Currently, there is no single agreement upon an ideal predictive model that characterizes the complexity of renal stones and predicts surgical outcomes following percutaneous nephrolithotomy (PCNL). New predictive tools have recently emerged to systematically and quantitatively assess kidney stone complexity to predict outcomes following PCNL: the Guy's Stone Score, the CROES nomogram, S.T.O.N.E. nephrolithometry, and S-ReSC score. An ideal scoring system should include variables that both influence surgical planning and are predictive of postoperative outcomes. This review discusses the strengths, weaknesses, and commonalities of each of the above scoring systems. Additionally, we propose future directions for the development and analysis of surgical treatment for stone disease, namely, the importance of assessing radiation exposure and patient quality of life when counseling patients on treatment options.
Reviews in urology. 2016 [Epub]
Simone L Vernez, Zhamshid Okhunov, Piruz Motamedinia, Vincent Bird, Zeph Okeke, Arthur Smith
Department of Urology, University of California, Irvine, Irvine, CA., Department of Urology, University of California, Irvine, Irvine, CA., Department of Urology, Yale School of Medicine, New Haven, CT., Department of Urology, University of Florida, Gainesville, FL., The Arthur DM Smith Institute for Urology, North Shore-LIJ Health System, New Hyde Park, NY., The Arthur DM Smith Institute for Urology, North Shore-LIJ Health System, New Hyde Park, NY.