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Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center

Use of a left ventricular assist device (LVAD) can benefit patients with end stage heart failure, but only with careful patient selection. In this study, previously derived Bayesian network models for predicting LVAD patient mortality at 1, 3, and 12 months post-implant were evaluated on retrospecti...

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Autores principales: Lohmueller, Lisa C., Kanwar, Manreet K., Bailey, Stephen, Murali, Srinivas, Antaki, James F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6176112/
https://www.ncbi.nlm.nih.gov/pubmed/30333978
http://dx.doi.org/10.3389/fmed.2018.00277
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author Lohmueller, Lisa C.
Kanwar, Manreet K.
Bailey, Stephen
Murali, Srinivas
Antaki, James F.
author_facet Lohmueller, Lisa C.
Kanwar, Manreet K.
Bailey, Stephen
Murali, Srinivas
Antaki, James F.
author_sort Lohmueller, Lisa C.
collection PubMed
description Use of a left ventricular assist device (LVAD) can benefit patients with end stage heart failure, but only with careful patient selection. In this study, previously derived Bayesian network models for predicting LVAD patient mortality at 1, 3, and 12 months post-implant were evaluated on retrospective data from a single implant center. The models performed well at all three time points, with a receiver operating characteristic area under the curve (ROC AUC) of 78, 76, and 75%, respectively. This evaluation of model performance verifies the utility of these models in “real life” scenarios at an individual institution.
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spelling pubmed-61761122018-10-17 Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center Lohmueller, Lisa C. Kanwar, Manreet K. Bailey, Stephen Murali, Srinivas Antaki, James F. Front Med (Lausanne) Medicine Use of a left ventricular assist device (LVAD) can benefit patients with end stage heart failure, but only with careful patient selection. In this study, previously derived Bayesian network models for predicting LVAD patient mortality at 1, 3, and 12 months post-implant were evaluated on retrospective data from a single implant center. The models performed well at all three time points, with a receiver operating characteristic area under the curve (ROC AUC) of 78, 76, and 75%, respectively. This evaluation of model performance verifies the utility of these models in “real life” scenarios at an individual institution. Frontiers Media S.A. 2018-10-02 /pmc/articles/PMC6176112/ /pubmed/30333978 http://dx.doi.org/10.3389/fmed.2018.00277 Text en Copyright © 2018 Lohmueller, Kanwar, Bailey, Murali and Antaki. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Lohmueller, Lisa C.
Kanwar, Manreet K.
Bailey, Stephen
Murali, Srinivas
Antaki, James F.
Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center
title Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center
title_full Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center
title_fullStr Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center
title_full_unstemmed Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center
title_short Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center
title_sort retrospective evaluation of bayesian risk models of lvad mortality at a single implant center
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6176112/
https://www.ncbi.nlm.nih.gov/pubmed/30333978
http://dx.doi.org/10.3389/fmed.2018.00277
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