To create and validate 2 models called RENSAFE (RENalSAFEty) to predict postoperative acute kidney injury (AKI) and development of chronic kidney disease (CKD) stage 3b in patients undergoing partial (PN) or radical nephrectomy (RN) for kidney cancer.
Methods
Primary objective was to develop a predictive model for AKI (reduction >25% of preoperative eGFR) and de novo CKD≥3b (<45 ml/min/1.73m2), through stepwise logistic regression. Secondary outcomes include elucidation of the relationship between AKI and de novo CKD≥3a (<60 ml/min/1.73m2). Accuracy was tested with receiver operator characteristic area under the curve (AUC).
Results
AKI occurred in 452/1,517 patients (29.8%) and CKD≥3b in 116/903 patients (12.8%). Logistic regression demonstrated male sex (OR = 1.3, P = 0.02), ASA score (OR = 1.3, P < 0.01), hypertension (OR = 1.6, P < 0.001), R.E.N.A.L. score (OR = 1.2, P < 0.001), preoperative eGFR<60 (OR = 1.8, P = 0.009), and RN (OR = 10.4, P < 0.0001) as predictors for AKI. Age (OR 1.0, P < 0.001), diabetes mellitus (OR 2.5, P < 0.001), preoperative eGFR <60 (OR 3.6, P < 0.001) and RN (OR 2.2, P < 0.01) were predictors for CKD≥3b. AUC for RENSAFE AKI was 0.80 and 0.76 for CKD≥3b. AKI was predictive for CKD≥3a (OR = 2.2, P < 0.001), but not CKD≥3b (P = 0.1). Using 21% threshold probability for AKI achieved sensitivity: 80.3%, specificity: 61.7% and negative predictive value (NPV): 88.1%. Using 8% cutoff for CKD≥3b achieved sensitivity: 75%, specificity: 65.7%, and NPV: 96%.
Conclusion
RENSAFE models utilizing perioperative variables that can predict AKI and CKD may help guide shared decision making. Impact of postsurgical AKI was limited to less severe CKD (eGFR<60 ml/min 71.73m2). Confirmatory studies are requisite.
Novel Score (RENSAFE) to predict kidney failure after RN and PN
Commentary by Dr. Teele Kuusk
The objective of the study was to create and validate two predictive models, RENSAFE (RENalSAFEty), for estimating the risk of postoperative acute kidney injury (AKI) and the development of chronic kidney disease (CKD) stage 3b in patients undergoing partial or radical nephrectomy for kidney cancer (RCC). The primary method involved developing predictive models for AKI and CKD. The accuracy of the models was assessed using the area under the curve (AUC).
Results showed that AKI occurred in 29.8% of patients, and CKD≥3b occurred in 12.8%. Various factors such as male sex, ASA score, hypertension, R.E.N.A.L. score, preoperative eGFR < 60, and radical nephrectomy were identified as predictors for AKI. Age, diabetes mellitus, preoperative eGFR <60, and radical nephrectomy were predictors for de novo CKD≥3b. The AUC for RENSAFE AKI was 0.80; AUC for CKD≥3b was 0.76. AKI was found to be predictive for CKD≥3a but not CKD≥3b. The models were suggested to be useful in guiding shared decision-making, particularly considering the limited impact of postsurgical AKI on more severe CKD. Yet, external validation is required to recommend the nomogram use in routine.
In their cohort comprising 1517 patients from tertiary referral centres, individuals underwent either partial or radical nephrectomy, with a median tumour size of 3.3 cm, and were subsequently monitored for 48 months. The nomogram demonstrated commendable performance when compared to previously reported models predicting the probability of de novo CKD≥3b (eGFR 30-44) (Ref). This prediction was based on straightforward clinical features, encompassing age, preoperative eGFR, diabetes, and surgical approach.
In their hypothetical scenario model, a 65-year-old non-diabetic patient with an eGFR of 50 ml/min was estimated to have a 37% likelihood of developing CKD≥3b (eGFR 30-44) after radical nephrectomy (RN) and a 20% likelihood after partial nephrectomy.
Conversely, a similar patient with diabetes exhibited a higher likelihood, reaching 60% and 39% after RN and PN, respectively. These findings suggest that the model could serve as a robust and valuable tool, aiding clinicians and patients alike in the decision-making process regarding partial and radical nephrectomy procedures. Nevertheless, its external validation is required. Although, many other similar models have been previously published, we hope that this model will be externally validated soon and, due to its robustness and easily extractable clinical data, could be used in clinical practice.
Reference:
Pecoraro A, Campi R, Bertolo R, Mir MC, Marchioni M, Serni S, Joniau S, Van Poppel H, Albersen M, Roussel E. Estimating Postoperative Renal Function After Surgery for Nonmetastatic Renal Masses: A Systematic Review of Available Prediction Models. Eur Urol Oncol. 2023 Apr;6(2):137-147. doi: 10.1016/j.euo.2022.11.007. Epub 2023 Jan 9. PMID: 36631353.