Identifying patient-level risk factors associated with non-β-lactam resistance outcomes in invasive MRSA infections in the United States using chain graphs.
Identifying patient-level risk factors associated with non-β-lactam resistance outcomes in invasive MRSA infections in the United States using chain graphs.
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DOI：
10.1093/jacamr/dlac068
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发表时间：
2022-08
影响因子：
3.4
通讯作者：
中科院分区：
文献类型：
--
作者：
MRSA 是医院和社区获得性感染的最常见原因之一。 MRSA 对多种抗生素具有耐药性，包括 β-内酰胺类抗生素、氟喹诺酮类、林可酰胺类、大环内酯类、氨基糖苷类、四环素类和氯霉素类。确定可能与表型变异相关的患者水平特征，这可能有助于改善处方实践和抗菌药物管理。耐药表型的链图是从 CDC 收集的侵入性 MRSA 监测数据中获得的，作为新发感染计划的一部分，以确定报告为 MIC 的个体耐药结果的患者水平风险因素，同时考虑耐药性特征之间的相关性。这些链图是多级概率图形模型 (PGM)，可用于量化和可视化多个阻力结果及其解释变量之间的复杂关联。一些表型耐药性与其他结果或预测因子（例如四环素、万古霉素、强力霉素和利福平）的相关性较低。只有左氧氟沙星敏感性与医疗保健相关感染相关。血培养是 MIC 最常见的预测指标。血培养阳性患者氯霉素、红霉素、庆大霉素、林可霉素和莫匹罗星的MIC显着升高，达托霉素和利福平的MIC显着降低。还观察到一些地区差异。既往接受过医疗保健或血培养呈阳性的患者之间或来自不同州的患者之间耐药表型的差异可能有助于为治疗临床 MRSA 病例的首选抗生素提供信息。此外，我们证明多级 PGM 对于量化和可视化多种耐药结果及其解释变量之间的相互作用很有用。
MRSA is one of the most common causes of hospital- and community-acquired infections. MRSA is resistant to many antibiotics, including β-lactam antibiotics, fluoroquinolones, lincosamides, macrolides, aminoglycosides, tetracyclines and chloramphenicol. To identify patient-level characteristics that may be associated with phenotype variations and that may help improve prescribing practice and antimicrobial stewardship. Chain graphs for resistance phenotypes were learned from invasive MRSA surveillance data collected by the CDC as part of the Emerging Infections Program to identify patient level risk factors for individual resistance outcomes reported as MIC while accounting for the correlations among the resistance traits. These chain graphs are multilevel probabilistic graphical models (PGMs) that can be used to quantify and visualize the complex associations among multiple resistance outcomes and their explanatory variables. Some phenotypic resistances had low connectivity to other outcomes or predictors (e.g. tetracycline, vancomycin, doxycycline and rifampicin). Only levofloxacin susceptibility was associated with healthcare-associated infections. Blood culture was the most common predictor of MIC. Patients with positive blood culture had significantly increased MIC of chloramphenicol, erythromycin, gentamicin, lincomycin and mupirocin, and decreased daptomycin and rifampicin MICs. Some regional variations were also observed. The differences in resistance phenotypes between patients with previous healthcare use or positive blood cultures, or from different states, may be useful to inform first-choice antibiotics to treat clinical MRSA cases. Additionally, we demonstrated multilevel PGMs are useful to quantify and visualize interactions among multiple resistance outcomes and their explanatory variables.
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