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Background: 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., Methods: 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., Results: 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., Conclusions: 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., Competing Interests: Potential conflicts of interest. V. S. C. reports serving on the Board of Directors, American Society of Microbiology, and reports being a co-founder, equity stakeholder of Microbial Genome Sequencing, LLC. G. M. S. reports serving as a scientific advisor, Infectious Diseases Connect (received personal payments as advisor on issues related to infection prevention and control) and Chair, Publications Committee, Society for Healthcare Epidemiology of America (volunteer). D. V. T. reports being a paid consultant for Century Therapeutics. L. H. H. reports receiving consulting fees from Infectious Diseases Connect for a presentation about the present work at a scientific advisory board meeting. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Severe infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are often complicated by persistent bacteremia (PB) despite active antibiotic therapy. Antibiotic resistance rarely contributes to MRSA-PB, suggesting an important role for antibiotic tolerance pathways. To identify bacterial factors associated with PB, we sequenced the whole genomes of 206 MRSA isolates derived from 20 patients with PB and looked for genetic signatures of adaptive within-host evolution. We found that genes involved in the tricarboxylic acid cycle ( citZ and odhA ) and stringent response ( rel ) bore repeated, independent, protein-altering mutations across multiple infections, indicative of convergent evolution. Both pathways have been linked previously to antibiotic tolerance. Mutations in citZ were identified most frequently, and further study showed they caused antibiotic tolerance through the loss of citrate synthase activity. Isolates harboring mutant alleles ( citZ , odhA , and rel ) were sampled at a low frequency from each patient but were detected in 10 (50%) of the patients. These results suggest that subpopulations of antibiotic-tolerant mutants emerge commonly during MRSA-PB. Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of hospital-acquired infection. In severe cases, bacteria invade the bloodstream and cause bacteremia, a condition associated with high mortality. We analyzed the genomes of serial MRSA isolates derived from patients with bacteremia that persisted through active antibiotic therapy and found a frequent evolution of pathways leading to antibiotic tolerance. Antibiotic tolerance is distinct from antibiotic resistance, and the role of tolerance in clinical failure of antibiotic therapy is defined poorly. Our results show genetic evidence that perturbation of specific metabolic pathways plays an important role in the ability of MRSA to evade antibiotics during severe infection.

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., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Multidrug-resistant bacteria pose a serious health threat, especially in hospitals. Horizontal gene transfer (HGT) of mobile genetic elements (MGEs) facilitates the spread of antibiotic resistance, virulence, and environmental persistence genes between nosocomial pathogens. We screened the genomes of 2173 bacterial isolates from healthcare-associated infections from a single hospital over 18 months, and identified identical nucleotide regions in bacteria belonging to distinct genera. To further resolve these shared sequences, we performed long-read sequencing on a subset of isolates and generated highly contiguous genomes. We then tracked the appearance of ten different plasmids in all 2173 genomes, and found evidence of plasmid transfer independent from bacterial transmission. Finally, we identified two instances of likely plasmid transfer within individual patients, including one plasmid that likely transferred to a second patient. This work expands our understanding of HGT in healthcare settings, and can inform efforts to limit the spread of drug-resistant pathogens in hospitals., Competing Interests: DE, MG, AS, KS, MS, MM, JM, VC, LH, DV No competing interests declared, (© 2020, Evans et al.)