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.
Prediction of significant renal function decline after open, laparoscopic, and robotic partial nephrectomy: External validation of the Martini’s nomogram on the RECORD2 project cohort
Gontero, Paolo;Longo, Nicola;Porpiglia, Francesco;Terrone, Carlo;
2022-01-01
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.
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