Prediction Model for Urinary Tract Infection in Pediatric Urological Surgery Patients

Abstract

Background: Urinary tract infection (UTI) is a common complication in pediatric urological surgery patients and is associated with long-term sequelae, including subsequent recurrent infections and renal scarring. In this study, we aimed to explore the risk factors for UTI in pediatric urological surgery patients and construct a predictive model for UTI.

Materials and methods: A total of 2,235 pediatric patients who underwent urological surgery at a tertiary hospital between February 2019 and January 2020 were included. A multivariate logistic regression model was applied to identify the predictive factors, and a predictive model was constructed using a receiver operating characteristic curve. A multifactorial predictive model was used to categorize the risk of UTI based on the weight of the evidence.

Results: A total of 341 patients with UTI were identified, which corresponded to a prevalence of 15.26% in pediatric urological surgery patients. Multivariate analysis identified six significant risk factors for UTI, including age <12.0 months, upper urinary tract disease, not using an indwelling drainage tube, hospital stay ≥10 days, administration of two or more types of antibiotics, and stent implantation. A combination of the aforementioned factors produced an area under the curve value of 88.37% for preventing UTI in pediatric urological surgery patients. A multifactorial predictive model was created based on the combination of these factors.

Conclusions: The constructed multifactorial model could predict UTI risk in pediatric urological surgery patients with a relatively high predictive value.

Keywords: pediatric; predictive model; risk factors; urinary tract infection; urological surgery.