New models for donor-recipient matching in lung transplantations.

<h4>Objective</h4>One of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of pa...

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Autores principales:	J M Dueñas-Jurado, P A Gutiérrez, A Casado-Adam, F Santos-Luna, A Salvatierra-Velázquez, S Cárcel, C J C Robles-Arista, C Hervás-Martínez
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d6c9e7861fc34eb08004885088f14e12
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spelling	oai:doaj.org-article:d6c9e7861fc34eb08004885088f14e122021-11-25T06:23:37ZNew models for donor-recipient matching in lung transplantations.1932-620310.1371/journal.pone.0252148https://doaj.org/article/d6c9e7861fc34eb08004885088f14e122021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252148https://doaj.org/toc/1932-6203<h4>Objective</h4>One of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process.<h4>Methods</h4>This was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtained via our experience of clinical practice in the donation and transplantation process. These were used to create various classification models, including classical statistical methods and also incorporating newer machine-learning approaches.<h4>Results</h4>The proposed model represents a powerful tool for donor-recipient matching, which in this current work, exceeded the capacity of classical statistical methods. The variables that predicted an increase in the probability of survival were: higher pre-transplant and post-transplant functional vital capacity (FVC), lower pre-transplant carbon dioxide (PCO2) pressure, lower donor mechanical ventilation, and shorter ischemia time. The variables that negatively influenced transplant survival were low forced expiratory volume in the first second (FEV1) pre-transplant, lower arterial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ratio, bilobar transplant, elderly recipient and donor, donor-recipient graft disproportion requiring a surgical reduction (Tailor), type of combined transplant, need for cardiopulmonary bypass during the surgery, death of the donor due to head trauma, hospitalization status before surgery, and female and male recipient donor sex.<h4>Conclusions</h4>These results show the difficulty of the problem which required the introduction of other variables into the analysis. The combination of classical statistical methods and machine learning can support decision-making about the compatibility between donors and recipients. This helps to facilitate reliable prediction and to optimize the grafts for transplantation, thereby improving the transplanted patient survival rate.J M Dueñas-JuradoP A GutiérrezA Casado-AdamF Santos-LunaA Salvatierra-VelázquezS CárcelC J C Robles-AristaC Hervás-MartínezPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252148 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q J M Dueñas-Jurado P A Gutiérrez A Casado-Adam F Santos-Luna A Salvatierra-Velázquez S Cárcel C J C Robles-Arista C Hervás-Martínez New models for donor-recipient matching in lung transplantations.
description	<h4>Objective</h4>One of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process.<h4>Methods</h4>This was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtained via our experience of clinical practice in the donation and transplantation process. These were used to create various classification models, including classical statistical methods and also incorporating newer machine-learning approaches.<h4>Results</h4>The proposed model represents a powerful tool for donor-recipient matching, which in this current work, exceeded the capacity of classical statistical methods. The variables that predicted an increase in the probability of survival were: higher pre-transplant and post-transplant functional vital capacity (FVC), lower pre-transplant carbon dioxide (PCO2) pressure, lower donor mechanical ventilation, and shorter ischemia time. The variables that negatively influenced transplant survival were low forced expiratory volume in the first second (FEV1) pre-transplant, lower arterial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ratio, bilobar transplant, elderly recipient and donor, donor-recipient graft disproportion requiring a surgical reduction (Tailor), type of combined transplant, need for cardiopulmonary bypass during the surgery, death of the donor due to head trauma, hospitalization status before surgery, and female and male recipient donor sex.<h4>Conclusions</h4>These results show the difficulty of the problem which required the introduction of other variables into the analysis. The combination of classical statistical methods and machine learning can support decision-making about the compatibility between donors and recipients. This helps to facilitate reliable prediction and to optimize the grafts for transplantation, thereby improving the transplanted patient survival rate.
format	article
author	J M Dueñas-Jurado P A Gutiérrez A Casado-Adam F Santos-Luna A Salvatierra-Velázquez S Cárcel C J C Robles-Arista C Hervás-Martínez
author_facet	J M Dueñas-Jurado P A Gutiérrez A Casado-Adam F Santos-Luna A Salvatierra-Velázquez S Cárcel C J C Robles-Arista C Hervás-Martínez
author_sort	J M Dueñas-Jurado
title	New models for donor-recipient matching in lung transplantations.
title_short	New models for donor-recipient matching in lung transplantations.
title_full	New models for donor-recipient matching in lung transplantations.
title_fullStr	New models for donor-recipient matching in lung transplantations.
title_full_unstemmed	New models for donor-recipient matching in lung transplantations.
title_sort	new models for donor-recipient matching in lung transplantations.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d6c9e7861fc34eb08004885088f14e12
work_keys_str_mv	AT jmduenasjurado newmodelsfordonorrecipientmatchinginlungtransplantations AT pagutierrez newmodelsfordonorrecipientmatchinginlungtransplantations AT acasadoadam newmodelsfordonorrecipientmatchinginlungtransplantations AT fsantosluna newmodelsfordonorrecipientmatchinginlungtransplantations AT asalvatierravelazquez newmodelsfordonorrecipientmatchinginlungtransplantations AT scarcel newmodelsfordonorrecipientmatchinginlungtransplantations AT cjcroblesarista newmodelsfordonorrecipientmatchinginlungtransplantations AT chervasmartinez newmodelsfordonorrecipientmatchinginlungtransplantations
_version_	1718413830317408256

New models for donor-recipient matching in lung transplantations.

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