呼吸机相关性肺炎风险预测模型的研究进展  

Research Progress of Risk Prediction Models for Ventilator-associated Pneumonia

作　　者：张丽玉[1] 王翠丽 郑洁[3] 刘琴琴 张颖惠[1] 侯林义[1] 路娇 王彩玲[1] ZHANG Liyu;WANG Cuili;ZHENG Jie;LIU Qinqin;ZHANG Yinghui;HOU Linyi;LU Jiao;WANG Cailing(Department of Critical Care Medicine,the Second Hospital of Shanxi Medical University,Taiyuan 030001,China;School of Nursing,Peking University,Beijing 100191,China;School of Nursing,Shanxi Medical University,Taiyuan 030001,China)

机构地区：[1]山西医科大学第二医院重症医学科,太原030001 [2]北京大学护理学院,北京100191 [3]山西医科大学护理学院,太原030001

出　　处：《医学综述》2024年第20期2510-2514,共5页Medical Recapitulate

基　　金：山西省产学研横向课题(YF-HZ-20230101)。

摘　　要：呼吸机相关性肺炎(VAP)是机械通气患者常见的并发症,不仅增加患者医疗费用,而且威胁其生命安全。风险预测模型能够预测某种结局事件的概率,有助于识别高风险人群。目前,国内外许多研究基于传统Logistic回归方法或机器学习法构建VAP的预测模型,并将预测模型转换为列线图或风险评分。VAP风险预测模型能够有效帮助临床医护人员快速、准确地筛查高风险人群,进而及时有效地针对高风险人群采取相应的预防措施,从而降低VAP发生率。Ventilator-associated pneumonia(VAP)is a common complication in mechanically ventilated patients,which not only increases their medical expenses but also threatens their life safety.Risk prediction models can predict the probabi-lity of a certain outcome event,which helps identify high-risk populations.At present,many studies at home and abroad are based on traditional Logistic regression methods or machine learning methods to construct prediction models for VAP,and convert the prediction models into column charts or risk scores.The VAP risk prediction model can effectively help the clinical medical staff quickly and accurately screen high-risk populations,and then take timely and effective preventive measures for high-risk populations,thereby reducing the incidence of VAP.

关 键 词：呼吸机相关性肺炎 机械通气 重症监护 风险 预测模型 

分 类 号：R563.1[医药卫生—呼吸系统]