Modeling graft loss in patients with donor-specific antibody at baseline using the Birmingham-Mayo (BirMay) predictor: Implications for clinical trials

Abstract

Predicting which renal allografts will fail and the likely cause of failure is important in clinical trial design to either enrich patient populations to be or as surrogate efficacy endpoints for trials aimed at improving long-term graft survival. This study tests our previous Birmingham-Mayo model (termed the BirMay Predictor) developed in a lowrisk kidney transplant population in order to predict the outcome of patients with donor specific alloantibody (DSA) at the time of transplantation and identify new factors to improve graft loss prediction in DSA+ patients. We wanted define ways to enrich the population for future therapeutic intervention trials. The discovery set included 147 patients from Mayo Cohort and the validation set included 111 patients from the Paris Cohort-all of whom had DSA at the time of transplantation. The BirMay predictor performed well predicting 5-year outcome well in DSA+ patients (Mayo C statistic = 0.784 and Paris C statistic = 0.860). Developing a new model did not improve on this performance. A high negative predictive value of greater than 90% in both cohorts excluded allografts not destined to fail within 5 years. We conclude that graft-survival models including histology predict graft loss well, both in DSA+ cohorts as well as DSA-patients. K E Y W O R D S alloantibody, clinical research/practice, kidney (allograft) function/dysfunction, kidney transplantation/nephrology, pathology/histopathology, protocol biopsy, risk assessment/risk stratification 1 | INTRODUC TI ON Predicting which renal allografts will fail and the likely cause of failure is important in clinical trial design to either enrich patient populations to be treated (eg, studies design to treat antibody-mediated rejection [ABMR]) or as surrogate efficacy endpoints for trials aimed at improving long-term graft survival. Several studies have demonstrated that kidney transplant recipients who have DSA at the time of transplantation have inferior outcomes to those without DSA. 1-3 Clearly, new therapy is needed to overcome the immunologic hurdle of preformed

Progressive decline of function in renal allografts with normal 1‐year biopsies: Gene expression studies fail to identify a classifier

“…The ability to identify grafts at risk for failure would improve our ability to counsel patients and to conduct clinical intervention trials. We and others have shown that in kidneys that survive a year, we can predict 5‐year graft loss using a combination of clinical factors and histologic findings on 1‐year surveillance biopsy 1–3 . However, we also have shown that these predictive factors are rare (almost 90% of patients have a low‐risk profile) and that approximately half of all graft losses at 5 years occur in patients with a low risk profile 1 …”

Longitudinal estimated glomerular filtration rate (eGFR) modeling in long‐term renal function to inform clinical trial design in kidney transplantation

“…Even models not limited to linear trajectories may ignore these early timepoints 13 . Current nonlinear models of renal trajectories following kidney transplantation tend to be latent class models, 13,14 which focus on grouping patients into different categories of risk groups, or survival models of graft failure 15–19 . While risk evaluation is valuable, a nonlinear, longitudinal model that captures both the early and later phases of kidney transplant trajectories would help explain how the underlying, clinically relevant mechanisms affect the time course of kidney allograft function.…”

“… 13 Current nonlinear models of renal trajectories following kidney transplantation tend to be latent class models, 13 , 14 which focus on grouping patients into different categories of risk groups, or survival models of graft failure. 15 , 16 , 17 , 18 , 19 While risk evaluation is valuable, a nonlinear, longitudinal model that captures both the early and later phases of kidney transplant trajectories would help explain how the underlying, clinically relevant mechanisms affect the time course of kidney allograft function. Other disease areas, like Duchenne's muscular dystrophy, have features of disease progression that are well captured by a nonlinear model 20 ; such a switch also has potential to benefit understanding of kidney allograft function.…”