{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T03:05:06Z","timestamp":1771297506554,"version":"3.50.1"},"reference-count":18,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T00:00:00Z","timestamp":1683331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004948","name":"Astellas Pharma","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004948","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Neurourology and Urodynamics"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims<\/jats:title><jats:p>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\u2010world data from the FAITH registry (NCT03572231).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2010day 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 \u201cmore effective\u201d if the composite outcome criteria for \u201csuccessful,\u201d \u201cno treatment change,\u201d and \u201csafe\u201d were met, otherwise treatment was deemed \u201cless effective.\u201d To explore the composite algorithm, a total of 14 clinical risk factors were included in the initial data set and a 10\u2010fold cross\u2010validation procedure was performed. A range of machine learning models were evaluated to determine the most effective algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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 \u201cmore effective\u201d group and 258 (65.2%) were in the \u201cless effective\u201d 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\u22120.85) when 15 was used for the min n parameter.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study successfully created a simple, rapid, and easy\u2010to\u2010use interface that could be further refined to produce a valuable educational or clinical decision\u2010making aid.<\/jats:p><\/jats:sec>","DOI":"10.1002\/nau.25190","type":"journal-article","created":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T15:13:25Z","timestamp":1683386005000},"page":"1227-1237","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A treatment prediction strategy for overactive bladder using a machine learning algorithm that utilized data from the FAITH study"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1761-424X","authenticated-orcid":false,"given":"Farid","family":"Hadi","sequence":"first","affiliation":[{"name":"Astellas Pharma Medical Affairs Singapore Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Budiwan","family":"Sumarsono","sequence":"additional","affiliation":[{"name":"Astellas Pharma Medical Affairs Singapore Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0891-2488","authenticated-orcid":false,"given":"Kyu\u2010Sung","family":"Lee","sequence":"additional","affiliation":[{"name":"Samsung Medical Center Sungkyunkwan University School of Medicine Seoul South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seung\u2010June","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Urology, Seoul National University College of Medicine Seoul National University Hospital Seoul South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4691-6159","authenticated-orcid":false,"given":"Sung Tae","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Urology, Hallym University Kangnam Sacred Heart Hospital Hallym University College of Medicine Seoul South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu\u2010Chao","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Urology Linkou Chang Gung Memorial Hospital Taipei Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Rasner","sequence":"additional","affiliation":[{"name":"Urological Department Moscow State University of Medicine and Dentistry Moscow Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cerys","family":"Jenkins","sequence":"additional","affiliation":[{"name":"Human Data Sciences Cardiff UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harry","family":"Fisher","sequence":"additional","affiliation":[{"name":"Human Data Sciences Cardiff UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,5,6]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/nau.22609"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2019.02.038"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/17562872211039034"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1159\/000447205"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2008.06.047"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2012.08.060"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/luts.12382"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2018.03.020"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1742-1241.2010.02626.x"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1464-410X.2009.09036.x"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2017.01.037"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/iju.13422"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1139\/gen-2020-0131"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0000000000003517"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/nau.24881"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cont.2022.100366"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2011.06.020"},{"key":"e_1_2_11_19_1","unstructured":"KuhnM QuinlanR.C50: C5.0 decision trees and rule\u2010based models 2022. 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