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Development of Prognostic Models for Bladder and Bowel Dysfunction in Traumatic Spinal Cord Injury Patients Using

Takaki Kitamura1, Satoshi Maki1,2, Takeo Furuya1

  • 1Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan.

Journal of Neurotrauma
|December 3, 2025
PubMed
Summary

Machine learning models accurately predict bladder and bowel dysfunction after spinal cord injury (SCI). Key predictors include L3 motor function and time to admission, with accessible web applications available.

Keywords:
artificial intelligencebladder and bowel dysfunctionmachine learningpredicting outcomesrehabilitationspinal cord injury

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Area of Science:

  • Neurology
  • Rehabilitation Medicine
  • Data Science

Background:

  • Spinal cord injury (SCI) frequently leads to bladder and bowel dysfunction.
  • Predictive models for neurological outcomes post-SCI are increasing, but models for bladder/bowel dysfunction are limited.

Purpose of the Study:

  • To develop and validate machine learning models predicting bladder and bowel dysfunction outcomes in traumatic SCI patients.
  • To integrate these predictive models into a user-friendly web application for clinical use.

Main Methods:

  • Utilized data from 4,181 traumatic SCI patients (1991-2015) from the Japan Association of Rehabilitation Database.
  • Employed machine learning (PyCaret) with feature selection (Boruta) and SHAP values for interpretability.
  • Evaluated model performance using Area Under the Curve (AUC), focusing on predicting natural urination and defecation at discharge.

Main Results:

  • Gradient boosting models achieved high accuracy: AUC of 0.9064 for bladder function and 0.8714 for bowel function.
  • Key predictors identified for both functions included L3 motor function, time from injury to admission, and Functional Independence Measure bowel management score.
  • A web application integrating these predictive models was developed and made publicly available.

Conclusions:

  • Machine learning models demonstrate high accuracy in predicting bladder and bowel dysfunction after traumatic SCI.
  • L3 motor function, time to admission, and bowel dysfunction severity are critical predictors for both bladder and bowel outcomes.
  • The developed web application provides a valuable tool for predicting and managing SCI-related bladder and bowel dysfunction.