Shinoda Kazunobu, Morita Shinya, Akita Hirotaka, Washizuka Fuyuki, Tamaki Satoshi, Takahashi Ryohei, Oguchi Hideyo, Sakurabayashi Kei, Mizutani Toshihide, Takahashi Yusuke, Hyodo Yoji, Itabashi Yoshihiro, Muramatsu Masaki, Kawamura Takeshi, Asanuma Hiroshi, Kikuchi Eiji, Jinzaki Masahiro, Shiraga Nobuyuki, Nakagawa Ken, Oya Mototsugu, Shishido Seiichiro, Sakai Ken
Department of Urology, Keio University School of Medicine, Tokyo, Japan; Department of Nephrology, Toho University Faculty of Medicine, Tokyo, Japan.
Department of Urology, Keio University School of Medicine, Tokyo, Japan.
Transplant Proc. 2019 Jun;51(5):1306-1310. doi: 10.1016/j.transproceed.2019.01.142. Epub 2019 May 7.
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.
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.
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).
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.
确保供体术后肾脏功能在其生存期内正常，是医生们关注的重要课题，而在判断肾脏捐献的可行性时，精确预测供体术后肾功能将大有裨益。本研究旨在探寻预测肾脏捐献后1年估算肾小球滤过率（eGFR）的最佳模型。
我们纳入了101名活体亲属肾供体作为开发队列，44名作为外部验证队列。每个队列中的所有患者均接受了薄层（1毫米）增强计算机断层扫描（CT）。我们将患有糖尿病、糖耐量异常或蛋白尿的个体排除在本研究之外。我们通过对薄层增强CT图像进行三维重建，评估术前因素，包括年龄、性别、高血压、体重指数（BMI）、血清尿酸、基线eGFR以及体表面积（BSA）校正后的保留肾体积（PKV）。为了检测独立预测因素，我们进行了多变量回归分析。
多变量回归分析显示，年龄、BMI、捐献前eGFR以及BSA校正后的PKV是肾脏捐献后1年eGFR的独立预测因素（相关系数分别为-0.15、-0.476、0.521、0.127）。在开发队列中，预测eGFR与观察到的eGFR之间存在强烈相关性（r = 0.839，P <.0001）。外部验证队列也证实了该预测模型的显著性（r = 0.797，P <.0001）。
年龄、BMI、捐献前eGFR以及BSA校正后的PKV可能有助于精确预测活体肾捐献后1年的eGFR，并有助于确定边缘供体进行肾脏捐献的可行性。
