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

Nityam Rathi1, Diego A Palacios1, Emily Abramczyk1

  • 1Center for Urologic Oncology, Glickman Urological and Kidney Institute, Cleveland Clinic, Room Q10-120, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.

World Journal of Urology
|January 13, 2022
PubMed
Summary

A new split-renal-function (SRF) model accurately predicts new-baseline-glomerular-filtration-rate (NBGFR) after radical nephrectomy (RN). This SRF-based approach is more accurate than non-SRF models for predicting post-operative renal function.

Keywords:
Functional compensationKidney cancerParenchymal volume analysisRadical nephrectomySplit renal function

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Area of Science:

  • Nephrology
  • Urology
  • Radiology

Background:

  • Radical nephrectomy (RN) necessitates accurate prediction of post-operative renal function.
  • New-baseline-glomerular-filtration-rate (NBGFR) is crucial for patient survival, with NBGFR > 45 ml/min/1.73m² linked to improved outcomes.
  • Existing models for NBGFR prediction are complex and lack key functional parameters.

Purpose of the Study:

  • To evaluate a simple model for predicting NBGFR after RN using split-renal-function (SRF) and renal-functional-compensation (RFC).
  • To compare the predictive accuracy of this SRF-based model against a validated non-SRF-based model.
  • To assess the clinical utility of predicting NBGFR, particularly the threshold of >45 ml/min/1.73m².

Main Methods:

  • A cohort of 236 patients undergoing RN was analyzed.
  • SRF was determined using semi-automated software for differential parenchymal-volume-analysis (PVA) from preoperative CT/MRI.
  • A novel SRF-based model (Predicted NBGFR = 1.24 × Global GFRPre-RN × SRFContralateral) was developed and compared with a non-SRF model.

Main Results:

  • The SRF-based model showed a higher correlation coefficient (r=0.87) compared to the non-SRF model (r=0.72).
  • For predicting NBGFR > 45 ml/min/1.73m², the SRF-based model achieved a superior area under the curve (AUC) of 0.94 versus 0.87 for the non-SRF model.
  • Statistical significance was observed for both correlation coefficients (p=0.005) and AUCs (p=0.044).

Conclusions:

  • The proposed SRF-based model offers a conceptually simple, accurate, and clinically implementable method for NBGFR prediction post-RN.
  • SRF, obtainable via affordable PVA software, is more accurate than nuclear renal scans.
  • The SRF-based model outperforms non-SRF models in predicting NBGFR, including the critical >45 ml/min/1.73m² threshold.