目的 探讨自回归求和时间序列模型（ARIMA）在耐碳青霉烯类肺炎克雷伯菌（CRKP）医院感染预测中的应用，为CRKP感染的科学防控提供策略。方法 选取贾汪区人民医院住院患者2018年1月—2024年12月共7年每月CRKP医院感染率，应用SPSS 19.0建立ARIMA模型，利用该模型对2024年1—12月每月CRKP医院感染数据进行验证，以评价模型的预测性能。结果 用2018—2024年住院患者每月CRKP医院感染率建模、拟合，建立最优模型ARIMA（1，1，1），模型拟合值与实际值较吻合，模型对CRKP医院感染率实际值与预测值吻合度较高，平均相对误差为5.40%。结论 用ARIMA模型可有效拟合、预测CRKP医院感染情况，为CRKP防控提供科学指导。

     Objective To explore the value of auto regressive integrated moving average（ARIMA）model in predicting theinfection rate of carbapenem-resistant Klebsiella pneumoniae（CRKP）．Methods The monthly incidence of nosocomial infection of CRKP in Jiawang District People’s Hospital from 2018 to 2024 was selected，and the ARIMA model was established by19.0 SPSS to analyze the fitting and prediction value of the model．Results The ARIMA was established based on the monthly CRKP infection rate from January 2018 to December 2024．The fitted value of the ARIMA（1，1，1）model was in good agreement with the actual value．The incidence of CRKP infection were in good agreement with the predicted value．The average relative errors were 5.40%．Conclusions The ARIMA model can effectively fit and predict the CRKP infection rate，providing scientific guidance for the prevention and control of CRKP infection．

目的 统计分析2011—2014年我院分离的肠杆菌科细菌数据,探讨耐碳青霉烯肠杆菌科细菌(CRE)的流行特征。方法 收集肠杆菌科细菌,根据药敏结果筛选出CRE菌株,并对相关临床资料进行统计分析。结果 共分离得到CRE菌株187株,标本来源依次为尿液(32.6%)、痰液(28.9%)和血液(10.7%)。从科室分布来看,39.0%的菌株来自重症监护室病区,23.0%的菌株来自泌尿外科病区,在其它病区呈散发分布。菌株的种属分布方面,肺炎克雷伯菌的比例为39.6%, 大肠埃希菌的比例为20.9%;从病人年龄构成来看,50岁以上高龄患者的分离比例达74.4%。CRE的分离数目随年份的递增而不断升高。结论 耐碳青霉烯肠杆菌科细菌的流行率呈现逐年递增的趋势,临床应合理使用相关抗生素,预防和控制CRE在医院环境中的流行。

Objective To investigate the epidemiological features of carbapenem resistant Enterobacteriaceae in a collection of clinical Enterobacteriaceae strains isolated during 2011-2014 from our hospital. Methods The Enterobacteriaceae strains were collected and CRE strains were screened by their resistance to carbapenems. Clinical information was analyzed to characterize the epidemiological traits of CRE strains. Results The total number of CRE isolates was 187. These CRE strains were isolated from various clinical specimens, including urine(32.6%), sputum (28.9%), blood (10.7%), and so on. These strains were frequently isolated from intensive care units (ICU) (39.0%) and department of Urology (23.0%). The most frequently isolated species were Klebsiella pneumoniae (39.6%), Escherichia coli (20.9%). The isolation rate is much higher in elderly patients more than 50 years old (74.4%). The percentage of CRE isolates were kept on increasing by years. Conclusion The prevalence carbapenem resistant Enterobacteriaceae in our hospital is increasing every year and it is important to prevent and control the transmission and outbreaks of CRE in the hospital by proper use of related antibiotics in clinical treatment.