Comparison of postvoid residual urine volumes among lower urinary tract parameters in pediatric and adolescent age groups: A cohort study at a tertiary referral center

Abstract

Introduction and objectives: Uroflowmetry and postvoid residual urine volume measurement remain essential in evaluating patients with lower urinary tract dysfunction. The objective was identify patients with a high risk of infection or renal damage based on the lower urinary tract parameters. Materials and methods: Over a one-year period we investigated patients who had an indication for uroflowmetry and postvoid residual urine volume measurement between 5-18 years of age. All parameters were prospectively recorded. After the initial evaluation, patients were divided into increased/normal postvoid residual urine volume groups. Lower urinary tract parameters, urinary tract infection and hydronephrosis status were analyzed. Results: A total of 176 girls and 140 boys with an age of 9 (5-16) were assessed. When increased = 135) / normal postvoid residual urine volume (n = 181) groups of patients were analysed, patients with a diagnosis of dysfunctional voiding, staccato and interrupted-shaped flow pattern, decreased Qavg, increased micrution duration, decreased daytime voiding frequency, and constipation were found to be prone to increased postvoid residual urine volume. A diagnosis of overactive bladder or dysfunctional voiding and high postvoid residual urine volume were both found to be related to infections. Conclusions: We contributed to the literature by evaluating different types of lower urinary tract conditions and their clinical parameters to better understand the predisposing factors detect patients at risk of infections or renal damage such as dysfunctional voiding, staccato and interrupted-shaped flow pattern, constipation, decreased Qavg, prolonged duration micturition and decreased daytime voiding frequency regardless of age and gender status. 2025 AEU. Published by Elsevier Espana, S.L.U. All rights are reserved, including those for text and data mining, AI training, and similar technologies.