Predicting GFR after radical nephrectomy: the importance of split renal function

03 medical and health sciences 0302 clinical medicine Humans Kidney Carcinoma, Renal Cell Nephrectomy Kidney Neoplasms Glomerular Filtration Rate Retrospective Studies 3. Good health
DOI: 10.1007/s00345-021-03918-9 Publication Date: 2022-01-12T17:02:52Z
ABSTRACT
To evaluate a conceptually simple model to predict new-baseline-glomerular-filtration-rate (NBGFR) after radical nephrectomy (RN) based on split-renal-function (SRF) and renal-functional-compensation (RFC), and to compare its predictive accuracy against a validated non-SRF-based model. RN should only be considered when the tumor has increased oncologic potential and/or when there is concern about perioperative morbidity with PN due to increased tumor complexity. In these circumstances, accurate prediction of NBGFR after RN can be important, with a threshold NBGFR > 45 ml/min/1.73m2 correlating with improved overall survival.236 RCC patients who underwent RN (2010-2012) with preoperative imaging (CT/MRI) and relevant functional data were included. NBGFR was defined as GFR 3-12 months post-RN. SRF was determined using semi-automated software that provides differential parenchymal-volume-analysis (PVA) from preoperative imaging. Our SRF-based model was: Predicted NBGFR = 1.24 (× Global GFRPre-RN) (× SRFContralateral), with 1.24 representing the mean RFC estimate from independent analyses. A non-SRF-based model was also assessed: Predicted NBGFR = 17 + preoperative GFR (× 0.65)-age (× 0.25) + 3 (if tumor > 7 cm)-2 (if diabetes). Alignment between predicted/observed NBGFR was assessed by comparing correlation coefficients and area-under-the-curve (AUC) analyses.The correlation-coefficients (r) were 0.87/0.72 for SRF-based/non-SRF-based models, respectively (p = 0.005). For prediction of NBGFR > 45 ml/min/1.73m2, the SRF-based/non-SRF-based models provided AUC of 0.94/0.87, respectively (p = 0.044).Previous non-SRF-based models to predict NBGFR post-RN are complex and omit two important parameters: SRF and RFC. Our proposed model prioritizes these parameters and provides a conceptually simple, accurate, and clinically implementable approach to predict NBGFR post-RN. SRF can be easily obtained using PVA software that is affordable, readily available (FUJIFILM-Medical-Systems), and more accurate than nuclear-renal-scans. The SRF-based model demonstrates greater predictive-accuracy than a non-SRF-based model, including the clinically-important predictive-threshold of NBGFR > 45 ml/min/1.73m2.
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