MP62-03 PREDICTING ESWL OUTCOME USING CLASSIFICATION AND REGRESSION TREE (CART) ANALYSIS

Abstract

You have accessJournal of UrologyStone Disease: Shock Wave Lithotripsy1 Apr 2017MP62-03 PREDICTING ESWL OUTCOME USING CLASSIFICATION AND REGRESSION TREE (CART) ANALYSIS Yasuo Kohjimoto, Shimpei Yamashita, Takashi Iguchi, Satoshi Nishizawa, Akinori Iba, Kazuro Kikkawa, and Isao Hara Yasuo KohjimotoYasuo Kohjimoto More articles by this author , Shimpei YamashitaShimpei Yamashita More articles by this author , Takashi IguchiTakashi Iguchi More articles by this author , Satoshi NishizawaSatoshi Nishizawa More articles by this author , Akinori IbaAkinori Iba More articles by this author , Kazuro KikkawaKazuro Kikkawa More articles by this author , and Isao HaraIsao Hara More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.1934AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Previous studies have developed various predictive models for stone-free rate following extracorporeal shock wave lithotripsy (ESWL). However, these models have several limitations such as difficulty in usage and lack of information derived from CT imaging. In this study, we sought to develop a user-friendly predictive model for ESWL outcome using the classification and regression tree (CART) analysis. METHODS We retrospectively reviewed charts of 245 patients who underwent ESWL for upper urinary tract calculi between January 2008 and January 2016. To create the CART decision tree, predictor variables were entered into the software (R version 3.2.2) to classify patients who achieved stone-free after a first session of ESWL. Variables included age, sex, hydronephrosis, urinary drainage, stone location, stone volume, number of stones and three indices based on CT attenuation of the stone, i.e., mean stone density (MSD), standard deviation of stone density (SDSD) and variation coefficient of stone density (VCSD). Stone-free status was determined as absence or residual fragments <4mm using NCCT within three months after a first session of ESWL. RESULTS Overall stone-free rate was 47.8%. In CART analysis, VCSD had the greatest variable importance (100%) followed by MSD (56.7%), stone volume (52.7%) and SDSD (49.3%). A total of five subgroups of patients with distinct stone-free rate were produced by three predictive variables (Figure). CONCLUSIONS We have generated a first CART decision tree for ESWL outcome that included VCSD as a novel and most important factor, as well as previously reported predictors such as MSD and stone volume. This model provides clinician with practical bedside tool for predicting stone-free following ESWL. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e828 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Yasuo Kohjimoto More articles by this author Shimpei Yamashita More articles by this author Takashi Iguchi More articles by this author Satoshi Nishizawa More articles by this author Akinori Iba More articles by this author Kazuro Kikkawa More articles by this author Isao Hara More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...