Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection

• Published in: Clinical infectious diseases Vol. 75; no. 3; pp. 476 - 482
• Main Authors: Sundermann, Alexander J, Chen, Jieshi, Kumar, Praveen, Ayres, Ashley M, Cho, Shu Ting, Ezeonwuka, Chinelo, Griffith, Marissa P, Miller, James K, Mustapha, Mustapha M, Pasculle, A William, Saul, Melissa I, Shutt, Kathleen A, Srinivasa, Vatsala, Waggle, Kady, Snyder, Daniel J, Cooper, Vaughn S, Van Tyne, Daria, Snyder, Graham M, Marsh, Jane W, Dubrawski, Artur, Roberts, Mark S, Harrison, Lee H
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 31.08.2022
• Subjects: Cross Infection - epidemiology; Cross Infection - microbiology; Cross Infection - prevention & control; Delivery of Health Care; Disease Outbreaks; Electronic Health Records; Genome, Bacterial; Humans; Machine Learning; Major; Whole Genome Sequencing - methods; outbreaks; surveillance; machine learning; hospital-associated infections; whole-genome sequencing
• ISSN: 1058-4838; 1537-6591
• DOI: 10.1093/cid/ciab946

Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole-genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively. We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared with traditional IP practice during the same period. Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2-14 patients. At least 1 transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192 408-$692 532. EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.