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

抗生素耐药性（AMR）的出现是现代医学目前面临的最大挑战之一（奥尼尔，2014）。AMR部分是由不适当的抗菌药物（AM）使用驱动的，因此，英国AMR战略的一个要素是专注于减少不适当的AM处方（英国五年国家计划，2019年）。虽然各种国家和地方AM管理计划已经取得了进展，但大多数规定的AM都是在缺乏具有相关AM敏感性的微生物培养物的情况下根据经验启动的假定细菌感染。国家管理指导方针（Start Smart then Focus，2015）要求在48至72小时审查AM的使用，此时AM可以停止、继续、转换为口服选择或改变，这取决于微生物学结果和临床反应。通过了解来自血培养分离株的局部表型AMR模式，以及所有急性NHS信托收集的其他样本类型。虽然这种局部表型耐药数据是广泛可用的，但由于缺乏对循环AMR机制的了解，机制之间的相互作用以及如何使用这些数据来指导处方，目前它是一种未充分利用的资源。以及“在初步治疗失败的情况下，我的二线抗生素选择是什么？““目前，这些决定是根据当地对抗生素X的耐药率做出的，而不是根据当地对抗生素X的耐药率做出的，因为假设对抗生素Y的耐药/敏感。我们打算将贝叶斯决策理论方法应用于我们现有的当地表型耐药数据，并使用现有和前瞻性收集的区域基因组数据来验证和改进该模型，以告知有关抗生素转换的决策。我们将首先关注常用的静脉注射抗生素（哌拉西林/他唑巴坦和第三代头孢菌素）与氨基糖苷类抗生素和三种常用的口服抗生素（复方新诺明，环丙沙星和coamoxiclav）之间的耐药机制的相互作用。我们选择了这些抗生素组合来解决最紧迫的日常临床问题，我们还将确定稳健结果所需的最小数据集大小，因此，这种方法可以在多大程度上应用于大量暴露于抗生素的较小患者群体，如血液学和骨髓移植（BMT）患者，抗生素选择可能存在问题。通过充分利用我们对当地AMR模式的了解，我们可以最大限度地提高抗菌治疗的成功率，并最大限度地减少抗生素的不当使用。一旦开发出来，这种方法可以迅速应用于英国（和国际）的其他地区，使用现有的表型数据，有或没有当地基因组监测计划的支持。随着耐药基因的传播，了解这些基因对患者管理的相互依赖性的重要性将不可避免地增加。这个项目将是一个有用的和及时的补充，我们了解这一重要问题。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的网站。https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/417032/Start_Smart_Then_Focus_FINAL.PDF

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