Conrad M Tobert1, Allen Shoemaker2, Richard J Kahnoski1, Brian R Lane3. 1. Urology, Michigan State University College of Human Medicine, Grand Rapids, MI; Urology, Spectrum Health Hospital System, Grand Rapids, MI. 2. Department of Biostatistics, Grand Rapids Medical Education Partners, Grand Rapids, MI. 3. Urology, Michigan State University College of Human Medicine, Grand Rapids, MI; Urology, Spectrum Health Hospital System, Grand Rapids, MI. Electronic address: brian.lane@spectrumhealth.org.
Abstract
OBJECTIVE: To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. PATIENTS AND METHODS: Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a "second-generation model." RESULTS: In multivariable analysis, each scoring system was a significant predictor of PN vs. RN (P<0.0001). Of the first-generation systems, CI was most highly correlated with surgery type (area under the curve [AUC] = 0.91), followed by RNS (AUC = 0.90) and PC (AUC = 0.88). Each individual component of these scoring systems was also a predictor of surgery type (P<0.0001). In a multivariable model incorporating each component individually, 4 were independent predictors of surgery type (each P<0.005): tumor size (RNS and PC), nearness to the collecting system (RNS), location along the lateral rim (PC), and centrality (CI). A novel model in which these 4 variables were rescaled outperformed each first-generation system (AUC = 0.91). CONCLUSIONS: Optimization of first-generation models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-generation model should aid comprehension, but future work is still needed to establish the most clinically useful model.
OBJECTIVE: To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. PATIENTS AND METHODS: Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a "second-generation model." RESULTS: In multivariable analysis, each scoring system was a significant predictor of PN vs. RN (P<0.0001). Of the first-generation systems, CI was most highly correlated with surgery type (area under the curve [AUC] = 0.91), followed by RNS (AUC = 0.90) and PC (AUC = 0.88). Each individual component of these scoring systems was also a predictor of surgery type (P<0.0001). In a multivariable model incorporating each component individually, 4 were independent predictors of surgery type (each P<0.005): tumor size (RNS and PC), nearness to the collecting system (RNS), location along the lateral rim (PC), and centrality (CI). A novel model in which these 4 variables were rescaled outperformed each first-generation system (AUC = 0.91). CONCLUSIONS: Optimization of first-generation models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-generation model should aid comprehension, but future work is still needed to establish the most clinically useful model.
Authors: Massimiliano Spaliviero; Bing Ying Poon; Christoph A Karlo; Giuliano B Guglielmetti; Pier Luigi Di Paolo; Renato Beluco Corradi; Alexandre G Martin-Malburet; Felix Campos-Juanatey; Eva Escudero-Fontano; Daniel D Sjoberg; Paul Russo; Jonathan A Coleman; Oguz Akin; Karim A Touijer Journal: Eur Urol Date: 2015-08-20 Impact factor: 20.096