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Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for
David Sheyn1, Mingxuan Ju, Sixiao Zhang
1Division of Female Pelvic Medicine and Reconstructive Surgery, Department of Urology, University Hospitals Cleveland Medical Center, the Case School of Engineering, Department of Electrical Engineering and Computer Science, Case Western Reserve University, the Division of Female Pelvic Medicine and Reconstructive Surgery, Department of Obstetrics and Gynecology, University Hospitals Cleveland Medical Center, and the Division of Female Pelvic Medicine and Reconstructive Surgery, MetroHealth Medical Center, Case Western Reserve University School of Medicine, Cleveland, Ohio.
This study developed a machine learning model to predict anticholinergic treatment failure in overactive bladder (OAB) patients. The validated model accurately identifies patients likely to benefit from OAB medication, improving treatment outcomes.
Area of Science:
- Urology
- Pharmacology
- Data Science
Background:
- Overactive bladder (OAB) affects millions, often treated with anticholinergic medications.
- Predicting treatment response is crucial for optimizing OAB management.
- Anticholinergic medications are a cornerstone of OAB therapy, but response varies significantly among patients.
Purpose of the Study:
- To develop and externally validate a machine learning model for predicting anticholinergic treatment response in OAB patients.
- To identify key factors influencing treatment success or failure.
- To provide a tool for clinicians to personalize OAB treatment strategies.
Main Methods:
- A retrospective dataset of 559 female OAB patients treated with anticholinergics was used to build a random forest model.
- Patients were stratified by age and prior medication history.
- The model was externally validated on a prospective dataset of 82 patients.
Main Results:
- The final model achieved 80.3% global accuracy and an AUC of 0.77.
- External validation showed 80.4% sensitivity and 77.4% specificity.
- Model performance varied by age, with younger women showing better prediction (AUC 0.84).
Conclusions:
- An externally validated machine learning model can predict anticholinergic treatment failure in OAB patients with over 80% accuracy.
- The model aids in predicting treatment outcomes within a standard 3-month trial.
- The prediction tool is accessible online for clinical use.
