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Computer model predicting breakthrough febrile urinary tract infection in children with primary vesicoureteral reflux
Angela M Arlen1, Siobhan E Alexander2, Moshe Wald2
1Department of Urology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA; Department of Pediatrics, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
Insights
A new computational model accurately predicts breakthrough febrile urinary tract infections (fUTI) in children with primary vesicoureteral reflux (VUR). This tool helps personalize management by identifying high-risk patients for better outcomes.
Area of Science:
- Pediatric Urology
- Computational Medicine
- Medical Informatics
Background:
- Management of primary vesicoureteral reflux (VUR) involves balancing risks of breakthrough febrile urinary tract infection (fUTI) and renal scarring against spontaneous resolution.
- Accurate prediction of fUTI risk is crucial for individualized VUR management strategies.
- Current methods for risk stratification may not fully capture the multifactorial nature of fUTI in children.
Purpose of the Study:
- To develop and validate a multivariable computational model for predicting the probability of breakthrough fUTI in children with primary VUR.
- To improve the identification of children at higher risk for recurrent fUTI.
- To create a tool that supports personalized VUR management decisions.
Main Methods:
- A cohort of children with primary VUR and complete clinical/voiding cystourethrogram (VCUG) data was analyzed.
- Patient demographics, VCUG findings (grade, laterality, bladder volume), UTI history, and bladder-bowel dysfunction (BBD) were assessed.
- A multivariable computational model, specifically a neural network, was developed and validated using a training and cross-validation dataset.
Main Results:
- The study included 255 children with primary VUR, with a median follow-up of 24 months.
- Breakthrough fUTI occurred in 26.7% of children (90 events).
- A neural network model demonstrated 76% accuracy in predicting breakthrough fUTI, identifying factors like VUR grade, low bladder volume, BBD, and UTI history as significant predictors.
Conclusions:
- A multivariable computational model accurately predicts individual risk of breakthrough fUTI in children with primary VUR.
- Key predictors include VUR grade, bladder volume at reflux onset, bladder-bowel dysfunction, and prior UTI history.
- A web-based prognostic calculator derived from this model can aid clinicians in personalized risk assessment and management of VUR.
Introduction And Objective:
Factors influencing the decision to surgically correct vesicoureteral reflux (VUR) include risk of breakthrough febrile urinary tract infection (fUTI) or renal scarring, and decreased likelihood of spontaneous resolution. Improved identification of children at risk for recurrent fUTI may impact management decisions, and allow for more individualized VUR management. We have developed and investigated the accuracy of a multivariable computational model to predict probability of breakthrough fUTI in children with primary VUR.
Study Design:
Children with primary VUR and detailed clinical and voiding cystourethrogram (VCUG) data were identified. Patient demographics, VCUG findings including grade, laterality, and bladder volume at onset of VUR, UTI history, presence of bladder-bowel dysfunction (BBD), and breakthrough fUTI were assessed. The VCUG dataset was randomized into a training set of 288 with a separate representational cross-validation set of 96. Various model types and architectures were investigated using neUROn++, a set of C++ programs.
Results:
Two hundred fifty-five children (208 girls, 47 boys) diagnosed with primary VUR at a mean age of 3.1 years (±2.6) met all inclusion criteria. A total 384 VCUGs were analyzed. Median follow-up was 24 months (interquartile range 12-52 months). Sixty-eight children (26.7%) experienced 90 breakthrough fUTI events. Dilating VUR, reflux occurring at low bladder volumes, BBD, and history of multiple infections/fUTI were associated with breakthrough fUTI (Table). A 2-hidden node neural network model had the best fit with a receiver operating characteristic curve area of 0.755 for predicting breakthrough fUTI.
Discussion:
The risk of recurrent febrile infections, renal parenchymal scarring, and likelihood of spontaneous resolution, as well as parental preference all influence management of primary VUR. The genesis of UTI is multifactorial, making precise prediction of an individual child's risk of breakthrough fUTI challenging. Demonstrated risk factors for UTI include age, gender, VUR grade, reflux at low bladder volume, BBD, and UTI history. We developed a prognostic calculator using a multivariable model with 76% accuracy that can be deployed for availability on the Internet, allowing input variables to be entered to calculate the odds of an individual child developing a breakthrough fUTI.
Conclusions:
A computational model using multiple variables including bladder volume at onset of VUR provides individualized prediction of children at risk for breakthrough fUTI. A web-based prognostic calculator based on this model will provide a useful tool for assessing personalized risk of breakthrough fUTI in children with primary VUR.
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