Stirrup Oliver, Hughes Joseph, Parker Matthew, Partridge David G, Shepherd James G, Blackstone James, Coll Francesc, Keeley Alexander, Lindsey Benjamin B, Marek Aleksandra, Peters Christine, Singer Joshua B, Tamuri Asif, de Silva Thushan I, Thomson Emma C, Breuer Judith
Institute for Global Health, University College London, London, United Kingdom.
MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom.
Elife. 2021 Jun 29;10:e65828. doi: 10.7554/eLife.65828.
Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult.
We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hr following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020.
We analysed data from 326 HOCIs. Among HOCIs with time from admission ≥8 days, the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time from admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%).
The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period.
COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute.
快速识别和调查医疗保健相关感染(HCAIs)对于抑制SARS-CoV-2很重要,但仅基于流行病学数据并不总是能轻易确定医院获得性COVID-19感染(HOCIs)的感染源。病毒测序数据提供了有关潜在传播集群的额外信息,但SARS-CoV-2的低突变率使得使用标准系统发育方法进行解读变得困难。
我们开发了一种新颖的统计方法和序列报告工具(SRT),该工具结合了流行病学和序列数据,以便快速评估HOCI病例(定义为入院后首次阳性检测>48小时)中发生HCAI的概率,并识别可能构成暴发事件的感染。该方法设计用于前瞻性使用,但使用2020年2月至5月从格拉斯哥和谢菲尔德的医院收集的回顾性数据集进行了验证。
我们分析了326例HOCI的数据。在入院时间≥8天的HOCI中,SRT算法在同一病房中识别出160/244(65.6%)的密切序列匹配,其余68/84(81.0%)在医院其他地方至少有一个相似序列,导致病房内和医院内传播的估计概率很高。对于入院时间为3 - 7天的HOCI,SRT评估的医疗保健获得概率在33/82(40.2%)中>0.5。
所开发的方法可以为HOCI提供快速反馈,这对感染预防和控制团队可能有用,并且值得进一步进行前瞻性评估。鉴于SARS-CoV-2的低突变率及其可变的潜伏期,整合流行病学和序列数据很重要。
由COG-UK联盟资助的COG-UK HOCI,由英国研究与创新、国家卫生研究院和惠康桑格研究所的资金支持。