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Prediction Model for Urinary Tract Infection in Pediatric Urological Surgery Patients
Yi Chen1, Xiao-Hua Ge2, Qun Yu2
1Department of Pediatric Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Insights
Urinary tract infections (UTIs) are a risk in pediatric urology surgery. This study identified six key risk factors and developed a predictive model to identify high-risk children, improving UTI prevention strategies.
Area of Science:
- Pediatric Urology
- Infectious Disease Epidemiology
Background:
- Urinary tract infection (UTI) is a frequent complication following pediatric urological surgery.
- UTIs can lead to serious long-term issues like recurrent infections and kidney scarring.
Purpose of the Study:
- To identify risk factors for UTI in pediatric urological surgery patients.
- To develop a predictive model for UTI risk in this population.
Main Methods:
- A retrospective study of 2,235 pediatric patients undergoing urological surgery.
- Multivariate logistic regression and receiver operating characteristic (ROC) curve analysis were used.
- A multifactorial predictive model was constructed.
Main Results:
- The prevalence of UTI was 15.26% (341 patients).
- Six significant risk factors were identified: young age (<12 months), upper urinary tract disease, absence of an indwelling drainage tube, prolonged hospital stay (≥10 days), multiple antibiotic administrations, and stent implantation.
- The predictive model achieved an area under the curve (AUC) of 88.37%.
Conclusions:
- A multifactorial model effectively predicts UTI risk in pediatric urological surgery patients.
- The model offers a valuable tool for risk stratification and targeted prevention strategies.
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.
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