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.
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.
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).
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.
The Journal of urology. 2024 Apr 16 [Epub ahead of print]
Blayne Welk, Tianyue Zhong, Jeremy Myers, John Stoffel, Sean Elliot, Sara M Lenherr, Daniel Lizotte
Department of Surgery, Western University, London, Ontario, Canada., Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada., Department of Surgery (Urology), University of Utah, Salt Lake City, Utah., Department of Urology, University of Michigan, Ann Arbor, Michigan., Department of Urology, University of Minnesota, Minneapolis, Minnesota.