Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling
Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling
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DOI：
10.1093/jac/dkaa408
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发表时间：
2021-01-01
影响因子：
5.2
通讯作者：
Obolski, Uri
中科院分区：
文献类型：
--
作者：
Cherny, Stacey S.;Nevo, Daniel;Obolski, Uri
目的：微生物耐药性表现出不同抗生素之间的依赖性模式，称为交叉耐药性和附带敏感性。这些模式在实验和临床环境中有所不同。目前尚不清楚这些差异是由生物学原因造成的，还是由临床环境中发现的混杂、有偏差的结果造成的。我们着手从临床数据中阐明对不同抗生素耐药性之间的潜在依赖性模式，同时考虑到患者特征和既往抗生素使用情况。方法：采用加法贝叶斯网络模型来同时估计来自住院患者的细菌培养物数据集中的变量之间的关系，并测试对多种抗生素的耐药性。数据包含五种细菌的耐药性结果、患者人口统计数据和既往抗生素使用情况：大肠杆菌 (n = 1054)、肺炎克雷伯菌 (n = 664)、铜绿假单胞菌 (n = 571)、CoNS (n = 495) 和奇异变形杆菌 (n = 415)。 结果：所有对各种抗生素的耐药性之间的关联均为阳性。在不同细菌物种中观察到不同类别抗生素耐药性之间的多重直接联系。例如，大肠杆菌对庆大霉素的耐药性与对环丙沙星（OR 8.39，95%可信区间5.58-13.30）和磺胺甲恶唑/甲氧苄啶（OR = 2.95，95%可信区间1.97-4.51）的耐药性直接相关。此外，对各种抗生素的耐药性与之前的抗生素使用直接相关。结论：即使考虑到多个协变量依赖性，对不同类别抗生素的耐药性以及与服用不同类别抗生素相关的耐药性之间也存在稳健关系。这些关系可以帮助指导临床环境中抗生素治疗的选择。
Objectives: Microbial resistance exhibits dependency patterns between different antibiotics, termed cross-resistance and collateral sensitivity. These patterns differ between experimental and clinical settings. It is unclear whether the differences result from biological reasons or from confounding, biasing results found in clinical settings. We set out to elucidate the underlying dependency patterns between resistance to different antibiotics from clinical data, while accounting for patient characteristics and previous antibiotic usage.Methods: Additive Bayesian network modelling was employed to simultaneously estimate relationships between variables in a dataset of bacterial cultures derived from hospitalized patients and tested for resistance to multiple antibiotics. Data contained resistance results, patient demographics and previous antibiotic usage, for five bacterial species: Escherichia coli (n =1054), Klebsiella pneumoniae (n = 664), Pseudomonas aeruginosa (n = 571), CoNS (n = 495) and Proteus mirabilis (n = 415).Results: ALL Links between resistance to the various antibiotics were positive. Multiple direct Links between resistance of antibiotics from different classes were observed across bacterial species. For example, resistance to gentamicin in E. coli was directly Linked with resistance to ciprofloxacin (OR 8.39, 95% credible interval 5.58-13.30) and sulfamethoxazole/trimethoprim (OR = 2.95, 95% credible interval 1.97-4.51). In addition, resistance to various antibiotics was directly Linked with previous antibiotic usage.Conclusions: Robust relationships among resistance to antibiotics belonging to different classes, as well as resistance being Linked to having taken antibiotics of a different class, exist even when taking into account multiple covariate dependencies. These relationships could help inform choices of antibiotic treatment in clinical settings.