BACKGROUND: Securing postdonation renal function in the lifetime of donors is a consequential subject for physicians, and precise prediction of postdonation renal function would be considerably beneficial when judging the feasibility of kidney donation. The aim of this study was to investigate the optimum model for predicting eGFR at 1 year after kidney donation. METHODS: We enrolled 101 living-related kidney donors for the development cohort and 44 for the external validation cohort. All patients in each cohort underwent thin-sliced (1 mm) enhanced computed tomography (CT) scans. We excluded individuals with diabetes, glucose intolerance, or albuminuria from this study. We evaluated preoperative factors including age, sex, hypertension, body mass index (BMI), serum uric acid, baseline eGFR, and body surface area (BSA)-adjusted preserved kidney volume (PKV) by using 3-dimensional reconstruction of thin-sliced enhanced CT images. To detect independent predictors, we performed multivariable regression analysis. RESULTS: The multivariable regression analysis revealed that age, BMI, predonation eGFR, and BSA-adjusted PKV were independent predictors of eGFR at 1 year after kidney donation (correlation coefficient: -0.15, -0.476, 0.521, 0.127, respectively). A strong correlation between predicted eGFR and observed eGFR was obtained in the development cohort (r = 0.839, P < .0001). The significance of this predictive model was also confirmed with the external validation cohort (r = 0.797, P < .0001). CONCLUSIONS: Age, BMI, predonation eGFR, and BSA-adjusted PKV may be useful for precisely predicting eGFR at 1 year after living kidney donation and be helpful to determine the feasibility of kidney donation from marginal donors.
BACKGROUND: Securing postdonation renal function in the lifetime of donors is a consequential subject for physicians, and precise prediction of postdonation renal function would be considerably beneficial when judging the feasibility of kidney donation. The aim of this study was to investigate the optimum model for predicting eGFR at 1 year after kidney donation. METHODS: We enrolled 101 living-related kidney donors for the development cohort and 44 for the external validation cohort. All patients in each cohort underwent thin-sliced (1 mm) enhanced computed tomography (CT) scans. We excluded individuals with diabetes, glucose intolerance, or albuminuria from this study. We evaluated preoperative factors including age, sex, hypertension, body mass index (BMI), serum uric acid, baseline eGFR, and body surface area (BSA)-adjusted preserved kidney volume (PKV) by using 3-dimensional reconstruction of thin-sliced enhanced CT images. To detect independent predictors, we performed multivariable regression analysis. RESULTS: The multivariable regression analysis revealed that age, BMI, predonation eGFR, and BSA-adjusted PKV were independent predictors of eGFR at 1 year after kidney donation (correlation coefficient: -0.15, -0.476, 0.521, 0.127, respectively). A strong correlation between predicted eGFR and observed eGFR was obtained in the development cohort (r = 0.839, P < .0001). The significance of this predictive model was also confirmed with the external validation cohort (r = 0.797, P < .0001). CONCLUSIONS: Age, BMI, predonation eGFR, and BSA-adjusted PKV may be useful for precisely predicting eGFR at 1 year after living kidney donation and be helpful to determine the feasibility of kidney donation from marginal donors.