Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool L7 8TX, UK.
Department of Medical Microbiology, Liverpool University Hospitals NHS Foundation Trust, Mount Vernon Street, Liverpool L7 8YE, UK.
J Antimicrob Chemother. 2024 Sep 3;79(9):2317-2326. doi: 10.1093/jac/dkae230.
Estimates of the prevalence of antimicrobial resistance (AMR) underpin effective antimicrobial stewardship, infection prevention and control, and optimal deployment of antimicrobial agents. Typically, the prevalence of AMR is determined from real-world antimicrobial susceptibility data that are time delimited, sparse, and often biased, potentially resulting in harmful and wasteful decision-making. Frequentist methods are resource intensive because they rely on large datasets.
To determine whether a Bayesian approach could present a more reliable and more resource-efficient way to estimate population prevalence of AMR than traditional frequentist methods.
Retrospectively collected, open-source, real-world pseudonymized healthcare data were used to develop a Bayesian approach for estimating the prevalence of AMR by combination with prior AMR information from a contextualized review of literature. Iterative random sampling and cross-validation were used to assess the predictive accuracy and potential resource efficiency of the Bayesian approach compared with a standard frequentist approach.
Bayesian estimation of AMR prevalence made fewer extreme estimation errors than a frequentist estimation approach [n = 74 (6.4%) versus n = 136 (11.8%)] and required fewer observed antimicrobial susceptibility results per pathogen on average [mean = 28.8 (SD = 22.1) versus mean = 34.4 (SD = 30.1)] to avoid any extreme estimation errors in 50 iterations of the cross-validation. The Bayesian approach was maximally effective and efficient for drug-pathogen combinations where the actual prevalence of resistance was not close to 0% or 100%.
Bayesian estimation of the prevalence of AMR could provide a simple, resource-efficient approach to better inform population infection management where uncertainty about AMR prevalence is high.
抗菌药物耐药性（AMR）的流行率估计是有效的抗菌药物管理、感染预防和控制以及最佳抗菌药物使用的基础。通常，AMR 的流行率是根据现实世界的抗菌药物敏感性数据来确定的，这些数据是时间限定的、稀疏的，而且往往存在偏差，这可能导致有害和浪费的决策。频率派方法资源密集，因为它们依赖于大型数据集。
确定贝叶斯方法是否可以比传统的频率派方法更可靠、更节省资源地估计人群中 AMR 的流行率。
使用回顾性收集的、开源的、真实世界的匿名医疗保健数据，结合文献综述中上下文化的 AMR 信息，开发了一种贝叶斯方法来估计 AMR 的流行率。迭代随机抽样和交叉验证用于评估贝叶斯方法与标准频率派方法相比的预测准确性和潜在资源效率。
与频率派估计方法相比，贝叶斯 AMR 流行率估计产生的极端估计误差更少[n=74（6.4%）比 n=136（11.8%）]，并且平均每个病原体需要更少的观察性抗菌药物敏感性结果[平均值=28.8（SD=22.1）比平均值=34.4（SD=30.1）]，以避免在 50 次交叉验证迭代中出现任何极端估计误差。对于实际耐药率接近 0%或 100%的药物-病原体组合，贝叶斯方法的效果和效率最高。
贝叶斯估计 AMR 的流行率可以为人群感染管理提供一种简单、节省资源的方法，在 AMR 流行率存在高度不确定性的情况下，可以更好地提供信息。
