A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study

Cited 57 time in Web of Science Cited 67 time in Scopus

Yoo, Kyung Don; Noh, Junhyug; Lee, Hajeong; Kim, Dong Ki; Lim, Chun Soo; Kim, Young Hoon; Lee, Jung Pyo; Kim, Gunhee; Kim, Yon Su

Issue Date
Nature Publishing Group
Scientific Reports, Vol.7 No.1, p. 8904
Accurate prediction of graft survival after kidney transplant is limited by the complexity and heterogeneity of risk factors influencing allograft survival. In this study, we applied machine learning methods, in combination with survival statistics, to build new prediction models of graft survival that included immunological factors, as well as known recipient and donor variables. Graft survival was estimated from a retrospective analysis of the data from a multicenter cohort of 3,117 kidney transplant recipients. We evaluated the predictive power of ensemble learning algorithms (survival decision tree, bagging, random forest, and ridge and lasso) and compared outcomes to those of conventional models (decision tree and Cox regression). Using a conventional decision tree model, the 3-month serum creatinine level post-transplant (cut-off, 1.65 mg/dl) predicted a graft failure rate of 77.8% (index of concordance, 0.71). Using a survival decision tree model increased the index of concordance to 0.80, with the episode of acute rejection during the first year post-transplant being associated with a 4.27-fold increase in the risk of graft failure. Our study revealed that early acute rejection in the first year is associated with a substantially increased risk of graft failure. Machine learning methods may provide versatile and feasible tools for forecasting graft survival.
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College of Medicine/School of Medicine (의과대학/대학원)Dept. of Medicine (의학과)Journal Papers (저널논문_의학과)
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Journal Papers (저널논문_컴퓨터공학부)
College of Medicine/School of Medicine (의과대학/대학원)Dept. of Biomedical Sciences (대학원 의과학과)Journal Papers (저널논문_의과학과)
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