RESUMEN
PURPOSE: Studies have shown that multiple genes influence antibiotic susceptibility, but the relationship between genotypic and phenotypic antibiotic susceptibility is unclear. We sought to analyze the concordance between the presence of antibiotic resistance (ABR) genes and antibiotic susceptibility results in urine samples collected from patients with symptomatic urinary tract infection (UTI). PATIENTS AND METHODS: Urine samples were collected from patients presenting to 37 geographically disparate urology clinics across the United States from July 2018 to February 2019. Multiplex polymerase chain reaction was used to detect 27 ABR genes. In samples containing at least one culturable organism at a concentration of ≥ 104 cells per mL, pooled antibiotic susceptibility testing (P-AST), which involves simultaneous growing all detected bacteria together in the presence of antibiotic and then measure susceptibility, was performed against 14 antibiotics. The concordance rate between the ABR genes and the P-AST results was generated for the overall group. The concordance rates for each antibiotic between monomicrobial and polymicrobial infection were compared using chi-square test. RESULTS: Results from ABR gene detection and P-AST of urine samples from 1155 patients were included in the concordance analysis. Overall, there was a 60% concordance between the presence or absence of ABR genes and corresponding antimicrobial susceptibility with a range of 49-78% across antibiotic classes. Vancomycin, meropenem, and piperacillin/tazobactam showed significantly lower concordance rates in polymicrobial infections than in monomicrobial infections. CONCLUSION: Given the 40% discordance rate, the detection of ABR genes alone may not provide reliable data to make informed clinical decisions in UTI management. However, when used in conjunction with susceptibility testing, ABR gene data can offer valuable clinical information for antibiotic stewardship.
RESUMEN
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.
