Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance. (25th December 2020)

• Record Type: Journal Article
• Authors: Sundermann, Alexander J; Chen, Jieshi; Miller, James K; Saul, Melissa I; Shutt, Kathleen A; Griffith, Marissa P; Mustapha, Mustapha M; Ezeonwuka, Chinelo; Waggle, Kady; Srinivasa, Vatsala; Kumar, Praveen; Pasculle, A William; Ayres, Ashley M; Snyder, Graham M; Cooper, Vaughn S; Van Tyne, Daria; Marsh, Jane W; Dubrawski, Artur W; Harrison, Lee H
• Is Part Of: Clinical infectious diseases. Volume 73:Number 3(2021)
• Page Start: e638
• Page End: e642
• Publication Date: 2020-12-25
• Subjects: outbreak detection -- healthcare-associated infections -- whole genome sequencing surveillance -- Pseudomonas aeruginosa -- machine learning; Communicable diseases -- Periodicals; 616.905
• DOI: 10.1093/cid/ciaa1887
• Languages: English
• ISSNs: 1058-4838

Abstract

Abstract: 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 aerugin osa 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. Abstract : Whole genome sequence surveillance combined with machine learning of the electronic health record discovered a previously undetected outbreak of Pseudomonas aeruginosa infections and accurately defined the transmission route as a contaminated gastroscope.