What is the problem?

Antimicrobial resistance is in part caused by the inappropriate prescribing of antibiotics. Since the publication of Lord Jim O’Neill’s AMR Review in 2014-16, there have been inroads to reduce the use of antbiotic prescribing through antimicrobial stewardship initiatives. However, the majority of antibiotics administered are prescribed empirically for presumed bacterial infection without any testing which can lead to the overuse and inappropriate use of antibiotics.

Sepsis is a life-threatening consequence of bacterial bloodstream infection. It requires rapid antibiotic therapy, and in almost all cases, the antibiotic susceptibility profile of the bacterium causing the infection is not known. Hence, working “in the dark”, prescribers must balance the use of antibiotics where rates of resistance are low – meaning treatment success is more likely – but also be mindful of unnecessarily using the latest antibiotics, because that will inevitably drive resistance. Such empiric antibiotic choice can therefore lead to treatment failure, or unnecessary antibiotic use. If treatment fails, then the clinician must decide which antibiotic to try next, often without any additional information.

What is the solution?

Most initial empiric antibiotic choices for bloodstream as well as other infections are informed by an understanding of local antibiotic resistance patterns from isolates recently microbiologically investigated. While this local antibiotic resistance data is widely available, particularly for urinary tract and bloodstream infections it is currently an underused resource due to a lack of understanding of circulating resistance mechanisms, the interplay between mechanisms and how this data can be used to guide prescribing.

The aim of this project is to improve empiric antibiotic prescribing, by supplementing antibiotic resistance data collected for the Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group with whole genome sequence data for Gram-negative bacterial isolates. The team will sequence all Gram-negative bloodstream isolates in 2020, and perform surveys of resistant urinary E. coli. The team will then build this data into a Bayesian decision making tool, integrating not just how common resistance to drug X is but how common resistance to drug X and drug Y occur together, and how common pre-resistance to drugs X and Y (meaning the presence of mutations or acquired genes that nearly, but not quite confer resistance) occur in the population. The aim is to choose antibiotics more wisely, to reduce the incidence of treatment failure and the emergence of resistance.

Next steps

Data collection is underway and the project is expected to conclude in 2023.