Director, Technical Supervisor, and Clinical Consultant University of Saskatchwean Saskatoon, SK, Canada
Aim: Kidney transplantation offers life-saving treatment for patients with ESKD, yet the development of de novo donor-specific antibodies (dnDSA) remains a major cause of chronic rejection and graft loss. Current molecular matching tools assess risk individually, missing key immune interactions. This study integrates three algorithms, HLA Epitope Registry (Epregistry), PIRCHE II, and SNOW, to improve risk prediction and enable personalized immunological monitoring.
Methods: A retrospective cohort of 594 kidney transplant recipients from SK (1981–2021) was conducted, tracking dnDSA development through January 2024. Epitope mismatch scores were calculated using the Epregistry, PIRCHE II, and SNOW. The ROC curve analysis determined optimal cutoffs for predicting DSA formation. Patients were stratified into high risk if all three scores exceeded the cutoff, intermediate risk if one or two scores were above the threshold, and low risk if all scores were below. Kaplan-Meier survival analysis (K-M) assessed DSA-free survival across risk categories.
Results: Among 594 recipients, 104 (17.5%) developed dnDSA, most frequently targeting HLA-DQ (72 cases, 69.2%), followed by HLA-A (24 cases, 23.1%). The distribution included 18 patients (Class I only), 67 (Class II only), and 19 (both Class I and II). The mean score for the DSA (+) group is higher than that of the (-) group across all three algorithms (P <0.05, Fig 1). ROC-derived cutoff values for predicting dnDSA were 22.5 (Epregistry), 30.5 (PIRCHE-II), and 5.5 (SNOW) for Class I, and 15.5, 16.5, and 5.5, respectively, for Class II (all p<0.05). K-M showed significantly lower dnDSA-free survival in high-risk recipients compared to low-risk recipients (log-rank test, p<0.001, Fig 2). Patients classified as high-risk by Epregistry, SNOW, and PIRCHE-II had the greatest likelihood of dnDSA formation and rejection, highlighting the advantage of multi-algorithm risk assessment.
Conclusion: Integrating Epregistry, PIRCHE-II, and SNOW enhances risk stratification for kidney transplant recipients. While Epregistry evaluates B-cell mismatches and PIRCHE-II focuses on T-cell mismatches, SNOW improves predictive accuracy, making this multi-algorithm approach more effective in identifying high-risk patients. By enabling earlier intervention and personalized immunosuppressive strategies, this model has the potential to improve long-term transplant success.
