AI model predicts indwelling catheter conversion in early neurogenic bladder
Researchers have developed a machine-learning model to predict the likelihood of patients with early-stage spinal cord injuries requiring long-term indwelling catheters. The study uses five key clinical variables to stratify patient prognosis with high accuracy.
Why it matters
This predictive tool could significantly improve clinical decision-making and patient outcomes in neuro-urology.
edited by Gaby Clark , reviewed by Robert Egan
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Add as preferred source A novel prognostic stratification model can predict indwelling catheter conversion (ICC) among patients with early-stage spinal cord injury (SCI) and neurogenic bladder (NB), according to a study published online Aug. 23 in Neurourology and Urodynamics .
Jiqiang Xie, from the Fourth Military Medical University in Xi'an, China, and colleagues retrospectively analyzed 135 consecutive patients with early-stage SCI-NB admitted between November 2017 and January 2026 to develop and validate interpretable machine-learning (ML) survival models for predicting indwelling catheter time (ICT). Five ML survival models were developed to estimate the probability of ICC within one year.
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