MP18-08 NOVEL MACHINE LEARNING ALGORITHMS ARE ACCURATE AND NONINFERIOR TO HUMAN EXPERTS IN PREDICTING SUBJECTIVE PATIENT OUTCOMES FOLLOWING THIRD LINE THERAPY FOR OVERACTIVE BLADDER

Abstract

You have accessJournal of UrologyCME1 May 2022MP18-08 NOVEL MACHINE LEARNING ALGORITHMS ARE ACCURATE AND NONINFERIOR TO HUMAN EXPERTS IN PREDICTING SUBJECTIVE PATIENT OUTCOMES FOLLOWING THIRD LINE THERAPY FOR OVERACTIVE BLADDER Glenn T. Werneburg, Eric Werneburg, Howard Goldman, Andrew Mullhaupt, and Sandip Vasavada Glenn T. WerneburgGlenn T. Werneburg More articles by this author , Eric WerneburgEric Werneburg More articles by this author , Howard GoldmanHoward Goldman More articles by this author , Andrew MullhauptAndrew Mullhaupt More articles by this author , and Sandip VasavadaSandip Vasavada More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002551.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We demonstrated machine learning (ML) algorithms accurately predict objective outcomes following third-line treatment for overactive bladder (OAB). Importantly, objective improvements (e.g. leak number) do not necessarily translate to subjective improvements, and thus here we developed ML algorithms to predict patient-reported subjective improvement in bladder leakage and function following OnabotulinumtoxinA (OBTX-A) or sacral neuromodulation (SNM) for OAB. We hypothesized algorithms would exhibit noninferiority to human experts. METHODS: ROSETTA datasets for OAB patients randomized to OBTX-A or SNM were obtained from NICHD. Novel ML algorithms, using reproducing kernel techniques, were trained and tasked to predict improvement in bladder leakage and function measured by Patient Global Impression of Improvement (PGI-I), a robust and valid patient-reported outcome measure of improvement following OAB treatment in a subset of patients in which algorithms were blinded to outcomes. Blinded expert FPMRS urologists given the same data also predicted outcomes. ROC curves with AUC and 95% CI were generated. RESULTS: Top algorithms exhibited excellent predictive accuracy for bladder function improvement in patients who underwent both OBTX-A (yellow; AUC 0.86, CI: 0.77–0.99, figure left) and SNM (AUC 0.96, CI: 0.88–1.0, figure right), and were noninferior to expert FPMRS urologists (purple and blue; Figure left: OBTX-A; Figure right: SNM). Top algorithms also exhibited excellent predictive accuracy for bladder leakage improvement in patients who underwent both OBTX-A (AUC 0.75, CI: 0.58–0.90) and SNM (AUC 0.80, CI: 0.62–0.98), and were noninferior to experts. CONCLUSIONS: Novel ML algorithms were accurate and noninferior to experts in prediction of patient-reported subjective improvements in bladder function and leakage in OAB. Objective outcomes do not necessarily translate to subjective improvement, and thus patient-reported outcomes are an optimal outcome measure. ML algorithms generated using the described novel techniques may complement clinical judgement in patient counseling, and their generalizability to other scenarios within and outside urology is promising and warrants investigation. Source of Funding: N/A © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e298 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Glenn T. Werneburg More articles by this author Eric Werneburg More articles by this author Howard Goldman More articles by this author Andrew Mullhaupt More articles by this author Sandip Vasavada More articles by this author Expand All Advertisement PDF DownloadLoading ...