Healthcare-associated infections (HAIs) are commonly associated with an increased risk of developing antimicrobial resistance (AMR). Globally, many patients are affected by HAIs, which has substantially increased the overall operational cost of the healthcare system. Although it is extremely important to identify pathogens with high transmission rates in hospital settings, diagnostic laboratory capacity is lacking to track them.
Study: Clinical Implementation of Routine Whole-genome Sequencing for Hospital Infection Control of Multi-drug Resistant Pathogens. Image Credit: nobeastsofierce/Shutterstock
Background
In Australia, more than 165,000 patients experience HAIs each year. An Australian 30-day survey revealed that mortality rates for methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) infections in hospital settings were 14.9% and 20%, respectively. The same survey also reported 18.6% mortality due to extended-spectrum beta-lactamase-producing Escherichia coli (ESBL-E) bloodstream infections in hospital settings.
Genomic analysis has proved to be an effective tool for characterizing transmission routes of pathogens. This tool could enhance infection prevention and control measures during pathogenic outbreaks. Nevertheless, it is rarely used as a real-time surveillance and prevention tool.
Conventional methods used for genetic analysis are typically time-consuming and the analytical instruments are not easily available outside specialized laboratories. Recently, whole genome sequencing (WGS) methods have been developed to analyze the transmission dynamics of bacterial pathogens, which helped assess their outbreak potential. This method could be used as a frontline tool to manage pathogens that could threaten human life.
In a recent Clinical Infectious Diseases study, scientists have developed a clinical WGS workflow that can detect transmission events of a pathogen before they become dominant. Therefore, this method can effectively prevent and control infections and help develop strategies to respond to outbreaks appropriately.
About the study
MRSA, VRE, ESBL-E, carbapenem-resistant Acinetobacter baumannii (CRAB), and carbapenemase-producing Enterobacterales (CPE) isolates were obtained from blood cultures, cerebrospinal fluid, sterile sites, and screening specimens (e.g., rectal swabs) across three large hospitals in Brisbane, Australia. A total of 2,660 bacterial isolates were obtained between 19 April 2017 and 1 July 2021 from the participating hospitals. These bacterial pathogens were isolated from 2336 patients, among which 259 patients provided multiple isolates.
In this study, samples were collected weekly, with an average of 8 samples per week. These samples were subjected to WGS analysis. WGS helped establish in silico multi-locus sequence typing (MLST). Additionally, resistance gene profiling was conducted using a bespoke genomic analysis pipeline.
The putative outbreak events were determined by comparing core genome single nucleotide polymorphisms (SNPs). Appropriate clinical data were analyzed along with genomic analysis data through customized automation. These findings were collated with hospital-specific reports regularly distributed to infection control teams.
Study findings
Among the total bacterial isolates sequenced during the study period, 293 were found to be MDR gram-negative bacilli, 620 MRSA, and 433 VRE. The combination of genomic and epidemiological data helped identify 37 clusters that had possibly occurred due to community instead of hospital transmission events.
Core genome SNP data revealed that 335 isolates formed 76 distinct clusters. Interestingly, among the 76 clusters, 43 were associated with the participating hospitals. This finding suggests the occurrence of ongoing bacterial transmission within hospital settings. The remaining 33 clusters were linked to either inter-hospital transmission events or bacterial strains circulating within a community.
Study implications
The availability of timely reports is crucial to develop an effective surveillance program. Importantly, the current protocol could provide genomic data within 10 days of sample collection. It must be noted that the mean report turnaround time of 33 days limits the clinical relevance of the data.
Some factors associated with long reporting periods are hindered sample transport to the central laboratory, lack of onsite or dedicated WGS infrastructure, and continual analysis pipeline development. Nevertheless, structural reorganization and workflow refinements could minimize these delays.
In this study, the WGS-based method helped identify two putative transmission clusters Ab1050-A1 and Eh90-A2, associated with previous outbreaks. This finding strongly suggests that WGS must be deployed as a prospective surveillance tool to prevent pathogenic outbreaks.
Conclusions
One key limitation of this study is that the prospective surveillance program was mainly based on multidrug-resistant bacteria. Hence, the current study failed to consider other antibiotic susceptible disease-causing organisms.
Even though it is challenging to incorporate WGS workflow and other appropriate computational infrastructure within existing systems in the healthcare setting, it is important to establish the same to prevent future outbreaks. The WGD-based establishment can reduce the overall cost of the healthcare system.
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