A rational approach to guide cost-effective de novo donor-specific antibody surveillance with tacrolimus immunosuppression

• DOI: 10.1016/j.ajt.2023.07.025
• Publication Date: 2023-08-04T07:40:09Z
• Authors: Chris Wiebe; Rob Balshaw; Ian W. Gibson; Julie Ho; Jamie Shaw; Martin Karpinski; Aaron Trachtenberg; Denise Pochinco; Aviva Goldberg; Patricia Birk; Maury Pinsk; David N. Rush; Peter W. Nickerson
• Keywords: Graft Rejection; Immunosuppression Therapy; Cost-Benefit Analysis; Graft Survival; HLA-DR Antigens; Tacrolimus; Antibodies; 3. Good health; HLA Antigens; Risk Factors; Isoantibodies; Child, Preschool; Humans; Prospective Studies; Child; Retrospective Studies

De novo donor-specific antibody (dnDSA) after renal transplantation has been shown to correlate with antibody-mediated rejection and allograft loss. However, the lack of proven interventions and the time and cost associated with annual screening for dnDSA are difficult to justify for all recipients. We studied a well-characterized consecutive cohort (n = 949) with over 15 years of prospective dnDSA surveillance to identify risk factors that would help institute a resource-responsible surveillance strategy. Younger recipient age and HLA-DR/DQ molecular mismatch were independent predictors of dnDSA development. Combining both risk factors into recipient age molecular mismatch categories, we found that 52% of recipients could be categorized as low-risk for dnDSA development (median subclinical dnDSA-free survival at 5 and 10 years, 98% and 97%, respectively). After adjustment, multivariate correlates of dnDSA development included tacrolimus versus cyclosporin maintenance immunosuppression (hazard ratio [HR], 0.37; 95% CI, 0.2-0.6; P < .0001) and recipient age molecular mismatch category: intermediate versus low (HR, 2.48; 95% CI, 1.5-4.2; P = .0007), high versus intermediate (HR, 2.56; 95% CI, 1.6-4.2; P = .0002), and high versus low (HR, 6.36; 95% CI, 3.7-10.8; P < .00001). When combined, recipient age and HLA-DR/DQ molecular mismatch provide a novel data-driven approach to reduce testing by >50% while selecting those most likely to benefit from dnDSA surveillance.