Eplet mismatch scores and de novo donor-specific antibody development in simultaneous pancreas-kidney transplantation.

Journal Article (Journal Article)

Antibody-mediated rejection is the principal cause of allotransplant graft failure. Available studies differ on the impact of de novo donor specific antibody (dnDSA) in pancreas transplants but are limited by patient sample size and sera sample collection. High-resolution HLA incompatibility scoring algorithms are able to more accurately predict dnDSA development. We hypothesized that HLA incompatibility scores as determined by the HLA-Matchmaker, HLA-EMMA, and PIRCHE-II algorithms would serve as a predictor of de novo donor specific antibody (dnDSA) development and clarify the role dnDSA as detrimental to simultaneous pancreas-kidney graft survival. Our results show that female sex and race were significantly associated with dnDSA development and dnDSA development resulted in worse kidney and pancreas graft survival. The majority of individuals who developed dnDSA (88%), developed anti-HLA-DQ antibody in some combination with anti-HLA class I or -DR. A multivariate analysis of the incompatibility scores showed that both HLA-Matchmaker and PIRCHE-II scores predicted anti-DQ dnDSA development. An optimal cutoff threshold for incompatibility matching was obtained for these scores and demonstrated statistical significance when predicting freedom from anti-DQ DSA development. In conclusion, increased scores from high-resolution HLA matching predict dnDSA development, and dnDSA is associated with antibody-mediated rejection and worse pancreas and kidney graft outcomes.

Full Text

Duke Authors

Cited Authors

  • Ladowski, JM; Mullins, H; Romine, M; Kloda, D; Young, C; Hauptfeld-Dolejsek, V; Houp, J; Locke, J

Published Date

  • March 2021

Published In

Volume / Issue

  • 82 / 3

Start / End Page

  • 139 - 146

PubMed ID

  • 33390268

Electronic International Standard Serial Number (EISSN)

  • 1879-1166

International Standard Serial Number (ISSN)

  • 0198-8859

Digital Object Identifier (DOI)

  • 10.1016/j.humimm.2020.12.009

Language

  • eng