Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Urology & Nephrology, antibody-mediated rejection, HLA-DQ, HLA matching, kidney transplantation, ANTIBODY-MEDIATED REJECTION, DONOR-SPECIFIC ANTIBODIES, CLINICAL-RELEVANCE, GRAFT-SURVIVAL, HUMORAL THEORY, ALLOCATION, MISMATCH, FAILURE, IMPACT, LIVER, Graft Rejection, Graft Survival, HLA Antigens, HLA-DQ Antigens, Histocompatibility Testing, Humans, Isoantibodies, Kidney Transplantation, Tissue Donors, 1103 Clinical Sciences, 3202 Clinical sciences

Abstract:

The weight of human leukocyte antigen (HLA) matching in kidney allocation algorithms, especially in the United States, has been devalued in a stepwise manner, supported by the introduction of modern immunosuppression. The intent was further to reduce the observed ethnic/racial disparity, as data emerged associating HLA matching with decreased access to transplantation for African American patients. In recent years, it has been increasingly recognized that a leading cause of graft loss is chronic antibody-mediated rejection, attributed to the development of de novo antibodies against mismatched donor HLA expressed on the graft. These antibodies are most frequently against donor HLA-DQ molecules. Beyond their impact on graft survival, generation of de novo donor-specific HLA antibodies also leads to increased sensitization, as measured by panel-reactive antibody metrics. Consequently, access to transplantation for patients returning to the waitlist in need of a second transplant is compromised. Herein, we address the implications of reduced HLA matching policies in kidney allocation. We highlight the observed diminished outcome data, the significant financial burden, the long-term health consequences, and, more important, the unintended consequences. We further provide recommendations to examine the impact of donor-recipient HLA class II and specifically HLA-DQα1β1 mismatching, focusing on collection of appropriate data, application of creative simulation approaches, and reconsideration of best practices to reduce inequalities while optimizing patient outcomes.