Predicting acute kidney injury after robot-assisted partial nephrectomy: Implications for patient selection and postoperative management

Abstract Background Acute Kidney Injury (AKI) is a common occurrence after partial nephrectomy and is a significant risk factor for chronic kidney disease. We aimed to create a model that predicts postoperative AKI in patients undergoing robot-assisted partial nephrectomy (RAPN). Methods We identified 1,190 patients who underwent RAPN between 2008 and 2017 from a multicenter database. AKI was defined as a >25% reduction in eGFR from pre-RAPN to discharge. A nomogram was built based on a binary logistic regression that ultimately included age, sex, BMI, diabetes, baseline eGFR, and RENAL Nephrometry score. Internal validation was performed using the leave-one-out cross validation. Calibration was graphically investigated. The decision curve analysis was used to evaluate the net clinical benefit; a classification tree was used to identify risk categories. The same model was fit adding ischemia time during RAPN. Results Median (IQR) age at surgery was 61 (50, 68) years; 505 (42%) patients were female, while 685 (58%) were male. Median (IQR) ischemia time during RAPN was 14 (10, 18) min. postoperative AKI occurred in 274 (23%) patients. All variables fitted in the model emerged as predictors of AKI (all P ≤ 0.005) and all were considered to build a nomogram. After internal validation, the area under the curve was 73%. The model demonstrated excellent calibration and improved clinical risk prediction at the decision curve analysis. In the low, intermediate, and high-risk groups the postoperative AKI rates were: 10%, 30%, and 48%, respectively. Adding ischemia time to the preoperative model fit the data better (likelihood ratio test: P Conclusion We developed a nomogram that accurately predicts AKI in patients undergoing RAPN. This model might serve (1) in the preoperative setting: for counsel patients according to their preoperative AKI risk (2) in the immediate postoperative: for identifying patients who would benefit from an early multidisciplinary evaluation, when considering also ischemia time.