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

PURPOSE: Patients with spinal cord injuries (SCIs) experience variable urinary symptoms and quality of life (QOL). Our objective was to use machine learning to identify bladder-relevant phenotypes after SCI and assess their association with urinary symptoms and QOL. MATERIALS AND METHODS: We used data from the Neurogenic Bladder Research Group SCI 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 analysis of variance 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 (ß = -0.12, P = .005), while the female predominant group had more deterioration (ß = 0.09, P = .047). CONCLUSIONS: This study demonstrates the utility of machine learning 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.

Aneurysmal dilation of bowel mimicking an abscess in pediatric primary gastrointestinal lymphoma.

Primary gastrointestinal lymphoma, though rare, is the most common gastrointestinal malignancy in children. Signs and symptoms are nonspecific, and include abdominal pain, nausea, emesis, and a palpable abdominal mass. Imaging is therefore typically required to differentiate gastrointestinal lymphoma from other abdominal conditions. We present a pediatric case of primary gastrointestinal lymphoma involving the distal bowel that was initially misdiagnosed as an intra-abdominal abscess. This case highlights the imaging findings of primary gastrointestinal lymphoma, potential pitfalls in imaging diagnosis, and the role of accurate imaging diagnosis in expediting patient management to reduce associated morbidity and mortality.