A K-mer Based Approach for Institutional AMR Surveillance, Transmission Monitoring, and Rapid Diagnostics
Antibiotic resistant organisms (AROs) have become increasingly difficult to treat, with rising morbidity and mortality worldwide. Healthcare institutions are often the epicenter for outbreaks of these antibiotic resistant organisms, and are also windows into their circulation within the broader community. Transmission of antibiotic resistant organisms within hospitals is under appreciated. Moreover, identification of linked strains that may be causing occult outbreaks is often not systematically performed. Genomic approaches can provide a better understanding of within-hospital transmission of AROs, which can be used to guide infection control practices. Some institutions have augmented their ARO surveillance with whole genome sequencing, but this is both expensive and time consuming, making it unsuitable for routine use. However, new approaches that use k-mer based algorithms along with genomic reference databases can provide rapid evaluation of pathogen lineage and potential for linked transmission. These same approaches can also be used to provide important rapid diagnostic information about the pathogen and likelihood of resistance to a given antibiotic. While there is much potential in these approaches, they need to be formally evaluated across care settings and geography before they can be trialled in the clinical setting. Here we propose a multi-continental prospective evaluation of the performance of a k-mer based approach for institutional surveillance of common multidrug resistant Gram-negative pathogens as well rapid prediction of antibiotic resistance patterns.
- Derek MacFadden, Ottawa Hospital Research Institute, Canada (Coordinator)
- Allison McGeer, Mt. Sinai Hospital, University of Toronto, Canada (Partner)
- Hajo Grundmann, University of Freiberg, Germany (Partner)
- Martin Antonio, Medical Research Council Unit, The Gambia, Gambia (Partner)
- William Hanage, Harvard Chan School of Public Health, USA (Observer)
Antibiotic resistance is one of our greatest public health threats globally. Rapid identification of antibiotic resistant organisms, and their paths of transmission, can help patients to receive effective treatment for their infections sooner and improve outcomes. We are using real-time genome sequencing of patient specimens to identify resistant organisms and predict their patterns of resistance to help guide therapy. We are using the same approaches to help identify whether bacteria that might have been transmitted from other patients and help with recognizing outbreaks of antibiotic resistant organisms.