Simmons Sarah, Wier Grady, Pedraza Antonio, Stibich Mark
Xenex Disinfection Services, 1074 Arion Circle, Suite 116, San Antonio, TX, USA.
BMC Infect Dis. 2021 Oct 20;21(1):1084. doi: 10.1186/s12879-021-06789-y.
The role of the environment in hospital acquired infections is well established. We examined the impact on the infection rate for hospital onset Clostridioides difficile (HO-CDI) of an environmental hygiene intervention in 48 hospitals over a 5 year period using a pulsed xenon ultraviolet (PX-UV) disinfection system.
Utilization data was collected directly from the automated PX-UV system and uploaded in real time to a database. HO-CDI data was provided by each facility. Data was analyzed at the unit level to determine compliance to disinfection protocols. Final data set included 5 years of data aggregated to the facility level, resulting in a dataset of 48 hospitals and a date range of January 2015-December 2019. Negative binomial regression was used with an offset on patient days to convert infection count data and assess HO-CDI rates vs. intervention compliance rate, total successful disinfection cycles, and total rooms disinfected. The K-Nearest Neighbor (KNN) machine learning algorithm was used to compare intervention compliance and total intervention cycles to presence of infection.
All regression models depict a statistically significant inverse association between the intervention and HO-CDI rates. The KNN model predicts the presence of infection (or whether an infection will be present or not) with greater than 98% accuracy when considering both intervention compliance and total intervention cycles.
The findings of this study indicate a strong inverse relationship between the utilization of the pulsed xenon intervention and HO-CDI rates.
环境在医院获得性感染中的作用已得到充分证实。我们使用脉冲氙气紫外线(PX-UV)消毒系统,在5年时间里对48家医院的环境卫生干预措施对医院获得性艰难梭菌感染(HO-CDI)率的影响进行了研究。
利用数据直接从自动化PX-UV系统收集,并实时上传至数据库。每家机构提供HO-CDI数据。在科室层面分析数据,以确定对消毒方案的依从性。最终数据集包括汇总至机构层面的5年数据,形成了一个包含48家医院、日期范围为2015年1月至2019年12月的数据集。使用负二项回归并以患者天数为偏移量,转换感染计数数据,评估HO-CDI率与干预依从率、成功消毒总周期数以及消毒房间总数之间的关系。使用K近邻(KNN)机器学习算法比较干预依从性和总干预周期与感染存在情况。
所有回归模型均显示干预与HO-CDI率之间存在统计学上显著的负相关。当同时考虑干预依从性和总干预周期时,KNN模型预测感染存在情况(或是否会出现感染)的准确率超过98%。
本研究结果表明,脉冲氙气干预措施的使用与HO-CDI率之间存在强烈的负相关关系。