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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8618905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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