Predicting Individual Renal Allograft Outcomes Using Risk Models with 1-Year Surveillance Biopsy and Alloantibody Data.

Abstract

The ability to predict outcomes for individual patients would be a significant advance for not only counseling, but also identifying those for whom interventions may be needed. The goals of this study were to validate an existing risk prediction score that incorporates easily obtainable clinical factors and determine if histologic findings at 1-year surveillance biopsy and/or serum donor-specific alloantibody status could improve predictability of graft loss by 5 years. We retrospectively studied 1465 adults who received a solitary kidney transplant between January of 1999 and December of 2008 and had sufficiently detailed 5-year follow-up data for modeling. In this cohort, the Birmingham risk model (incorporating recipient factors at 1 year, including age, sex, ethnicity, renal function, proteinuria, and prior acute rejection) predicted death-censored and overall graft survival (c statistics =0.84 and 0.78, respectively). The presence of glomerulitis or chronic interstitial fibrosis (g and ci scores by Banff, respectively) on 1-year biopsy specimens independently correlated with graft loss by 5 years. Adding these variables to the model for death-censored graft loss increased predictability (c statistic =0.90), improved calibration (ability to stratify risk from high to low), and reclassified risk of failure in 29% of patients. Adding the presence of donor-specific alloantibody at 1 year did not improve predictability or reclassification but did improve calibration marginally. We conclude that, at 1 year after kidney transplant, a risk model of graft survival that incorporates clinical factors and histologic findings at surveillance biopsy is highly predictive of individual risk and well calibrated.