Preoperative vaginal microbiome as a predictor of postoperative urinary tract infection

This is a single Institute, prospective cohort study. We enrolled twenty-two postmenopausal women with pelvic organ prolapse planning to undergo vaginal hysterectomy with transvaginal pelvic reconstructive surgery, with or without a concomitant anti-incontinence procedure. Vaginal swabs and urine samples were longitudinally collected at five time points: preoperative consult visit (T1), day of surgery prior to surgical scrub (T2), immediately postoperative (T3), day of hospital discharge (T4), and at the postoperative exam visit (T5). Women experiencing urinary tract infection symptoms provided a sample set prior to antibiotic administration (T6). Microbiome analysis was performed on vaginal and urinary specimens at each time point. Region V3-V5 of the 16S ribosomal RNA gene was amplified and sequenced. DNA samples were analyzed for visit T1, T2, T5 and T6. Six (27.3%) participants developed postoperative urinary tract infection whose vaginal sample at first clinical visit (T1) revealed beta-diversity analysis with significant differences in microbiome structure and composition. Women diagnosed with a postoperative urinary tract infection had a vaginal microbiome characterized by low abundance of Lactobacillus and high prevalence of Prevotella and Gardnerella species. In our cohort, preoperative vaginal swabs can predict who will develop a urinary tract infection following transvaginal surgery for pelvic organ prolapse.ClinicalTrials.gov Identifier: NCT02751073.

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Publication Details

Journal
Scientific Reports
Published
2024-11-22
DOI
https://doi.org/10.1038/s41598-024-78809-1
Citations
2
Primary Topic
Urinary Tract Infections Management
Type
article
Field-Weighted Citation Impact
0.77
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article

Preoperative vaginal microbiome as a predictor of postoperative urinary tract infection

John A. Occhino, Marina Walther-António, Pei‐Ying Wu, Jun Chen et al.
2 citations
Scientific Reports
Urinary Tract Infections Management
0.77
article

Preoperative vaginal microbiome as a predictor of postoperative urinary tract infection

John A. Occhino, Marina Walther-António, Pei‐Ying Wu, Jun Chen, Jenifer N. Byrnes
article en
2 citations

Abstract

This is a single Institute, prospective cohort study. We enrolled twenty-two postmenopausal women with pelvic organ prolapse planning to undergo vaginal hysterectomy with transvaginal pelvic reconstructive surgery, with or without a concomitant anti-incontinence procedure. Vaginal swabs and urine samples were longitudinally collected at five time points: preoperative consult visit (T1), day of surgery prior to surgical scrub (T2), immediately postoperative (T3), day of hospital discharge (T4), and at the postoperative exam visit (T5). Women experiencing urinary tract infection symptoms provided a sample set prior to antibiotic administration (T6). Microbiome analysis was performed on vaginal and urinary specimens at each time point. Region V3-V5 of the 16S ribosomal RNA gene was amplified and sequenced. DNA samples were analyzed for visit T1, T2, T5 and T6. Six (27.3%) participants developed postoperative urinary tract infection whose vaginal sample at first clinical visit (T1) revealed beta-diversity analysis with significant differences in microbiome structure and composition. Women diagnosed with a postoperative urinary tract infection had a vaginal microbiome characterized by low abundance of Lactobacillus and high prevalence of Prevotella and Gardnerella species. In our cohort, preoperative vaginal swabs can predict who will develop a urinary tract infection following transvaginal surgery for pelvic organ prolapse.ClinicalTrials.gov Identifier: NCT02751073.

Scientific ReportsVol. 14(1)
Summa Health System (US), Mayo Clinic (US), Mayo Clinic in Arizona (US), National Cheng Kung University Hospital (TW)
Good health and well-being
Openalex Percentile: Top 24%
Urinary Tract Infections Management
0.77
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Preoperative vaginal microbiome as a predictor of postoperative urinary tract infection — John A. Occhino, Marina Walther-António, et al. · Scientific Reports (2024) | TGRS Research Map | TGRS