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Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges

The “virtual” crossmatch (VXM) has become a critical tool to predict the compatibility between an organ donor and a potential recipient. Yet, nonstandardized laboratory practice can lead to variability in VXM interpretation. Therefore, UCLA’s VXM Exchange survey was designed to understand factors th...

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Autores principales: Locke, Arlene F., Hickey, Michelle, Valenzuela, Nicole M., Butler, Carrie, Sosa, Rebecca, Zheng, Ying, Gjertson, David, Reed, Elaine F., Zhang, Qiuheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358445/
https://www.ncbi.nlm.nih.gov/pubmed/36944607
http://dx.doi.org/10.1097/TP.0000000000004586
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author Locke, Arlene F.
Hickey, Michelle
Valenzuela, Nicole M.
Butler, Carrie
Sosa, Rebecca
Zheng, Ying
Gjertson, David
Reed, Elaine F.
Zhang, Qiuheng
author_facet Locke, Arlene F.
Hickey, Michelle
Valenzuela, Nicole M.
Butler, Carrie
Sosa, Rebecca
Zheng, Ying
Gjertson, David
Reed, Elaine F.
Zhang, Qiuheng
author_sort Locke, Arlene F.
collection PubMed
description The “virtual” crossmatch (VXM) has become a critical tool to predict the compatibility between an organ donor and a potential recipient. Yet, nonstandardized laboratory practice can lead to variability in VXM interpretation. Therefore, UCLA’s VXM Exchange survey was designed to understand factors that influence the variability of VXM prediction in the presence of HLA donor-specific antibody (DSA). Thirty-six donor blood samples and 72 HLA reference sera were sent to 35 participating laboratories to perform HLA antibody testing, flow crossmatch (FXM), and VXM from 2014 to 2019, consisting of 144 T/B-cell FXM pairs and 112 T/B-cell VXM pairs. In the FXM survey, 86% T-cell FXM and 84% B-cell FXM achieved >80% concordance among laboratories. In the VXM survey, 81% T-cell VXM and 80% VXM achieved >80% concordance. The concordance between FXM and VXM was 79% for T cell and 87% for B cell. The consensus between VXM and FXM was high with strong DSA. However, significant variability was observed in sera with (1) very high titer antibodies that exit prozone effect; (2) weak-to-moderate DSA, particularly in the presence of multiple weak DSAs; and (3) DSA against lowly expressed antigens. With the increasing use the VXM, standardization and continuous learning via exchange surveys will provide better understanding and quality controls for VXM to improve accuracy across all centers.
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spelling pubmed-103584452023-07-21 Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges Locke, Arlene F. Hickey, Michelle Valenzuela, Nicole M. Butler, Carrie Sosa, Rebecca Zheng, Ying Gjertson, David Reed, Elaine F. Zhang, Qiuheng Transplantation Original Clinical Science—General The “virtual” crossmatch (VXM) has become a critical tool to predict the compatibility between an organ donor and a potential recipient. Yet, nonstandardized laboratory practice can lead to variability in VXM interpretation. Therefore, UCLA’s VXM Exchange survey was designed to understand factors that influence the variability of VXM prediction in the presence of HLA donor-specific antibody (DSA). Thirty-six donor blood samples and 72 HLA reference sera were sent to 35 participating laboratories to perform HLA antibody testing, flow crossmatch (FXM), and VXM from 2014 to 2019, consisting of 144 T/B-cell FXM pairs and 112 T/B-cell VXM pairs. In the FXM survey, 86% T-cell FXM and 84% B-cell FXM achieved >80% concordance among laboratories. In the VXM survey, 81% T-cell VXM and 80% VXM achieved >80% concordance. The concordance between FXM and VXM was 79% for T cell and 87% for B cell. The consensus between VXM and FXM was high with strong DSA. However, significant variability was observed in sera with (1) very high titer antibodies that exit prozone effect; (2) weak-to-moderate DSA, particularly in the presence of multiple weak DSAs; and (3) DSA against lowly expressed antigens. With the increasing use the VXM, standardization and continuous learning via exchange surveys will provide better understanding and quality controls for VXM to improve accuracy across all centers. Lippincott Williams & Wilkins 2023-07-20 2023-08 /pmc/articles/PMC10358445/ /pubmed/36944607 http://dx.doi.org/10.1097/TP.0000000000004586 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Clinical Science—General
Locke, Arlene F.
Hickey, Michelle
Valenzuela, Nicole M.
Butler, Carrie
Sosa, Rebecca
Zheng, Ying
Gjertson, David
Reed, Elaine F.
Zhang, Qiuheng
Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges
title Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges
title_full Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges
title_fullStr Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges
title_full_unstemmed Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges
title_short Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges
title_sort virtual and reality: an analysis of the ucla virtual crossmatch exchanges
topic Original Clinical Science—General
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358445/
https://www.ncbi.nlm.nih.gov/pubmed/36944607
http://dx.doi.org/10.1097/TP.0000000000004586
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