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Bacterial Interactions as Detected by Pooled Antibiotic Susceptibility Testing (P-AST) in Polymicrobial Urine Specimens. 混合抗生素药敏试验(P-AST)检测多微生物尿液标本中的细菌相互作用。
Pub Date : 2020-01-01 Epub Date: 2020-05-29
Annah Vollstedt, David Baunoch, Alan Wolfe, Natalie Luke, Kirk J Wojno, Kevin Cline, Laurence Belkoff, Aaron Milbank, Neil Sherman, Rashel Haverkorn, Natalie Gaines, Laurence Yore, Neal Shore, Michael Opel, Howard Korman, Colleen Kelly, Mohammad Jafri, Meghan Campbell, Patrick Keating, Dylan Hazelton, Bridget Makhlouf, David Wenzler, Mansour Sabry, Frank Burks, Miguel Penaranda, David E Smith, Patrick Cacdac, Larry Sirls

Introduction: Antimicrobial susceptibility is well characterized in monomicrobial infections, but bacterial species often coexist with other bacterial species. Antimicrobial susceptibility is often tested against single bacterial isolates; this approach ignores interactions between cohabiting bacteria that could impact susceptibility. Here, we use Pooled Antibiotic Susceptibility Testing to compare antimicrobial susceptibility patterns exhibited by polymicrobial and monomicrobial urine specimens obtained from patients with urinary tract infection symptoms.

Methods: Urine samples were collected from patients who had symptoms consistent with a urinary tract infection. Multiplex polymerase chain reaction testing was performed to identify and quantify 31 bacterial species. Antibiotic susceptibility was determined using a novel Pooled Antibiotic Susceptibility Testing method. Antibiotic resistance rates in polymicrobial specimens were compared with those in monomicrobial infections. Using a logistic model, resistance rates were estimated when specific bacterial species were present. To assess interactions between pairs of bacteria, the predicted resistance rates were compared when a pair of bacterial species were present versus when just one bacterial species was present.

Results: Urine specimens were collected from 3,124 patients with symptoms of urinary tract infection. Of these, multiplex polymerase chain reaction testing detected bacteria in 61.1% (1910) of specimens. Pooled Antibiotic Susceptibility Testing results were available for 70.8% (1352) of these positive specimens. Of these positive specimens, 43.9% (594) were monomicrobial, while 56.1% (758) were polymicrobial. The odds of resistance to ampicillin (p = 0.005), amoxicillin/clavulanate (p = 0.008), five different cephalosporins, vancomycin (p = <0.0001), and tetracycline (p = 0.010) increased with each additional species present in a polymicrobial specimen. In contrast, the odds of resistance to piperacillin/tazobactam decreased by 75% for each additional species present (95% CI 0.61, 0.94, p = 0.010). For one or more antibiotics tested, thirteen pairs of bacterial species exhibited statistically significant interactions compared with the expected resistance rate obtained with the Highest Single Agent Principle and Union Principle.

Conclusion: Bacterial interactions in polymicrobial specimens can result in antimicrobial susceptibility patterns that are not detected when bacterial isolates are tested by themselves. Optimizing an effective treatment regimen for patients with polymicrobial infections may depend on accurate identification of the constituent species, as well as results obtained by Pooled Antibiotic Susceptibility Testing.

在单菌感染中,抗菌素敏感性具有很好的特征,但细菌种类往往与其他细菌种类共存。通常对单一细菌分离物进行抗菌药物敏感性测试;这种方法忽略了同居细菌之间可能影响易感性的相互作用。在这里,我们使用抗生素药敏试验来比较从尿路感染症状患者获得的多微生物和单微生物尿液标本所表现出的抗生素药敏模式。方法:收集有尿路感染症状的患者的尿液样本。多重聚合酶链反应试验鉴定和定量31种细菌。采用一种新型的抗生素药敏试验方法测定抗生素的药敏。比较了多微生物标本与单微生物标本的耐药率。使用逻辑模型,当特定细菌种类存在时,估计耐药率。为了评估细菌对之间的相互作用,预测的耐药率被比较当一对细菌物种存在和当只有一个细菌物种存在。结果:收集尿路感染患者尿液标本3124例。其中,多元聚合酶链反应检测在61.1%(1910)的标本中检测到细菌。1352例阳性标本中,有70.8%(1352例)可获得综合药敏试验结果。阳性标本中,594例(43.9%)为单微生物,758例(56.1%)为多微生物。对氨苄西林(p = 0.005)、阿莫西林/克拉维酸酯(p = 0.008)、5种不同头孢菌素、万古霉素的耐药几率(p =结论:多微生物标本中细菌相互作用可导致细菌敏感性模式,而这些模式在细菌分离株单独检测时无法检测到。优化多微生物感染患者的有效治疗方案可能取决于对组成物种的准确鉴定,以及汇集抗生素药敏试验获得的结果。
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Journal of surgical urology
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