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Personalized Prediction of Kidney Function Decline and Network Analysis of the Risk Factors after Kidney Transplantation Using Nationwide Cohort Data

Authors
Hong, Moongi SimonLee, Yu-HoKong, Jin-MinKwon, Oh-JungJung, Cheol-WoongYang, JaeseokKim, Myoung-SooHan, Hyun-WookNam, Sang-MinKorean Organ Transplantation Registry Study Group
Issue Date
Mar-2022
Publisher
MDPI AG
Keywords
kidney transplantation; machine learning; risk factors; graft survival
Citation
Journal of Clinical Medicine, v.11, no.5
Indexed
SCIE
SCOPUS
Journal Title
Journal of Clinical Medicine
Volume
11
Number
5
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/55599
DOI
10.3390/jcm11051259
Abstract
We developed a machine-learning-based model that could predict a decrease in one-year graft function after kidney transplantation, and investigated the risk factors of the decreased function. A total of 4317 cases were included from the Korean Organ Transplant Registry (2014-2019). An XGBoost model was trained to predict the recipient's one-year estimated glomerular filtration rate (eGFR) below 45 mL/min/1.73 m(2) using 112 pre- and peri-transplantation variables. The network of model factors was drawn using inter-factor partial correlations and the statistical significance of each factor. The model with seven features achieved an area under the curve of 0.82, sensitivity of 0.73, and specificity of 0.79. The model prediction was associated with five-year graft and rejection-free survival. Post-transplantation hospitalization >25 days and eGFR >= 88.0 were the prominent risk and preventive factors, respectively. Donor age and post-transplantation eGFR < 59.8 were connected to multiple risk factors on the network. Therefore, careful donor-recipient matching in older donors, and avoiding pre-transplantation risk factors, would reduce the risk of graft dysfunction. The model might improve long-term graft outcomes by supporting early detection of graft dysfunction, and proactive risk factor control.
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2. Clinical Science > Department of Nephrology and Hypertension > 1. Journal Articles
2. Clinical Science > Department of Transplantation and Vascular Surgery > 1. Journal Articles

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Kim, Myung Gyu
Anam Hospital (Department of Nephrology and Hypertension, Anam Hospital)
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