Defining Risk Categories for a Significant Decline in Estimated Glomerular Filtration Rate after Robotic Partial Nephrectomy – Beyond the Abstract

The renal functional outcome after kidney surgery is of utmost importance since it affects overall survival and recent studies suggest its implication in cancer-specific survival as well.1,2 Additionally, it also profoundly affects a patient’s quality of life. In fact, the renal function, as a result of kidney surgery, can precipitate ultimately leading to dialysis. Thus, when counseling patients regarding surgery on such a delicate organ, urologists have to take into account several factors.

Presently, there are no data-driven recommendations as to when a postoperative nephrological consultation should be obtained. A more stringent follow-up with aggressive management of hypertension and/or dietary and lifestyle modifications might help to prevent renal function decline after surgery. The American Urological Association guidelines list four criteria that should prompt a nephrologist referral; however, these criteria derived from expert opinion rather than evidence-based medicine.3 The European Association of Urology guidelines suggests repeated long-term eGFR monitoring when renal function is impaired before or after surgery.4 Despite that, sometimes patients with high functional reserve might be at higher risk of renal function decline relative to those with pre-existing chronic kidney disease.

In an effort to address this point, our group has worked on creating models aimed at the prediction of postoperative AKI5 and renal function decline within 15 months from robotic partial nephrectomy.6 

Based on this model, in our most recent study, we used our nomogram to define risk categories for significant estimated glomerular filtration rate (eGFR) reduction after robotic partial nephrectomy.7 We considered a significant eGFR reduction as a 25% reduction from the preoperative baseline eGFR. Briefly, we used data from 999 patients who underwent robotic partial nephrectomy between 2008 and 2017 at five tertiary referral centers. Increasing age, female gender, higher Charlson comorbidity index, higher renal nephrometry score, higher baseline eGFR, and postoperative AKI (before discharge) were all significant predictors of a significant eGFR decline between 3 and 15 months. In this study, we defined four risk categories for significant eGFR reduction. The probability of this occurrence was 4%, 14%, 29%, and 79% in the low, intermediate, high and very high-risk group. Our data suggest that patients with a high or very high risk of eGFR decline (individuals with a risk ≥21%, according to your nomogram) are those who would potentially benefit the most from an early multidisciplinary evaluation.

We believe that our risk stratification tool is helpful when counseling patients. The risk group concept is easy for patients to understand and can lead to more informative shared decision-making. These categories, if validated, could be implemented in the design of future prospective studies to potentially identify the best follow-up strategy for each risk group.


Written by: Alberto Martini, MD, Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Urology, Vita-Salute San Raffaele University, Milan, Italy

References:
1. Antonelli A, Minervini A, Sandri M, et al. Below safety limits, every unit of glomerular filtration rate counts: assessing the relationship between renal function and cancer-specific mortality in renal cell carcinoma. Eur Urol 2018;74:661–7.
2. Martini A, Cumarasamy S, Hemal AK, Badani KK. Renal cell carcinoma: the oncological outcome is not the only endpoint. Transl Androl Urol 2019;S93–5.
3. Campbell S, Uzzo RG, Allaf ME, et al. Renal mass and localized renal cancer: AUA guideline. J Urol 2017;198:520–9.
4. Ljungberg B, Bensalah K, Canfield S, et al. EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol 2015;67:913–24.
5. Martini A, Sfakianos JP, Paulucci DJ, et al. Predicting acute kidney injury after robot-assisted partial nephrectomy: implications for patient selection and postoperative management. Urol Oncol 2019;37:445–51.
6. Martini A, Cumarasamy S, Beksac AT, et al. A nomogram to predict significant estimated glomerular filtration rate reduction after robotic partial nephrectomy. Eur Urol 2018;74:833–9.
7. Martini A, Falagario UG, Cumarasamy S et al. Defining Risk Categories for a Significant Decline in Estimated Glomerular Filtration Rate After Robotic Partial Nephrectomy: Implications for Patient Follow-up. Eur Urol Oncol 2019. https://doi.org/ 10.1016/j.euo.2019.07.001