Time to Move on: HLA Matching Should Be Reconsidered in Modern Deceased Donor Kidney Allocation

Balancing equity and human leukocyte antigen matching in deceased-donor kidney allocation with eplet mismatch

Human leukocyte antigen-level matching in US kidney allocation has been deemphasized due to its role in elevating racial disparities. Molecular matching based on eplets might improve risk stratification compared to antigen matching, but the magnitude of racial disparities in molecular matching is not known. To assign eplets unambiguously, we utilized a cohort of 5193 individuals with high-resolution allele-level human leukocyte antigen genotypes from the National Kidney Registry. Using repeated random sampling to simulate donor-recipient genotype pairings based on the ethnic composition of the historical US deceased-donor pool, we profiled the percentage of well-matched donors available for candidates by ethnicity. The prevalence of well-matched donors with 0-DR/DQ eplet mismatch was 3-fold less racially disparate for Black and Asian candidates and 2-fold less for Latino candidates compared to 0-ABDR antigen mismatches. Compared to 0-DR antigen mismatch, 0-DR eplet mismatch was 1.33-fold more racially disparate for Asian and 1.28-fold more for Latino, with similar disparity for Black candidates, whereas 0-DQ eplet mismatch reduced disparities, showing 1.26-fold less disparity for Black, 1.14-fold less for Latino, but 1.26-fold higher for Asian candidates. The prevalence of well-matched donors for candidates of different ethnicities varied according to which molecules were chosen to define a low-risk match.

Human leukocyte antigen mismatch and circulating donor-specific antibodies predict graft loss after kidney transplantation: A retrospective study from Campania region - Italy

Donor-specific antibodies (DSA) are an established biomarker predicting antibody-mediated rejection, as the leading cause of graft loss after kidney transplantation. Furthermore, human leukocyte antigen (HLA) matching offers a more precise assessment of donor-recipient HLA compatibility and may prevent more effectively sensitization against allograft tissue. Indeed, increased number of HLA mismatches (MM) is significantly associated with a higher risk of immunological rejection, de novo DSA (dnDSA) development, and graft failure. Over the last decade, a comprehensive approach to optimize kidney matching and monitor transplant recipients for acute and chronic graft dysfunction was the goal for the success of the kidney transplantation. In our long-term retrospective study, we have found that pre- and post-transplantation HLA antibodies were significantly associated with de novo dnDSA occurrence (pre-transplant HLA Class I antibodies p = 0.039p < 0.05; pre-transplant HLA Class II antibodies p = 0.011p < 0.05; post-transplant HLA Class I non-DSA antibodies p < 0.01; post-transplant HLA Class II non-DSA antibodies p < 0.01). In addition, HLA MM at locus A (hazard ratio (HR), 2.44; 95 % confidence interval (CI): 1.15-5.16; p = 0.01 hazard ratio (HR), 2.33; 95 % confidence interval (CI):1.132-4.805; p = 0.02) and DSA Class I (HR, 10.24; 95 % CI: 1.44-72.62; p = 0.02 HR, 5.539; 95 % CI: 1.264-24.272; p = 0.02) appeared to be significant predictors of poorer graft survival. Our investigation demonstrates the long medium-term experience of DSA development occurrence in patients with after kidney transplantation in Campania region - Italy.

Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching

(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor-recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor-recipient matching. A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. The first scenario used the original dataset, the second used a merged version of the dataset, and the last scenario used a hierarchical architecture model. Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. Finally, the ML-based donor-recipient matching model was integrated into a web-based platform called Nephron. (3) Results: The gradient boost model was the top performer, achieving a remarkable and consistent accuracy rate of 98% across the three experimental scenarios. Furthermore, the custom ranking algorithm outperformed the conventional cosine and Jaccard similarity methods in identifying the most suitable recipients. Importantly, the platform not only facilitated efficient patient selection and prioritisation for kidney allocation but can be flexibly adapted for other solid organ allocation systems built on similar criteria. (4) Conclusions: This study proposes an ML-based approach to optimize donor-recipient matching within the kidney allocation process. Successful implementation of this methodology demonstrates significant potential to enhance both efficiency and fairness in kidney transplantation.

Role of HLA in cardiothoracic transplantation

In current clinical practice, transplant clinicians create collaborative working relationships with histocompatibility laboratory scientists to identify the risk of long-term graft failure, which may assist in establishing strategies for treatment and surveillance. Transplant immunology research also focuses on optimizing human leukocyte antibody tissue typing and defines the most effective test for detecting the presence of donor-specific antibodies. Although several studies have been conducted, data on pediatric heart transplant recipients are limited. Epitope load information may be utilized to identify donors with permissible human leukocyte antibody mismatches to increase transplant success. Although current guidelines do not consider human leukocyte antibody epitope-based matching tools, these guidelines could be useful for identifying recipients at a high risk of donor-specific antibody production, which would be appropriate for routine donor-specific antibody screening to initiate early interventions to prevent antibody-mediated rejection. Human leukocyte antibody matching at the epitope level offers an effective approach for identifying acceptable mismatches in sensitized patients and provides information about epitope loads. In the future, eplet matching may be used to define the best immunosuppressive therapy protocol for cardiothoracic organ transplantation. This report provides an overview of the role of human leukocyte antibodies in heart and lung transplantation.

Predicting Kidney Transplantation Outcomes from Donor and Recipient Characteristics at Time Zero: Development of a Mobile Application for Nephrologists

(1) Background: We report on the development of a predictive tool that can estimate kidney transplant survival at time zero. (2) Methods: This was an observational, retrospective study including 5078 transplants. Death-censored graft and patient survivals were calculated. (3) Results: Graft loss was associated with donor age (hazard ratio [HR], 1.021, 95% confidence interval [CI] 1.018-1.024, p < 0.001), uncontrolled donation after circulatory death (DCD) (HR 1.576, 95% CI 1.241-2.047, p < 0.001) and controlled DCD (HR 1.567, 95% CI 1.372-1.812, p < 0.001), panel reactive antibody percentage (HR 1.009, 95% CI 1.007-1.011, p < 0.001), and previous transplants (HR 1.494, 95% CI 1.367-1.634, p < 0.001). Patient survival was associated with recipient age (> 60 years, HR 5.507, 95% CI 4.524-6.704, p < 0.001 vs. < 40 years), donor age (HR 1.019, 95% CI 1.016-1.023, p < 0.001), dialysis vintage (HR 1.0000263, 95% CI 1.000225-1.000301, p < 0.01), and male sex (HR 1.229, 95% CI 1.135-1.332, p < 0.001). The C-statistics for graft and patient survival were 0.666 (95% CI: 0.646, 0.686) and 0.726 (95% CI: 0.710-0.742), respectively. (4) Conclusions: We developed a mobile app to estimate survival at time zero, which can guide decisions for organ allocation.

Heart Transplant Human Leukocyte Antigen Matching in the Modern Era

BACKGROUND

Although numerous reports have studied the consequences of human leukocyte antigen (HLA) mismatching in renal transplantation, there are limited and outdated data analyzing this association in thoracic organ transplantation. Therefore, our study reviewed the impact of HLA mismatching at both the total and the loci levels in the modern-era heart-transplant procedure on survival and chronic rejection outcomes.

METHODS

We performed a retrospective analysis of adult patients after heart transplant by using the United Network for Organ Sharing database from January 2005-July 2021. Total HLA and HLA-A, HLA-B and HLA-DR mismatches were analyzed. Survival and cardiac allograft vasculopathy were the outcomes of interest during a 10-year follow-up period using Kaplan-Meier curves, log-rank tests and multivariable regression models.

RESULTS

A total of 33,060 patients were included in this study. Recipients with a high degree of HLA mismatching had increased incidences of acute organ rejection. There were no significant differences in mortality rates among any of the total or loci level groups. Similarly, there were no significant differences between total HLA mismatch groups in time to first cardiac allograft vasculopathy, though mismatching at the HLA-DR locus was associated with an increased risk of cardiac allograft vasculopathy.

CONCLUSION

Our analysis suggests that HLA mismatch is not a significant predictor of survival in the modern era. Overall, the clinical implications of this study provide reassuring data for the continued use of non-HLA-matched donors in an effort to increase the donor pool. If HLA matching is to be considered for heart transplant donor-recipient selection, matching at the HLA-DR locus should take priority due to its association with cardiac allograft vasculopathy.