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Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States

Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, do...

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Autores principales: Thongprayoon, Charat, Mao, Shennen A., Jadlowiec, Caroline C., Mao, Michael A., Leeaphorn, Napat, Kaewput, Wisit, Vaitla, Pradeep, Pattharanitima, Pattharawin, Tangpanithandee, Supawit, Krisanapan, Pajaree, Qureshi, Fawad, Nissaisorakarn, Pitchaphon, Cooper, Matthew, Cheungpasitporn, Wisit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224965/
https://www.ncbi.nlm.nih.gov/pubmed/35743357
http://dx.doi.org/10.3390/jcm11123288
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author Thongprayoon, Charat
Mao, Shennen A.
Jadlowiec, Caroline C.
Mao, Michael A.
Leeaphorn, Napat
Kaewput, Wisit
Vaitla, Pradeep
Pattharanitima, Pattharawin
Tangpanithandee, Supawit
Krisanapan, Pajaree
Qureshi, Fawad
Nissaisorakarn, Pitchaphon
Cooper, Matthew
Cheungpasitporn, Wisit
author_facet Thongprayoon, Charat
Mao, Shennen A.
Jadlowiec, Caroline C.
Mao, Michael A.
Leeaphorn, Napat
Kaewput, Wisit
Vaitla, Pradeep
Pattharanitima, Pattharawin
Tangpanithandee, Supawit
Krisanapan, Pajaree
Qureshi, Fawad
Nissaisorakarn, Pitchaphon
Cooper, Matthew
Cheungpasitporn, Wisit
author_sort Thongprayoon, Charat
collection PubMed
description Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m(2). Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m(2). In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.
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spelling pubmed-92249652022-06-24 Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States Thongprayoon, Charat Mao, Shennen A. Jadlowiec, Caroline C. Mao, Michael A. Leeaphorn, Napat Kaewput, Wisit Vaitla, Pradeep Pattharanitima, Pattharawin Tangpanithandee, Supawit Krisanapan, Pajaree Qureshi, Fawad Nissaisorakarn, Pitchaphon Cooper, Matthew Cheungpasitporn, Wisit J Clin Med Article Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m(2). Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m(2). In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation. MDPI 2022-06-08 /pmc/articles/PMC9224965/ /pubmed/35743357 http://dx.doi.org/10.3390/jcm11123288 Text en © 2022 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
Thongprayoon, Charat
Mao, Shennen A.
Jadlowiec, Caroline C.
Mao, Michael A.
Leeaphorn, Napat
Kaewput, Wisit
Vaitla, Pradeep
Pattharanitima, Pattharawin
Tangpanithandee, Supawit
Krisanapan, Pajaree
Qureshi, Fawad
Nissaisorakarn, Pitchaphon
Cooper, Matthew
Cheungpasitporn, Wisit
Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States
title Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States
title_full Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States
title_fullStr Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States
title_full_unstemmed Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States
title_short Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States
title_sort machine learning consensus clustering of morbidly obese kidney transplant recipients in the united states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224965/
https://www.ncbi.nlm.nih.gov/pubmed/35743357
http://dx.doi.org/10.3390/jcm11123288
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