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Improving prediction of heart transplantation outcome using deep learning techniques

The primary objective of this study is to compare the accuracy of two risk models, International Heart Transplantation Survival Algorithm (IHTSA), developed using deep learning technique, and Index for Mortality Prediction After Cardiac Transplantation (IMPACT), to predict survival after heart trans...

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Autores principales: Medved, Dennis, Ohlsson, Mattias, Höglund, Peter, Andersson, Bodil, Nugues, Pierre, Nilsson, Johan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5827028/
https://www.ncbi.nlm.nih.gov/pubmed/29483521
http://dx.doi.org/10.1038/s41598-018-21417-7
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author Medved, Dennis
Ohlsson, Mattias
Höglund, Peter
Andersson, Bodil
Nugues, Pierre
Nilsson, Johan
author_facet Medved, Dennis
Ohlsson, Mattias
Höglund, Peter
Andersson, Bodil
Nugues, Pierre
Nilsson, Johan
author_sort Medved, Dennis
collection PubMed
description The primary objective of this study is to compare the accuracy of two risk models, International Heart Transplantation Survival Algorithm (IHTSA), developed using deep learning technique, and Index for Mortality Prediction After Cardiac Transplantation (IMPACT), to predict survival after heart transplantation. Data from adult heart transplanted patients between January 1997 to December 2011 were collected from the UNOS registry. The study included 27,860 heart transplantations, corresponding to 27,705 patients. The study cohorts were divided into patients transplanted before 2009 (derivation cohort) and from 2009 (test cohort). The receiver operating characteristic (ROC) values, for the validation cohort, computed for one-year mortality, were 0.654 (95% CI: 0.629–0.679) for IHTSA and 0.608 (0.583–0.634) for the IMPACT model. The discrimination reached a C-index for long-term survival of 0.627 (0.608–0.646) for IHTSA, compared with 0.584 (0.564–0.605) for the IMPACT model. These figures correspond to an error reduction of 12% for ROC and 10% for C-index by using deep learning technique. The predicted one-year mortality rates for were 12% and 22% for IHTSA and IMPACT, respectively, versus an actual mortality rate of 10%. The IHTSA model showed superior discriminatory power to predict one-year mortality and survival over time after heart transplantation compared to the IMPACT model.
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spelling pubmed-58270282018-03-01 Improving prediction of heart transplantation outcome using deep learning techniques Medved, Dennis Ohlsson, Mattias Höglund, Peter Andersson, Bodil Nugues, Pierre Nilsson, Johan Sci Rep Article The primary objective of this study is to compare the accuracy of two risk models, International Heart Transplantation Survival Algorithm (IHTSA), developed using deep learning technique, and Index for Mortality Prediction After Cardiac Transplantation (IMPACT), to predict survival after heart transplantation. Data from adult heart transplanted patients between January 1997 to December 2011 were collected from the UNOS registry. The study included 27,860 heart transplantations, corresponding to 27,705 patients. The study cohorts were divided into patients transplanted before 2009 (derivation cohort) and from 2009 (test cohort). The receiver operating characteristic (ROC) values, for the validation cohort, computed for one-year mortality, were 0.654 (95% CI: 0.629–0.679) for IHTSA and 0.608 (0.583–0.634) for the IMPACT model. The discrimination reached a C-index for long-term survival of 0.627 (0.608–0.646) for IHTSA, compared with 0.584 (0.564–0.605) for the IMPACT model. These figures correspond to an error reduction of 12% for ROC and 10% for C-index by using deep learning technique. The predicted one-year mortality rates for were 12% and 22% for IHTSA and IMPACT, respectively, versus an actual mortality rate of 10%. The IHTSA model showed superior discriminatory power to predict one-year mortality and survival over time after heart transplantation compared to the IMPACT model. Nature Publishing Group UK 2018-02-26 /pmc/articles/PMC5827028/ /pubmed/29483521 http://dx.doi.org/10.1038/s41598-018-21417-7 Text en © The Author(s) 2018 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
Medved, Dennis
Ohlsson, Mattias
Höglund, Peter
Andersson, Bodil
Nugues, Pierre
Nilsson, Johan
Improving prediction of heart transplantation outcome using deep learning techniques
title Improving prediction of heart transplantation outcome using deep learning techniques
title_full Improving prediction of heart transplantation outcome using deep learning techniques
title_fullStr Improving prediction of heart transplantation outcome using deep learning techniques
title_full_unstemmed Improving prediction of heart transplantation outcome using deep learning techniques
title_short Improving prediction of heart transplantation outcome using deep learning techniques
title_sort improving prediction of heart transplantation outcome using deep learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5827028/
https://www.ncbi.nlm.nih.gov/pubmed/29483521
http://dx.doi.org/10.1038/s41598-018-21417-7
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