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Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database

Background: Black kidney transplant recipients have worse allograft outcomes compared to White recipients. The feature importance and feature interaction network analysis framework of machine learning random forest (RF) analysis may provide an understanding of RF structures to design strategies to p...

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Autores principales: Thongprayoon, Charat, Jadlowiec, Caroline C., Leeaphorn, Napat, Bruminhent, Jackrapong, Acharya, Prakrati C., Acharya, Chirag, Pattharanitima, Pattharawin, Kaewput, Wisit, Boonpheng, Boonphiphop, Cheungpasitporn, Wisit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621202/
https://www.ncbi.nlm.nih.gov/pubmed/34822363
http://dx.doi.org/10.3390/medicines8110066
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author Thongprayoon, Charat
Jadlowiec, Caroline C.
Leeaphorn, Napat
Bruminhent, Jackrapong
Acharya, Prakrati C.
Acharya, Chirag
Pattharanitima, Pattharawin
Kaewput, Wisit
Boonpheng, Boonphiphop
Cheungpasitporn, Wisit
author_facet Thongprayoon, Charat
Jadlowiec, Caroline C.
Leeaphorn, Napat
Bruminhent, Jackrapong
Acharya, Prakrati C.
Acharya, Chirag
Pattharanitima, Pattharawin
Kaewput, Wisit
Boonpheng, Boonphiphop
Cheungpasitporn, Wisit
author_sort Thongprayoon, Charat
collection PubMed
description Background: Black kidney transplant recipients have worse allograft outcomes compared to White recipients. The feature importance and feature interaction network analysis framework of machine learning random forest (RF) analysis may provide an understanding of RF structures to design strategies to prevent acute rejection among Black recipients. Methods: We conducted tree-based RF feature importance of Black kidney transplant recipients in United States from 2015 to 2019 in the UNOS database using the number of nodes, accuracy decrease, gini decrease, times_a_root, p value, and mean minimal depth. Feature interaction analysis was also performed to evaluate the most frequent occurrences in the RF classification run between correlated and uncorrelated pairs. Results: A total of 22,687 Black kidney transplant recipients were eligible for analysis. Of these, 1330 (6%) had acute rejection within 1 year after kidney transplant. Important variables in the RF models for acute rejection among Black kidney transplant recipients included recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration. The three most frequent interactions consisted of two numerical variables, including recipient age:donor age, recipient age:serum albumin, and recipient age:BMI, respectively. Conclusions: The application of tree-based RF feature importance and feature interaction network analysis framework identified recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration as important variables in the RF models for acute rejection among Black kidney transplant recipients in the United States.
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spelling pubmed-86212022021-11-27 Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database Thongprayoon, Charat Jadlowiec, Caroline C. Leeaphorn, Napat Bruminhent, Jackrapong Acharya, Prakrati C. Acharya, Chirag Pattharanitima, Pattharawin Kaewput, Wisit Boonpheng, Boonphiphop Cheungpasitporn, Wisit Medicines (Basel) Article Background: Black kidney transplant recipients have worse allograft outcomes compared to White recipients. The feature importance and feature interaction network analysis framework of machine learning random forest (RF) analysis may provide an understanding of RF structures to design strategies to prevent acute rejection among Black recipients. Methods: We conducted tree-based RF feature importance of Black kidney transplant recipients in United States from 2015 to 2019 in the UNOS database using the number of nodes, accuracy decrease, gini decrease, times_a_root, p value, and mean minimal depth. Feature interaction analysis was also performed to evaluate the most frequent occurrences in the RF classification run between correlated and uncorrelated pairs. Results: A total of 22,687 Black kidney transplant recipients were eligible for analysis. Of these, 1330 (6%) had acute rejection within 1 year after kidney transplant. Important variables in the RF models for acute rejection among Black kidney transplant recipients included recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration. The three most frequent interactions consisted of two numerical variables, including recipient age:donor age, recipient age:serum albumin, and recipient age:BMI, respectively. Conclusions: The application of tree-based RF feature importance and feature interaction network analysis framework identified recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration as important variables in the RF models for acute rejection among Black kidney transplant recipients in the United States. MDPI 2021-11-02 /pmc/articles/PMC8621202/ /pubmed/34822363 http://dx.doi.org/10.3390/medicines8110066 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
Thongprayoon, Charat
Jadlowiec, Caroline C.
Leeaphorn, Napat
Bruminhent, Jackrapong
Acharya, Prakrati C.
Acharya, Chirag
Pattharanitima, Pattharawin
Kaewput, Wisit
Boonpheng, Boonphiphop
Cheungpasitporn, Wisit
Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database
title Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database
title_full Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database
title_fullStr Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database
title_full_unstemmed Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database
title_short Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database
title_sort feature importance of acute rejection among black kidney transplant recipients by utilizing random forest analysis: an analysis of the unos database
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621202/
https://www.ncbi.nlm.nih.gov/pubmed/34822363
http://dx.doi.org/10.3390/medicines8110066
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