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

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 t...

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Autores principales: Yoo, Kyung Don, Noh, Junhyug, Lee, Hajeong, Kim, Dong Ki, Lim, Chun Soo, Kim, Young Hoon, Lee, Jung Pyo, Kim, Gunhee, Kim, Yon Su
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5567098/
https://www.ncbi.nlm.nih.gov/pubmed/28827646
http://dx.doi.org/10.1038/s41598-017-08008-8
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author Yoo, Kyung Don
Noh, Junhyug
Lee, Hajeong
Kim, Dong Ki
Lim, Chun Soo
Kim, Young Hoon
Lee, Jung Pyo
Kim, Gunhee
Kim, Yon Su
author_facet Yoo, Kyung Don
Noh, Junhyug
Lee, Hajeong
Kim, Dong Ki
Lim, Chun Soo
Kim, Young Hoon
Lee, Jung Pyo
Kim, Gunhee
Kim, Yon Su
author_sort Yoo, Kyung Don
collection PubMed
description 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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spelling pubmed-55670982017-09-01 A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study Yoo, Kyung Don Noh, Junhyug Lee, Hajeong Kim, Dong Ki Lim, Chun Soo Kim, Young Hoon Lee, Jung Pyo Kim, Gunhee Kim, Yon Su Sci Rep Article 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. Nature Publishing Group UK 2017-08-21 /pmc/articles/PMC5567098/ /pubmed/28827646 http://dx.doi.org/10.1038/s41598-017-08008-8 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Yoo, Kyung Don
Noh, Junhyug
Lee, Hajeong
Kim, Dong Ki
Lim, Chun Soo
Kim, Young Hoon
Lee, Jung Pyo
Kim, Gunhee
Kim, Yon Su
A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study
title A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study
title_full A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study
title_fullStr A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study
title_full_unstemmed A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study
title_short A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study
title_sort machine learning approach using survival statistics to predict graft survival in kidney transplant recipients: a multicenter cohort study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5567098/
https://www.ncbi.nlm.nih.gov/pubmed/28827646
http://dx.doi.org/10.1038/s41598-017-08008-8
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