Alberto Martini1, John P Sfakianos2, David J Paulucci2, Ronney Abaza3, Daniel D Eun4, Akshay Bhandari5, Ashok K Hemal6, Ketan K Badani2. 1. Department of Urology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY. Electronic address: alberto.martini@mountsinai.org. 2. Department of Urology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY. 3. Robotic Urologic Surgery, OhioHealth Dublin Methodist Hospital. Columbus, OH. 4. Temple University School of Medicine, Philadelphia, PA. 5. Division of Urology, Columbia University at Mount Sinai, Miami Beach, FL. 6. Wake Forest School of Medicine, Winston-Salem, NC.
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 < 0.001) and yielded an incremental area under the curve of 3% (95% confidence interval: 1, 5%) 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.
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 < 0.001) and yielded an incremental area under the curve of 3% (95% confidence interval: 1, 5%) 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.
Authors: Ugo G Falagario; Alberto Martini; John Pfail; Patrick-Julien Treacy; Kennedy E Okhawere; Bheesham D Dayal; John P Sfakianos; Ronney Abaza; Daniel D Eun; Akshay Bhandari; James R Porter; Ashok K Hemal; Ketan K Badani Journal: Transl Androl Urol Date: 2020-04