Clinical implementation of continuous genomic surveillance to identify, track, and interrupt transmission of multidrug-resistant pathogenic bacteria

Clinical implementation of continuous genomic surveillance to identify, track, and interrupt transmission of multidrug-resistant pathogenic bacteria

Healthcare-associated infections (HAIs) are often associated with an increased risk of developing antimicrobial resistance (AMR). HAIs affect many patients worldwide, which has significantly increased the total cost of ownership of the healthcare system. Although it is extremely important to identify pathogens with high transmission rates in hospitals, there is a lack of diagnostic laboratory capacity to track them.

Lernen: Klinische Implementierung der routinemäßigen Gesamtgenomsequenzierung zur Kontrolle von Krankenhausinfektionen mit multiresistenten Krankheitserregern. Bildnachweis: nobeastsofierce/Shutterstock

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In Australia, more than 165,000 patients suffer HAIs each year. An Australian 30-day survey found that mortality rates for methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) infections in hospitals were 14.9% and 20%, respectively. The same survey also reported an 18.6% mortality rate due to extended-spectrum beta-lactamase-producing Escherichia coli (ESBL-E) bloodstream infections in hospitals.

Genome analysis has proven to be an effective tool for characterizing pathogen transmission routes. This tool could improve infection prevention and control measures during pathogenic outbreaks. Yet it is rarely used as a real-time monitoring and prevention tool.

Traditional methods of genetic analysis are typically time-consuming and the analysis tools are not readily available outside of specialized laboratories. Recently, whole genome sequencing (WGS) methods were 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 combat pathogens that could threaten human life.

In a recent Clinical infectious diseases Scientists have developed a clinical WGS workflow that can detect pathogen transmission events before they become dominant. Therefore, this method can effectively prevent and control infections and help develop strategies to adequately respond to outbreaks.

About studying

Isolates of MRSA, VRE, ESBL-E, carbapenem-resistant Acinetobacter baumannii (CRAB), and carbapenemase-producing Enterobacterales (CPE) were obtained from blood cultures, CSF, sterile sites, and screening samples (e.g., rectal swabs) from three large hospitals in Brisbane, Australia. A total of 2,660 bacterial isolates were obtained from participating hospitals between April 19, 2017 and July 1, 2021. These bacterial pathogens were isolated from 2336 patients, of 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 contributed to the establishment of in silico multi-locus sequence typing (MLST). In addition, resistance gene profiling was performed using a customized 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 custom automation. These results were compiled with hospital-specific reports that were regularly distributed to infection control teams.

Study results

Among the total bacterial isolates sequenced during the study period, 293 were gram-negative MDR bacilli, 620 were MRSA, and 433 were VRE. The combination of genomic and epidemiological data helped identify 37 clusters that may have arisen due to community transmission events rather than hospitals.

Core genome SNP data showed that 335 isolates formed 76 distinct clusters. Interestingly, of the 76 clusters, 43 were associated with the participating hospitals. This finding suggests the occurrence of ongoing bacterial transmission within hospitals. The remaining 33 clusters were associated with either interhospital transmission events or with bacterial strains circulating within a community.

Effects on studies

The availability of timely reports is critical to developing an effective monitoring program. Importantly, the current protocol could provide genomic data within 10 days of sample collection. It is important to note that the average report turnaround time of 33 days limits the clinical relevance of the data.

Some factors associated with long reporting periods include hindered sample transportation to the central laboratory, lack of dedicated or dedicated on-site WGS infrastructure, and ongoing development of analytical pipelines. Nevertheless, these delays could be minimized through structural reorganization and workflow refinements.

In this study, the WGS-based method helped to identify two putative transmission clusters Ab1050-A1 and Eh90-A2 that were associated with previous outbreaks. This finding strongly suggests that WGS needs to be used as a prospective surveillance tool to prevent pathogenic outbreaks.

Conclusions

A major limitation of this study is that the prospective surveillance program was based primarily on multidrug-resistant bacteria. Therefore, other antibiotic-sensitive disease-causing organisms were not considered in the current study.

Although it is difficult to integrate the WGS workflow and other appropriate computing infrastructure into existing healthcare systems, it is important to establish them to prevent future outbreaks. The WGD based facility can reduce the overall cost of the healthcare system.

Reference:

• Förde, B. et al. (2022) „Klinische Implementierung der routinemäßigen Gesamtgenomsequenzierung zur Krankenhausinfektionskontrolle multiresistenter Krankheitserreger“, Clinical Infectious Diseases. doi: 10.1093/cid/ciac726. https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciac726/6691363?searchresult=1&login=false#google_vignette

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