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

Cited 72 time in Web of Science Cited 86 time in Scopus
Authors

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
2017-08
Publisher
Nature Publishing Group
Citation
Scientific Reports, Vol.7 No.1, p. 8904
Abstract
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.
ISSN
2045-2322
Language
English
URI
https://hdl.handle.net/10371/139249
DOI
https://doi.org/10.1038/s41598-017-08008-8
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  • College of Medicine
  • Department of Medicine
Research Area Nephrology, Transplantation, Urology

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