目的 探索甘肃省三级甲等医院医院感染的独立危险因素，建立预测模型并进行验证。方法 选取2021年1月-12月甘肃省人民医院690例发生医院感染的住院患者为感染组，1∶1匹配入院科室、年龄因素，选取同期住院的690例未发生医院感染的患者为对照组。对比两组患者基础疾病、内镜操作、输血和免疫抑制剂使用情况等信息，采用多因素logistic回归分析住院患者医院感染的影响因素，并建立logistic预测模型。以甘肃省人民医院数据的80％作为模型的训练集，其余20％作为测试集用于内部验证;以甘肃省内其他3所医院的病例数据用于外部验证。采用灵敏度、特异度、准确率、受试者操作特征曲线下面积(area under the curve，AUC)评价模型效能。结果 多因素logistic回归分析显示，内镜治疗操作[比值比(odds ratio，OR)=3。360，95％置信区间(confidence interval，CI)(2。496，4。523)]、留置导尿管[OR=3。100，95％CI(2。352，4。085)]、器官移植/人工制品植入[O R=3。133，95％CI(1。780，5。516)]、输血及血液制品[O R=3。412，95％CI(2。626，4。434)]、糖皮质激素[OR=2。253，95％CI(1。608，3。157)]、基础疾病数量[OR=1。197，95％CI(1。068，1。342)]和住院期间手术次数[OR=1。221，95％CI(1。096，1。361)]是医院感染的危险因素。预测模型回归方程为:logit(P)=-2。208+1。212×内镜治疗操作+1。131×留置导尿管+1。142×器官移植/人工制品植入+1。227×输血及血液制品+0。812×糖皮质激素+0。180×基础疾病数量+0。200×住院期间手术次数。内部验证集模型灵敏度为72。857％，特异度为77。206％，准确率为76。692％，AUC为0。817;外部验证模型灵敏度为63。705％，特异度为70。934％，准确率为68。669％，AUC为0。726。结论 内镜治疗操作、留置导尿管、器官移植/人工制品植入、输血及血液制品、糖皮质激素、基础疾病数量、住院期间手术次数是医院感染的影响因素。建立的模型可有效预测医院感染发生情况，指导临床采取预防措施，减少医院感染发生。
Risk factor analysis and prediction model construction for hospital infections in tertiary hospitals in Gansu Province
Objective To explore the independent risk factors for hospital infections in tertiary hospitals in Gansu Province,and establish and validate a prediction model.Methods A total of 690 patients hospitalized with hospital infections in Gansu Provincial Hospital between January and December 2021 were selected as the infection group;matched with admission department and age at a 1∶1 ratio,690 patients who were hospitalized during the same period without hospital infections were selected as the control group.The information including underlying diseases,endoscopic operations,blood transfusion and immunosuppressant use of the two groups were compared,the factors influencing hospital infections in hospitalized patients were analyzed through multiple logistic regression,and the logistic prediction model was established.Eighty percent of the data from Gansu Provincial Hospital were used as the training set of the model,and the remaining 20％were used as the test set for internal validation.Case data from other three hospitals in Gansu Province were used for external validation.Sensitivity,specificity,accuracy,and area under the receiver operating characteristic curve(AUC)were used to evaluate the model effectiveness.Results Multiple logistic regression analysis showed that endoscopic therapeutic manipulation[odds ratio(OR)=3.360,95％confidence interval(CI)(2.496,4.523)],indwelling catheter[OR=3.100,95％CI(2.352,4.085)],organ transplantation/artifact implantation[OR=3.133,95％CI(1.780,5.516)],blood or blood product transfusions[OR=3.412,95％CI(2.626,4.434)],glucocorticoids[OR=2.253,95％CI(1.608,3.157)],the number of underlying diseases[OR=1.197,95％CI(1.068,1.342)],and the number of surgical procedures performed during hospitalization[OR=1.221,95％CI(1.096,1.361)]were risk factors for hospital infections.The regression equation of the prediction model was:logit(P)=-2.208+1.212×endoscopic therapeutic operations+1.131×indwelling urinary catheters+1.142×organ transplantation/artifact implantation+1.227×transfusion of blood or blood products+0.812×glucocorticosteroids+0.180×number of underlying diseases+0.200×number of surgical procedures performed during the hospitalization.The internal validation set model had a sensitivity of 72.857％,a specificity of 77.206％,an accuracy of 76.692％,and an AUC value of 0.817.The external validation model had a sensitivity of 63.705％,a specificity of 70.934％,an accuracy of 68.669％,and an AUC value of 0.726.Conclusions Endoscopic treatment operation,indwelling catheter,organ transplantation/artifact implantation,blood or blood product transfusion,glucocorticoid,number of underlying diseases,and number of surgical cases during hospitalization are influencing factors of hospital infections.The model can effectively predict the occurrence of hospital infections and guide the clinic to take preventive measures to reduce the occurrence of hospital infections.
Hospital infectionrisk factorlogistic regressionpredictive model
NETL
NSTL
万方数据
