ArticleInternational neurourology journal2026
Risk Assessment for Upper Urinary Tract Deterioration in Patients With Neurogenic Bladder: A Retrospective Study Using Interpretable Machine Learning Algorithms.
Article in International neurourology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeUpper urinary tract deterioration (UUTD) is a burdensome complication in patients with neurogenic bladder (NB). This study aimed to develop a practical machine learning (ML) tool for assessing UUTD risk.
methodsThe study included 2,393 patients with NB who underwent clinical and urodynamic examinations. Of these, 2,001 patients from northern regions of our country were assigned to training and validation sets, and 392 patients from southern regions formed the test set for internal-external validation. Clinical and urodynamic parameters were screened using the Boruta, least absolute shrinkage and selection operator regression, and decision tree algorithms; features identified by all 3 algorithms were used to develop the ML models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. SHapley Additive exPlanations analysis provided visual interpretations for individual patients.
resultsSix features, notably bladder management method and detrusor sphincter dyssynergia, were identified as risk factors for UUTD. Of the 4 ML algorithms, the Gaussian Naive Bayes (GNB) model had the best overall performance, with AUCs (95% confidence intervals) of 0.884 (0.867-0.901), 0.868 (0.829-0.907), and 0.878 (0.839-0.916) in the training, validation, and test sets, respectively. Subgroup analyses by imaging outcome, NB etiology, and age showed consistent performance of the GNB model, with AUCs ranging from 0.815 to 0.895. The GNB model incorporating the core variables was presented through a practical web-based tool for individualized risk assessment.
conclusionWe developed a ML model to assess UUTD risk in patients with NB. The findings emphasize the importance of combining bladder management method with urodynamic evaluation when assessing UUTD risk, and the model provides a practical tool for individualized assessment. Independent external validation of the model is warranted.
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