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Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant

Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive...

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Autores principales: Konieczny, Andrzej, Stojanowski, Jakub, Rydzyńska, Klaudia, Kusztal, Mariusz, Krajewska, Magdalena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618905/
https://www.ncbi.nlm.nih.gov/pubmed/34830526
http://dx.doi.org/10.3390/jcm10225244
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author Konieczny, Andrzej
Stojanowski, Jakub
Rydzyńska, Klaudia
Kusztal, Mariusz
Krajewska, Magdalena
author_facet Konieczny, Andrzej
Stojanowski, Jakub
Rydzyńska, Klaudia
Kusztal, Mariusz
Krajewska, Magdalena
author_sort Konieczny, Andrzej
collection PubMed
description Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes.
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spelling pubmed-86189052021-11-27 Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant Konieczny, Andrzej Stojanowski, Jakub Rydzyńska, Klaudia Kusztal, Mariusz Krajewska, Magdalena J Clin Med Article Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes. MDPI 2021-11-11 /pmc/articles/PMC8618905/ /pubmed/34830526 http://dx.doi.org/10.3390/jcm10225244 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Konieczny, Andrzej
Stojanowski, Jakub
Rydzyńska, Klaudia
Kusztal, Mariusz
Krajewska, Magdalena
Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant
title Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant
title_full Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant
title_fullStr Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant
title_full_unstemmed Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant
title_short Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant
title_sort artificial intelligence—a tool for risk assessment of delayed-graft function in kidney transplant
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618905/
https://www.ncbi.nlm.nih.gov/pubmed/34830526
http://dx.doi.org/10.3390/jcm10225244
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