Optimizing prediction of new-baseline glomerular filtration rate after radical nephrectomy: are algorithms really necessary?

Abstract

Radical nephrectomy (RN) is an important consideration for the management of localized renal-cell-carcinoma (RCC) whenever the tumor appears aggressive, although reduced renal function is a concern. Split-renal-function (SRF) in the contralateral kidney and postoperative renal functional compensation (RFC) are fundamentally important for the accurate prediction of new baseline GFR (NBGFR) post-RN. SRF can be estimated either from nuclear renal scans (NRS) or from preoperative imaging using parenchymal-volume-analysis (PVA). We compare two SRF-based models for predicting NBGFR after RN with a subjective prediction of NBGFR by an experienced urologic-oncologist. 187 RCC patients managed with RN (2006-16) were included based on the availability of preoperative CT/MRI and NRS, and preoperative/postoperative eGFR. NBGFR was defined as the final GFR 3-12months post-RN. For the SRF-based approaches, SRF was derived from either NRS or PVA, and RFC was estimated at 25% based on previous independent analyses. Thus, the formula (Global GFRPre-RN × SRFcontralateral) × 1.25 was used to predict NBGFR after RN. For subjective-assessment, a blinded, independent urologic oncologist provided NBGFR predictions based on preoperative eGFR, CT/MRI, and clinical/tumor characteristics. Predictive accuracies were assessed by correlation coefficients (r). The r values for subjective-assessment, NRS/SRF-based, and PVA/SRF-based approaches were 0.72/0.72/0.85, respectively (p < 0.05). The PVA/SRF-based model also demonstrated significant improvement across other performance parameters. The PVA/SRF-based model more accurately predicts NBGFR post-RN than NRS/SRF-based and Subjective Estimation. PVA software (Fujifilm-medical-systems) is readily available and affordable and provides accurate SRF estimations from routine preoperative imaging. This novel approach may inform clinical management regarding RN/PN for complex RCC cases.