Jeon Junseok, Yu Jae Yong, Song Yeejun, Jung Weon, Lee Kyungho, Lee Jung Eun, Huh Wooseong, Cha Won Chul, Jang Hye Ryoun
Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
Front Med (Lausanne). 2023 Jul 14;10:1222973. doi: 10.3389/fmed.2023.1222973. eCollection 2023.
Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors' high life expectancy and elderly donors' comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning.
The study included 823 living kidney donors who underwent nephrectomy in 2009-2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m and ≥ 65% of the pre-donation values, respectively.
The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762-0.930) and 0.626 (0.541-0.712), while the areas under the precision-recall curve were 0.965 (0.944-0.978) and 0.709 (0.647-0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed.
The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application.
考虑到年轻供体的高预期寿命以及老年供体的合并症会影响肾功能，活体肾供体的捐肾后肾脏转归是一个关键问题。我们使用可解释的机器学习方法开发了一种活体肾捐献后肾脏适应性的预测模型。
该研究纳入了2009年至2020年期间接受肾切除术的823名活体肾供体。使用基于机器学习的评分生成器AutoScore来开发预测模型。良好和优秀的肾脏适应性分别定义为捐肾后估计肾小球滤过率（eGFR）≥60 mL/min/1.73 m²以及≥捐肾前值的65%。
平均年龄为45.2岁；51.6%为女性。该模型纳入了捐肾前的人口统计学和实验室变量、通过二乙烯三胺五乙酸扫描测量的肾小球滤过率，以及双侧肾脏和剩余肾脏的计算机断层扫描肾脏体积/体重。对于良好和优秀的肾脏适应性，受试者工作特征曲线下面积分别为0.846（95%置信区间，0.762 - 0.930）和0.626（0.541 - 0.712），而精确召回率曲线下面积分别为0.965（0.944 - 0.978）和0.709（0.647 - 0.788）。开发了一个交互式临床决策支持系统。
捐肾后肾脏适应性的预测工具显示出良好的预测能力，并可能通过易于使用的基于网络的应用程序帮助临床决策。
