背景:血管内导管是医疗实践中的关键设备,会增加医疗保健相关感染(HAIs)的风险,以及相关的健康经济不良结果。本范围审查旨在全面概述已发布的用于监测导管相关血流感染(CRBSI)和中心线相关血流感染(CLABSI)的自动算法。
方法:我们根据2000年1月1日至2021年12月31日在PubMed和EMBASE中对文献的系统搜索进行了范围审查。如果他们评估CLABSI/CRBSI检测的自动监测算法的预测性能并使用手动收集的监测数据作为参考,则包括研究。我们评估了自动化系统的设计,包括用于开发算法的定义(CLABSI与CRBSI),使用的数据集和分母,和每个研究中评估的算法。
结果:我们根据标题和摘要筛选了586项研究,99人基于全文进行评估。九项研究被纳入范围审查。大多数研究是单中心的(n=5),他们确定CLABSI(n=7)作为结果。大多数研究使用管理和微生物数据(n=9),五项研究包括在其自动化系统中存在血管中心线。六项研究解释了他们选择的分母,其中五个选择了中线日。算法中使用的最常见的规则和步骤被归类为医院获取的规则,感染规则(感染与污染),重复数据删除,剧集分组,辅助BSI规则(辅助与主BSI),和导管相关规则。
结论:我们确定的自动监测系统在定义方面是异构的,使用的数据集和分母,每个算法中的规则组合。需要进一步的指南和研究来开发和实施检测CLABSI/CRBSI的算法,有了标准化的定义,适当的数据源和适当的分母。
BACKGROUND: Intravascular catheters are crucial devices in medical practice that increase the risk of healthcare-associated infections (HAIs), and related health-economic adverse outcomes. This scoping
review aims to provide a comprehensive overview of published automated algorithms for surveillance of catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI).
METHODS: We performed a scoping
review based on a systematic search of the literature in PubMed and EMBASE from 1 January 2000 to 31 December 2021. Studies were included if they evaluated predictive performance of automated surveillance algorithms for CLABSI/CRBSI detection and used manually collected surveillance data as reference. We assessed the design of the automated systems, including the definitions used to develop algorithms (CLABSI versus CRBSI), the datasets and denominators used, and the algorithms evaluated in each of the studies.
RESULTS: We screened 586 studies based on title and abstract, and 99 were assessed based on full text. Nine studies were included in the scoping
review. Most studies were monocentric (n = 5), and they identified CLABSI (n = 7) as an outcome. The majority of the studies used administrative and microbiological data (n = 9) and five studies included the presence of a vascular central line in their automated system. Six studies explained the denominator they selected, five of which chose central line-days. The most common rules and steps used in the algorithms were categorized as hospital-acquired rules, infection rules (infection versus contamination), deduplication, episode grouping, secondary BSI rules (secondary versus primary BSI), and catheter-associated rules.
CONCLUSIONS: The automated surveillance systems that we identified were heterogeneous in terms of definitions, datasets and denominators used, with a combination of rules in each algorithm. Further guidelines and studies are needed to develop and implement algorithms to detect CLABSI/CRBSI, with standardized definitions, appropriate data sources and suitable denominators.