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Predicting non-response to urotherapy in pediatric bowel and bladder dysfunction: A machine learning approach
Journal article   Peer reviewed

Predicting non-response to urotherapy in pediatric bowel and bladder dysfunction: A machine learning approach

Jackson M. Dunning, Adree Khondker, Christopher S. Cooper, Jacob Hansen, Mandy Rickard, Lauren Erdman, Joana Dos Santos, Armando J. Lorenzo and Douglas W. Storm
Journal of pediatric urology, Vol.22(1), 105642
02/2026
DOI: 10.1016/j.jpurol.2025.10.009
PMID: 41198494

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Abstract

Introduction Urotherapy remains the first-line conservative treatment of pediatric bowel and bladder dysfunction (BBD), however, some patients show limited or no response. Early identification of patients likely to fail urotherapy alone could influence early management and outcomes. This study aimed to develop predictive models to identify pediatric patients unlikely to respond to urotherapy alone (Summary Figure). Methods A retrospective cohort of 123 pediatric patients aged 5–10 years diagnosed with BBD who completed a validated 18-question BBD symptomology questionnaire at their initial and follow-up visit was analyzed. Patients underwent urotherapy as the primary intervention and symptom improvement was assessed at 6 months or less through a standardized scoring system. Machine learning (ML) models, including multivariable logistic regression and random Forest classifiers, were developed to identify predictors of non-response to urotherapy. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Results 123 patients met inclusion criteria with 92 (75%) females, and the median age was 6 years (IQR 5, 8). The median time from the initial to the next follow-up visit was 3 months (IQR 1, 4). Overall, 26 (21%) patients had complete improvement, 28 (23%) had moderate improvement, 23 (19%) patients had minimal improvement (19%), and 46 (38%) had no improvement. Older age (OR 1.45, 95% CI 1.09, 1.98; p=0.01) and presence of dysuria (OR 1.54, 95% CI 1.06, 2.37; p=0.03) at initial visit were associated with an increased likelihood of response to urotherapy, whereas the presence of daytime incontinence (OR 0.67, 95% CI 0.46, 0.97; p=0.04) was associated with a lower likelihood of response. The logistic regression model achieved an AUROC of 0.67, while the random Forest model slightly outperformed it with an AUROC of 0.71. Conclusion ML models using demographic and standardized questionnaire data predicted non-response to urotherapy in pediatric BBD patients. Age, dysuria, and daytime incontinence were identified as significant predictors. Early identification of potential non-responders could permit clinicians to implement additional therapeutic strategies sooner, improving overall patient care and outcomes.
 Machine Learning Bladder and bowel dysfunction Voiding dysfunction Artificial intelligence Urotherapy

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