Hospital-acquired infections (HAIs) are generally associated with an increased risk of developing antimicrobial resistance (AMR). Globally, many patients are affected by HAIs, which has significantly increased the overall operational cost of the healthcare system. Although it is extremely important to identify pathogens with high transmission rates in hospital settings, the capacity of diagnostic laboratories is lacking to track them.
Background
In Australia, more than 165,000 patients suffer from IASS each year. A 30-day Australian survey found that death rates for methicillin-resistant patients Staphylococcus aureus (MRSA) and vancomycin resistant Enterococcus (ERV) in the hospital setting were 14.9% and 20%, respectively. The same survey also reported an 18.6% mortality due to extended-spectrum beta-lactamase production Escherichia coli (ESBL-E) bacteremia in the hospital setting.
Genomic analysis has proven to be an effective tool for characterizing pathogen transmission pathways. This tool could improve infection prevention and control measures during pathogen outbreaks. Nevertheless, it is rarely used as a real-time monitoring and prevention tool.
Conventional methods used for genetic analysis are generally time consuming and analytical instruments are not readily available outside of specialized laboratories. Recently, whole genome sequencing (WGS) methods have been developed to analyze the transmission dynamics of bacterial pathogens, which has enabled their epidemic potential to be assessed. This method could be used as a first-line tool to manage pathogens that could threaten human life.
In a recent Clinical infectious diseases study, scientists 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 appropriately to outbreaks.
About the study
Isolates of MRSA, VRE, ESBL-E, carbapenem-resistant Acinetobacter baumannii (CRAB) and carbapenemase-producing Enterobacteriaceae (CPE) were obtained from blood cultures, cerebrospinal fluid, sterile sites and screening specimens (e.g. hospitals in Brisbane, Australia. A total of 2,660 bacterial isolates were obtained between April 19, 2017 and July 1, 2021 from participating hospitals. These bacterial pathogens were isolated from 2336 patients, of which 259 patients provided multiple isolates.
In this study, samples were taken weekly, with an average of 8 samples per week. These samples were subjected to WGS analysis. WGS helped establish silicone multi-locus sequence typing (MLST). Additionally, resistance gene profiling was performed using a bespoke genomic analysis pipeline.
Putative hatching events were determined by comparing core genome single nucleotide polymorphisms (SNPs). Relevant clinical data was analyzed along with genomic analysis data through custom automation. These results were collated with hospital-specific reports regularly distributed to infection control teams.
Study results
Of 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 identified 37 clusters that may have occurred due to community rather than hospital transmission events.
Core genome SNP data revealed that 335 isolates formed 76 distinct clusters. Interestingly, of the 76 clusters, 43 were associated with participating hospitals. This finding suggests the occurrence of ongoing bacterial transmission in the hospital setting. The remaining 33 clusters were linked either to events of inter-hospital transmission or to bacterial strains circulating within a community.
Implications of the study
The availability of timely reports is crucial to developing an effective monitoring program. Importantly, the current protocol could provide genomic data within 10 days of sample collection. It should be noted that the average report processing time of 33 days limits the clinical relevance of the data.
Some factors associated with long reporting periods are impeded transport of samples to the central laboratory, lack of on-site or dedicated WGS infrastructure, and ongoing development of the analysis pipeline. Nevertheless, structural reorganization and workflow improvements could minimize these delays.
In this study, the WGS-based method identified two putative transmission clusters Ab1050-A1 and Eh90-A2 associated with previous outbreaks. This finding strongly suggests that WGS should be deployed as a prospective surveillance tool to prevent pathogen outbreaks.
conclusion
One of the main limitations of this study is that the prospective surveillance program was mainly based on multidrug-resistant bacteria. Therefore, the current study did not consider other pathogenic organisms susceptible to antibiotics.
Even though it is difficult to integrate the WGS workflow and other suitable computing frameworks into the existing systems in the healthcare setting, it is important to establish the same to prevent future outbreaks. The establishment based on WGD can reduce the overall cost of the health system.
#Clinical #implementation #continuous #genomic #surveillance #identify #track #interrupt #transmission #multidrugresistant #bacterial #pathogens