Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance
Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance
复制标题
全基因组测序监测检测污染胃镜中铜绿假单胞菌感染暴发
DOI：
10.1093/cid/ciaa1887
复制
发表时间：
2021-08-01
影响因子：
11.8
通讯作者：
Harrison, Lee H.
中科院分区：
文献类型：
--
作者：
Sundermann, Alexander J.;Chen, Jieshi;Harrison, Lee H.
背景传统的疫情调查方法利用反应性全基因组测序（WGS）来确认或反驳疫情。我们已经实施了WGS监测和用于电子健康记录（EHR）的机器学习（ML）算法，以回顾性地检测以前未识别的疫情，并确定负责的传播途径。我们进行了WGS监测，以确定和表征集群的遗传相关的铜绿假单胞菌感染在24个月的时间。EHR的ML用于确定潜在的传播途径。由感染预防专家对EHR进行手动审查，以确定最可能的途径，并将结果与ML算法进行比较。我们确定了一组在7个月内发生的6例遗传相关的铜绿假单胞菌病例。ML算法将胃镜检查确定为6例患者中4例的潜在传播途径。手动EHR审查确认胃镜检查是5例患者最可能的途径。通过对5例患者中的4例进行胃镜检查时偶然培养出的遗传相关铜绿假单胞菌的鉴定，证实了该传播途径。如果ML算法实时运行，可以预防3例感染，其中2例为血流感染。WGS监测与EHR的ML算法相结合，发现了以前未发现的胃镜相关铜绿假单胞菌感染爆发。这些结果强调了WGS监测和EHR的ML对于加强医院爆发检测和预防严重感染的价值。
Background. Traditional methods of outbreak investigations utilize reactive whole genome sequencing (WGS) to confirm or refute the outbreak. We have implemented WGS surveillance and a machine learning (ML) algorithm for the electronic health record (EHR) to retrospectively detect previously unidentified outbreaks and to determine the responsible transmission routes.Methods. We performed WGS surveillance to identify and characterize clusters of genetically-related Pseudomonas aeruginosa infections during a 24-month period. ML of the EHR was used to identify potential transmission routes. A manual review of the EHR was performed by an infection preventionist to determine the most likely route and results were compared to the ML algorithm.Results. We identified a cluster of 6 genetically related P. aeruginosa cases that occurred during a 7-month period. The ML algorithm identified gastroscopy as a potential transmission route for 4 of the 6 patients. Manual EHR review confirmed gastroscopy as the most likely route for 5 patients. This transmission route was confirmed by identification of a genetically-related P. aeruginosa incidentally cultured from a gastroscope used on 4of the 5 patients. Three infections, 2 of which were blood stream infections, could have been prevented if the ML algorithm had been running in real-time.Conclusions. WGS surveillance combined with a ML algorithm of the EHR identified a previously undetected outbreak of gastroscope-associated P. aeruginosa infections. These results underscore the value of WGS surveillance and ML of the EHR for enhancing outbreak detection in hospitals and preventing serious infections.