Bacterial Whole Genome Sequencing And The Clinic: The Future Is Almost Here

I heard this talk almost a year ago*, so I’m glad this paper is finally out (I’ll translate into English below; boldface mine):

The increasing prevalence of multidrug-resistant (MDR) bacteria is a serious global challenge. Here, we studied prospectively whether bacterial whole genome sequencing (WGS) for real-time MDR surveillance is technical feasible, returns actionable results, and is cost-beneficial. WGS was applied to all MDR isolates of four species (methicillin-resistant Staphylococcus aureus [MRSA], vancomycin-resistant Enterococcus faecium, MDR Escherichia coli, and MDR Pseudomonas aeruginosa) at the University Hospital Muenster, Germany, a tertiary care hospital with 1,450 beds, during two six-month intervals. Turn-around times (TAT) were measured and total costs for sequencing per isolate were calculated. After cancelling prior policies of preemptive isolation of patients harboring certain Gram-negative MDR bacteria in risk areas, the second interval was conducted. During interval I, 645 bacterial isolates were sequenced. From culture, TATs ranged from 4.4 to 5.3 days, and costs were 202.49 € per isolate. During interval II, 550 bacterial isolates were sequenced. Hospital-wide transmission rates of the two most common species (MRSA and MDR E. coli) were low during intervals I (5.8 and 2.3 %, respectively) and II (4.3 % and 5.0 %, respectively). Cancellation of isolation of non-pan-resistant MDR E. coli in risk wards did not increase transmission. When comparing sequencing costs with avoided costs mostly due to less blocked beds during interval II, we saved in excess of at least 200.000 €. Real-time microbial WGS was in our institution feasible, produced precise actionable results, helped to monitor transmission rates that remained low following a modification in isolation procedures, and ultimately saved costs.

Essentially, genome sequencing allowed much more targeted interventions, saving money while not significantly leading to increased infections. Why? Rather than treating every MDR (multi-drug resistant) infection as a possible outbreak, sequencing let the doctors realize that most of these infections were not part of outbreaks. That meant extra–and expensive–precautions, such as patient isolation, could be avoided in most cases. It’s worth noting that the costs of the non-genome sequencing approach are very conservative, so they probably saved even more money.

But you’ll note that I wrote the future is almost here. The next step is to integrate whole genome sequencing data at a scale larger than a single hospital, both to identify inter-facility spread as well as determine new trends and problems. In other words, this genomic information needs to be shared. As Duncan MacCannell noted, this is hard as there are a lot of patient privacy and data ownership issues.

We also need to figure out data standardization. This includes the very basic problem of how do two different hospitals conclude if they have the same bacterial strain: the hospitals might differ in everything from the basic analysis and production of the genomic data to ‘higher level analysis’ used to determine if isolates are closely related (for the genomic cognoscenti, I mean either calling SNPs or whole genome MLST).

So there’s a lot to do–and the study needs to reproduced elsewhere. But this is very promising for infection control and limiting the spread of antibiotic resistance. If nothing else, we might not have to choose between detailed genomic epidemiology and cost.

*I point I’ve made many times is that talks are how biologists communicate with each other (for better and for worse), while papers, to a considerable extent, are currency for grants.

This entry was posted in Antibiotics, E. coli, Genomics, KPC, MRSA, NDM-1, Public Health. Bookmark the permalink.

1 Response to Bacterial Whole Genome Sequencing And The Clinic: The Future Is Almost Here

  1. bckirkup
    BenK says:

    I resist the idea of MLST or SNPs as the key to strain ID; let alone infection ID. Worst case, we should be identifying mobile elements, insertions, plasmids, etc, in the isolates. Better yet, we would identify copy number variation as well, in minimally passaged isolates. Even better, would be data that captures genetic variation within the infection (clearly more important for certain infections than for others).

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