PDF(Pubmed)
Abstract：
BACKGROUND: Detection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns.
METHODS: In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species.
RESULTS: All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms.
CONCLUSIONS: Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.
METHODS: In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species.
RESULTS: All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms.
CONCLUSIONS: Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.
摘要：
背景：在医院内检测与病原体相关的集群是早期干预以防止继续传播的关键。已经在医院环境中实施了用于爆发检测的各种自动监视方法。然而,由于数据源和方法的异质性，直接比较是困难的。在医院环境中，当应用于具有不同发生模式的各种病原体时，我们评估了三种不同的微生物簇识别方法的性能。
方法：在这项回顾性队列研究中，我们使用WHONET-SaTScan，CLAR（CLusterAleRt系统）和我们目前使用的基于百分位数的系统（P75）用于聚类检测。这三种方法适用于2014年1月1日至2021年12月31日三级医院收集的相同数据。我们展示了以下案例研究的结果：引入一种新的病原体，随后的地方性，一种特有物种，地方性生物的水平不断上升，和偶尔出现的物种。
结果：所有三种簇检测方法仅在特有生物中显示一致性。然而,与CLAR(n=319)和P75系统(n=472)相比,WHONET-SaTScan(n=9)发出的警报很少.与CLAR和P75系统相比，WHONET-SaTScan并未发现地方性生物和零星生物的基线数量变化较小。CLAR和P75系统显示出地方性和零星生物的警报一致。
结论：使用基于统计的自动群集警报系统（如CLAR和WHONET-Satscan）与仅针对地方性病原体的基于规则的警报系统相当。与基于规则的警报系统相比，对于散发性病原体，WHONET-SaTScan返回的警报较少。关于临床相关性需要进一步的工作，集群警报和实施的时间表。
方法：在这项回顾性队列研究中，我们使用WHONET-SaTScan，CLAR（CLusterAleRt系统）和我们目前使用的基于百分位数的系统（P75）用于聚类检测。这三种方法适用于2014年1月1日至2021年12月31日三级医院收集的相同数据。我们展示了以下案例研究的结果：引入一种新的病原体，随后的地方性，一种特有物种，地方性生物的水平不断上升，和偶尔出现的物种。
结果：所有三种簇检测方法仅在特有生物中显示一致性。然而,与CLAR(n=319)和P75系统(n=472)相比,WHONET-SaTScan(n=9)发出的警报很少.与CLAR和P75系统相比，WHONET-SaTScan并未发现地方性生物和零星生物的基线数量变化较小。CLAR和P75系统显示出地方性和零星生物的警报一致。
结论：使用基于统计的自动群集警报系统（如CLAR和WHONET-Satscan）与仅针对地方性病原体的基于规则的警报系统相当。与基于规则的警报系统相比，对于散发性病原体，WHONET-SaTScan返回的警报较少。关于临床相关性需要进一步的工作，集群警报和实施的时间表。
