Urinary tract infection after clean-contaminated pelvic surgery: a retrospective cohort study and prediction model
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Introduction and hypothesis
Postoperative urinary tract infection (UTI) leads to increased patient morbidity and health care costs. A prediction model may identify patients at highest risk for UTI development. Our primary objective was to determine the rate of UTI in the first 6 weeks after benign gynecologic surgery. Our secondary objective was to identify risk factors and build a predictive model for postoperative UTI.
We reviewed 310 patient records, which represent all patients who underwent clean-contaminated surgery at a tertiary center (2016–2017). UTI was defined as positive urine culture (> 100,000,000 CFU/l) in a symptomatic patient. Pre-, intra- and postoperative variables were collected. The relation between these variables and UTI was assessed through logistic regression. A clinical prediction model was built.
Patients’ mean age was 58.5 years and mean body mass index was 27.5 kg/m2. Most were inpatients (65.8%) and 269 had urogynecologic procedures, with the remainder undergoing pelvic surgery for other indications. The most common operation was vaginal reconstruction for prolapse (59.7%), associated with concomitant synthetic midurethral sling in 1/3 cases. Forty patients (12.9%) developed UTI. Multivariate prediction modeling showed increasing age (OR 1.33, CI 1.01–1.75), increasing number of procedures (OR 1.42, CI 1.14–1.78) and prolonged voiding dysfunction (OR 3.78, CI 1.66–8.60) to be significant UTI predictors.
Urinary tract infection in the first 6 weeks after complex pelvic surgery is common. Our prediction model identifies that patients who are older women, have prolonged voiding dysfunction and have a greater number of concomitant pelvic floor surgeries have higher risk of postoperative UTI.
KeywordsDecision support techniques Pelvic floor reconstruction Postoperative infection Prediction model Urinary tract infection Urinary retention
MS Sanaee: Protocol/project development, data collection, data management, data analysis, manuscript writing/editing.
K Pan: Data collection, data management, manuscript editing.
T Lee: Data analysis, manuscript editing.
N Koenig: Protocol/project development, data collection, data management, data analysis, manuscript writing/editing.
R Geoffrion: Protocol/project development, data collection, data management, data analysis, manuscript writing/editing.
Funding for this study was provided by institutional funding; a competitive Providence Health Care Foundation grant and a University of British Columbia Faculty of Medicine Summer Student Research Program grant.
Compliance with ethical standards
Conflict of interest
M. Sanaee, K. Pan, T. Lee and N. Koenig: None.
R. Geoffrion reports personal fees from Boston Scientific and from Duchesnay Canada, outside the submitted work.
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