Aims: To descriptively evaluate treatment persistence among adults who received mirabegron or antimuscarinics in South Korea. Methods: This study involved a retrospective analysis of the Health Insurance Review and Assessment (HIRA) database. Patients (≥18 years) who had a new prescription for an overactive bladder (OAB) target medication (mirabegron/ antimuscarinic) within an 8-month index period (July 1, 2015-February 29, 2016) were included. The date when the target (index) medication was dispensed was the index date. The 6-month period before the index date was used to assess patient eligibility. A 12-month post-index period was used to assess medication persistence, which was defined as the time to discontinuation.Overall data were analyzed and the results were also stratified by age group (≤65, >65 years), sex, or prior OAB medication experience. Persistence rates were calculated after the 1st, 3rd, 6th, 9th, and 12th months.Results: A data set of 52 722 cases was obtained (mirabegron: 11 424, antimuscarinics: 41 298). The mean age was 60.9 ± 16.1 years and the majority of the patients were female (30 862 [58.5%] patients). Median persistence was longer with mirabegron (51 days) versus antimuscarinics (25 days). The persistence rate with mirabegron was higher throughout the study compared with all the antimuscarinics (12-month data: 13.5% and 4.9%, respectively). Longer treatment persistence was noted in older, male, and treatment-experienced patients. Conclusion:The results from the HIRA database showed that persistence was longer with mirabegron than with antimuscarinics in South Korea. This finding may help inform clinical decision-making within the South Korean healthcare system.
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.
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