annually. The patterns of causative pathogens were scatteringly distributed and had less and less difference between nosocomial infections and infections acquired outside of hospital（ P 〉 0. 05 ）. The majority of antimicrobial agents had deterioration of susceptibility, especially cephalosporins. Extended spectrum β-lactamase（ESBL） （ 70. 9% ） and methicillin-resistant staphylococcus（MRS） （ 58.0% ） were the prominent problem of antimicrobial resistance both gram-negative and gram-positive bacteria. There were no significant changes of total sensitive rates in most antimicrobial agents （ P 〉 0. 05 ） ; a lot of changes were slight decreasing, but some antimicrobial agents had increasing susceptibility in last two years. In time series, greater part of antimicrobial agents could be estimated and forecasted well with ordinary precision （ MAPE 〈 50 ）. Conclusion This system can successfully grasp the dynamic changes of causative pathogens and antimicrobial susceptibility,find out the changes in composition ratio of causative pathogens, find uncommon pathogens caused infections and emerging antimicrobial resistant strains, discover new variation of antimicrobiai susceptibility, estimate and foreeast future trends of antimierobial sensitive rates well.