Healthcare-associated infections (HAIs) are commonly associated with an increased risk of developing antimicrobial resistance (AMR). Globally, many patients are affected by a healthcare-related disease, which has greatly increased the overall operating cost of the healthcare system. Although it is extremely important to identify pathogens with high rates of transmission in hospitals, the capacity of diagnostic laboratories to track them is lacking.
In Australia, more than 165,000 patients suffer from a healthcare peripheral infection each year. A 30-day Australian survey revealed methicillin-resistant mortality rates Staphylococcus aureus (MRSA) and vancomycin resistance Enterococcus The hospitalized infections (VRE) were 14.9% and 20%, respectively. The same survey also reported a mortality rate of 18.6% due to extended-spectrum beta-lactamase production. Escherichia coli (ESBL-E) Nosocomial bloodstream infections.
Genomic analysis has proven to be an effective tool for characterizing pathogen transmission routes. This tool can enhance infection prevention and control measures during outbreaks of pathogenic diseases. However, it is rarely used as a real-time monitoring and prevention tool.
Traditional methods used in genetic analysis are usually time consuming and analytical tools are not readily available outside specialized laboratories. Recently, whole genome sequencing (WGS) methods have been developed to analyze the transmission dynamics of bacterial pathogens, which has helped to assess the potential for outbreaks. This method can be used as a frontline tool for managing pathogens that can threaten human life.
at recent days Clinical infectious diseases In the study, scientists developed a clinical workflow for WGS that can detect transmission events of a pathogen before it becomes dominant. Therefore, this method can effectively prevent and control infections and help develop strategies to respond to outbreaks appropriately.
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 samples (eg, rectal swabs) across three large samples. 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. Bacterial pathogens were isolated from 2336 patients, of which 259 patients were given 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 create in silico Multi-position sequence writing (MLST). In addition, resistance genotyping was performed using a customized genome analysis pipeline.
Presumptive outbreak events were identified by comparing primary genome single-nucleotide polymorphisms (SNPs). Appropriate clinical data were analyzed along with genomic analysis data through custom automation. These results have been combined with hospital-specific reports that are distributed regularly to infection control teams.
Among the total bacterial isolates sequenced during the study period, 293 Gram-negative MDR isolates, 620 MRSA and 433 VRE were found. The combination of genomic and epidemiological data helped identify 37 clusters that may have occurred due to community rather than hospital transmission events.
Primary genome SNP data revealed that 335 isolates formed 76 distinct groups. Interestingly, of the 76 groups, 43 were associated with participating hospitals. This result indicates that continuous bacterial transmission occurs within hospitals. The remaining 33 clusters were linked to either nosocomial transmission events or bacterial strains circulating within the community.
Timely availability of reports is critical to the development of an effective monitoring programme. Importantly, the current protocol can provide genome data within 10 days of sample collection. It should be noted that the median time to report of 33 days limits the clinical significance of the data.
Some of the factors associated with long reporting periods impede transportation of samples to the central laboratory, lack of on-site or dedicated WGS infrastructure, and development of a continuous analysis pipeline. However, structural reorganization and workflow improvements can reduce these delays.
In this study, the WGS-based method helped to identify two putative transmission groups Ab1050-A1 and Eh90-A2, which are associated with previous outbreaks. This result strongly indicates that WGS should be deployed as a potential surveillance tool to prevent outbreaks of pathogenic diseases.
A major limitation of this study is that the prospective surveillance program was primarily based on multidrug-resistant bacteria. Hence, the current study fails to consider other pathogenic organisms susceptible to antibiotics.
Although it is difficult to integrate WGS workflows and other appropriate computing infrastructure within existing systems into a healthcare setting, it is important to establish the same to prevent future outbreaks. A WGD-based organization can reduce the overall cost of the healthcare system.