# Machine-learning prediction model for personalized urinary tract infection care in children

> **NIH AHRQ K08** · BOSTON CHILDREN'S HOSPITAL · 2024 · $153,050

## Abstract

PROJECT SUMMARY
The overarching goal of Dr. Wang’s proposal is to reduce the known risk of renal injury from febrile urinary tract
infection (fUTI) in children by implementing a practical, validated clinical decision support algorithm to promptly
identify unsafe anatomy before injury occurs. Dr. Wang’s proposal has identified significant care gaps in the
care of fUTI children. The long-term goal is to contribute to optimal management for fUTI in children through
implementation of novel, high-value, self-renewing machine learning (ML) models. The overall objective is to
identify those critical elements necessary for the development and implementation of predictive modeling to
identify children who would benefit most from early vs later voiding cystourethrogram (VCUG) in primary care
(NOT-HS-22-011). The central hypothesis is that ML models can provide accurate prediction of risky fUTI, and
thus assist clinicians/families to choose the best timing for VCUG. The rationale is to offer a scientific roadmap
and pilot new strategies that incorporate and implement prediction models to provide true value-based care
(NOT-HS-19-011) and equitable resource utilization for children (NOT-HS-21-015, NOT-HS-21-014). This
hypothesis has been formulated based on Dr. Wang’s previous work that demonstrates 1) high variability in
post-UTI VCUG practice patterns; and 2) ML models can serve as a promising basis to reliably identify children
with high risk for damaging UTI. Leveraging the data from a large pediatric practice network within Boston
Children’s Hospital, the following specific aims are proposed: 1) assess determinants for successful ML
algorithm implementation for pediatric fUTI care, 2) prospectively collect data to optimize and validate novel ML
algorithms in fUTI children, 3) pilot prediction of pediatric fUTI algorithm implementation to iteratively testing,
implementing, and adapting the algorithm using the principles of implementation and behavioral science to
maximize adoption and sustained implementations. In this proposal, Dr. Wang has assembled a multi-
disciplinary mentorship team consisting of experts in, qualitative methods, informatics, infectious disease,
machine learning, implementation, and behavioral science to help him achieve his goals and has designed a
comprehensive training plan to acquire necessary expertise. Dr. Wang’s unique background combined with his
career development plan, and the rich supporting environment (Boston Children’s Hospital, Harvard system,
and MIT) position him well to attain the proposed training goals and specific aims, and eventually lead to his
transition to independent surgeon-scientist. Combining machine-learning technology and real-life
implementation to tackle the challenge and change the status quo by translating actionable ML results to the
bedside is novel and innovative. The study is significant in that successful implementation of an algorithm for
UTI will be proof-of-concept to catalyze a similar appr...

## Key facts

- **NIH application ID:** 10983858
- **Project number:** 1K08HS029526-01A1
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Hsin-Hsiao Scott Wang
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2024
- **Award amount:** $153,050
- **Award type:** 1
- **Project period:** 2024-07-01 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10983858

## Citation

> US National Institutes of Health, RePORTER application 10983858, Machine-learning prediction model for personalized urinary tract infection care in children (1K08HS029526-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10983858. Licensed CC0.

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