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Published on: June 24, 2025
Targeted Workup after Initial Febrile Urinary Tract Infection: Using a Novel Machine Learning Model to Identify
Insights
A new machine learning model can predict which children with urinary tract infections (UTIs) are at high risk for recurrent UTIs and associated vesicoureteral reflux (VUR), guiding targeted imaging. This approach avoids unnecessary voiding cystourethrograms in low-risk children.
Area of Science:
- Pediatric Nephrology
- Medical Informatics
Background:
- Urinary tract infections (UTIs) in children often lead to debate regarding diagnostic workup.
- Current guidelines may not optimally stratify risk for recurrent UTIs and associated vesicoureteral reflux (VUR).
- A targeted approach is needed to identify high-risk children for imaging, such as voiding cystourethrogram (VCUG).
Purpose of the Study:
- To develop and validate a predictive model for identifying children at high risk of recurrent UTIs with VUR after an initial UTI.
- To enable selective use of VCUG, improving diagnostic yield and reducing overuse.
Main Methods:
- Utilized data from the RIVUR and CUTIE trials, excluding the RIVUR prophylaxis arm.
- Developed a machine learning model incorporating clinical variables (age, sex, race, weight, blood pressure, dysuria, urine albumin-to-creatinine ratio, antibiotic exposure, medication).
- Validated the model's performance using training, validation, and testing datasets with optimal tree imputation for missing data.
Main Results:
- Included 500 children (90% female, mean age 21 months).
- Identified recurrent UTI with VUR in 10.6% of subjects.
- The final model achieved an AUC of 0.761 (95% CI 0.714-0.808) in predicting recurrent UTI associated VUR in the testing set.
Conclusions:
- A novel machine learning model demonstrates promising performance in predicting recurrent UTI associated VUR in children.
- The model facilitates individualized treatment strategies post-initial UTI.
- Enables more selective application of VCUG, enhancing diagnostic utility and minimizing unnecessary procedures.
Purpose:
Significant debate persists regarding the appropriate workup in children with an initial urinary tract infection. Greatly preferable to all or none approaches in the current guideline would be a model to identify children at highest risk for a recurrent urinary tract infection plus vesicoureteral reflux to allow for targeted voiding cystourethrogram while children at low risk could be observed. We sought to develop a model to predict the probability of recurrent urinary tract infection associated vesicoureteral reflux in children after an initial urinary tract infection.
Materials And Methods:
We included subjects from the RIVUR (Randomized Intervention for Children with Vesico-Ureteral Reflux) and CUTIE (Careful Urinary Tract Infection Evaluation) trials in our study, excluding the prophylaxis treatment arm of the RIVUR. The main outcome was defined as recurrent urinary tract infection associated vesicoureteral reflux. Missing data were imputed using optimal tree imputation. Data were split into training, validation and testing sets. Machine learning algorithm hyperparameters were tuned by the validation set with fivefold cross-validation.
Results:
A total of 500 subjects, including 305 from the RIVUR and 195 from the CUTIE trials, were included in study. Of the subjects 90% were female and mean ± SD age was 21 ± 19 months. A recurrent urinary tract infection developed in 72 patients, of whom 53 also had vesicoureteral reflux (10.6% of the total). The final model included age, sex, race, weight, the systolic blood pressure percentile, dysuria, the urine albumin-to-creatinine ratio, prior antibiotic exposure and current medication. The model predicted recurrent urinary tract infection associated vesicoureteral reflux with an AUC of 0.761 (95% CI 0.714-0.808) in the testing set.
Conclusions:
Our predictive model using a novel machine learning algorithm provided promising performance to facilitate individualized treatment of children with an initial urinary tract infection and identify those most likely to benefit from voiding cystourethrogram after the initial urinary tract infection. This would allow for more selective application of this test, increasing the yield while also minimizing overuse.
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