PURPOSE: Imaging is routinely done preoperatively and postoperatively to assess patients treated with percutaneous nephrolithotomy. We developed a nomogram for percutaneous nephrolithotomy success. MATERIALS AND METHODS: From November 2007 to December 2009 the CROES (Clinical Research Office of the Endourological Society) collected data on consecutive patients at 96 centers globally. Patients were evaluated for stone-free status using plain x-ray of the kidneys, ureters and bladder. Treatment success was defined as no visible stones or residual fragments less than 4 mm. Multivariate regression was used to model the relationship between preoperative descriptors and the stone-free rate. Variables included case load, prior treatment, body mass index, staghorn stones, renal anomalies, and stone burden, location and count. Bootstrapping techniques were used to validate the model. Adjusted chi-square statistic values were used to rank the prognostic value of variables. A nomogram was developed using significant predictors from the model. We assessed the predictive accuracy of the nomogram using the ROC curve AUC. The nomogram was calibrated. RESULTS: Stone burden was the best predictor of the stone-free rate (chi-square = 30.27, p <0.001). Other factors associated with the stone-free rate were case volume (chi-square = 35.75, p <0.001), prior stone treatment (chi-square = 14.55, p <0.012), staghorn stone (adjusted chi-square = 4.73, p <0.029), stone location (chi-square = 14.74, p <0.001) and stone count (chi-square = 4.78, p <0.004). A nephrolithometric nomogram was developed with predictive accuracy (AUC 0.76). CONCLUSIONS: The percutaneous nephrolithotomy stone-free rate can be predicted using preclinical data and radiological information. We present a nephrolithometric nomogram for percutaneous nephrolithotomy.
PURPOSE: Imaging is routinely done preoperatively and postoperatively to assess patients treated with percutaneous nephrolithotomy. We developed a nomogram for percutaneous nephrolithotomy success. MATERIALS AND METHODS: From November 2007 to December 2009 the CROES (Clinical Research Office of the Endourological Society) collected data on consecutive patients at 96 centers globally. Patients were evaluated for stone-free status using plain x-ray of the kidneys, ureters and bladder. Treatment success was defined as no visible stones or residual fragments less than 4 mm. Multivariate regression was used to model the relationship between preoperative descriptors and the stone-free rate. Variables included case load, prior treatment, body mass index, staghorn stones, renal anomalies, and stone burden, location and count. Bootstrapping techniques were used to validate the model. Adjusted chi-square statistic values were used to rank the prognostic value of variables. A nomogram was developed using significant predictors from the model. We assessed the predictive accuracy of the nomogram using the ROC curve AUC. The nomogram was calibrated. RESULTS: Stone burden was the best predictor of the stone-free rate (chi-square = 30.27, p <0.001). Other factors associated with the stone-free rate were case volume (chi-square = 35.75, p <0.001), prior stone treatment (chi-square = 14.55, p <0.012), staghorn stone (adjusted chi-square = 4.73, p <0.029), stone location (chi-square = 14.74, p <0.001) and stone count (chi-square = 4.78, p <0.004). A nephrolithometric nomogram was developed with predictive accuracy (AUC 0.76). CONCLUSIONS: The percutaneous nephrolithotomy stone-free rate can be predicted using preclinical data and radiological information. We present a nephrolithometric nomogram for percutaneous nephrolithotomy.
Authors: Stavros Sfoungaristos; Ofer N Gofrit; Ioannis Mykoniatis; Ezekiel H Landau; Ioannis Katafigiotis; Dov Pode; Constantinos A Constantinides; Mordechai Duvdevani Journal: Int Urol Nephrol Date: 2016-05-09 Impact factor: 2.370
Authors: Ahmed M Harraz; Yasser Osman; Ahmed R El-Nahas; Amr A Elsawy; Islam Fakhreldin; Osama Mahmoud; Ahmed El-Assmy; Ahmed A Shokeir Journal: World J Urol Date: 2016-12-24 Impact factor: 4.226
Authors: Muhammad Waqas Iqbal; Richard H Shin; Ramy F Youssef; Adam G Kaplan; Fernando J Cabrera; Jonathan Hanna; Charles D Scales; Michael N Ferrandino; Glenn M Preminger; Michael E Lipkin Journal: Urolithiasis Date: 2016-05-30 Impact factor: 3.436