Identifying Bladder Phenotypes After Spinal Cord Injury With Unsupervised Machine Learning: A New Way to Examine Urinary Symptoms and Quality of Life.

BACKGROUND: Patients with spinal cord injuries (SCI) experience variable urinary symptoms and QOL. Our objective was to use machine learning to identify bladder-relevant phenotypes after SCI and assess their association with urinary symptoms and QOL.

METHODS: We used data from the Neurogenic Bladder Research Group SCI (NBRG) registry. Baseline variables that were previously shown to be associated with bladder symptoms/QOL were included in the machine learning environment. An unsupervised consensus clustering approach (k-prototypes) was used to identify 4 patient clusters. After qualitative review of the clusters, 2 outcomes of interest were assessed: the total neurogenic bladder symptom score (NBSS) and the NBSS-satisfaction question (QOL). The NBSS and NBSS-satisfaction question at baseline and after 1 year were compared between clusters using ANOVA and linear regression.

RESULTS: Among the 1263 included participants, the 4 identified clusters were termed "female predominant," "high function, low SCI complication," "quadriplegia with bowel/bladder morbidity," and "older, high SCI complication." Using outcome data from baseline, significant differences were observed in the NBSS score, with the "female predominant" group exhibiting worse bladder symptoms. After 1 year, the overall bladder symptoms (NBSS Total) did not change significantly by cluster, however the QOL score for the "high function, low SCI complication" group had more improvement (β = -0.12, P = .005), while the "female predominant" group had more deterioration (β = 0.09, P = .047).

CONCLUSION: This study demonstrates the utility of machine larning in uncovering bladder-relevant phenotypes among SCI patients. Future research should explore cluster-based targeted strategies to enhance bladder-related outcomes and QOL in SCI.