Improving empiric antibiotic prescribing by applying a Bayesian decision theory approach to phenotypic and genomic resistance data.

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The emergence of antimicrobial resistance (AMR) is one of the greatest challenges currently facing modern medicine (O'Neill, 2014). AMR is partly driven by inappropriate antimicrobial (AM) use, hence, one element of the UK's AMR strategy it to focus on reducing inappropriate AM prescribing (UK five year national plan, 2019). Although in-roads have been made by various national and local AM stewardship initiatives, the majority of AMs that are prescribed are initiated empirically for presumed bacterial infection in the absence of a microbiological culture with associated AM sensitivities. The national stewardship guideline (Start Smart then Focus, 2015) requires a review of AM use at 48 to 72 hours, when the AMs can be stopped, continued, switched to an oral option or changed, dependent on microbiological results and clinical response.The initial empiric AM choices for the management of bacterial infections are informed by an understanding of local phenotypic AMR patterns from blood culture isolates, and other sample types which are collected by all acute NHS trusts. While this local phenotypic resistance data is wildly available, it is currently an underused resource due to a lack of understanding of circulating AMR mechanisms, the interplay between mechanisms and how this data can be used to guide prescribing.Amongst the most common AM decisions hospital clinicians make are "which oral antibiotic is optimal following successful treatment with this intravenous (iv) antibiotic?" and "what is my second line choice of antibiotic given failure of initial treatment?" Currently these decisions are informed by the local resistance rates to antibiotic X, rather than the local resistance rate of antibiotic X given the presumed resistance/sensitivity to antibiotic Y. We intend to apply a Bayesian decision theory approach to our existing local phenotypic resistance data, and to verify and improve this model using existing and prospectively collected regional genomic data to inform decision making regarding antibiotic switches. We will initially focus on interplay of resistance mechanisms between commonly used iv antibiotics (piperacillin/tazobactam and third generation cephalosporins) with aminoglycoside antibiotics and three commonly use oral antibiotics (cotrimoxazole, ciprofloxacin, and coamoxiclav). We have selected these antibiotic combinations to address the most pressing day-to-day clinical concerns.We will also determine the minimum size of dataset required for robust results, and therefore the extent to which this approach can be applied to smaller groups of patients that are heavily exposed to antibiotics such as Haematology and bone marrow transplant (BMT) patients, were antibiotic selection can be problematic. By fully utilising our understanding of local AMR patterns we can maximise the chance of successful antimicrobial treatment and minimise the inappropriate use of antibiotics. Once developed this approach could be rapidly applied to other regions of the UK (and internationally), using existing phenotypic data with or without the support of a local genomic surveillance program. As resistance genes spread over time the importance of understanding the co-dependencies of these genes to the management of patients will inevitably increase. This project will be a useful and timely addition to our understanding of this important problem.1. https://amr-review.org/sites/default/files/160525_Final%20paper_with%20cover.pdf2. https://www.gov.uk/government/publications/uk-5-year-action-plan-for-antimicrobial-resistance-2019-to-20243. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/417032/Start_Smart_Then_Focus_FINAL.PDF