MP16-01 MACHINE LEARNING MODEL TO PREDICT LIKELIHOOD OF SPONTANEOUS URETERAL STONE PASSAGE

Abstract

You have accessJournal of UrologyCME1 Apr 2023MP16-01 MACHINE LEARNING MODEL TO PREDICT LIKELIHOOD OF SPONTANEOUS URETERAL STONE PASSAGE Katherine Fischer, Abhay Singh, Joey Logan, Benjamin Schurhamer, Brent Cao, Roby Daniel, Ryan McGregor, Iqra Nadeem, Curran Uppaluri, Alice Xiang, Ester Choi, Yuemeng Li, Yong Fan, Justin Ziemba, and Gregory Tasian Katherine FischerKatherine Fischer More articles by this author , Abhay SinghAbhay Singh More articles by this author , Joey LoganJoey Logan More articles by this author , Benjamin SchurhamerBenjamin Schurhamer More articles by this author , Brent CaoBrent Cao More articles by this author , Roby DanielRoby Daniel More articles by this author , Ryan McGregorRyan McGregor More articles by this author , Iqra NadeemIqra Nadeem More articles by this author , Curran UppaluriCurran Uppaluri More articles by this author , Alice XiangAlice Xiang More articles by this author , Ester ChoiEster Choi More articles by this author , Yuemeng LiYuemeng Li More articles by this author , Yong FanYong Fan More articles by this author , Justin ZiembaJustin Ziemba More articles by this author , and Gregory TasianGregory Tasian More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003236.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Standard treatment options for patients with a ureteral stone are a trial of passage or surgical intervention. Prior studies have tried to identify factors that predict the likelihood of spontaneous passage, such as stone size and location, but this remains difficult. Successful prediction of spontaneous passage can help avoid the risks of unnecessary surgery or shorten the symptomatic period in patients that will ultimately require surgery. Our goal was to create a machine learning model incorporating patient clinical and imaging characteristics to accurately predict the likelihood of ureteral stone passage. METHODS: We performed a retrospective cohort study of pediatric and adult patients presenting with ureteral stones on CT scan. Chart review was performed to ascertain patient clinical and imaging characteristics and to determine the primary outcome of spontaneous stone passage. A random forest model was built using these characteristics to predict spontaneous ureteral stone passage. RESULTS: 103 pediatric (median age 14 years, 56% female) and 153 adult (median age 57 years, 35.9% female) patients with confirmed ureteral stones were identified. Spontaneous passage occurred in 54% of pediatric and 44.4% of adult patients. Separate models were created for pediatric and adult patients since different features were important for prediction in each cohort. The pediatric model had an accuracy of 70% (95% CI 67-74%) and the adult model 63% (95% CI 58-66%). Stone area was the most important feature in both models (Figure 2). CONCLUSIONS: We created a machine learning model that predicted ureteral stone passage based on clinical and imaging features with 63-70% accuracy. Our long-term aim is to create a deep learning model that incorporates clinical characteristics and automated segmentation of CT imaging to accurately predict stone passage without the need for human abstraction and input. Our hope is that this will allow for better individualized patient care through early identification of those who will have a successfully trial of passage versus those that require surgical intervention. Source of Funding: NIDDK P20 CHOP/ Penn Center for Machine Learning in Urology (P20DK127488)AUA Care Foundation and SPU Sushil Lacy Research Scholar Award (KMF) © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e201 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Katherine Fischer More articles by this author Abhay Singh More articles by this author Joey Logan More articles by this author Benjamin Schurhamer More articles by this author Brent Cao More articles by this author Roby Daniel More articles by this author Ryan McGregor More articles by this author Iqra Nadeem More articles by this author Curran Uppaluri More articles by this author Alice Xiang More articles by this author Ester Choi More articles by this author Yuemeng Li More articles by this author Yong Fan More articles by this author Justin Ziemba More articles by this author Gregory Tasian More articles by this author Expand All Advertisement PDF downloadLoading ...