Adv Clin Exp Med. 2026 Jun 23. doi: 10.17219/acem/209761. Online ahead of print.
ABSTRACT
BACKGROUND: Postoperative urinary tract infection (UTI) following pyeloplasty remains a significant complication and continues to pose challenges in pediatric urological care.
OBJECTIVES: This study aimed to develop a simplified predictive model to identify risk factors for postoperative UTI after unilateral pyeloplasty and to support clinicians in implementing preventive strategies targeting modifiable risk factors.
MATERIAL AND METHODS: Clinical data from children who underwent unilateral pyeloplasty at the Children’s Hospital of Capital Institute of Pediatrics (Beijing, China) between January 2012 and January 2022 were retrospectively analyzed. Variables including sex, age, body mass index (BMI), surgical modality, drainage tube type, and parameters from blood and urine tests were evaluated. Statistical analyses, including least absolute shrinkage and selection operator (LASSO) regression, logistic regression, and random forest modeling, were performed to identify significant predictive factors. Variables with the greatest predictive importance were used to develop a nomogram, and its clinical utility was evaluated using decision curve analysis (DCA).
RESULTS: Among 764 patients, 265 (35%) developed postoperative UTI. Key risk factors included surgical modality, laterality of ureteropelvic junction obstruction (UPJO), drainage tube type, blood urea nitrogen (BUN) level, and patient height. LASSO regression identified 14 predictive variables, while logistic regression determined independent risk and protective factors. Ultimately, 8 variables (e.g., sex, operative time, drainage tube type, history of infection, history of fistula, age, BUN level, and renal cortical thickness) were selected for development of the nomogram predicting postoperative UTI risk after unilateral pyeloplasty.
CONCLUSIONS: This study identified 8 factors associated with postoperative UTI following unilateral pyeloplasty in children. The developed predictive model may assist clinicians in identifying high-risk patients, thereby supporting improved perioperative planning and postoperative management.
PMID:42335386 | DOI:10.17219/acem/209761
