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基于Logistic回归分析的风险评估对医院感染的预防作用
引用本文:张泽波,陈莉华.基于Logistic回归分析的风险评估对医院感染的预防作用[J].中华实验和临床感染病杂志(电子版),2018,12(3):230-234.
作者姓名:张泽波  陈莉华
作者单位:1. 432000 孝感市,武汉科技大学附属孝感市中心医院感染科
基金项目:国家自然科学基金资助项目(No. 30570628)
摘    要:目的探讨基于Logistic回归分析的风险评估对医院感染的预防作用。 方法回顾性分析本院2008年1月至2014年12月武汉科技大学附属孝感市中心医院1 626例住院患者的临床资料,根据患者住院期间是否发生医院感染分为感染组(520例)和对照组(1 106例)。通过Logistic回归分析确定医院感染危险因素并形成风险评估量表,对2015年1月至2016年2月收治的352例住院患者进行医院感染风险评估。 结果住院时间> 15 d、使用三联抗菌药物、使用呼吸机、留置导尿管、全身麻醉、合并肝病、合并血液性疾病、合并糖尿病、激素治疗、放疗或化疗、手术时间> 3 h以及侵入性操作均为医院感染的独立危险因素(P均< 0.05)。建立风险评估量表后医院感染发生率为25.00%,低于评估量表建立前的31.98%(χ2 = 6.622,P < 0.05)。 结论基于Logistic回归分析的风险评估模型可有效评估患者感染风险,为医院感染的预防提供依据,并可有效降低医院感染风险。

关 键 词:医院感染  Logistic回归分析  风险评估  预防  
收稿时间:2017-02-27

Role of risk assessment model based on Logistic regression analysis in preventing nosocomial infection
Zebo Zhang,Lihua Chen.Role of risk assessment model based on Logistic regression analysis in preventing nosocomial infection[J].Chinese Journal of Experimental and Clinical Infectious Diseases(Electronic Version),2018,12(3):230-234.
Authors:Zebo Zhang  Lihua Chen
Institution:1. Department of Infectious Diseases, The Center Hospital of Xiaogan Affiliated to Wuhan University of Science and Technology, 432000 Xiaogan, China
Abstract:ObjectiveTo investigate the preventive effect of risk assessment model based on Logistic regression analysis on nosocomial infection. MethodsThe clinical data of 1 626 hospitalized patients in our hospital from January 2008 to December 2014 in the Center Hospital of Xiaogan Affiliated to Wuhan University of Science and Technology were analyzed, retrospectively, the patients were divided into the infection group (520 cases) and the control group (1 106 cases) according to whether with hospital infection during hospitalization. Logistic regression analysis was used to determine the risk factors of nosocomial infection and form a risk assessment scale. A total of 352 hospitalized patients admitted from January 2015 to February 2016 were assessed by this sacle for nosocomial infection risk. ResultsHospitalized time > 15 days, the application of triple antibiotics and ventilator, indwelling catheter, general anesthesia, liver disease, complicated with blood diseases, diabetes, hormone therapy, radiotherapy or chemotherapy, surgery time > 3 h, invasive operation were all independent risk factors for nosocomial infection (all P < 0.05). The incidence of nosocomial infection after establishment of the risk assessment scale was 25.00%, which was lower than that before the scale established (31.98%), with significant difference (χ2 = 6.622, P < 0.05). ConclusionsThe risk assessment model based on Logistic regression analysis could effectively assess the risk of infection in patients, provide a basis for the prevention of nosocomial infections, and reduce the risk of nosocomial infections.
Keywords:Hospital infection  Logistic regression analysis  Risk assessment  Prevention  
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