The Simplified PADUA REnal (SPARE) nephrometry system: a novel classification of parenchymal renal tumours suitable for partial nephrectomy.

OBJECTIVE: To simplify the original Preoperative Aspects and Dimensions Used for an Anatomical (PADUA) classification of renal tumours, generating a new system able to predict equally or better the risk of overall complications in patients undergoing partial nephrectomy (PN); and to test if the addition of the contact surface area (CSA) parameter improves the accuracy of the original PADUA and new Simplified PADUA REnal (SPARE) nephrometry classification systems. PATIENTS AND METHODS: We analysed the clinical records of 531 patients who underwent PN (open, laparoscopic and robot-assisted) for renal tumours at five tertiary academic referral centres from January 2014 to December 2016. The ability of each variable included in the PADUA classification to predict overall complications was tested using binary logistic regression analysis. The variables that were not statistically significant were excluded from the SPARE classification. In addition to the original PADUA and SPARE systems, another two models were generated adding tumour CSA. Receiver operating characteristic curve analysis was used to compare the ability of the four different models to predict overall complications. Binary logistic regression was used to perform both univariable and multivariable analyses looking for predictors of postoperative complications. Linear regression analysis was used to identify independent predictors of absolute change in estimated glomerular filtration rate (eGFR; ACE). RESULTS: The SPARE nephrometry score system including: (i) rim location, (ii) renal sinus involvement, (iii) exophytic rate, and (iv) tumour dimension; showed equal performance in comparison with the original PADUA score (area under the curve [AUC] 0.657 vs 0.664). Adding tumour CSA to the original PADUA (AUC 0.661) or to the SPARE (AUC 0.658) scores did not increase the accuracy of either system to predict overall complications. The SPARE system (odds ratio 1.2, 95% confidence interval 1.1-1.3) was an independent predictor of postoperative overall complications. Age (P < 0.001), body mass index (P < 0.001), Charlson Comorbidity Index (P = 0.02), preoperative eGFR (P < 0.001), and tumour CSA (P = 0.005) were independent predictors of ACE. Limitations include the retrospective design and the lack of central imaging review. CONCLUSIONS: The new SPARE score is comprised of only four variables instead of the original six and its accuracy to predict overall complications is similar to that of the original PADUA score. Addition of tumour CSA was not associated with an increase in prognostic accuracy. The SPARE system could replace the original PADUA score to evaluate the complexity of tumours suitable for PN.

Tumour contact surface area as a predictor of postoperative complications and renal function in patients undergoing partial nephrectomy for renal tumours.

OBJECTIVES: To evaluate the ability of original tumour contact surface area (CSA) to predict postoperative complications and renal function impairment in a series of patients who underwent elective partial nephrectomy (PN) for renal masses. MATERIALS AND METHODS: We analysed the clinical records of 531 consecutive patients who underwent elective PN because of a suspicion of kidney cancer at five academic, high-volume centres between January 2014 and December 2016. Each participating centre evaluated prospectively the radiological images to evaluate the CSA and to assign a PADUA score. Several expert surgeons performed the surgical procedures in each participating centre. Binary logistic regression was used to perform both univariable and multivariable analyses to identify predictors of postoperative complications. Linear regression analysis was used to identify independent predictors of absolute change in estimated glomerular filtration rate (eGFR; ACE). RESULTS: The median (interquartile range) CSA value was 14.2 (7.4-25.1) cm2 . A total of 349 tumours (65.7%) had a CSA ≤ 20 cm2 and the remaining 182 (34.3%) had a CSA > 20 cm2 . PNs were performed using an open approach in 237 (44.6%) cases, a pure laparoscopic approach in 152 cases (28.6%), and a robot-assisted approach in the remaining 142 cases (26.7%). Multivariable analyses found that only age (odds ratio [OR] 1.037, 95% confidence interval [CI] 1.018-1.057) and PADUA score (OR 1.289, 95%CI 1.132-1.469) were independent predictors of postoperative complications. Tumour CSA (OR 1.020, 95%CI 1.010-1.030) was found to be an independent predictor of postoperative complications only when PADUA score was removed from the model. Age (from -0.639 to -0.306; P < 0.001); body mass index (from 0.267 to 1.076; P = 0.001), age-adjusted Charlson score (from -3.193 to -0.259; P = 0.02), preoperative eGFR value (from -0.939 to -0.862; P < 0.001) and tumour CSA (from -0.260 to -0.048; P = 0.005) were found to be independent predictors of ACE. CONCLUSIONS: Tumour CSA is an independent predictor of postoperative renal function. Conversely, at multivariable analysis, PADUA score outperformed tumour CSA to predict postoperative complications after PN. The complexity of The Leslie et al. formula for calculating tumour CSA is a potential limitation with regard to its diffusion and application in clinical practice.

Three-dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores.

OBJECTIVES: To apply the standard PADUA and RENAL nephrometry score variables to three-dimensional (3D) virtual models (VMs) produced from standard bi-dimensional imaging, thereby creating three-dimensional (3D)-based (PADUA and RENAL) nephrometry scores/categories for the reclassification of the surgical complexity of renal masses, and to compare the new 3D nephrometry score/category with the standard 2D-based nephrometry score/category, in order to evaluate their predictive role for postoperative complications. MATERIALS AND METHODS: All patients with localized renal tumours scheduled for minimally invasive partial nephrectomy (PN) between September 2016 and September 2018 underwent 3D and 2D nephrometry score/category assessments preoperatively. After nephrometry score/category evaluation, all the patients underwent surgery. Chi-squared tests were used to evaluate the individual patients' grouping on the basis of the imaging tool (3D VMs and 2D imaging) used to assess the nephrometry score/category, while Cohen's κ coefficient was used to test the concordance between classifications. Receiver-operating characteristic curves were produced to evaluate the sensitivity and specificity of the 3D nephrometry score/category vs the 2D nephrometry score/category in predicting the occurrence of postoperative complications. A general linear model was used to perform multivariable analyses to identify predictors of overall and major postoperative complications. RESULTS: A total of 101 patients were included in the study. The evaluation of PADUA and RENAL nephrometry scores via 3D VMs showed a downgrading in comparison with the same scores evaluated with 2D imaging in 48.5% and 52.4% of the cases. Similar results were obtained for nephrometry categories (29.7% and 30.7% for PADUA risk and RENAL complexity categories, respectively). The 3D nephrometry score/category demonstrated better accuracy than the 2D nephrometry score/category in predicting overall and major postoperative complications (differences in areas under the curve for each nephrometry score/category were statistically significant comparing the 3D VMs with 2D imaging assessment). Multivariable analyses confirmed 3D PADUA/RENAL nephrometry category as the only independent predictors of overall (P = 0.007; P = 0.003) and major postoperative complications (P = 0.03; P = 0.003). CONCLUSIONS: In the present study, we showed that 3D VMs were more precise than 2D standard imaging in evaluating the surgical complexity of renal masses according to nephrometry score/category. This was attributable to a better perception of tumour depth and its relationships with intrarenal structures using the 3D VM, as confirmed by the higher accuracy of the 3D VM in predicting postoperative complications.

RPN (Radius, Position of tumour, iNvasion of renal sinus) Classification and Nephrometry Scoring System: An Internationally Developed Clinical Classification To Describe the Surgical Difficulty for Renal Masses for Which Robotic Partial Nephrectomy Is Planned.

Background: The surgical difficulty of partial nephrectomy (PN) varies depending on the operative approach. Existing nephrometry classifications for assessment of surgical difficulty are not specific to the robotic approach. Objective: To develop an international robotic-specific classification of renal masses for preoperative assessment of surgical difficulty of robotic PN. Design setting and participants: The RPN classification (Radius, Position of tumour, iNvasion of renal sinus) considers three parameters: tumour size, tumour position, and invasion of the renal sinus. In an international survey, 45 experienced robotic surgeons independently reviewed de-identified computed tomography images of 144 patients with renal tumours to assess surgical difficulty of robot-assisted PN using a 10-point Likert scale. A separate data set of 248 patients was used for external validation. Outcome measurements and statistical analysis: Multiple linear regression was conducted and a risk score was developed after rounding the regression coefficients. The RPN classification was correlated with the surgical difficulty score derived from the international survey. External validation was performed using a retrospective cohort of 248 patients. RPN classification was also compared with the RENAL (Radius; Exophytic/endophytic; Nearness; Anterior/posterior; Location), PADUA (Preoperative Aspects and Dimensions Used for Anatomic), and SPARE (Simplified PADUA REnal) scoring systems. Results and limitation: The median tumour size was 38 mm (interquartile range 27-49). The majority (81%) of renal tumours were peripheral, followed by hilar (12%) and central (7.6%) locations. Noninvasive and semi-invasive tumours accounted for 37% each, and 26% of the tumours were invasive. The mean surgical difficulty score was 5.2 (standard deviation 1.9). Linear regression analysis indicated that the RPN classification correlated very well with the surgical difficulty score (R2 = 0.80). The R2 values for the other scoring systems were: 0.66 for RENAL, 0.75 for PADUA, and 0.70 for SPARE. In an external validation cohort, the performance of all four classification systems in predicting perioperative outcomes was similar, with low R2 values. Conclusions: The proposed RPN classification is the first nephrometry system to assess the surgical difficulty of renal masses for which robot-assisted PN is planned, and is a useful tool to assist in surgical planning, training and data reporting. Patient summary: We describe a simple classification system to help urologists in preoperative assessment of the difficulty of robotic surgery for partial kidney removal for kidney tumours.

Predicting Complications After Robotic Partial Nephrectomy: Back to Simplicity.

BACKGROUND: Robotic partial nephrectomy (RPN) has a significant morbidity. Nephrometry scores have been described to predict the occurrence of complications. Their usefulness is debated. OBJECTIVE: To evaluate the clinical utility of three nephrometry scores (radius, exophytic/endophytic, nearness, anterior/posterior, location [RENAL], preoperative aspects and dimensions used for an anatomical [PADUA], and simplified PADUA Renal [SPARE]) to predict perioperative outcomes and compare their performance to the simple measurement of tumor size in a large cohort of patients who underwent RPN. DESIGN, SETTING, AND PARTICIPANTS: We analyzed 1581 consecutive patients who underwent RPN for small renal masses. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Tumor size, RENAL, PADUA, and SPARE scores were calculated based on preoperative imaging. Correlation between scores, estimated blood loss (EBL), operative time (OT), and warm ischemia time (WIT) were calculated. Logistic regression analyses were performed to identify predictors of overall and major complications. The area under the curve was used to identify models with the highest discrimination. Decision curve analyses determined the net benefit associated with their use. RESULTS AND LIMITATIONS: The median age was 62 yr (interquartile range [IQR]: 52-70) and the median tumor size was 35 mm (IQR: 25-47). Postoperative complications were observed in 346 patients (21.9%), including 5.6% of major complications. All scores were significantly correlated with EBL, OT, and WIT. However, correlation coefficients were all <0.3, suggesting a weak association. Nephrometry scores and tumor size were significant predictors of overall complications in univariate and adjusted multivariable logistic regression model analysis. However, decision curve analysis demonstrated net benefit of tumor size comparable with all nephrometry scores. Finally, neither nephrometry scores nor tumor size was found to be associated with the risk of major complications. CONCLUSIONS: Tumor size has the same ability as nephrometry scores to predict perioperative outcomes of RPN. PATIENT SUMMARY: We evaluated the association between tumor size, nephrometry scores, and perioperative outcomes of robotic partial nephrectomy (RPN). We found that tumor size could predict perioperative outcomes of RPN as well as nephrometry scores.

What Is Better for Predicting Morbidity of Robotic Partial Nephrectomy-A Score or Your Clinical Judgement?

BACKGROUND: Little is known about the predictive value of surgeon's judgement to estimate perioperative outcomes following robotic partial nephrectomy (RPN). OBJECTIVE: To evaluate the accuracy of surgeon's intuition to estimate perioperative outcomes of patients undergoing RPN and compare its predictive value with that of objective scoring systems. DESIGN, SETTING, AND PARTICIPANTS: We prospectively analysed 100 consecutive patients who underwent RPN. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: RENAL, PADUA, and MAP scores were calculated based on preoperative imaging. The surgeon gave a subjective estimation of the technical difficulty and the risk of postoperative complications of RPN immediately before and after surgery using a visual analogue scale (VAS). Correlation between scores, VAS, estimated blood loss (EBL), operative time (OT), and warm ischaemia time (WIT) were examined. Logistic regression analyses were performed to identify the best predictors of overall complications. Receiver operating characteristic (ROC) curve analysis was used to assess the accuracy of VAS and scoring systems to predict trifecta achievement. RESULTS AND LIMITATIONS: RENAL, PADUA, and MAP scores significantly correlated with surgeon's pre- and postoperative VAS evaluation, with the RENAL score showing the strongest correlation (r=0.49 and r=0.34, respectively). Pre- and postoperative VAS scores had the strongest correlation with EBL (r=0.48 and r=0.59, respectively), OT (r=0.44 and r=0.65, respectively), and WIT (r=0.37 and r=0.47, respectively). In multivariate analysis adjusted for anticoagulant/antiplatelet treatment, body mass index, surgeon's experience, and Charlson comorbidity index, only surgeon's prediction could significantly predict overall complications (odds ratio=5.42, p <0.001). Finally, surgeon's intuition was better to predict trifecta accomplishment than all radiological scores (ROC areas under the curves were 0.76 and 0.77 for pre- and postoperative VAS scores, respectively). CONCLUSIONS: Surgeon's clinical assessment is a good predictor of perioperative outcomes of RPN and seems to perform better than conventional scores. PATIENT SUMMARY: In this report, we found that surgeon's clinical feeling can better predict perioperative morbidity of robotic partial nephrectomy than conventional radiological scores.

Contact surface area and its association with outcomes in robotic-assisted partial nephrectomy.

BACKGROUND: Renal tumor scoring systems, such as the contact surface area value, aim to assist in predicting outcomes following robotic-assisted partial nephrectomy. The aim of this study is to identify associations between specific postoperative outcomes and the contact surface area of renal masses. METHODS: We analyzed 332 consecutive robotic-assisted partial nephrectomies and calculated contact surface area for renal tumors with the contact surface area formula (CSA = 2πrd), where π ≈ 3.14, r = greatest tumor radius (cm), and d = greatest tumor depth (cm). RESULTS: Higher contact surface area was associated with longer warm ischemia time (P < .001), higher estimated blood loss (P < .001), and longer length of hospital stay (LOS) (P < .001). Higher contact surface area was significantly associated with decreased renal function at 1 day, 1 month, and 6 months following robotic-assisted partial nephrectomy. CONCLUSIONS: Contact surface area is associated with certain outcomes following robotic-assisted partial nephrectomy and may be a useful predictive tool.

A preoperative nomogram to predict major complications after robot assisted partial nephrectomy (UroCCR-57 study).

OBJECTIVE: To generate a nomogram based on preoperative parameters to predict the occurrence of a major complication within 30-days of robotic partial nephrectomy. MATERIALS AND METHODS: The study included 1,342 patients with a clinically localized renal tumor who underwent robotic partial nephrectomy (RPN) between 2010 and 2017 at 7 academic centers. The primary outcome was the major complication rate. A multivariable logistic regression model was fitted to predict the risk of major complications after RPN. Model-derived coefficients were used to calculate the risk of major complications. Local regression smoothing technique was used to plot the observed rate against the predicted risk of major complications. RESULTS: In multivariate logistic regression, male gender (odds ratio [OR]: 2.93; P = 0.03), Charlson comorbidity index (OR: 1.13; P = 0.05), ECOG PS (OR: 1.66; P = 0.02), low hospital volume (P < 0.05), and high RENAL score (OR: 4.73; P = 0.01) were significant predictors of major postoperative complications. A preoperative nomogram incorporating these risk factors was constructed with an area under curve of 75%. CONCLUSIONS: Using standard preoperative variables from this multi-institutional RPN experience, we constructed and validated a nomogram to predict postoperative complications after RPN. We believe this tool can be relevant to help weighing treatment options for a more tailored management of patients with small renal masses.

Predicting morbidity after robotic partial nephrectomy: The effect of tumor, environment, and patient-related factors.

PURPOSE: To investigate the effect of tumor and nontumor related parameters on perioperative outcomes of robotic partial nephrectomy (RPN). PATIENTS AND METHODS: Patients who underwent RPN for a localized renal tumor at 2 institutions between June 2010 and November 2016 were reviewed. RENAL and Mayo adhesive probability (MAP) scores were calculated and information on comorbid conditions including ASA score, performance status, Charlson's comorbidity index (CCI), and history of cardiovascular disease was collected. Correlations between each variable and warm ischemia time, estimated blood loss (EBL), operative time, change in estimated glomerular filtration rate, and length of hospital stay were assessed. Logistic regression analyses were performed to identify the best predictors of overall complications, major complications, risk of conversion, and Trifecta achievement. RESULTS: A total of 500 patients were included. RENAL score was found to have a statistically significant (P<0.05) correlation with warm ischemia time, EBL, and change in estimated glomerular filtration rate. MAP score showed significant association (P<0.05) with operative time and EBL. CCI had a significant correlation (P<0.05) with length of hospital stay and postoperative complications. In multivariable analyses, MAP score as a continuous variable (OR = 7.66; P<0.001) and MAP risk group stratification (OR = 3.29; P = 0.005) were independent predictors of the risk of conversion. Major complications were significantly associated with the cardiovascular disease in both univariable (OR = 2.35; P = 0.01) and multivariable analysis (OR = 4.52, P = 0.01). Finally, the MAP score as a continuous variable was an independent factor of Trifecta achievement (OR = 0.56; P = 0.04). CONCLUSION: Patients related factors were the most important determinants of postoperative complications after RPN. RENAL and MAP scores had some influence on intraoperative parameters.

Nephrometry Scores: The Effect of Imaging on Routine Read-out and Prediction of Outcome of Nephron-sparing Surgery.

BACKGROUND/AIM: This study investigated the impact of available preoperative imaging on the reliability and predictive accuracy of RENAL and PADUA nephrometry-scoring systems for renal tumors. PATIENTS AND METHODS: Five urologists determined RENAL and PADUA scores using preoperative imaging data (computed tomography and magnetic resonance imaging) of 100 patients admitted for partial nephrectomy with the following combinations: T0: transverse planes without excretory phase (EP), TC0: transverse and coronal planes without EP, TC1: transverse and coronal planes with EP. Reference standard was obtained by a uro-radiologist. Ischemia time was used as surrogate for surgical complexity. RESULTS: Assignment of EP significantly reduced interobserver-variability among urologists (p<0.0001). Predictive accuracy for surgical complexity or correct assignment to nephrometry risk groups did not depend on image planes or EP. CONCLUSION: Interobserver variability, but not predictive accuracy of nephrometric systems, is affected by additional usage of EP.

External Validation of the Arterial-Based Complexity Score and First Head-to-Head Comparison With the R.E.N.A.L. and PADUA Scores and C-index.

INTRODUCTION: We performed an external validation of the arterial-based complexity (ABC) score using a head-to-head comparison with the R.E.N.A.L. (radius, exophytic/endophytic tumor properties, nearness of tumor to deepest portion of collecting system or sinus, anterior/posterior descriptor, location relative to the polar line), PADUA (preoperative aspects and dimension for anatomic classification of renal tumors), and C-index scores for the prediction of surgical outcomes after partial nephrectomy. MATERIALS AND METHODS: The data from a series of consecutive open or robot-assisted partial nephrectomies performed from January 2014 to July 2016 by 4 expert surgeons at a tertiary academic institution were reviewed. After dedicated training, 1 urologist not involved in the surgical procedures evaluated the cross-sectional imaging studies and assigned the nephrometry score using the 4 nephrometry scoring systems. The predictive performance of the ABC and other scoring systems was tested in univariate and multivariable fashion. RESULTS: Overall, 234 patients were recruited (148 men and 86 women; age, 63 ± 10.9 years). The scores were all related to the estimated blood loss, use of hilar clamping, ischemia time, operative time, length of stay, and MIC (margin status, ischemia time, complications) score. They were not related to the occurrence of postoperative complications or, for the C-index and ABC score, the length of stay. In a head-to-head comparison, the ABC was not inferior only to the C-index relative to the occurrence of complications and MIC score, with borderline statistical significance. On multivariate analysis, the ABC score provided significant improvement only for the prediction of the operative and ischemia times. However, its performance was inferior to that of the other scoring systems. In addition, only the PADUA score improved the prediction of artery clamping and MIC score, and only the R.E.N.A.L. score showed an advantage for the prediction of the estimated blood loss. CONCLUSION: The predictive ability of ABC was inferior to that of well-established existing nephrometry scoring systems, such as the PADUA and R.E.N.A.L. scores.

External validation of four nephrometry scores for trans-peritoneal robotic partial nephrectomy.

INTRODUCTION: External validation of four nephrometry scores (NS): Centrality index (C-index), arterial based complexity (ABC), preoperative aspects and dimensions used for an anatomical (PADUA) and radius expohytic/endophytic nearness anterior/posterior location (RENAL) scoring systems in patients who have undergone trans-peritoneal robotic assisted partial nephrectomy (RAPN). MATERIAL AND METHODS: A prospective database for RAPN has been maintained. Individual NSs were performed on 3-dimensional reconstructions of MDCT/MRI studies retrospectively by a board certified uroradiologist. Univariate Cox Proportional-Hazard Regression Analysis was performed for each NSs to valuate its predictability for the following parameters: Warm Ischemia Time (WIT), Estimated Blood Loss (EBL), Operative Time (OT), Complication Rates and Positive Margin Rates. RESULTS: 78 RAPNs were performed for suspected renal malignancies. The mean OT, EBL and WIT time was 186.5 minutes (SD - 33.8), 125.5 mls (SD - 188.91) and 16.7 minutes (SD - 5.6) respectively. The overall complication rate was 20.5% (16/78) of which only 2.6% (2/78) were Clavien Grade 3 or higher complications. The mean change in creatinine change at Day - 1 was 12.54 µmol/L (SD - 18.05). On the Cox regression analysis only the Centrality index predicted prolonged WIT with statistical significance: C-Index (0.02), ABC (0.2), PADUA (0.2), RENAL (0.9). ABC predicted operative time with statistical significance: C-index (0.45), ABC (0.0004), PADUA (0.25), RENAL (0.3). None of the NSs could predict overall complication: C-index (0.5), ABC (0.2), PADUA (0.13), RENAL (0.5). None of the NSs predicted EBL: C-index (0.3)0, ABC (0.8), PADUA (0.2), RENAL (0.7). None of the NSs predicted Positive Margin Rates: C-index (0.4), ABC (0.4), PADUA (0.9), RENAL (0.8). CONCLUSIONS: C-index was able to predict prolonged WIT. ABC was a strong predictor of OT. PADUA and RENAL were poor predictors for all measured parameters.