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

抗菌素耐药性（AMR）的出现是现代医学目前面临的最大挑战之一（O'Neill, 2014）。抗菌素耐药性部分是由不适当的抗菌素（AM）使用造成的，因此，英国抗菌素耐药性战略的一个要素是重点减少不适当的抗菌素处方（英国五年国家计划，2019年）。尽管各种国家和地方AM管理倡议已经取得了进展，但大多数处方AM是在缺乏与AM敏感性相关的微生物培养的情况下根据推定的细菌感染而开始的。国家管理指南（Start Smart then Focus, 2015）要求在48至72小时内对AM的使用进行审查，此时可以根据微生物结果和临床反应停止、继续、切换到口服选择或改变AM。细菌感染管理的最初经验性抗微生物药物选择是通过对血液培养分离物的局部表型抗微生物药物耐药性模式的了解，以及所有急性NHS信托机构收集的其他样本类型。虽然这种局部表型耐药数据广泛可用，但由于缺乏对循环AMR机制，机制之间的相互作用以及如何使用这些数据来指导处方的了解，目前它是一种未充分利用的资源。医院临床医生做出的最常见的AM决策是“在这种静脉注射抗生素成功治疗后，哪种口服抗生素是最佳的？”和“在初始治疗失败的情况下，我的二线抗生素选择是什么？”目前，这些决策是由当地对抗生素X的耐药率决定的，而不是基于对抗生素y的假定耐药性/敏感性的抗生素X的当地耐药率决定的。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，并使用现有和未来收集的区域基因组数据来验证和改进该模型，以告知有关抗生素切换的决策。我们将首先关注常用的静脉抗生素（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、环丙沙星和科莫昔拉）之间的相互作用的耐药机制。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，从而确定该方法在多大程度上可以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，这些患者的抗生素选择可能存在问题。通过充分利用我们对当地抗菌素耐药性模式的了解，我们可以最大限度地提高成功抗微生物治疗的机会，并最大限度地减少抗生素的不当使用。一旦开发，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。随着耐药基因随着时间的推移而传播，了解这些基因的相互依赖性对患者管理的重要性将不可避免地增加。这个项目对我们理解这个重要问题将是一个有益而及时的补充。https://amr-review.org/sites/default/files/160525_Final%20paper_with%20cover.pdf2。https://www.gov.uk/government/publications/uk - 5年行动计划-抗菌素耐药性- 2019 - 20243。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