Introduction and Objectives Many women with multiple sclerosis (MS) experience neurogenic overactive bladder (NOAB) characterized by urinary frequency, urinary urgency and urgency incontinence. The objective of the study was to create machine learning (ML) models utilizing clinical and imaging data to predict NOAB treatment success stratified by treatment type.

Methods This was a retrospective cohort study of female patients with diagnosis of NOAB and MS seen at a tertiary academic center from 2017-2022. Clinical and imaging data were extracted. Three types of NOAB treatment options evaluated included behavioral therapy, medication therapy and minimally invasive therapies. The primary outcome – treatment success was defined as > 50% reduction in urinary frequency, urinary urgency or a subjective perception of treatment success. For the construction of the logistic regression ML models, bivariate analyses were performed with backward selection of variables with p-values of < 0.10 and clinically relevant variables applied. For ML, the cohort was split into a training dataset (70%) and a test dataset (30%). Area under the curve (AUC) scores are calculated to evaluate model performance.

Results The 110 patients included had a mean age of patients were 59 years old (SD 14 years), with a predominantly White cohort (91.8%), post-menopausal (68.2%). Patients were stratified by NOAB treatment therapy type received with 70 patients (63.6%) at behavioral therapy, 58 (52.7%) with medication therapy and 44 (40%) with minimally invasive therapies. On MRI brain imaging, 63.6% of patients had > 20 lesions though majority were not active lesions. The lesions were mostly located within the supratentorial (94.5%), infratentorial (68.2%) and 58.2 infratentorial brain (63.8%) as well as in the deep white matter (53.4%). For MRI spine imaging, most of the lesions were in the cervical spine (71.8%) followed by thoracic spine (43.7%) and lumbar spine (6.4%).10.3%). After feature selection, the top 10 highest ranking features were used to train complimentary LASSO-regularized logistic regression (LR) and extreme gradient-boosted tree (XGB) models. The top-performing LR models for predicting response to behavioral, medication, and minimally invasive therapies yielded AUC values of 0.74, 0.76, and 0.83, respectively.

Conclusions Using these top-ranked features, LR models achieved AUC values of 0.74-0.83 for prediction of treatment success based on individual factors. Further prospective evaluation is needed to better characterize and validate these identified associations.

Dr. Peter Chang is a co-founder and CMO at Avicenna.ai with stock/stock options, conducts grant-funded research at Novocure, and receives consulting fees from Bayer and Canon Medical. Dr. Lane is a consultant for Axonics Inc for teaching and research.

The project described was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2 TR001416. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This retrospective study was conducted at the University of California, Irvine, from 2017-2022 and was approved by the Institutional Review Board (IRB No.3930).

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Data availability statement The data that support the findings of this study are available from the corresponding author, OC, upon reasonable request.

Financial funding The project described was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2 TR001416. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Conflicts of Interest Dr. Peter Chang is a co-founder and CMO at Avicenna.ai with stock/stock options, conducts grant-funded research at Novocure, and receives consulting fees from Bayer and Canon Medical. Dr. Lane is a consultant for Axonics Inc for teaching and research.

Ethics Statement This retrospective study was conducted at the University of California, Irvine, from 2017-2022 and was approved by the Institutional Review Board (IRB No.3930). Findings were presented at the Society of Urodynamics and Female Urology 2025 as Oral Poster in Rancho mirage, CA.

The data that support the findings of this study are available from the corresponding author, OC, upon reasonable request.