Maximizing matching, equity and survival in kidney transplantation using molecular HLA immunogenicity quantitation

• Published in: Computers in biology and medicine 2024-05, Vol.174, p.108452, Article 108452
• Main Authors: Syed, Fayeq Jeelani, Bekbolsynov, Dulat, Stepkowski, Stanislaw, Kaur, Devinder, Green, Robert C.
• Format: Article
• Language: English
• Subjects: African Americans equality; Algorithms; Allocation; Black or African American; Clustering; Female; Graft survival; Graft Survival - immunology; Histocompatibility antigen HLA; Histocompatibility Testing - methods; HLA; HLA Antigens - immunology; Humans; Hydrophobicity; Immunogenicity; Kidney matching; Kidney Transplantation; Kidney transplants; Male; Matching; Survival; Transplants; Unsupervised survival analysis; White
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108452

HLA matching improves long-term outcomes of kidney transplantation, yet implementation challenges persist, particularly within the African American (Black) patient demographic due to donor scarcity. Consequently, kidney survival rates among Black patients significantly lag behind those of other racial groups. A refined matching scheme holds promise for improving kidney survival, with prioritized matching for Black patients potentially bolstering rates of HLA-matched transplants. To facilitate quantity, quality and equity in kidney transplants, we propose two matching algorithms based on quantification of HLA immunogenicity using the hydrophobic mismatch score (HMS) for prospective transplants. We mined the national transplant patient database (SRTR) for a diverse group of donors and recipients with known racial backgrounds. Additionally, we use novel methods to infer survival assessment in the simulated transplants generated by our matching algorithms, in the absence of actual target outcomes, utilizing modified unsupervised clustering techniques. Our allocation algorithms demonstrated the ability to match 87.7% of Black and 86.1% of White recipients under the HLA immunogenicity threshold of 10. Notably, at the lowest HMS threshold of 0, 4.4% of Black and 12.1% of White recipients were matched, a marked increase from the 1.8% and 6.6% matched under the prevailing allocation scheme. Furthermore, our allocation algorithms yielded similar or improved survival rates, as illustrated by Kaplan–Meier (KM) curves, and enhanced survival prediction accuracy, evidenced by C-indices and Integrated Brier Scores. •Two algorithms maximize post-transplant kidney allocation survival using HLA.•Molecular HLA type-based immunogenicity scale improved survival and survival prediction.•Unsupervised learning predicts survival well in the absence of outcomes.