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

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.

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).

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 primary bladder management method and good bowel QOL at baseline had the lowest (best) mean baseline NBSS at 13.44 ± 0.83. In contrast, females with baseline spontaneous voiding had the highest (worst) mean baseline NBSS at 34.42 ± 1.05. Our second decision tree evaluated the change in the NBSS at 1-year follow-up. Of the 711 participants that performed better than the median change (i.e., improved), 45% were accounted for jointly by women who did not use bladder relaxing medications at baseline, and men without a history of prior urinary tract infections who used a single bladder management method at follow-up. The predictive capacity the decision tree was 57%.

Decision tree models help identify combinations of patient characteristics which correlate with urinary symptoms after SCI. However, there was a limited predictive capacity of the decision tree to forecast future bladder symptoms.