Improving prediction of heart transplantation outcome using deep learning techniques
Medved, Dennis LU
; Ohlsson, Mattias
LU
; Höglund, Peter
LU
; Andersson, Bodil
LU
; Nugues, Pierre
LU
and Nilsson, Johan
LU
(2018)
In Scientific Reports
- Abstract
- 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,... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/178c033b-df0e-4e70-8a38-278bf704bf52
- author
- Medved, Dennis
LU
; Ohlsson, Mattias
LU
; Höglund, Peter
LU
; Andersson, Bodil
LU
; Nugues, Pierre
LU
and Nilsson, Johan
LU
- organization
-
- Department of Computer Science
- Artificial Intelligence in CardioThoracic Sciences (AICTS) (research group)
- eSSENCE: The e-Science Collaboration
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
- Computational Biology and Biological Physics - Undergoing reorganization
- Department of Astronomy and Theoretical Physics - Undergoing reorganization
- Division of Clinical Chemistry and Pharmacology
- Clinical studies in CKD (research group)
- Protease Inhibitor Research (research group)
- Thoracic Surgery
- Heart and Lung transplantation (research group)
- Surgery (Lund)
- Hepato-Pancreato-Biliary Surgery (research group)
- Robotics and Semantic Systems
- publishing date
- 2018-02-26
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Scientific Reports
- issue
- 8
- article number
- 3613
- pages
- 9 pages
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85043235571
- pmid:29483521
- ISSN
- 2045-2322
- DOI
- 10.1038/s41598-018-21417-7
- project
- Lund University AI Research
- language
- English
- LU publication?
- yes
- id
- 178c033b-df0e-4e70-8a38-278bf704bf52
- date added to LUP
- 2018-02-27 11:49:52
- date last changed
- 2022-12-22 18:17:03
@article{178c033b-df0e-4e70-8a38-278bf704bf52,
abstract = {{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.}},
author = {{Medved, Dennis and Ohlsson, Mattias and Höglund, Peter and Andersson, Bodil and Nugues, Pierre and Nilsson, Johan}},
issn = {{2045-2322}},
language = {{eng}},
month = {{02}},
number = {{8}},
publisher = {{Nature Publishing Group}},
series = {{Scientific Reports}},
title = {{Improving prediction of heart transplantation outcome using deep learning techniques}},
url = {{http://dx.doi.org/10.1038/s41598-018-21417-7}},
doi = {{10.1038/s41598-018-21417-7}},
year = {{2018}},
}