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Updated: Aug 30, 2025

An In Vitro Bladder Model of Catheter-Associated Urinary Tract Infection
Published on: June 24, 2025
Building nomogram plots for predicting urinary tract infections in children less than three years of age
Shang-Chien Li1, Hsin Chi2, Fu-Yuan Huang3
1Department of Pediatric Infectious Diseases, MacKay Children's Hospital, Taipei, Taiwan; Department of Pediatrics, Far Eastern Memorial Hospital, New Taipei, Taiwan.
Background And Purpose:
Urinary tract infections (UTIs) are the most common bacterial infection in young children. This study aimed to formulate nomogram plots for clinicians to predict UTIs in children aged <3 years by evaluating the risk factors for UTIs in these children.
Methods:
This retrospective study was conducted at a tertiary medical center from December 2017 to November 2020. Children less than three years of age were eligible for the study if they had undergone both urine culture and urinalysis during the study period. Mixed-effects logistic regression models with a stepwise procedure were used to determine the relationship between outcome (positive/negative UTI) and covariates of interest (e.g., weight percentile, laboratory) for each patient. Nomogram plots were constructed on the basis of significant factors. We repeated the analysis thrice to adapt it to three different medical settings: medical centers, regional hospitals, and local clinics.
Results:
In the medical center setting, the two most significant factors were urine leukocyte count ≥100 (OR =8.87; 95% CI (Confidence Interval), 4.135-19.027) and urine nitrite level (OR =8.809; 95% CI, 5.009-15.489). The two factors showed similar significance at the regional hospital and local clinic settings. Abnormal renal echo findings were positively correlated with UTI in the medical center setting (OR =2.534; 95% CI 1.757-3.655). Three nomogram plots for the prediction of UTIs were drawn for medical centers, regional hospitals, and local clinics.
Conclusion:
Using the three nomogram plots, frontline doctors can formulate the probabilities of pediatric UTIs for better decision-making.
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