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

通过对表型和基因组耐药性数据应用贝叶斯决策理论方法来改进经验性抗生素处方。

• 批准号: MR/T005408/1
• 负责人: Philip Williams
• 金额: $ 28.48万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT005408%2F1
• 关键词: Improving; empiric; antibiotic; prescribing; applying

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

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）。 AMR 的部分原因是抗菌药物 (AM) 的不当使用，因此，英国 AMR 战略的要素之一是专注于减少不当的 AM 处方（英国五年国家计划，2019 年）。尽管各个国家和地方的 AM 管理举措已取得进展，但大多数处方 AM 都是在缺乏与 AM 敏感性相关的微生物培养的情况下凭经验针对假定的细菌感染而启动的。国家管理指南（Start Smart then Focus，2015）要求在 48 至 72 小时内审查 AM 的使用，此时 AM 可以停止、继续、切换为口服选项或更改，具体取决于微生物学结果和临床反应。管理细菌感染的最初经验性 AM 选择是通过了解血培养分离物的局部表型 AMR 模式以及所有急性感染者收集的其他样本类型来确定的。 NHS 信任。虽然这种局部表型耐药性数据随处可见，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，目前该资源尚未得到充分利用。医院临床医生最常见的 AM 决策是“在成功使用这种静脉 (iv) 抗生素治疗后，哪种口服抗生素是最佳选择？”以及“如果初始治疗失败，我的二线抗生素选择是什么？”目前，这些决策是根据抗生素 X 的局部耐药率得出的，而不是根据抗生素 Y 的假定耐药性/敏感性得出的抗生素 X 的局部耐药率。我们打算将贝叶斯决策理论方法应用于我们现有的局部表型耐药性数据，并使用现有的和前瞻性收集的区域基因组数据验证和改进该模型，为有关抗生素转换的决策提供信息。我们首先将重点关注常用静脉注射抗生素（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口服抗生素（复方新诺明、环丙沙星和考阿莫昔拉）之间的耐药机制。我们选择这些抗生素组合来解决最紧迫的日常临床问题。我们还将确定获得可靠结果所需的最小数据集大小，因此，如果抗生素选择可能存在问题，那么这种方法可以在多大程度上应用于大量接触抗生素的患者，例如血液学和骨髓移植 (BMT) 患者。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抗菌治疗成功的机会，并最大限度地减少抗生素的不当使用。一旦开发出来，这种方法可以在有或没有当地基因组监测计划的支持下，使用现有的表型数据快速应用于英国其他地区（以及国际上）。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于患者管理的重要性将不可避免地增加。这个项目将是对我们对这个重要问题的理解的有用和及时的补充。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

项目成果

Rational Antibiotic Escalation Applied to Specific Patient Groups

针对特定患者群体合理使用抗生素

• DOI: 10.1101/2023.11.03.23298025
• 发表时间: 2023
• 作者: Bhamber R

Limited phylogenetic overlap between fluoroquinolone-resistant Escherichia coli isolated on dairy farms and those causing bacteriuria in humans living in the same geographical region.

• DOI: 10.1093/jac/dkab310
• 发表时间: 2021-11-12
• 期刊: The Journal of antimicrobial chemotherapy
• 作者: Mounsey O;Schubert H;Findlay J;Morley K;Puddy EF;Gould VC;North P;Bowker KE;Williams OM;Williams PB;Barrett DC;Cogan TA;Turner KM;MacGowan AP;Reyher KK;Avison MB
• 通讯作者: Avison MB

Trade-Offs Between Antibacterial Resistance and Fitness Cost in the Production of Metallo-ß-Lactamase by Enteric Bacteria Manifest as Sporadic Emergence of Carbapenem Resistance in a Clinical Setting

肠道细菌生产金属-内酰胺酶的抗菌耐药性和健身成本之间的权衡表现为临床环境中碳青霉烯类耐药性的零星出现

• DOI: 10.1101/2020.10.24.353581
• 发表时间: 2020
• 作者: Cheung C

Identification and characterisation of Klebsiella pneumoniae and Pseudomonas aeruginosa clinical isolates with atypical ß-lactam susceptibility profiles using Orbitrap liquid chromatography-tandem mass spectrometry

使用 Orbitrap 液相色谱-串联质谱法对具有非典型 β-内酰胺敏感性的肺炎克雷伯菌和铜绿假单胞菌临床分离株进行鉴定和表征

• DOI: 10.1101/2022.02.27.482154
• 发表时间: 2022
• 作者: Takebayashi Y

Diagnostic MALDI-TOF MS can differentiate between high and low toxic Staphylococcus aureus bacteraemia isolates as a predictor of patient outcome.

• DOI: 10.1099/mic.0.001223
• 发表时间: 2022-08
• 期刊: MICROBIOLOGY-SGM
• 影响因子: 2.8
• 作者: Brignoli, Tarcisio;Recker, Mario;Lee, Winnie W. Y.;Dong, Tim;Bhamber, Ranjeet;Albur, Mahableshwar;Williams, Philip;Dowsey, Andrew W.;Massey, Ruth C.
• 通讯作者: Massey, Ruth C.