PloS one · 2026
Methods for cost-efficient, whole genome sequencing surveillance for enhanced detection of outbreaks in a hospital setting.
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Waggle Kady D, Griffith Marissa Pacey, Rokes Alecia B, Rangachar Srinivasa Vatsala, Nawrocki Erin M, Ereifej Deena, Patrick Rose, Coyle Hunter, Chaudhary Shurmin, Raabe Nathan J, Shutt Kathleen, Sundermann Alexander J, Cooper Vaughn S, Harrison Lee H, Pless Lora Lee
Аннотация
INTRODUCTION: Outbreaks of healthcare-associated infections (HAI) result in substantial patient morbidity and mortality; mitigation efforts by infection prevention teams have the potential to curb outbreaks and prevent transmission to additional patients. The incorporation of whole genome sequencing (WGS) surveillance of suspected high-risk pathogens often identifies outbreaks that are not detected by traditional infection prevention methods and provides evidence for transmission. Our approach to real-time WGS surveillance, the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), has 1) identified serious outbreaks that were otherwise undetected and 2) shown the potential to be cost saving.
METHODS: We describe our cost-efficient methods to perform WGS surveillance and data analysis of pathogens for institutions that are interested in expanding infection prevention surveillance. We provide an overview of the weekly workflow of EDS-HAT during two distinct phases over three years.
RESULTS: In an average week at our tertiary healthcare system, we sequenced 60 samples at a cost of less than $100 each during Phase 1, and 80 samples for less than $70 each in Phase 2, inclusive of laboratory reagents and staff salaries. The average turnaround time, from sample collection to reporting data to infection prevention, was nine days.
CONCLUSIONS: Performing EDS-HAT in real-time can be both feasible and time-efficient. Providing such timely information to aid in outbreak detection could identify transmission events sooner and thus could increase patient safety.
METHODS: We describe our cost-efficient methods to perform WGS surveillance and data analysis of pathogens for institutions that are interested in expanding infection prevention surveillance. We provide an overview of the weekly workflow of EDS-HAT during two distinct phases over three years.
RESULTS: In an average week at our tertiary healthcare system, we sequenced 60 samples at a cost of less than $100 each during Phase 1, and 80 samples for less than $70 each in Phase 2, inclusive of laboratory reagents and staff salaries. The average turnaround time, from sample collection to reporting data to infection prevention, was nine days.
CONCLUSIONS: Performing EDS-HAT in real-time can be both feasible and time-efficient. Providing such timely information to aid in outbreak detection could identify transmission events sooner and thus could increase patient safety.