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Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes

Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT) recipients with DGF using OPTN/UNOS data. DGF...

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Autores principales: Jadlowiec, Caroline C., Thongprayoon, Charat, Leeaphorn, Napat, Kaewput, Wisit, Pattharanitima, Pattharawin, Cooper, Matthew, Cheungpasitporn, Wisit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773391/
https://www.ncbi.nlm.nih.gov/pubmed/36568137
http://dx.doi.org/10.3389/ti.2022.10810
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author Jadlowiec, Caroline C.
Thongprayoon, Charat
Leeaphorn, Napat
Kaewput, Wisit
Pattharanitima, Pattharawin
Cooper, Matthew
Cheungpasitporn, Wisit
author_facet Jadlowiec, Caroline C.
Thongprayoon, Charat
Leeaphorn, Napat
Kaewput, Wisit
Pattharanitima, Pattharawin
Cooper, Matthew
Cheungpasitporn, Wisit
author_sort Jadlowiec, Caroline C.
collection PubMed
description Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT) recipients with DGF using OPTN/UNOS data. DGF was observed in 20.9% (n = 17,073) of KT and most kidneys had a KDPI score <85%. Four distinct clusters were identified. Cluster 1 recipients were young, high PRA re-transplants. Cluster 2 recipients were older diabetics and more likely to receive higher KDPI kidneys. Cluster 3 recipients were young, black, and non-diabetic; they received lower KDPI kidneys. Cluster 4 recipients were middle-aged, had diabetes or hypertension and received well-matched standard KDPI kidneys. By cluster, one-year patient survival was 95.7%, 92.5%, 97.2% and 94.3% (p < 0.001); one-year graft survival was 89.7%, 87.1%, 91.6%, and 88.7% (p < 0.001). There were no differences between clusters after accounting for death-censored graft loss (p = 0.08). Clinically meaningful differences in recipient characteristics were noted between clusters, however, after accounting for death and return to dialysis, there were no differences in death-censored graft loss. Greater emphasis on recipient comorbidities as contributors to DGF and outcomes may help improve utilization of DGF at-risk kidneys.
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spelling pubmed-97733912022-12-23 Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes Jadlowiec, Caroline C. Thongprayoon, Charat Leeaphorn, Napat Kaewput, Wisit Pattharanitima, Pattharawin Cooper, Matthew Cheungpasitporn, Wisit Transpl Int Health Archive Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT) recipients with DGF using OPTN/UNOS data. DGF was observed in 20.9% (n = 17,073) of KT and most kidneys had a KDPI score <85%. Four distinct clusters were identified. Cluster 1 recipients were young, high PRA re-transplants. Cluster 2 recipients were older diabetics and more likely to receive higher KDPI kidneys. Cluster 3 recipients were young, black, and non-diabetic; they received lower KDPI kidneys. Cluster 4 recipients were middle-aged, had diabetes or hypertension and received well-matched standard KDPI kidneys. By cluster, one-year patient survival was 95.7%, 92.5%, 97.2% and 94.3% (p < 0.001); one-year graft survival was 89.7%, 87.1%, 91.6%, and 88.7% (p < 0.001). There were no differences between clusters after accounting for death-censored graft loss (p = 0.08). Clinically meaningful differences in recipient characteristics were noted between clusters, however, after accounting for death and return to dialysis, there were no differences in death-censored graft loss. Greater emphasis on recipient comorbidities as contributors to DGF and outcomes may help improve utilization of DGF at-risk kidneys. Frontiers Media S.A. 2022-12-08 /pmc/articles/PMC9773391/ /pubmed/36568137 http://dx.doi.org/10.3389/ti.2022.10810 Text en Copyright © 2022 Jadlowiec, Thongprayoon, Leeaphorn, Kaewput, Pattharanitima, Cooper and Cheungpasitporn. 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 Health Archive
Jadlowiec, Caroline C.
Thongprayoon, Charat
Leeaphorn, Napat
Kaewput, Wisit
Pattharanitima, Pattharawin
Cooper, Matthew
Cheungpasitporn, Wisit
Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes
title Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes
title_full Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes
title_fullStr Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes
title_full_unstemmed Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes
title_short Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes
title_sort use of machine learning consensus clustering to identify distinct subtypes of kidney transplant recipients with dgf and associated outcomes
topic Health Archive
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773391/
https://www.ncbi.nlm.nih.gov/pubmed/36568137
http://dx.doi.org/10.3389/ti.2022.10810
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