External validation of the Palacios' equation: a simple and accurate tool to estimate the new baseline renal function after renal cancer surgery

Performance of Future Glomerular Filtration Rate Equation by Race in a Large, Racially Diverse Patient Cohort Undergoing Nephrectomy for Renal Cell Carcinoma

OBJECTIVE

To examine the performance of the Palacios et al [Aguilar Palacios D, Wilson B, Ascha M, et al. New baseline renal function after radical or partial nephrectomy: a simple and accurate predictive model. J Urol. 2021;205:1310-1320] post-nephrectomy future glomerular function rate (fGFR) equation in a diverse cohort using both the Chronic Kidney Disease Epidemiology (CKD-EPI) 2009 equation with race, used in the creation of the formula, as well as the CKD-EPI 2021 equation without race.

METHODS

Patients who underwent partial or radical nephrectomy for renal cell carcinoma from 2005-2021 were identified in our institutional database. Patients with creatinine values preoperatively and 3-12 months postoperatively were included. Correlation/bias/accuracy/precision of the fGFR equation (fGFR = 35+ [preoperative eGFR × 0.65] - 18 [if radical] - [age × 0.25] + 3 [if tumor >7 cm] - 2 [if diabetes]) with observed postoperative eGFR was determined by both the CKD-EPI-2021 and CKD-EPI 2009 equations.

RESULTS

A total of 1443 patients were analyzed. Seventy-one percent (1024) were White and 22.9% (331) were Black. Most underwent radical nephrectomy (60.3%). 40% T3-T4 renal cell carcinoma (RCC), with 14.8% of patients having M1 disease. Median observed vs predicted fGFR was 58.0 vs 58.7 mL/min/1.73 m2 for CKD-EPI 2021 and 56.0 vs 57.5 for CKD-EPI 2009. For the total cohort, the correlation/bias/accuracy/precision of the fGFR equation was 0.805/-0.5/81.7/7.9-9.0 for CKD-EPI 2021 and 0.809/-0.8/81.3/-8.1 to 8 for CKD-EPI 2009. In Black patients, fGFR equation demonstrated >75% accuracy with both CKD-EPI equations; however, accuracy was lower in black patients with the CKD-EPI2021 equation (76.1% vs 83.4%, P = .003).

CONCLUSION

The fGFR equation performed well in our large, diverse cohort, though accuracy was relatively lower when using CKD-EPI 2021 compared to CKD-EPI 2009.

The role of warm ischemia time on functional outcomes after robotic partial nephrectomy: a radionuclide renal scan study from the clock randomized trial

PURPOSE

To evaluate the relationship between warm ischemia time (WIT) duration and renal function after robot-assisted partial nephrectomy (RAPN).

METHODS

The CLOCK trial is a phase 3 randomized controlled trial comparing on- vs off-clamp RAPN. All patients underwent pre- and postoperative renal scintigraphy. Six-month absolute variation of eGFR (AV-GFR), rate of relative variation in eGFR over 25% (RV-GFR > 25), absolute variation of split renal function (SRF) at scintigraphy (AV-SRF). The relationships WIT/outcomes were assessed by correlation graphs and then modeled by uni- and multivariable regression.

RESULTS

324 patients were included (206 on-clamp, 118 off-clamp RAPN). Correlation graphs showed a threshold on WIT equal to 10 min. The differences in outcome measures between cases with WIT < vs ≥ 10 min were: AV-GFR - 3.7 vs - 7.5 ml/min (p < 0.001); AV-SRF - 1% vs - 3.6% (p < 0.001); RV-GFR > 25 9.3% vs 17.8% (p = 0.008). Multivariable models found that AV-GFR was related to WIT ≥ 10 min (regression coefficient [RC] - 0.52, p = 0.019), age (RC - 0.35, p = 0.001) and baseline eGFR (RC - 0.30, p < 0.001); RV-GFR > 25 to WIT ≥ 10 min (odds ratio [OR] 1.11, p = 0.007) and acute kidney injury defined as > 50% increase in serum creatinine (OR 19.7, p = 0.009); AV-SRF to WIT ≥ 10 min (RC - 0.30, p = 0.018), baseline SRF (RC - 0.76, p < 0.001) and RENAL score (RC - 0.60. p = 0.028). The main limitation was that the CLOCK trial was designed on a different endpoint and therefore the present analysis could be underpowered.

CONCLUSIONS

Up to 10 min WIT had no consequences on functional outcomes. Above the 10-min threshold, a statistically significant, but clinically negligible impact was found.

Estimating Postoperative Renal Function After Surgery for Nonmetastatic Renal Masses: A Systematic Review of Available Prediction Models

CONTEXT

A variety of models predicting postoperative renal function following surgery for nonmetastatic renal tumors have been reported, but their validity and clinical usefulness have not been formally assessed.

OBJECTIVE

To summarize prediction models available for estimation of mid- to long-term (>3 mo) postoperative renal function after partial nephrectomy (PN) or radical nephrectomy (RN) for nonmetastatic renal masses that include only preoperative or modifiable intraoperative variables.

EVIDENCE ACQUISITION

A systematic review of the English-language literature was conducted using the MEDLINE, Embase, and Web of Science databases from January 2000 to March 2022 according to the PRISMA guidelines (PROSPERO ID: CRD42022303492). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool.

EVIDENCE SYNTHESIS

Overall, 21 prediction models from 18 studies were included (nine for PN only; eight for RN only; four for PN or RN). Most studies relied on retrospective patient cohorts and had a high risk of bias and high concern regarding the overall applicability of the proposed model. Patient-, kidney-, surgery-, tumor-, and provider-related factors were included among the predictors in 95%, 86%, 100%, 61%, and 0% of the models, respectively. All but one model included both patient age and preoperative renal function, while only a few took into account patient gender, race, comorbidities, tumor size/complexity, and surgical approach. There was significant heterogeneity in both the model building strategy and the performance metrics reported. Five studies reported external validation of six models, while three assessed their clinical usefulness using decision curve analysis.

CONCLUSIONS

Several models are available for predicting postoperative renal function after kidney cancer surgery. Most of these are not ready for routine clinical practice, while a few have been externally validated and might be of value for patients and clinicians.

PATIENT SUMMARY

We reviewed the tools available for predicting kidney function after partial or total surgical removal of a kidney for nonmetastatic cancer. Most of the models include patient and kidney characteristics such as age, comorbidities, and preoperative kidney function, and a few also include tumor characteristics and intraoperative variables. Some models have been validated by additional research groups and appear promising for improving counseling for patients with nonmetastatic cancer who are candidates for surgery.

Validation of new baseline renal function predictive model in robotic-assisted partial nephrectomy cohort

OBJECTIVE

To validate a new baseline estimated glomerular filtration rate (NB-GFR) formula in a cohort of robotic-assisted partial nephrectomies (RAPN).

METHODS

NB-GFR = 35 + preoperative GFR (× 0.65) - 18 (if radical nephrectomy) - age (× 0.25) + 3 (if tumor size >7 cm) - 2 (if diabetes). NB-GFR was calculated in 464 consecutive RAPN from a single surgeon cohort. 143 patients were excluded secondary to insufficient eGFR follow up. We analyzed NB-GFR accuracy utilizing the last observed eGFR 3-12 months post RAPN. Categorical variables were summarized with the frequency and percentage of patients. Numerical variables were summarized with the median, 25th percentile, and 75th percentile.

RESULTS

The mean difference between observed and predicted NB-GFR was 4.6 ml/min/1.73m2 (95% CI -6.9 to 16.1 ml/min/1.73m2 ). There was a pattern of higher observed NB-GFRs being underestimated by the NB-GFR equation while lower observed NB-GFRs were overestimated by the NB-GFR equation. The NB-GFR formula had a high level of accuracy with 98.8% of predicted NB-GFRs falling within 30% of the observed NB-GFR (95% CI 86.8% to 99.5%). The median and interquartile range of the difference between observed and predicted NB-GFR was 3.9 ml/min/1.73m2 (IQR 0.7 to 8.2 ml/min/1.73m2 ). The sensitivity, specificity, positive predictive value, and negative predictive value for the ability of predicted NB-GFR to identify those with an observed NB-GFR <60 ml/min/1.73m2 after RAPN was 98%, 92%, 88%, and 99%, respectively.

CONCLUSION

The NB-GFR equation developed with partial and radical nephrectomy cohorts is accurate in predicting post-operative eGFR 3-12 months following RAPN.

How to optimize the use of adjuvant pembrolizumab in renal cell carcinoma: which patients benefit the most?

PURPOSE

The KEYNOTE-564 trial showed improved disease-free survival (DFS) for patients with high-risk renal cell carcinoma (RCC) receiving adjuvant pembrolizumab as compared to placebo. However, if systematically administered to all high-risk patients, it might lead to the overtreatment in a non-negligible proportion of patient. Therefore, we aimed to determine the optimal candidate for adjuvant pembrolizumab.

METHODS

Within a prospectively maintained database we selected patients who fulfilled the inclusion criteria of the KEYNOTE-564. We compared baseline characteristics and oncologic outcomes in this cohort with those of the placebo arm of the KEYNOTE-564. Regression tree analyses was used to generate a risk stratification tool to predict 1-year DFS after surgery.

RESULTS

In the off-trial setting, patients had worse tumor characteristics then in the KEYNOTE-564 placebo arm, i.e. there were more pT4 (5.4 vs. 2.7%, p = 0.046) and pN1 (15 vs. 6.3%, p < 0.001) cases. Median DFS was 29 (95% CI 21-35) months as compared to value not reached in KEYNOTE-564 and 1-year DFS was 64.2% (95% CI 59.6-69.2) as compared to 76.2% (95% CI 72.2-79.7), respectively. Patients with pN1 were at the highest risk of 1-year recurrence (1-year DFS 28.6% [95% CI 20.2-40.3]); patients without LNI, but necrosis were at intermediate risk (1-year DFS 62.5% [95% CI 56.9-68.8]); those without LNI and necrosis were at the lowest risk (1-year DFS 83.8% [95% CI 79.1-88.9]). LVI substratification furtherly improved the accuracy in the prediction of early recurrence.

CONCLUSIONS

Patients potentially eligible for adjuvant pembrolizumab have worse characteristics and DFS in the off-trial setting as compared to the placebo arm of the KEYNOTE-564. Patients with either LNI or necrosis were at the highest risk of early-recurrence, which make them the ideal candidate to adjuvant pembrolizumab.

Predicting GFR after radical nephrectomy: the importance of split renal function

PURPOSE

To evaluate a conceptually simple model to predict new-baseline-glomerular-filtration-rate (NBGFR) after radical nephrectomy (RN) based on split-renal-function (SRF) and renal-functional-compensation (RFC), and to compare its predictive accuracy against a validated non-SRF-based model. RN should only be considered when the tumor has increased oncologic potential and/or when there is concern about perioperative morbidity with PN due to increased tumor complexity. In these circumstances, accurate prediction of NBGFR after RN can be important, with a threshold NBGFR > 45 ml/min/1.73m² correlating with improved overall survival.

METHODS

236 RCC patients who underwent RN (2010-2012) with preoperative imaging (CT/MRI) and relevant functional data were included. NBGFR was defined as GFR 3-12 months post-RN. SRF was determined using semi-automated software that provides differential parenchymal-volume-analysis (PVA) from preoperative imaging. Our SRF-based model was: Predicted NBGFR = 1.24 (× Global GFR_Pre-RN) (× SRF_{Contralateral}), with 1.24 representing the mean RFC estimate from independent analyses. A non-SRF-based model was also assessed: Predicted NBGFR = 17 + preoperative GFR (× 0.65)-age (× 0.25) + 3 (if tumor > 7 cm)-2 (if diabetes). Alignment between predicted/observed NBGFR was assessed by comparing correlation coefficients and area-under-the-curve (AUC) analyses.

RESULTS

The correlation-coefficients (r) were 0.87/0.72 for SRF-based/non-SRF-based models, respectively (p = 0.005). For prediction of NBGFR > 45 ml/min/1.73m², the SRF-based/non-SRF-based models provided AUC of 0.94/0.87, respectively (p = 0.044).

CONCLUSION

Previous non-SRF-based models to predict NBGFR post-RN are complex and omit two important parameters: SRF and RFC. Our proposed model prioritizes these parameters and provides a conceptually simple, accurate, and clinically implementable approach to predict NBGFR post-RN. SRF can be easily obtained using PVA software that is affordable, readily available (FUJIFILM-Medical-Systems), and more accurate than nuclear-renal-scans. The SRF-based model demonstrates greater predictive-accuracy than a non-SRF-based model, including the clinically-important predictive-threshold of NBGFR > 45 ml/min/1.73m².