Alessandro Antonelli1, Andrea Mari2, Alessandro Tafuri1, Riccardo Tellini2, Umberto Capitanio3, Paolo Gontero4, Antonio Andrea Grosso2, Vincenzo Li Marzi5,4, Nicola Longo6, Francesco Porpiglia7, Angelo Porreca8, Bernardo Rocco9, Claudio Simeone10, Riccardo Schiavina11,12, Luigi Schips13, Salvatore Siracusano1, Carlo Terrone14, Vincenzo Ficarra15, Marco Carini2, Andrea Minervini2. 1. Department of Urology, Azienda Ospedaliera Universitaria Integrata, Verona, Italy. 2. Unit of Oncologic Minimally-Invasive Urology and Andrology, Department of Experimental and Clinical Medicine, Careggi Hospital, University of Florence, Florence, Italy. 3. Unit of Urology, Division of Experimental Oncology, Urological Research Institute, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Milan, Italy. 4. Division of Urology, Department of Surgical Sciences, San Giovanni Battista Hospital, University of Studies of Torino, Turin, Italy. 5. Unit of Urological Minimally Invasive Robotic Surgery and Renal Transplantation, Department of Experimental and Clinical Medicine, Careggi Hospital, University of Florence, Florence, Italy. 6. Department of Urology, University Federico II of Naples, Naples, Italy. 7. Division of Urology, Department of Oncology, School of Medicine, San Luigi Gonzaga Hospital, Turin, Italy. 8. Department of Robotic Urologic Surgery, Abano Terme Hospital, Abano Terme, Italy. 9. Urology Department, University of Modena and Reggio Emilia, Modena, Italy. 10. Department of Urology, Spedali Civili Hospital, University of Brescia, Brescia, Italy. 11. Department of Urology, University of Bologna, Bologna, Italy. 12. Department of Experimental, Diagnostic, and Specialty Medicine, University of Bologna, Bologna, Italy. 13. Department of Medical, Oral and Biotechnological Sciences, G. d'Annunzio University of Chieti, Urology Unit, SS. Annunziata Hospital, Chieti, Italy. 14. Department of Urology, Policlinico San Martino Hospital, University of Genova, Genova, Italy. 15. Department of Human and Paediatric Pathology, Gaetano Barresi, Urologic Section, University of Messina, Messina, Italy.
Abstract
OBJECTIVES: Martini et al. developed a nomogram to predict significant (>25%) renal function loss after robot-assisted partial nephrectomy and identified four risk categories. We aimed to externally validate Martini's nomogram on a large, national, multi-institutional data set including open, laparoscopic, and robot-assisted partial nephrectomy. METHODS: Data of 2584 patients treated with partial nephrectomy for renal masses at 26 urological Italian centers (RECORD2 project) were collected. Renal function was assessed at baseline, on third postoperative day, and then at 6, 12, 24, and 48 months postoperatively. Multivariable models accounting for variables included in the Martini's nomogram were applied to each approach predicting renal function loss at all the specific timeframes. RESULTS: Multivariable models showed high area under the curve for robot-assisted partial nephrectomy at 6- and 12-month (87.3% and 83.6%) and for laparoscopic partial nephrectomy (83.2% and 75.4%), whereas area under the curves were lower in open partial nephrectomy (78.4% and 75.2%). The predictive ability of the model decreased in all the surgical approaches at 48 months from surgery. Each Martini risk group showed an increasing percentage of patients developing a significant renal function reduction in the open, laparoscopic and robot-assisted partial nephrectomy group, as well as an increased probability to develop a significant estimated glomerular filtration rate reduction in the considered time cutoffs, although the predictive ability of the classes was <70% at 48 months of follow-up. CONCLUSIONS: Martini's nomogram is a valid tool for predicting the decline in renal function at 6 and 12 months after robot-assisted partial nephrectomy and laparoscopic partial nephrectomy, whereas it showed a lower performance at longer follow-up and in patients treated with open approach at all these time cutoffs.
OBJECTIVES: Martini et al. developed a nomogram to predict significant (>25%) renal function loss after robot-assisted partial nephrectomy and identified four risk categories. We aimed to externally validate Martini's nomogram on a large, national, multi-institutional data set including open, laparoscopic, and robot-assisted partial nephrectomy. METHODS: Data of 2584 patients treated with partial nephrectomy for renal masses at 26 urological Italian centers (RECORD2 project) were collected. Renal function was assessed at baseline, on third postoperative day, and then at 6, 12, 24, and 48 months postoperatively. Multivariable models accounting for variables included in the Martini's nomogram were applied to each approach predicting renal function loss at all the specific timeframes. RESULTS: Multivariable models showed high area under the curve for robot-assisted partial nephrectomy at 6- and 12-month (87.3% and 83.6%) and for laparoscopic partial nephrectomy (83.2% and 75.4%), whereas area under the curves were lower in open partial nephrectomy (78.4% and 75.2%). The predictive ability of the model decreased in all the surgical approaches at 48 months from surgery. Each Martini risk group showed an increasing percentage of patients developing a significant renal function reduction in the open, laparoscopic and robot-assisted partial nephrectomy group, as well as an increased probability to develop a significant estimated glomerular filtration rate reduction in the considered time cutoffs, although the predictive ability of the classes was <70% at 48 months of follow-up. CONCLUSIONS: Martini's nomogram is a valid tool for predicting the decline in renal function at 6 and 12 months after robot-assisted partial nephrectomy and laparoscopic partial nephrectomy, whereas it showed a lower performance at longer follow-up and in patients treated with open approach at all these time cutoffs.