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Prediction tool for renal adaptation after living kidney donation using interpretable machine learning

INTRODUCTION: Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors’ high life expectancy and elderly donors’ comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable...

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Autores principales: Jeon, Junseok, Yu, Jae Yong, Song, Yeejun, Jung, Weon, Lee, Kyungho, Lee, Jung Eun, Huh, Wooseong, Cha, Won Chul, Jang, Hye Ryoun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375292/
https://www.ncbi.nlm.nih.gov/pubmed/37521345
http://dx.doi.org/10.3389/fmed.2023.1222973
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author Jeon, Junseok
Yu, Jae Yong
Song, Yeejun
Jung, Weon
Lee, Kyungho
Lee, Jung Eun
Huh, Wooseong
Cha, Won Chul
Jang, Hye Ryoun
author_facet Jeon, Junseok
Yu, Jae Yong
Song, Yeejun
Jung, Weon
Lee, Kyungho
Lee, Jung Eun
Huh, Wooseong
Cha, Won Chul
Jang, Hye Ryoun
author_sort Jeon, Junseok
collection PubMed
description INTRODUCTION: Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors’ high life expectancy and elderly donors’ comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning. METHODS: The study included 823 living kidney donors who underwent nephrectomy in 2009–2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m(2) and ≥ 65% of the pre-donation values, respectively. RESULTS: The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762–0.930) and 0.626 (0.541–0.712), while the areas under the precision-recall curve were 0.965 (0.944–0.978) and 0.709 (0.647–0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed. CONCLUSION: The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application.
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spelling pubmed-103752922023-07-29 Prediction tool for renal adaptation after living kidney donation using interpretable machine learning Jeon, Junseok Yu, Jae Yong Song, Yeejun Jung, Weon Lee, Kyungho Lee, Jung Eun Huh, Wooseong Cha, Won Chul Jang, Hye Ryoun Front Med (Lausanne) Medicine INTRODUCTION: Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors’ high life expectancy and elderly donors’ comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning. METHODS: The study included 823 living kidney donors who underwent nephrectomy in 2009–2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m(2) and ≥ 65% of the pre-donation values, respectively. RESULTS: The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762–0.930) and 0.626 (0.541–0.712), while the areas under the precision-recall curve were 0.965 (0.944–0.978) and 0.709 (0.647–0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed. CONCLUSION: The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application. Frontiers Media S.A. 2023-07-14 /pmc/articles/PMC10375292/ /pubmed/37521345 http://dx.doi.org/10.3389/fmed.2023.1222973 Text en Copyright © 2023 Jeon, Yu, Song, Jung, Lee, Lee, Huh, Cha and Jang. https://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
Jeon, Junseok
Yu, Jae Yong
Song, Yeejun
Jung, Weon
Lee, Kyungho
Lee, Jung Eun
Huh, Wooseong
Cha, Won Chul
Jang, Hye Ryoun
Prediction tool for renal adaptation after living kidney donation using interpretable machine learning
title Prediction tool for renal adaptation after living kidney donation using interpretable machine learning
title_full Prediction tool for renal adaptation after living kidney donation using interpretable machine learning
title_fullStr Prediction tool for renal adaptation after living kidney donation using interpretable machine learning
title_full_unstemmed Prediction tool for renal adaptation after living kidney donation using interpretable machine learning
title_short Prediction tool for renal adaptation after living kidney donation using interpretable machine learning
title_sort prediction tool for renal adaptation after living kidney donation using interpretable machine learning
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375292/
https://www.ncbi.nlm.nih.gov/pubmed/37521345
http://dx.doi.org/10.3389/fmed.2023.1222973
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