PD56-01 IMPROVED PREDICTION OF RENAL FUNCTION AFTER RADICAL NEPHRECTOMY

Abstract

You have accessJournal of UrologyCME1 May 2022PD56-01 IMPROVED PREDICTION OF RENAL FUNCTION AFTER RADICAL NEPHRECTOMY Nityam Rathi, Yosuke Yasuda, Diego Aguilar Palacios, Yunlin Ye, Jianbo Li, Christopher Weight, Mohammed Eltemamy, Robert Abouassaly, and Steven Campbell Nityam RathiNityam Rathi More articles by this author , Yosuke YasudaYosuke Yasuda More articles by this author , Diego Aguilar PalaciosDiego Aguilar Palacios More articles by this author , Yunlin YeYunlin Ye More articles by this author , Jianbo LiJianbo Li More articles by this author , Christopher WeightChristopher Weight More articles by this author , Mohammed EltemamyMohammed Eltemamy More articles by this author , Robert AbouassalyRobert Abouassaly More articles by this author , and Steven CampbellSteven Campbell More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002636.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Radical nephrectomy (RN) is an important consideration for the surgical management of localized renal cell carcinoma (RCC), particularly in cases of increased oncologic potential. However, reduced renal function after RN is a concern. Preoperative global glomerular filtration rate (GFR) and split renal function (SRF) in the contralateral kidney have been shown to be strong predictors of new baseline GFR (NBGFR) post-RN. Recently, software has been developed to perform parenchymal volume analysis (PVA) on preoperative imaging to determine SRF (FUJIFILM Medical Systems). We compare the abilities of two SRF-based models, using either nuclear renal scan (NRS) or PVA to determine SRF, to predict NBGFR after RN. These SRF-based models were also compared with subjective prediction of NBGFR by an experienced urologic oncologist, because some believe that an expert can predict such outcomes accurately and that predictive models may not be needed in this domain. METHODS: All 187 RCC patients who underwent RN (2006-16) with preoperative CT/MRI, NRS, and preop/postoperative GFR estimations were analyzed. NBGFR was defined as GFR 3-12 months after RN. For the two SRF-based approaches, SRF was derived from either NRS or semi-automated PVA software, and renal functional compensation was estimated at 25% based on previous studies. Thus, the formula (Global GFRPre-RN × SRFcontralateral) x 1.25 was used to predict NBGFR. For the “Subjective Approach”, a blinded, independent urologic oncologist provided predictions of NBGFR based on preoperative CT/MRI, baseline global GFR, tumor features, and patient age/comorbidities. Predictive accuracies were assessed by correlation coefficients (r). RESULTS: The r for the Subjective Assessment, NRS/SRF-based, and PVA/SRF-based approaches were 0.72, 0.72, and 0.85, respectively (Fig. 1). An SRF-based model using PVA to determine SRF performed better than NRS/SRF-based and Subjective approaches (both p < 0.05). CONCLUSIONS: Software-based PVA more accurately predicts NBGFR after RN than NRS and Subjective Assessment. The readily-available, inexpensive PVA software provides a precise and accurate estimation of SRF from routine preoperative CT/MRI. Clinically, this practical and innovative approach can contribute to the assessment of the merits and risks of RN versus partial nephrectomy in complex RCC cases. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e953 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nityam Rathi More articles by this author Yosuke Yasuda More articles by this author Diego Aguilar Palacios More articles by this author Yunlin Ye More articles by this author Jianbo Li More articles by this author Christopher Weight More articles by this author Mohammed Eltemamy More articles by this author Robert Abouassaly More articles by this author Steven Campbell More articles by this author Expand All Advertisement PDF DownloadLoading ...