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Article

Comparing Prescribing Behaviors and Clinician Experiences Between Multiplex PCR/Pooled Antibiotic Susceptibility Testing and Standard Urine Culture in Complicated UTI Cases

by
Emery Haley
1,
Natalie Luke
1,
Howard Korman
2,
Ganesh Srinvas Rao
3,
David Baunoch
4,
Xiaofei Chen
5,
Jim Havrilla
5 and
Mohit Mathur
6,*
1
Department of Clinical Research, Pathnostics, Irvine, CA 92618, USA
2
School of Medicine, Wayne State University, Detroit, MI 48201, USA
3
Department of Urology, First Urology, Louisville, KY 40207, USA
4
Department of Research and Development, Pathnostics, Irvine, CA 92618, USA
5
Department of Informatics, Pathnostics, Irvine, CA 92618, USA
6
Department of Medical Affairs, Pathnostics, Irvine, CA 92618, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(23), 7453; https://doi.org/10.3390/jcm13237453
Submission received: 2 October 2024 / Revised: 28 November 2024 / Accepted: 3 December 2024 / Published: 7 December 2024
(This article belongs to the Section Infectious Diseases)
Browse Figure
Versions Notes

Abstract

:
Background/Objectives: We aimed to compare the prescribing behavior and clinical experience of urology providers when using the combined multiplex polymerase chain reaction (M-PCR)/Pooled Antibiotic Susceptibility Testing (P-AST) diagnostic test versus the standard urine culture (SUC) in the same set of patients previously reported to have improved clinical outcomes with M-PCR/P-AST. Methods: We conducted a multi-centered, prospective, observational study (clinical trial registration: NCT05091931) with Western Institutional Review Board (IRB) approval (20214705). Adult subjects were split between the M-PCR/P-AST (n = 250) and SUC arms (n = 135). Treatment details were determined by clinician and subject surveys. Differences in prescribed antibiotics were compared using the Chi-square or Fisher’s exact test. Results: There was no significant difference in the overall use of “access” antibiotics (p = 1.0) or first-line drugs (p = 0.4483) between M-PCR/P-AST and SUC. Nitrofurantoin (p = 0.0172) and metronidazole (p = 0.0309) were more frequently used with M-PCR/P-AST, while amoxicillin/clavulanate (p = 0.0008), cefuroxime (p = 0.0378), and ertapenem (p = 0.0378) were more frequently used with SUC. Conclusions: The use of M-PCR/P-AST to guide complicated UTI management was not associated with the increased use of non-first-line antibiotics, such as carbapenems, compared to SUC. Combined with the prior reported evidence of improved patient outcomes in this same set of patients, this test should be considered for utilization when managing complicated UTI cases.

1. Introduction

Urinary tract infections (UTIs) account for significant healthcare costs [1] and are a major source of outpatient antibiotic prescriptions [2], especially empirically prescribed antibiotics [3]. Over the past decade, 16SrRNA sequencing and other culture-independent, molecular-based methods have revealed the existence of a resident urobiome, which includes a variety of microorganisms, including viruses, yeasts, and fastidious/anaerobic bacterial species, in addition to the previously recognized Gram-positive and Gram-negative bacterial species. [4,5,6,7,8] While the clinical relevance of these “emerging” or “opportunistic” uropathogens is still under study, the composition of the urobiome was found to change in response to aging, hormonal changes, and antibiotic use, suggesting a role for urobiome dysbiosis in UTI risk and pathogenesis [9,10,11,12]. Critically, in contrast to molecular methods, SUC has been repeatedly demonstrated to lack sensitivity for detecting most of these organisms [13,14,15,16]. The culture conditions of SUC favor the growth of Gram-negative bacteria, especially Escherichia coli (E. coli), which is the most commonly identified organism in acute UTIs [17]. Yet, “emerging” or “opportunistic” uropathogens found in the urobiome have been demonstrated to contribute to clinically significant lower urinary tract symptoms [18]. Consistent with this limitation in sensitivity, a recent publication has shown poor outcomes for patients with negative SUC results but whose urine specimens contained viable microorganisms, which grew under more sensitive expanded culture conditions [19]. In this study, many SUC-positive cases also experienced poor outcomes. This result may be due to heteroresistance [20,21,22,23,24,25,26,27,28,29,30,31], limitations in the recognition of polymicrobial infections, and/or the inability to assess full antibiotic susceptibility within polymicrobial infections [32]. Recent research in the field has demonstrated that polymicrobial infections are common, particularly in older adults and complicated UTIs [32,33,34,35,36,37,38]. Also, SUC followed by a standard antibiotic susceptibility test (AST) frequently requires ≥3 days to return the results, which is longer than many clinicians and patients are willing to delay treatment; a standard AST typically measures the susceptibility using a few isolates, and therefore, may fail to capture heteroresistant infections [39]. Advanced diagnostic tests are now available that seek to address these limitations of SUC.
One such test is Guidance® UTI, a combined multiplex polymerase chain reaction (M-PCR) and Pooled Antibiotic Susceptibility Testing (P-AST) assay, which addresses both the limited sensitivity of SUC and the inability of M-PCR alone to provide phenotypic antibiotic susceptibility results. The M-PCR component provides pathogen identification and the detection of resistance genes and has shown improved sensitivity and high specificity compared to SUC [33,35,40,41,42]. Positive M-PCR results also exhibit high concordance with the identification of viable organisms grown by the expanded culture methodology [19], indicating that M-PCR is capable of detecting viable organisms. It is important to note that while the organisms targeted by the M-PCR assay have all been associated with UTI pathogenesis, their detection by M-PCR or SUC does not inherently indicate pathogenicity. In a prior study of cases that were clinically assessed as having a presumptive UTI, organisms detected by M-PCR were highly associated with elevated inflammatory markers of the urinary tract [40,41,42]. This indicates that there is a high correlation between organism detection and pathogenicity in the appropriate patient population and highlights the importance of assessing UTI symptoms and the clinical workup when utilizing diagnostic tests.
Additionally, M-PCR produces quantitative results (in cells/mL) that correlate linearly 1:1 with the CFU/mL units used in SUC [43]. The P-AST component tests for pooled organism growth in Mueller–Hinton broth against multiple dilutions of UTI-relevant antibiotics using a fluorescent probe. This accounts for the effects of multi-species interactions that may alter susceptibility in polymicrobial infections and for the presence of multiple strains of a single species, which can co-occur in a UTI with varying susceptibilities (heteroresistance) [44,45,46]. The M-PCR/P-AST assay has prior publications demonstrating that (1) the clinical use of this test is associated with improved patient outcomes and (2) the test is rapid, typically completed within one day of the specimen’s receipt at the testing lab [32,47,48,49].
When assessing the utility of these new diagnostic tests compared to the culture, an important consideration beyond improvements in patient outcomes is whether first-line antibiotics are prioritized before resorting to antibiotics reserved for non-responsive and multi-drug-resistant infections. In 2017, the World Health Organization (WHO) developed the Access, Watch, and Reserve (AWaRe) classification system of antibiotics to aid in global antimicrobial stewardship efforts [50,51], AWaRe classifies antibiotics according to both their spectrum of activity and their potential to contribute to increasing antimicrobial resistance. The “access” classification applies to antibiotics used in first-line treatment with a low risk of developing resistance. The “watch” classification applies to antibiotics with a broader spectrum of activity and a higher risk of developing resistance. According to the WHO, nitrofurantoin is the preferred treatment option for acute lower UTIs, and amoxicillin/clavulanate, trimethoprim, or sulfamethoxazole/trimethoprim are additional first-line “access” antibiotic options [51].
The Infectious Diseases Society of America (IDSA) and the European Society for Microbiology and Infectious Disease joint guidelines also recommend nitrofurantoin or sulfamethoxazole/trimethoprim for a first-line lower UTI treatment [52]. They also include fosfomycin as a first-line treatment in contrast to the WHO classification of fosfomycin as a “watch” antibiotic [51,52]. Due to high resistance rates in a growing number of locations, sulfamethoxazole/trimethoprim is recommended as a frontline treatment for acute lower UTIs only when current local antibiograms indicate that resistance rates are <20% or when an individual patient’s specimen is tested and found susceptible, which makes it less useful as a form of empiric therapy [52]. Similarly to IDSA, the American Urological Association’s (AUA’s) guidelines for recurrent UTIs recommend nitrofurantoin, sulfamethoxazole/trimethoprim, and fosfomycin as first-line antibiotics, with the caveat that sulfamethoxazole/trimethoprim should not be used where local resistance exceeds 20%.
The M-PCR/P-AST assay studied here has previously demonstrated an improvement in patient outcomes and a reduction in empiric therapy use [47]. The objective of this study is to analyze those exact same cases and compare the use of first-line antibiotics and provider experience when using M-PCR/P-AST compared to SUC.

2. Materials and Methods

2.1. Study Design and Methodology

This study was a multi-centered, prospective, observational study (clinical trial registration: NCT05091931, https://clinicaltrials.gov/ct2/show/NCT05091931 (accessed on 20 September 2024). It was conducted in accordance with the Declaration of Helsinki and with the approval of the Western Institutional Review Board—Copernicus Group (WGC-IRB) (ID: 20214705), who determined that the study protocol met all requirements for a partial waiver of authorization and required only that verbal informed consent was given prior to enrollment by all subjects.
As an observational study that aimed to compare patient outcomes and provider prescribing behavior when either MCR/P-AST or SUC are utilized, the test cohort assignment was at the clinicians’ discretion, and only the assigned test result was provided. Additionally, the clinicians were free to treat the patient (whether based on test results, clinical experience, expertise, or both) at their discretion without direction from the trial protocol. This study was designed to determine if any significant differences exist between test cohorts in patient outcomes, including the choice of whether to prescribe any antibiotic treatment, whether to prescribe empiric versus directed antibiotic treatment and which antibiotic treatments were prescribed empirically vs. which antibiotic treatments were prescribed as directed therapy. The study included a broad patient population representing a typical clinical caseload for a urology office.
The cohort consisted of 385 adult (≥18 years old) subjects (n = 112 males and n = 273 females) who presented to urology or urogynecology clinics with symptoms and clinical presentations consistent with complicated UTIs from 2 U.S. states between 30 March 2022 and 24 May 2023, who required microbial diagnostic testing according to their clinician’s judgment, and who were willing and able to provide informed consent in English or Spanish. Individuals who were pregnant, incarcerated, currently taking antibiotics, currently receiving radiotherapy, relying on self-catheterization or a chronic indwelling catheter, diagnosed with bladder/urologic cancer, chronic pelvic pain, untreated overactive bladder, or who had urinary diversion were excluded. The full inclusion and exclusion criteria, including the definition of cUTI, are described in the prior publication of this study dataset [47]. The subjects entered either the M-PCR/P-AST (n = 250) or the SUC (n = 135) arm based on the test requisition order at their clinician’s discretion. The healthcare provider documented the subjects’ clinical presentations, medical history, and treatment plans. The subjects provided midstream voided or in-and-out catheter-collected urine specimens and completed baseline and daily surveys [47].
Treatment status was determined, as previously described [47], based on both the clinical evaluation forms completed by healthcare providers and on the subjects’ responses in daily surveys [47]. “Treated” was defined as treated with antimicrobials, including antibiotics and anti-fungal medications, between day 1 and day 14 of the study. Subjects who either did not receive any medication(s) at all or who only received medication other than antimicrobials (ex: pain medication) were defined as “untreated”.
Treated subjects were categorized as receiving “empiric treatment” if the healthcare provider or subjects reported empirical treatment via the questions on the forms/surveys. If the form did not have this information, antibiotics reported as being given prior to the test result being provided were considered empiric. Treated subjects were categorized as receiving “directed treatment” if the healthcare provider or subjects reported the use of directed treatment via the questions on the forms/surveys. If the form did not have this information, antibiotics given after the test result was provided were considered as receiving “directed treatment”. Subjects who received antimicrobial treatment both before and after the test results were made available were categorized as receiving both empiric and directed treatment, whether the provider continued the same antibiotic or changed it.

2.2. Standard Urine Culture and Antibiotic Susceptibility Testing

In the case that SUC was selected, the clinician submitted a test requisition per their usual clinical practices. These tests were performed by separate clinical diagnostic labs with high-complexity CLIA certifications that were already routinely being used by the providers/medical office prior to the study, following the standard protocols of each laboratory. These labs are independent of the authors, and no changes were requested to their standard operation procedures for this study. This setup best provides an unbiased SUC comparator that mirrors general clinical lab results for SUC at multiple sites.

2.3. M-PCR/P-AST Assay (Guidance® UTI, Offered by Pathnostics, Irvine, CA, USA)

As described previously [33,47], DNA extracted from the subject’s urine sample using a King Fisher/MagMAX™ automated DNA extraction instrument and the MagMAX™ DNA Multi-Sample Ultra Kit (Thermo Fisher, Carlsbad, CA, USA) was mixed with a universal PCR master mix and amplified using TaqMan technology in a Life Technologies 12K Flex 112-format Open Array System (Thermo Fisher Scientific, Wilmington, NC, USA). For the M-PCR component of the assay, probes, and primers are used to detect 32 antibiotic-resistance genes as well as the following microorganisms, which included 4 yeast species, and 26 bacterial species or groups, both fastidious and non-fastidious:
Candida albicans (C. albicans), Candida glabrata (C. glabrata), Candida parapsilosis (C. parapsilosis), Citrobacter freundii (C. freundii), Citrobacter koseri (C. koseri), Enterococcus faecalis (E. faecalis), Enterococcus faecium (E. faecium), Escherichia coli (E. coli), Klebsiella oxytoca (K. oxytoca), Klebsiella pneumoniae (K. pneumoniae), Morganella morganii (M. morganii), Pantoea agglomerans (P. agglomerans), Proteus mirabilis (P. mirabilis), Providencia stuartii (P. stuartii), Pseudomonas aeruginosa (P. aeruginosa), Serratia marcescens (S. marcescens), Staphylococcus aureus (S. aureus), Streptococcus agalactiae (S. agalactiae), Enterobacter group [including Klebsiella aerogenes (formally known as Enterobacter aerogenes) and Enterobacter cloacae], Acinetobacter baumannii (A. baumannii), Actinotignum schaalii (A. schaalii), Aerococcus urinae (A. urinae), Alloscardovia omnicolens (A. omnicolens), Candida auris (C. auris), Corynebacterium riegelii (C. riegelii), Gardnerella vaginalis (G. vaginalis), Mycoplasma hominis (M. hominis), Ureaplasma urealyticum (U. urealyticum), coagulase-negative staphylococci group (CoNS) (including Staphylococcus epidermidis, Staphylococcus haemolyticus, Staphylococcus lugdunesis, and Staphylococcus saprophyticus), and Viridans group streptococci (VGS) (including Streptococcus anginosus, Streptococcus oralis, and Streptococcus pasteuranus).
In addition, the fluorescence-based P-AST was performed on all specimens in which the M-PCR test component identified at least one non-fastidious bacterial species or group capable of growth in the assay conditions. The method was performed, as described previously [47] to determine susceptibility to the following 19 antibiotics commonly used for UTI treatment: amoxicillin/clavulanate, ampicillin, ampicillin/sulbactam, cefaclor, cefazolin, cefepime, cefoxitin, ceftazidime, ceftriaxone, ciprofloxacin, fosfomycin, gentamicin, levofloxacin, meropenem, nitrofurantoin, piperacillin/tazobactam, sulfamethoxazole, tetracycline, and vancomycin.

2.4. Statistical Analysis

To reduce bias, subjects were matched between the two arms on categories of age, sex, and baseline United States Food and Drug Administration (FDA) symptom scores, as described in the 2023 publication by Haley et al. [47], prior to analysis. A total of 385 subjects were matched between the M-PCR and SUC cohorts for this analysis. The Chi-squared test or Fisher’s exact test was used to test whether the difference between the SUC and M-PCR/PAST cohorts was significant. For all statistical tests, significance was defined as p < 0.05. No adjustments were made for multiple comparisons. All statistical calculations were performed using Python 3.10.12 (Figure 1).

3. Results

3.1. Subject Demographics

A total of 395 individuals were successfully matched between the M-PCR/P-AST (n = 250) and SUC (n = 145) arms based on sex, age, and baseline symptom scores (Table 1). The matched cases were approximately 70% female (n = 176/250 for M-PCR and n = 103/145 for SUC) with no statistical difference in sex distribution between the cohorts (p = 0.4931). The resulting mean age was approximately 65 years and was similar between the M-PCR and SUC cohorts (p = 0.9093). Baseline symptom scores between the two arms were also similar after matching (p = 0.5306). Details of organisms detected and the number of polymicrobial cases are provided in Supplemental Tables S1 and S2.

3.2. Treatment Decisions

There were no significant differences between the cohorts for the proportion of treated versus untreated subjects (p = 0.0548) (Table 2a). However, there was a significant difference in the distribution of empiric-only, mixed, or directed-only treatments (p = 0.0034), with the higher utilization of empiric therapy in the SUC arm (Table 2b).
For empiric therapy, only amoxicillin/clavulanate had a statistically significant difference (p = 0.0309) with increased use in the SUC cohort compared to M-PCR/P-AST (Supplemental Table S3).
Comparing directed therapies, nitrofurantoin (p = 0.0172) and metronidazole (p = 0.0309) were used more in the M-PCR/P-AST cohort, while amoxicillin/clavulanate (p = 0.0008), cefuroxime (p = 0.0378), and ertapenem (p = 0.0378) were used more in the SUC cohort (Table 3). Metronidazole was used in the M-PCR/P-AST arm for nine cases where G. vaginalis was detected, which is not detectable by SUC.
There was no significant difference between the cohorts for the utilization of “watch” classification antibiotics versus “access” classification antibiotics selected for directed treatment (p = 1.0) (Table 4a).
There was also no significant difference between the cohorts in the utilization of “first-line” classification antibiotics, fosfomycin, nitrofurantoin, and sulfamethoxazole/trimethoprim, during the directed treatment (p = 0.4483) (Table 4b).
The results of a survey for the providers in this study show the use and importance of each component of the M-PCR/P-AST assay (Supplemental Figure S1), with 71% of providers indicating that all components together were important. For those who ranked the individual components of the M-PCR/P-AST results in order of importance, the P-AST result was ranked the most important (47% gave P-AST the highest rank).

4. Discussion

There is a major gap in the efficacy of clinical care for complicated and recurrent UTIs due to the significant limitations of the current standard of care diagnostic tests, leading to high rates of hospitalization and ER/Urgent Care visits [47]. Equally important, these patients have a significantly reduced quality of life due to the persistence and recurrence of symptoms, including pain [53,54,55,56,57,58,59,60,61,62,63]. Prior published studies have already demonstrated that the M-PCR/P-AST diagnostic test is associated with reduced hospitalizations, ER visits, Urgent Care visits, and the recurrence of UTI symptoms [47,48]. Unlike M-PCR tests alone, the P-AST component of the test provides antibiotic susceptibility information, thereby providing phenotypic susceptibility results to the clinician. Urology providers indicated that these results are important to them as they make prescribing and patient management decisions (Supplemental Figure S1), with both the M-PCR and P-AST components being selected as important to decision-making.
The primary aim of this report was to assess whether the utilization of the novel M-PCR/P-AST diagnostic assay resulted in any change to the use of first-line or “access” antibiotics versus the standard-of-care diagnostic test, SUC, for patients with a suspected complicated UTI. Secondarily, the total number of antibiotic prescriptions, the category of prescription timing (empiric or test-result-directed), and the specific antibiotics chosen were analyzed. Prior reports have assessed the impact on patient outcomes of using this advanced diagnostic, showing an improvement compared to SUC and a significant reduction in empiric therapy in the same set of patients studied here [47].
Overall, the results show there was no significant difference in the use of WHO-classified “watch” antibiotics nor “access” antibiotics between the M-PCR/P-AST assay and standard urine culture cohorts (p = 1.0). Both tests also had no significant differences in their use of the first-line therapies listed by the Infectious Diseases Society of America (IDSA) and the American Urological Association (AUA) (p = 0.4483). This demonstrates that there was no significant association between the utilization of the Guidance UTI test (M-PCR/P-AST) and increased use of non-first-line antibiotics. The results do not imply equivalency between the antibiotics used in the two arms but do show that the improved outcomes shown previously for the M-PCR/P-AST arm were not associated with the increased utilization of non-first-line antibiotics.
Additionally, comparing the antibiotics chosen for empiric therapy, there was only one statistically significant difference between the SUC and M-PCR/P-AST cohorts, specifically, that amoxicillin/clavulanate was used more in the SUC cohort (p = 0.0309). Given that the empiric selection of an antibiotic is made based on clinical judgment and local antibiograms rather than on actual test results, the lack of significant difference for almost all antibiotics is expected.
There was a slightly larger number of antibiotic prescriptions in the M-PCR/P-AST cohort, which did not reach statistical significance between the cohorts in the proportion of treated versus untreated subjects (p = 0.0548). There was a significantly higher utilization of empiric therapy in the SUC arm compared to M-PCR/P-AST (p = 0.0034). These results are consistent with our previous data, which also showed a reduction in empiric therapy with the utilization of M-PCR/P-AST [47,48].
For directed therapies, nitrofurantoin (p = 0.0172) was used more in the M-PCR/P-AST cohort than with SUC. Nitrofurantoin is widely recommended as a first-line treatment for UTIs [51,52], and increased utilization is consistent with good antimicrobial stewardship. Metronidazole was used as directed therapy for nine cases in the M-PCR/P-AST cohort but not at all in the SUC cohort, presumably due to the inability of SUC to detect G. vaginalis. The M-PCR/P-AST assay adds a comment that metronidazole, along with other relevant antimicrobials, are commonly used for management when G. vaginalis is detected, though it does not run pooled susceptibility (P-AST) against metronidazole. G. vaginalis is a fastidious, anaerobic bacteria of the urogenital microbiome [64,65,66,67,68,69,70,71], historically considered a contaminant in urine [69,70,72,73]. However, recent data have shown that in the bladder, G. vaginalis is associated with immune activation, urothelial exfoliation, recurrent UTIs, and urosepsis [74,75,76,77,78]. Metronidazole has demonstrated efficacy in treating G. vaginalis UTIs [79].
In the SUC cohort, amoxicillin/clavulanate (p = 0.0008) and cefuroxime (p = 0.0378) were used more for directed treatment than in the M-PCR/P-AST cohort. Amoxicillin/clavulanate is a beta-lactamase inhibitor combination class “access” antibiotic recommended by the WHO for frontline lower UTI treatment [51]. However, this combination is not a first-line treatment according to the IDSA and the European Society for Microbiology and Infectious Disease joint guidelines [52] and is strongly associated with gastrointestinal side effects, including Clostridium difficile infection [80]. Cefuroxime, a cephalosporin antibiotic, is a “watch” classification antibiotics whose use should be minimized [51].
Ertapenem, as a directed therapy, was used exclusively in the SUC cohort with no use in the M-PCR/P-AST cohort (p = 0.0378), though the number of cases where it was used was small (n = 3 in the SUC arm). This does not imply that ertapenem usage was inappropriate in the SUC arm since the presence of ESBL or other factors can make its use necessary. It does indicate that M-PCR/P-AST is not associated with an increase in the use of this non-first-line carbapenem class antibiotic.

Limitations and Future Directions

The focus of this study was on clinician-prescribing behavior to assess if there were any changes to the use of first-line and “access” antibiotics with the use of the new diagnostic, using the same dataset of patients that demonstrated improved outcomes in a prior report [47]. Future studies may assess more detailed criteria by which clinicians can arrive at their treatment decisions and examine case-by-case correlations between which organism(s) are present, what the antibiotic susceptibility test results were, which specific antibiotic(s) have been prescribed, and the patient’s resulting clinical outcome(s). This study also does not imply causation between the use of first-line therapy and the improved outcomes reported in prior work, nor does it imply equivalency with SUC antibiotic choices, which may also be a focus of further research. As a novel diagnostic, further study of the Pooled Antibiotic Susceptibility (P-AST) component, including a comparison to standard methods of measuring isolates’ antibiotic susceptibility, would be useful and are currently ongoing. These studies will also standardize the SUC assay at a central laboratory instead of the real-world method used here, where SUC testing was performed at multiple independent laboratories. A larger study may also be considered to further assess outcomes such as overall antibiotic usage rates and those outlined above.

5. Conclusions

This study demonstrated that the use of the M-PCR/P-AST assay to guide complicated UTI diagnosis and treatment did not significantly change the proportion of first-line or “access” antibiotics used, nor the overall proportion of treatment versus non-treatment, compared to standard urine culture. The previous finding that M-PCR/P-AST was associated with the reduced utilization of empiric antibiotic therapy was also replicated here. This study, combined with previous studies on the same set of patients, demonstrates that the M-PCR/P-AST assay has the potential to improve patient outcomes in complicated and recurrent UTI cases without increasing the use of non-first-line antibiotics. The use of this novel diagnostic should be part of a broader clinical assessment, and management should be based on clinical judgment, including the consideration of our current understanding of the urinary microbiome.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jcm13237453/s1. Supplemental Table S1: The proportion of monomicrobial versus polymicrobial infections detected by M-PCR/P-AST compared to SUC; Supplemental Table S2: Histogram of detected organisms; Supplemental Table S3: Antibiotic selections for empiric treatments between SUC and M-PCR/P-AST cohorts; Table S4. Survey Questions and Response Options; Supplemental Figure S1: Results of survey ranking importance of report components.

Author Contributions

Conceptualization, D.B. and M.M.; methodology, N.L.; software, J.H.; validation, E.H., J.H. and X.C.; formal analysis, X.C. and J.H.; investigation, H.K. and G.S.R.; resources, D.B.; data curation, J.H.; writing—original draft preparation, E.H.; writing—review and editing, E.H., N.L., M.M. and D.B.; visualization, E.H. and M.M.; supervision, N.L.; project administration, N.L.; funding acquisition, D.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. It was funded internally by Pathnostics.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the independent Institutional Review Board WIRB-Copernicus Group (WCG® IRB) (protocol code 20214705 on 20 March 2022).

Informed Consent Statement

The IRB determined that the study protocol met the requirements for a partial waiver of authorization, requiring only verbal informed consent, which was given by all subjects prior to enrollment: the use of protected health information (PHI) involved no more than minimal risk to this study’s subjects, and this research could not be practicably conducted without the access to PHI or without the waiver. All subjects gave verbal informed consent prior to enrollment.

Data Availability Statement

The original data presented in the study are openly available in FigShare at DOI: 10.6084/m9.figshare.27324660.

Acknowledgments

We wish to thank Franklin R. Cockerill and Rick Pesano for their review of the manuscript.

Conflicts of Interest

E.H., N.L., D.B., X.C., J.H., and M.M. are employees of Pathnostics. H.K. is a paid consultant of Pathnostics.

References

  1. Zilberberg, M.D.; Nathanson, B.H.; Sulham, K.; Shorr, A.F. Descriptive epidemiology and outcomes of hospitalizations with complicated urinary tract infections in the United States, 2018. Open Forum Infect. Dis. 2022, 9, ofab591. [Google Scholar] [CrossRef] [PubMed]
  2. Grigoryan, L.; Nash, S.; Zoorob, R.; Germanos, G.J.; Horsfield, M.S.; Khan, F.M.; Martin, L.; Trautner, B.W. Qualitative analysis of primary care provider prescribing decisions for urinary tract infections. Antibiotics 2019, 8, 84. [Google Scholar] [CrossRef] [PubMed]
  3. Waller, T.A.; Pantin, S.A.L.; Yenior, A.L.; Pujalte, G. Urinary tract infection antibiotic resistance in the United States. Prim. Care Clin. Off. Pract. 2018, 45, 455–466. [Google Scholar] [CrossRef] [PubMed]
  4. Groah, S.L.; Pérez-Losada, M.; Caldovic, L.; Ljungberg, I.H.; Sprague, B.M.; Castro-Nallar, E.; Chandel, N.J.; Hsieh, M.H.; Pohl, H.G. Redefining healthy urine: A cross-sectional exploratory metagenomic study of people with and without bladder dysfunction. J. Urol. 2016, 196, 579–587. [Google Scholar] [CrossRef]
  5. Wolfe, A.J.; Brubaker, L. Sterile urine and the presence of bacteria. Eur. Urol. 2015, 68, 173–174. [Google Scholar] [CrossRef]
  6. Mueller, E.R.; Wolfe, A.J.; Brubaker, L. Female urinary microbiota. Curr. Opin. Urol. 2017, 27, 282–286. [Google Scholar] [CrossRef]
  7. Nelson, D.E.; Pol, B.V.D.; Dong, Q.; Revanna, K.V.; Fan, B.; Easwaran, S.; Sodergren, E.; Weinstock, G.M.; Diao, L.; Fortenberry, J.D. Characteristic male urine microbiomes associate with asymptomatic sexually transmitted infection. PLoS ONE 2010, 5, e14116. [Google Scholar] [CrossRef]
  8. Fouts, D.E.; Pieper, R.; Szpakowski, S.; Pohl, H.; Knoblach, S.; Suh, M.-J.; Huang, S.-T.; Ljungberg, I.; Sprague, B.M.; Lucas, S.K.; et al. Integrated next-generation sequencing of 16S RDNA and metaproteomics Differentiate the healthy urine microbiome from asymptomatic bacteriuria in neuropathic bladder associated with spinal cord injury. J. Transl. Med. 2012, 10, 174. [Google Scholar] [CrossRef]
  9. Curtiss, N.; Balachandran, A.; Krska, L.; Peppiatt-Wildman, C.; Wildman, S.; Duckett, J. Age, Menopausal status and the bladder microbiome. Eur. J. Obstet. Gynecol. Reprod. Biol. 2018, 228, 126–129. [Google Scholar] [CrossRef]
  10. Liu, F.; Ling, Z.; Xiao, Y.; Yang, Q.; Zheng, L.; Jiang, P.; Li, L.; Wang, W. Characterization of the urinary microbiota of elderly women and the effects of type 2 diabetes and urinary tract infections on the microbiota. Oncotarget 2017, 8, 100678–100690. [Google Scholar] [CrossRef]
  11. Mulder, M.; Radjabzadeh, D.; Hassing, R.J.; Heeringa, J.; Uitterlinden, A.G.; Kraaij, R.; Stricker, B.H.; Verbon, A. The effect of antimicrobial drug use on the composition of the genitourinary microbiota in an elderly population. BMC Microbiol. 2019, 19, 9. [Google Scholar] [CrossRef] [PubMed]
  12. Jayalath, S.; Magana-Arachchi, D. Dysbiosis of the human urinary microbiome and its association to diseases affecting the urinary system. Indian J. Microbiol. 2022, 62, 153–166. [Google Scholar] [CrossRef] [PubMed]
  13. Wolfe, A.J.; Toh, E.; Shibata, N.; Rong, R.; Kenton, K.; FitzGerald, M.; Mueller, E.R.; Schreckenberger, P.; Dong, Q.; Nelson, D.E.; et al. Evidence of uncultivated bacteria in the adult female bladder. J. Clin. Microbiol. 2012, 50, 1376–1383. [Google Scholar] [CrossRef] [PubMed]
  14. Thomas-White, K.; Brady, M.; Wolfe, A.J.; Mueller, E.R. The Bladder Is Not Sterile: History and current discoveries on the urinary microbiome. Curr. Bladder Dysfunct. Rep. 2016, 11, 18–24. [Google Scholar] [CrossRef]
  15. Hilt, E.E.; McKinley, K.; Pearce, M.M.; Rosenfeld, A.B.; Zilliox, M.J.; Mueller, E.R.; Brubaker, L.; Gai, X.; Wolfe, A.J.; Schreckenberger, P.C. Urine is not sterile: Use of enhanced urine culture techniques to detect resident bacterial flora in the adult female bladder. J. Clin. Microbiol. 2014, 52, 871–876. [Google Scholar] [CrossRef]
  16. Barnes, H.C.; Wolff, B.; Abdul-Rahim, O.; Harrington, A.; Hilt, E.E.; Price, T.K.; Halverson, T.; Hochstedler, B.R.; Pham, T.; Acevedo-Alvarez, M.; et al. A randomized clinical trial of standard versus expanded cultures to diagnose urinary tract infections in women. J. Urol. 2021, 206, 1212–1221. [Google Scholar] [CrossRef]
  17. Price, T.K.; Hilt, E.E.; Dune, T.J.; Mueller, E.R.; Wolfe, A.J.; Brubaker, L. Urine trouble: Should we think differently about UTI? Int. Urogynecol. J. 2018, 29, 205–210. [Google Scholar] [CrossRef]
  18. Brubaker, L.; Chai, T.C.; Horsley, H.; Khasriya, R.; Moreland, R.B.; Wolfe, A.J. Tarnished gold—The standard urine culture: Reassessing the characteristics of a criterion standard for detecting urinary microbes. Front. Urol. 2023, 3, 1206046. [Google Scholar] [CrossRef]
  19. Festa, R.A.; Luke, N.; Mathur, M.; Parnell, L.; Wang, D.; Zhao, X.; Magallon, J.; Remedios-Chan, M.; Nguyen, J.; Cho, T.; et al. A test combining multiplex-PCR with pooled antibiotic susceptibility testing has high correlation with expanded urine culture for detection of live bacteria in urine samples of suspected UTI patients. Diagn. Microbiol. Infect. Dis. 2023, 107, 116015. [Google Scholar] [CrossRef]
  20. Stegger, M.; Leihof, R.F.; Baig, S.; Sieber, R.N.; Thingholm, K.R.; Marvig, R.L.; Frimodt-Møller, N.; Nielsen, K.L. A snapshot of diversity: Intraclonal Variation of Escherichia Coli clones as commensals and pathogens. Int. J. Med. Microbiol. 2020, 310, 151401. [Google Scholar] [CrossRef]
  21. Band, V.I.; Crispell, E.K.; Napier, B.A.; Herrera, C.M.; Tharp, G.K.; Vavikolanu, K.; Pohl, J.; Read, T.D.; Bosinger, S.E.; Trent, M.S.; et al. Antibiotic failure mediated by a resistant subpopulation in Enterobacter cloacae. Nat. Microbiol. 2016, 1, 16053. [Google Scholar] [CrossRef] [PubMed]
  22. Roch, M.; Sierra, R.; Andrey, D.O. Antibiotic heteroresistance in ESKAPE pathogens, from bench to bedside. Clin. Microbiol. Infect. 2023, 29, 320–325. [Google Scholar] [CrossRef] [PubMed]
  23. Choby, J.E. Antibiotic Heteroresistance: What Is It and How Does It Impact Patients? Available online: https://www.contagionlive.com/view/antibiotic-heteroresistance-what-is-it-and-how-does-it-impact-patients- (accessed on 29 April 2024).
  24. El-Halfawy, O.M.; Valvano, M.A. Antimicrobial heteroresistance: An emerging field in need of clarity. Clin. Microbiol. Rev. 2015, 28, 191–207. [Google Scholar] [CrossRef] [PubMed]
  25. Dewachter, L.; Fauvart, M.; Michiels, J. Bacterial heterogeneity and antibiotic survival: Understanding and combatting persistence and heteroresistance. Mol. Cell 2019, 76, 255–267. [Google Scholar] [CrossRef]
  26. El-Halfawy, O.M.; Valvano, M.A. Chemical communication of antibiotic resistance by a highly resistant subpopulation of bacterial cells. PLoS ONE 2013, 8, e68874. [Google Scholar] [CrossRef]
  27. Andam, C.P. Clonal yet different: Understanding the causes of genomic heterogeneity in microbial species and impacts on public health. mSystems 2019, 4, e00097-19. [Google Scholar] [CrossRef]
  28. Band, V.I.; Satola, S.W.; Smith, R.D.; Hufnagel, D.A.; Bower, C.; Conley, A.B.; Rishishwar, L.; Dale, S.E.; Hardy, D.J.; Vargas, R.L.; et al. Colistin heteroresistance is largely undetected among carbapenem-resistant enterobacterales in the United States. mBbio 2021, 12, e02881-20. [Google Scholar] [CrossRef]
  29. Band, V.I.; Weiss, D.S. Heteroresistance to beta-lactam antibiotics may often be a stage in the progression to antibiotic resistance. PLoS Biol. 2021, 19, e3001346. [Google Scholar] [CrossRef]
  30. Band, V.I.; Weiss, D.S. Heteroresistance: A cause of unexplained antibiotic treatment failure? PLoS Pathog. 2019, 15, e1007726. [Google Scholar] [CrossRef]
  31. Al-Shebiny, A.; Shawky, R.; Emara, M. Heteroresistance: A gray side of antimicrobial susceptibility testing. J. Adv. Pharm. Res. 2023, 7, 101–110. [Google Scholar] [CrossRef]
  32. Korman, H.J.; Baunoch, D.; Luke, N.; Wang, D.; Zhao, X.; Levin, M.; Wenzler, D.L.; Mathur, M. A diagnostic test combining molecular testing with phenotypic pooled antibiotic susceptibility improved the clinical outcomes of patients with non-E. coli or polymicrobial complicated urinary tract infections. Res. Rep. Urol. 2023, 15, 141–147. [Google Scholar] [CrossRef] [PubMed]
  33. Vollstedt, A.; Baunoch, D.; Wojno, K.; Luke, N.; Cline, K.; Belkoff, L.; Sirls, L. Multisite prospective comparison of multiplex polymerase chain reaction testing with urine culture for diagnosis of urinary tract infections in symptomatic patients. J. Surg. Urol. 2020, 1, JSU-102. [Google Scholar]
  34. Kline, K.A.; Lewis, A.L. Gram-Positive Uropathogens, polymicrobial urinary tract infection, and the emerging microbiota of the urinary tract. Microbiol. Spectr. 2016, 4. [Google Scholar] [CrossRef] [PubMed]
  35. Vollstedt, A.; Baunoch, D.; Wolfe, A.; Luke, N.; Wojno, K.J.; Cline, K.; Belkoff, L.; Milbank, A.; Sherman, N.; Haverkorn, R.; et al. Bacterial interactions as detected by pooled antibiotic susceptibility testing (P-AST) in polymicrobial urine specimens. J. Surg. Urol. 2020, 1, 101. [Google Scholar] [PubMed]
  36. Pavlaki, M.; Poulakou, G.; Drimousis, P.; Adamis, G.; Apostolidou, E.; Gatselis, N.K.; Kritselis, I.; Mega, A.; Mylona, V.; Papatsoris, A.; et al. Polymicrobial bloodstream infections: Epidemiology and impact on mortality. J. Glob. Antimicrob. Resist. 2013, 1, 207–212. [Google Scholar] [CrossRef]
  37. Nityadarshini, N.; Mohapatra, S.; Gautam, H.; Jain, V.; Chaudhry, R.; Kapil, A. Polymicrobial growth in standard urine culture: Time to act or ignore? Trop. Doct. 2022, 52, 335–336. [Google Scholar] [CrossRef]
  38. Dhamgaye, S.; Qu, Y.; Peleg, A.Y. Polymicrobial infections involving clinically relevant gram-negative bacteria and fungi. Cell. Microbiol. 2016, 18, 1716–1722. [Google Scholar] [CrossRef]
  39. Nicoloff, H.; Hjort, K.; Levin, B.R.; Andersson, D.I. The high prevalence of antibiotic heteroresistance in pathogenic bacteria is mainly caused by gene amplification. Nat. Microbiol. 2019, 4, 504–514. [Google Scholar] [CrossRef]
  40. Haley, E.; Luke, N.; Mathur, M.; Festa, R.A.; Wang, J.; Jiang, Y.; Anderson, L.; Baunoch, D. Comparison Shows that multiplex polymerase chain reaction identifies infection-associated urinary biomarker–positive urinary tract infections that are missed by standard urine culture. Eur. Urol. Open Sci. 2023, 58, 73–81. [Google Scholar] [CrossRef]
  41. Haley, E.; Luke, N.; Mathur, M.; Festa, R.A.; Wang, J.; Jiang, Y.; Anderson, L.A.; Baunoch, D. The prevalence and association of different uropathogens detected by M-PCR with infection-associated urine biomarkers in urinary tract infections. Res. Rep. Urol. 2024, 16, 19–29. [Google Scholar] [CrossRef]
  42. Akhlaghpour, M.; Haley, E.; Parnell, L.; Luke, N.; Mathur, M.; Festa, R.A.; Percaccio, M.; Magallon, J.; Remedios-Chan, M.; Rosas, A.; et al. Urine biomarkers individually and as a consensus model show high sensitivity and specificity for detecting UTIs. BMC Infect. Dis. 2024, 24, 153. [Google Scholar] [CrossRef] [PubMed]
  43. Festa, R.A.; Opel, M.; Mathur, M.; Luke, N.; Parnell, L.K.S.; Wang, D.; Zhao, X.; Magallon, J.; Percaccio, M.; Baunoch, D. Quantitative multiplex PCR in copies Ml–1 linearly correlates with standard urine culture in colonies Ml–1 for urinary tract infection (UTI) pathogens. Lett. Appl. Microbiol. 2023, 76, ovad085. [Google Scholar] [CrossRef] [PubMed]
  44. Moreno, E.; Andreu, A.; Pérez, T.; Sabaté, M.; Johnson, J.R.; Prats, G. Relationship between Escherichia coli strains causing urinary tract infection in women and the dominant faecal flora of the same hosts. Epidemiol. Infect. 2006, 134, 1015–1023. [Google Scholar] [CrossRef] [PubMed]
  45. Willner, D.; Low, S.; Steen, J.A.; George, N.; Nimmo, G.R.; Schembri, M.A.; Hugenholtz, P. Single clinical isolates from acute uncomplicated urinary tract infections are representative of dominant in situ populations. mBio 2014, 5, e01064-13. [Google Scholar] [CrossRef] [PubMed]
  46. Qin, J.; Wu, N.; Bao, J.; Shi, X.; Ou, H.; Ye, S.; Zhao, W.; Wei, Z.; Cai, J.; Li, L.; et al. Heterogeneous Klebsiella pneumoniae co-infections complicate personalized bacteriophage therapy. Front. Cell. Infect. Microbiol. 2021, 10, 608402. [Google Scholar] [CrossRef]
  47. Haley, E.; Luke, N.; Korman, H.; Baunoch, D.; Wang, D.; Zhao, X.; Mathur, M. Improving patient outcomes while reducing empirical treatment with multiplex-polymerase-chain-reaction/pooled-antibiotic-susceptibility-testing assay for complicated and recurrent urinary tract infections. Diagnostics 2023, 13, 3060. [Google Scholar] [CrossRef]
  48. Korman, H.J.; Mathur, M.; Luke, N.; Wang, D.; Zhao, X.; Levin, M.; Wenzler, D.L.; Baunoch, D. Multiplex Polymerase chain reaction/pooled antibiotic susceptibility testing was not associated with increased antibiotic resistance in management of complicated urinary tract infections. Infect. Drug. Resist. 2023, 16, 2841–2848. [Google Scholar] [CrossRef]
  49. Ko, D.S.-C.; Lukacz, E.S.; Juster, I.A.; Niecko, T.; Ashok, A.; Vollstedt, A.J.; Baunoch, D.; Mathur, M. Real-World evidence that a novel diagnostic combining molecular testing with pooled antibiotic susceptibility testing is associated with reduced infection severity and lower cost compared with standard urine culture in patients with complicated or persistently recurrent urinary tract infections. JU Open Plus 2023, 1, e00021. [Google Scholar] [CrossRef]
  50. Sharland, M.; Zanichelli, V.; Ombajo, L.A.; Bazira, J.; Cappello, B.; Chitatanga, R.; Chuki, P.; Gandra, S.; Getahun, H.; Harbarth, S.; et al. The WHO essential medicines list AWaRe Book: From a list to a quality improvement system. Clin. Microbiol. Infect. 2022, 28, 1533–1535. [Google Scholar] [CrossRef]
  51. The WHO AWaRe (Access, Whatch, Reserve) Antibiotic Book—Web Annex. Infographics. Available online: https://iris.who.int/bitstream/handle/10665/365135/WHO-MHP-HPS-EML-2022.02-eng.pdf?sequence=1 (accessed on 3 June 2024).
  52. Gupta, K.; Hooton, T.M.; Naber, K.G.; Wullt, B.; Colgan, R.; Miller, L.G.; Moran, G.J.; Nicolle, L.E.; Raz, R.; Schaeffer, A.J.; et al. International clinical practice guidelines for the treatment of acute uncomplicated cystitis and pyelonephritis in women: A 2010 update by the Infectious Diseases Society of America and the European Society for Microbiology and Infectious Diseases. Clin. Infect. Dis. 2011, 52, e103–e120. [Google Scholar] [CrossRef]
  53. Thompson, J.; Marijam, A.; Mitrani-Gold, F.S.; Wright, J.; Joshi, A.V. Activity Impairment, health-related quality of life, productivity, and self-reported resource use and associated costs of uncomplicated urinary tract infection among women in the United States. PLoS ONE 2023, 18, e0277728. [Google Scholar] [CrossRef]
  54. Newlands, A.F.; Kramer, M.; Roberts, L.; Maxwell, K.; Price, J.L.; Finlay, K.A. Evaluating the Quality of life impact of recurrent urinary tract infection: Validation and refinement of the recurrent UTI impact questionnaire (RUTIIQ). Neurourol. Urodyn. 2024. [Google Scholar] [CrossRef] [PubMed]
  55. Scott, V.C.S.; Thum, L.W.; Sadun, T.; Markowitz, M.; Maliski, S.L.; Ackerman, A.L.; Anger, J.T.; Kim, J.-H. Fear and frustration among women with recurrent urinary tract infections: Findings from patient focus groups. J. Urol. 2021, 206, 688–695. [Google Scholar] [CrossRef] [PubMed]
  56. Anderson, D.J.; Aucoin, A.; Toups, C.R.; Cormier, D.; McDonald, M.; Hasoon, J.; Viswanath, O.; Kaye, A.D.; Urits, I. Lower urinary tract symptoms in depression: A review. Health Psychol. Res. 2023, 11, 81040. [Google Scholar] [CrossRef] [PubMed]
  57. Maxwell, K.; Roberts, L.; Kramer, M.; Price, J.; Newlands, A.; Finlay, K.A. Psychosocial burden and healthcare disillusionment in recurrent UTI: A large-scale international survey of patient perspectives. Front. Urol. 2023, 3, 1264299. [Google Scholar] [CrossRef]
  58. Naber, K.G.; Tirán-Saucedo, J.; Wagenlehner, F.M.E.; RECAP group. Psychosocial burden of recurrent uncomplicated urinary tract infections. GMS Infect. Dis. 2022, 10, Doc01. [Google Scholar] [CrossRef] [PubMed]
  59. Ellis, A.K.; Verma, S. Quality of life in women with urinary tract infections: Is benign disease a misnomer? J. Am. Board Fam. Pr. 2000, 13, 392–397. [Google Scholar] [CrossRef]
  60. Renard, J.; Ballarini, S.; Mascarenhas, T.; Zahran, M.; Quimper, E.; Choucair, J.; Iselin, C.E. Recurrent lower urinary tract infections have a detrimental effect on patient quality of life: A prospective, observational study. Infect. Dis. Ther. 2015, 4, 125–135. [Google Scholar] [CrossRef]
  61. Breyer, B.N.; Kenfield, S.A.; Blaschko, S.D.; Erickson, B.A. The association of lower urinary tract symptoms, depression and suicidal ideation: Data from the 2005–2006 and 2007–2008 National Health and Nutrition examination survey. J. Urol. 2014, 191, 1333–1339. [Google Scholar] [CrossRef]
  62. Grigoryan, L.; Mulgirigama, A.; Powell, M.; Schmiemann, G. The emotional impact of urinary tract infections in women: A qualitative analysis. BMC Women’s Health 2022, 22, 182. [Google Scholar] [CrossRef]
  63. Ernst, E.J.; Ernst, M.E.; Hoehns, J.D.; Bergus, G.R. Women’s quality of life is decreased by acute cystitis and antibiotic adverse effects associated with treatment. Heal Qual. Life Outcomes 2005, 3, 45. [Google Scholar] [CrossRef] [PubMed]
  64. Jespers, V.; Hardy, L.; Buyze, J.; Loos, J.; Buvé, A.; Crucitti, T. Association of Sexual debut in adolescents with microbiota and inflammatory markers. Obstet. Gynecol. 2016, 128, 22–31. [Google Scholar] [CrossRef] [PubMed]
  65. Hickey, R.J.; Zhou, X.; Settles, M.L.; Erb, J.; Malone, K.; Hansmann, M.A.; Shew, M.L.; Pol, B.V.D.; Fortenberry, J.D.; Forney, L.J. Vaginal microbiota of adolescent girls prior to the onset of menarche resemble those of reproductive-age women. mBio 2015, 6, e00097-15. [Google Scholar] [CrossRef] [PubMed]
  66. Yoo, J.-J.; Song, J.S.; Kim, W.B.; Yun, J.; Shin, H.B.; Jang, M.-A.; Ryu, C.B.; Kim, S.S.; Chung, J.C.; Kuk, J.C.; et al. Gardnerella vaginalis in recurrent urinary tract infection is associated with dysbiosis of the bladder microbiome. J. Clin. Med. 2022, 11, 2295. [Google Scholar] [CrossRef]
  67. Curley, T.; Forster, C.S. Recurrent UTIs in girls: What is the role of the microbiome? Urology 2021, 151, 94–97. [Google Scholar] [CrossRef]
  68. Morrill, S.; Gilbert, N.M.; Lewis, A.L. Gardnerella vaginalis as a cause of bacterial vaginosis: Appraisal of the Evidence from in vivo models. Front. Cell. Infect. Microbiol. 2020, 10, 168. [Google Scholar] [CrossRef]
  69. Thomas-White, K.; Forster, S.C.; Kumar, N.; Kuiken, M.V.; Putonti, C.; Stares, M.D.; Hilt, E.E.; Price, T.K.; Wolfe, A.J.; Lawley, T.D. Culturing of female bladder bacteria reveals an interconnected urogenital microbiota. Nat. Commun. 2018, 9, 1557. [Google Scholar] [CrossRef]
  70. Komesu, Y.M.; Dinwiddie, D.L.; Richter, H.E.; Lukacz, E.S.; Sung, V.W.; Siddiqui, N.Y.; Zyczynski, H.M.; Ridgeway, B.; Rogers, R.G.; Arya, L.A.; et al. Defining the relationship between vaginal and urinary microbiomes. Am. J. Obstet. Gynecol. 2020, 222, 154.e1–154.e10. [Google Scholar] [CrossRef]
  71. Li, T.; Zhang, Z.; Wang, F.; He, Y.; Zong, X.; Bai, H.; Liu, Z. Antimicrobial susceptibility testing of metronidazole and clindamycin against Gardnerella vaginalis in planktonic and biofilm formation. Can. J. Infect. Dis. Med. Microbiol. 2020, 2020, 1361825. [Google Scholar] [CrossRef]
  72. Sze, C.; Pressler, M.; Lee, J.R.; Chughtai, B. The gut, vaginal, and urine microbiome in overactive bladder: A systematic review. Int. Urogynecol. J. 2022, 33, 1157–1164. [Google Scholar] [CrossRef]
  73. Perez-Carrasco, V.; Soriano-Lerma, A.; Soriano, M.; Gutiérrez-Fernández, J.; Garcia-Salcedo, J.A. Urinary microbiome: Yin and yang of the urinary. Tract. Front. Cell. Infect. Microbiol. 2021, 11, 617002. [Google Scholar] [CrossRef] [PubMed]
  74. O’Brien, V.P.; Lewis, A.L.; Gilbert, N.M. Bladder exposure to Gardnerella activates host pathways necessary for Escherichia coli recurrent UTI. Front. Cell. Infect. Microbiol. 2021, 11, 788229. [Google Scholar] [CrossRef] [PubMed]
  75. Lewis, A.L.; Gilbert, N.M. Roles of the vagina and the vaginal microbiota in urinary tract infection: Evidence from clinical correlations and experimental models. GMS Infect. Dis. 2020, 8, Doc02. [Google Scholar] [CrossRef] [PubMed]
  76. Holst, E.; Mårdh, P.A.; Thelin, I. Recovery of anaerobic curved rods and Gardnerella vaginalis from the urethra of men, including male heterosexual consorts of female carriers. Scand. J. Urol. Nephrol. Suppl. 1984, 86, 173–177. [Google Scholar]
  77. Akamine, C.M.; Chou, A.; Tavakoli-Tabasi, S.; Musher, D.M. Gardnerella vaginalis bacteremia in male patients: A Case series and review of the literature. Open Forum Infect. Dis. 2022, 9, ofac176. [Google Scholar] [CrossRef]
  78. Ruiz-Gómez, M.L.; Martín-Way, D.A.; Pérez-Ramírez, M.D.; Gutiérrez-Fernández, J. Male deep infections by Gardnerella vaginalis. A literature review and a case report. Rev. Esp. Quimioter. 2019, 32, 469–472. (In Spanish) [Google Scholar]
  79. Pedraza-Avilés, A.G.; Zaragoza, M.C.; Mota-Vázquez, R.; Hernández-Soto, C.; Ramírez-Santana, M.; Terrazas-Maldonado, M.L. Treatment of urinary tract infection by Gardnerella vaginalis: A comparison of oral metronidazole versus ampicillin. Cochrane Cent. Regist. Control. Trials 2018, 2007, 65–69. [Google Scholar]
  80. Huttner, A.; Bielicki, J.; Clements, M.N.; Frimodt-Møller, N.; Muller, A.E.; Paccaud, J.-P.; Mouton, J.W. Oral amoxicillin and amoxicillin–clavulanic acid: Properties, indications and usage. Clin. Microbiol. Infect. 2020, 26, 871–879. [Google Scholar] [CrossRef]
Jcm 13 07453 g001
Figure 1. Study events and subject selection for analysis.
Figure 1. Study events and subject selection for analysis.
Jcm 13 07453 g001
Table 1. Demographics of the matched study cohorts.
Table 1. Demographics of the matched study cohorts.
All Subjects (n = 395)
SUC (n = 145)M-PCR/P-AST (n = 250)p-Value
Age0.4931
Mean (SD)63.0 (14.8)64.0 (13.7)
Median (Min, Max)65.8 (21.0, 95.7)66.5 (22.0, 96.1)
Sex [n (%)]0.9093
Male42 (29.0%)74 (29.6%)
Female103 (71.0%)176 (70.4%)
Mean Baseline
Symptom Score (SD)		6.5 (3.5)6.3 (3.2)0.5306
Table 2. (a) Prevalence of antimicrobial treatment between SUC and M-PCR/P-AST cohorts. (b) Comparing proportions of empiric versus directed treatments between SUC and M-PCR/P-AST cohorts.
Table 2. (a) Prevalence of antimicrobial treatment between SUC and M-PCR/P-AST cohorts. (b) Comparing proportions of empiric versus directed treatments between SUC and M-PCR/P-AST cohorts.
(a)
Total (n = 395)SUCM-PCR/P-ASTp-Value
(n = 145)(n = 250)
n%n%0.0548
Not Treated with Antimicrobial Agents4531.05522.0
Treated with Antimicrobial Agents10069.019578.0
(b)
Total (n = 295)SUCM-PCR/P-ASTp-Value
(n = 100)(n = 195)
n%n%0.0034
Empiric Only22.042.1
Empiric to Directed7171.09950.8
Directed Only2727.09247.2
Table 3. Antibiotic selections for directed treatments between SUC and M-PCR/P-AST cohorts.
Table 3. Antibiotic selections for directed treatments between SUC and M-PCR/P-AST cohorts.
2019 WHO
AWaRe
Classification		Antibiotic ClassAntibiotic NameDirected Treatmentp-Value
SUC (n = 96)M-PCR/P-AST
(n = 188)
n%n%
AccessNitrofuranNitrofurantoin1818.8%6132.4%0.0172
WatchPhosphonicFosfomycin33.1%63.2%1.0000
AccessSulfonamide/
Dihydrofolate Reductase Inhibitor		Sulfamethoxazole/
Trimethoprim		1717.7%179.0%0.0517
AccessDihydrofolate Reductase InhibitorTrimethoprim *11.0%00.0%0.3380
AccessPenicillinAmoxicillin *00.0%10.5%1.0000
AccessAmpicillin11.0%84.3%0.2810
AccessBeta-lactamase Inhibitor
Combination		Amoxicillin/
Clavulanate		1717.7%94.8%0.0008
WatchCephalosporinsCeftriaxone00.0%00.0%1.0000
WatchCefdinir *11.0%10.5%1.0000
AccessCefuroxime *33.1%00.0%0.0378
AccessCephalexin *22.1%84.3%0.5032
WatchFluoroquinolonesLevofloxacin99.4%2714.4%0.2629
WatchCiprofloxacin1414.6%2412.8%0.7138
WatchCarbapenemsErtapenem *33.1%00.0%0.0378
AccessTetracyclinesDoxycycline *77.3%179.0%0.8219
AccessNitroimidazolesMetronidazole00.0%94.8%0.0309
Antibiotics marked with an * were not tested by P-AST. Bolded p-values indicate statistical significance.
Table 4. (a) Comparison of utilization of watch versus access antibiotics for directed treatments between SUC and M-PCR/P-AST cohorts. (b) Comparison of utilization of first-line antibiotics (nitrofurantoin, fosfomycin, and sulfamethoxazole/trimethoprim) for directed treatments between SUC and M-PCR/P-AST cohorts.
Table 4. (a) Comparison of utilization of watch versus access antibiotics for directed treatments between SUC and M-PCR/P-AST cohorts. (b) Comparison of utilization of first-line antibiotics (nitrofurantoin, fosfomycin, and sulfamethoxazole/trimethoprim) for directed treatments between SUC and M-PCR/P-AST cohorts.
(a)
All Directed Treatments (n = 284)SUC (n = 96)M-PCR/P-AST (n = 188)p-Value
n%n%
Watch Antibiotics (n = 88)3031.25830.91.0
Access Antibiotics (n = 196)6668.813069.1
(b)
All Directed Treatments (n = 284)SUC (n = 96)M-PCR/P-AST (n = 188)p-Value
n%n%
First-Line Antibiotics (n = 122)3839.68444.70.4483
Other Antibiotics (n = 162)5860.410455.3
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Haley, E.; Luke, N.; Korman, H.; Rao, G.S.; Baunoch, D.; Chen, X.; Havrilla, J.; Mathur, M. Comparing Prescribing Behaviors and Clinician Experiences Between Multiplex PCR/Pooled Antibiotic Susceptibility Testing and Standard Urine Culture in Complicated UTI Cases. J. Clin. Med. 2024, 13, 7453. https://doi.org/10.3390/jcm13237453

AMA Style

Haley E, Luke N, Korman H, Rao GS, Baunoch D, Chen X, Havrilla J, Mathur M. Comparing Prescribing Behaviors and Clinician Experiences Between Multiplex PCR/Pooled Antibiotic Susceptibility Testing and Standard Urine Culture in Complicated UTI Cases. Journal of Clinical Medicine. 2024; 13(23):7453. https://doi.org/10.3390/jcm13237453

Chicago/Turabian Style

Haley, Emery, Natalie Luke, Howard Korman, Ganesh Srinvas Rao, David Baunoch, Xiaofei Chen, Jim Havrilla, and Mohit Mathur. 2024. "Comparing Prescribing Behaviors and Clinician Experiences Between Multiplex PCR/Pooled Antibiotic Susceptibility Testing and Standard Urine Culture in Complicated UTI Cases" Journal of Clinical Medicine 13, no. 23: 7453. https://doi.org/10.3390/jcm13237453

APA Style

Haley, E., Luke, N., Korman, H., Rao, G. S., Baunoch, D., Chen, X., Havrilla, J., & Mathur, M. (2024). Comparing Prescribing Behaviors and Clinician Experiences Between Multiplex PCR/Pooled Antibiotic Susceptibility Testing and Standard Urine Culture in Complicated UTI Cases. Journal of Clinical Medicine, 13(23), 7453. https://doi.org/10.3390/jcm13237453

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