PD12-04 PREDICTING RENAL FUNCTION AFTER RADICAL NEPHRECTOMY: THE IMPORTANCE OF SPLIT RENAL FUNCTION

Abstract

You have accessJournal of UrologyKidney Cancer: Localized: Surgical Therapy I (PD12)1 Sep 2021PD12-04 PREDICTING RENAL FUNCTION AFTER RADICAL NEPHRECTOMY: THE IMPORTANCE OF SPLIT RENAL FUNCTION Nityam Rathi, Diego Palacios, Hajime Tanaka, Yunlin Ye, Jianbo Li, Robert Abouassaly, and Steven Campbell Nityam RathiNityam Rathi More articles by this author , Diego PalaciosDiego Palacios More articles by this author , Hajime TanakaHajime Tanaka More articles by this author , Yunlin YeYunlin Ye More articles by this author , Jianbo LiJianbo Li 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.0000000000001987.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Accurate prediction of new baseline glomerular filtration rate (NBGFR) after radical nephrectomy (RN) has significant clinical implications for renal cell carcinoma (RCC) management, including decisions about RN versus partial nephrectomy. Current models to predict NBGFR post-RN are complex and omit two important parameters: split renal function (SRF) and renal functional compensation (RFC, also known as compensatory hypertrophy). We propose a simple model to predict NBGFR based solely on preoperative GFR in the contralateral kidney and RFC, and compare its predictive accuracy against a previously published, externally validated non-SRF-based model. METHODS: All 272 RCC patients who underwent RN (2006-16) with preoperative nuclear renal scans and necessary functional data were included. Each patient had estimations of global preoperative and postoperative GFR based on CKD-EPI. NBGFR was defined as GFR 3-12 months after RN. RFC was calculated as percent change in GFR of the preserved kidney after RN. Our SRF-based model was: Predicted NBGFR=Global GFRPre-RN×SRF×RFC, with RFC representing an average for all 272 patients. A recently published non-SRF-based model was also assessed on the same cohort: Predicted NBGFR=35+preoperative GFR (× 0.65) – 18 – age (× 0.25)+3 (if tumor size >7 cm). Alignment between predicted and observed NBGFR was assessed for each model by the correlation coefficient (r) obtained from linear regression analysis. A bootstrapping method was conducted to compare the two r values. RESULTS: Average RFC among the 272 patients was a 24% increase in function at 3-12 months. The correlation coefficients for the SRF-based model and the non-SRF-based model were 0.82 (95% CI: 0.77-0.86) and 0.77 (95% CI: 0.69-0.81), respectively (figure 1). The difference between correlation coefficients was statistically significant (p=0.03). CONCLUSIONS: Incorporation of SRF and RFC generates a simple model to predict NBGFR after RN that can readily be implemented in clinical practice. SRF can be determined at point of care using software that provides differential parenchymal volume analysis, which is more accurate than nuclear renal scans. The SRF-based model demonstrates greater predictive accuracy than the non-SRF-based model. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e204-e204 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nityam Rathi More articles by this author Diego Palacios More articles by this author Hajime Tanaka More articles by this author Yunlin Ye More articles by this author Jianbo Li More articles by this author Robert Abouassaly More articles by this author Steven Campbell More articles by this author Expand All Advertisement Loading ...