Prediction tool for renal adaptation after living kidney donation using interpretable machine learning - Jeon, Junseok; Yu, Jae Yong; Song, Yeejun; Jung, Weon; Lee, Kyungho; Lee, Jung Eun; Huh, Wooseong; Cha, Won Chul; Jang, Hye Ryoun (Frontiers in Medicine, 2023)
Introduction: 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. Methods: 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 2 and ≥65% of the pre-donation values, respectively. Results: The mean age was 45.2 years; 51.6% were female. The model included predonation 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 (https://jaeyongyu.shinyapps.io/rapto/). This is a provisional file, not the final typeset articleThe prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application.