Utilizing Predictive Analytics to Understand Neurogenic Bladder Symptom Score (NBSS) Variations in Adults With Acquired Spinal Cord Injury.

ABSTRACT:

Introduction

Individuals with spinal cord injury (SCI) have varying bladder health trajectories after their injury. We explored whether a predictive machine learning model could identify which variables impact urinary symptoms.

Methods

We used 238 variables from the Neurogenic Bladder Research Group SCI registry for a Decision Tree analysis (eCHAID technique). The primary outcomes were the baseline Neurogenic Bladder Symptom Score (NBSS), and the change from the baseline NBSS at 1-year follow up (measured as better/worse than the median change).

Results

Among the 1479 participants, mean baseline NBSS was 24.16 ± 0.28 (standard error of the mean). Our decision tree that evaluated the NBSS at baseline predicted that individuals with a suprapubic tube/urostomy as their p

SUBMITTER: Nejad-Mansouri M 

PROVIDER: S-EPMC12319491 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature