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Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering

Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving incre...

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Autores principales: Thongprayoon, Charat, Miao, Jing, Jadlowiec, Caroline C., Mao, Shennen A., Mao, Michael A., Vaitla, Pradeep, Leeaphorn, Napat, Kaewput, Wisit, Pattharanitima, Pattharawin, Tangpanithandee, Supawit, Krisanapan, Pajaree, Nissaisorakarn, Pitchaphon, Cooper, Matthew, Cheungpasitporn, Wisit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223300/
https://www.ncbi.nlm.nih.gov/pubmed/37241209
http://dx.doi.org/10.3390/medicina59050977
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author Thongprayoon, Charat
Miao, Jing
Jadlowiec, Caroline C.
Mao, Shennen A.
Mao, Michael A.
Vaitla, Pradeep
Leeaphorn, Napat
Kaewput, Wisit
Pattharanitima, Pattharawin
Tangpanithandee, Supawit
Krisanapan, Pajaree
Nissaisorakarn, Pitchaphon
Cooper, Matthew
Cheungpasitporn, Wisit
author_facet Thongprayoon, Charat
Miao, Jing
Jadlowiec, Caroline C.
Mao, Shennen A.
Mao, Michael A.
Vaitla, Pradeep
Leeaphorn, Napat
Kaewput, Wisit
Pattharanitima, Pattharawin
Tangpanithandee, Supawit
Krisanapan, Pajaree
Nissaisorakarn, Pitchaphon
Cooper, Matthew
Cheungpasitporn, Wisit
author_sort Thongprayoon, Charat
collection PubMed
description Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients.
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spelling pubmed-102233002023-05-28 Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering Thongprayoon, Charat Miao, Jing Jadlowiec, Caroline C. Mao, Shennen A. Mao, Michael A. Vaitla, Pradeep Leeaphorn, Napat Kaewput, Wisit Pattharanitima, Pattharawin Tangpanithandee, Supawit Krisanapan, Pajaree Nissaisorakarn, Pitchaphon Cooper, Matthew Cheungpasitporn, Wisit Medicina (Kaunas) Article Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients. MDPI 2023-05-18 /pmc/articles/PMC10223300/ /pubmed/37241209 http://dx.doi.org/10.3390/medicina59050977 Text en © 2023 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
Miao, Jing
Jadlowiec, Caroline C.
Mao, Shennen A.
Mao, Michael A.
Vaitla, Pradeep
Leeaphorn, Napat
Kaewput, Wisit
Pattharanitima, Pattharawin
Tangpanithandee, Supawit
Krisanapan, Pajaree
Nissaisorakarn, Pitchaphon
Cooper, Matthew
Cheungpasitporn, Wisit
Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering
title Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering
title_full Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering
title_fullStr Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering
title_full_unstemmed Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering
title_short Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering
title_sort differences between very highly sensitized kidney transplant recipients as identified by machine learning consensus clustering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223300/
https://www.ncbi.nlm.nih.gov/pubmed/37241209
http://dx.doi.org/10.3390/medicina59050977
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