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

Abstract

Recent advancements in machine learning have increased studies predicting neurological outcomes following spinal cord injury (SCI). However, there is limited research on predictive models for bladder and bowel dysfunction outcomes postinjury. This study aims to develop predictive models for bladder and bowel dysfunction outcomes in patients with traumatic SCI and integrate the models into a web application. This study utilized data from 4181 patients with traumatic SCI, registered in the Japan Association of Rehabilitation Database between 1991 and 2015, to develop and validate predictive models. The explanatory variables were categorized into three groups: neurological findings at admission (such as American Spinal Injury Association scores and Functional Independence Measure scores), patient background (including demographics, comorbidities, and insurance status), and SCI pathology (including injury mechanism, vertebral fractures, surgical history, presence of ossification of the posterior longitudinal ligament/OLF, and time to admission). Feature selection was performed using Boruta, excluding features with more than 25% missing values. The target variables were the bladder and bowel functions at discharge, classified into a binary outcome of whether natural urination and defecation were possible. Machine learning models were implemented using PyCaret, and model performance was evaluated using the area under the curve (AUC). Shapley Additive Explanation (SHAP) values assessed the contribution of individual features. A total of 3,949 cases were analyzed, with an average age of 50.3 years. The model with the highest accuracy for predicting bladder function was the gradient boosting model, achieving an AUC of 0.9064 on the test data. For predicting bowel function, the gradient boosting model showed the highest accuracy with an AUC of 0.8714. The top three key predictive factors identified using SHAP values included L3 motor function, time from injury to admission, and the Functional Independence Measure bowel management score, which were common predictors for both bladder and bowel function. The web application of the predictive models can be found at https://takakikitamura-bladder-prediction.hf.space/ and https://takakikitamura-bowel-prediction.hf.space. In conclusion, we developed a predictive model for bladder and bowel dysfunction outcomes after traumatic SCI using machine learning, confirming its high predictive accuracy. Critical predictors included L3 motor function, time from injury to admission, and the degree of bowel dysfunction, all of which were relevant for predicting both bladder and bowel function. These models were made publicly available as a web application.