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

Jackson M Dunning1, Adree Khondker2, Christopher S Cooper1

  • 1Department of Urology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.

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|November 6, 2025
PubMed
Summary

Machine learning models can predict which pediatric patients with bowel and bladder dysfunction (BBD) are unlikely to respond to urotherapy alone. Identifying these non-responders early allows for timely adjustments to treatment plans, improving patient outcomes.

Keywords:
Artificial intelligenceBladder and bowel dysfunctionMachine learningUrotherapyVoiding dysfunction

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Area of Science:

  • Pediatric Urology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Urotherapy is a primary treatment for pediatric bowel and bladder dysfunction (BBD).
  • A significant portion of pediatric patients exhibit limited or no response to urotherapy alone.
  • Early identification of non-responders is crucial for optimizing management and improving outcomes.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting non-response to urotherapy in pediatric BBD patients.
  • To identify key predictors of urotherapy treatment failure in children.
  • To enable earlier clinical intervention for non-responsive cases.

Main Methods:

  • Retrospective analysis of 123 pediatric patients (aged 5-10 years) with BBD.
  • Utilized a validated 18-question BBD symptomology questionnaire for data collection.
  • Developed and compared multivariable logistic regression and random Forest machine learning models to predict urotherapy non-response, evaluating performance with AUROC.

Main Results:

  • Older age and the presence of dysuria were associated with a higher likelihood of response to urotherapy.
  • Daytime incontinence at the initial visit was linked to a lower likelihood of response.
  • The random Forest model achieved a superior AUROC of 0.71 compared to logistic regression (0.67).

Conclusions:

  • Machine learning models effectively predict non-response to urotherapy in pediatric BBD.
  • Age, dysuria, and daytime incontinence are significant predictors of urotherapy outcomes.
  • Early identification of non-responders facilitates timely implementation of alternative or additional therapeutic strategies, enhancing patient care.