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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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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. |
format | Online Article Text |
id | pubmed-5827028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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