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最小抑菌浓度(MIC)分布分析确定了人群亚组之间抗菌药物耐药性(AMR)的差异。

MIC distribution analysis identifies differences in AMR between population sub-groups.

作者信息

Wildfire Jacob, Waterlow Naomi R, Clements Alastair, Fuller Naomi M, Knight Gwen M

机构信息

Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, London, WC1E 7HT, UK.

Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK.

出版信息

Wellcome Open Res. 2024 May 9;9:244. doi: 10.12688/wellcomeopenres.21269.1. eCollection 2024.

Abstract

BACKGROUND

Phenotypic data, such as the minimum inhibitory concentrations (MICs) of bacterial isolates from clinical samples, are widely available through routine surveillance. MIC distributions inform antibiotic dosing in clinical care by determining cutoffs to define isolates as susceptible or resistant. However, differences in MIC distributions between patient sub-populations could indicate strain variation and hence differences in transmission, infection, or selection.

METHODS

The Vivli AMR register contains a wealth of MIC and metadata for a vast range of bacteria-antibiotic combinations. Using a generalisable methodology followed by multivariate regression, we explored MIC distribution variations across 4 bacteria, covering 7,135,070 samples, by key population sub-groups such as age, sex and infection type, and over time.

RESULTS

We found clear differences between MIC distributions across various patient sub-groups for a subset of bacteria-antibiotic pairings. For example, within , MIC distributions by age group and infection site displayed clear trends, especially for levofloxacin with higher resistance levels in older age groups (odds of 2.17 in those aged 85+ compared to 19-64), which appeared more often in men. This trend could reflect greater use of fluoroquinolones in adults than children but also reveals an increasing MIC level with age, suggesting either transmission differences or accumulation of resistance effects. We also observed high variations by WHO region, and over time, with the latter likely linked to changes in surveillance.

CONCLUSIONS

We found that MIC distributions can be used to identify differences in AMR levels between population sub-groups. Our methodology could be used more widely to unveil hidden transmission sources and effects of antibiotic use in different patient sub-groups, highlighting opportunities to improve stewardship programmes and interventions, particularly at local scales.

摘要

背景

表型数据,如临床样本中细菌分离株的最低抑菌浓度(MIC),可通过常规监测广泛获取。MIC分布通过确定定义分离株为敏感或耐药的临界值,为临床治疗中的抗生素给药提供依据。然而,患者亚组之间MIC分布的差异可能表明菌株变异,进而提示传播、感染或选择方面的差异。

方法

Vivli AMR登记册包含大量细菌 - 抗生素组合的MIC和元数据。我们采用一种通用方法,随后进行多变量回归,通过年龄、性别和感染类型等关键人群亚组以及随时间推移,探索了4种细菌(涵盖7,135,070个样本)的MIC分布变化。

结果

我们发现,对于一部分细菌 - 抗生素配对,不同患者亚组的MIC分布存在明显差异。例如,在[具体范围未给出]内,年龄组和感染部位的MIC分布呈现出明显趋势,尤其是左氧氟沙星,老年组的耐药水平更高(85岁及以上人群与19 - 64岁人群相比,优势比为2.17),且在男性中更常见。这种趋势可能反映了氟喹诺酮类药物在成人中比儿童中使用更广泛,但也揭示了MIC水平随年龄增长而升高,这表明要么存在传播差异,要么存在耐药效应的积累。我们还观察到不同世界卫生组织区域以及随时间推移存在高度差异,后者可能与监测变化有关。

结论

我们发现MIC分布可用于识别不同人群亚组之间的抗菌药物耐药水平差异。我们的方法可更广泛地用于揭示不同患者亚组中隐藏的传播源和抗生素使用的影响,突出改善管理计划和干预措施的机会,特别是在地方层面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea6c/11306957/d53fd57986c7/wellcomeopenres-9-23524-g0000.jpg

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