A treatment prediction strategy for overactive bladder using a machine learning algorithm that utilized data from the FAITH study

Aims: To use machine learning algorithms to develop a model to accurately predict treatment responses to mirabegron or antimuscarinic agents in patients with overactive bladder (OAB), using real-world data from the FAITH registry (NCT03572231). Methods: The FAITH registry data included patients who had been diagnosed with OAB symptoms for at least 3 months and were due to initiate monotherapy with mirabegron or any antimuscarinic. For the development of the machine learning model, data from patients were included if they had completed the 183-day study period, had data for all timepoints and had completed the overactive bladder symptom scores (OABSS) at baseline and end of study. The primary outcome of the study was a composite outcome combining efficacy, persistence, and safety outcomes. Treatment was deemed “more effective” if the composite outcome criteria for “successful,” “no treatment change,” and “safe” were met, otherwise treatment was deemed “less effective.” To explore the composite algorithm, a total of 14 clinical risk factors were included in the initial data set and a 10-fold cross-validation procedure was performed. A range of machine learning models were evaluated to determine the most effective algorithm. Results: In total, data from 396 patients were included (266 [67.2%] treated with mirabegron and 130 [32.8%] treated with an antimuscarinic). Of these, 138 (34.8%) were in the “more effective” group and 258 (65.2%) were in the “less effective” group. The groups were comparable in terms of their characteristic distributions across patient age, sex, body mass index, and Charlson Comorbidity Index. Of the six models initially selected and tested, the decision tree (C5.0) model was chosen for further optimization, and the receiver operating characteristic of the final optimized model had an area under the curve result of 0.70 (95% confidence interval: 0.54−0.85) when 15 was used for the min n parameter. Conclusions: This study successfully created a simple, rapid, and easy-to-use interface that could be further refined to produce a valuable educational or clinical decision-making aid.