Background. Innovation in renal transplant management would benefit from identification of early markers that accurately predict long-term graft survival.
Methods. Data from the United States Renal Data System for kidney transplant recipients (1995–2004) were analyzed to develop prediction models for all-cause graft survival based on estimated glomerular filtration rate (eGFR), the presence or absence of acute rejection within 1 year, and recipient and donor demographic characteristics. The prediction models were applied to participants in the Belatacept Evaluation of Nephroprotection and Efficacy as First-line Immunosuppression Trial and Belatacept Evaluation of Nephroprotection and Efficacy as First-line Immunosuppression Trial—EXTended criteria donors trials comparing belatacept with cyclosporine in standard criteria donor (SCD) and expanded criteria donor (ECD) graft recipients, respectively, as an external validation of the model predictions in a diverse population.
Results. Compared with eGFR 60 mL/min/1.73 m2, the relative hazard for all-cause graft loss increased in an accelerating pattern with lower GFR to approximately eight and seven times, respectively, among SCD and ECD recipients with eGFR less than 15 mL/min/1.73 m2. When applied to the clinical trial samples, the predicted differences in all-cause graft survival of less intensive belatacept versus cyclosporine at the second transplant anniversary (SCD: 3.9%, 95% confidence interval [CI]: 3.6% to 4.2%; ECD: 4.1%, 95% CI: 3.5% to 4.7%) were similar to observed differences (SCD: 4.2%, 97.3% CI: −1.3% to 10.1%; ECD: 1.4%, 97.3% CI: −7.5% to 10.2%).
Conclusions. Accurate models of long-term graft survival can be developed using eGFR, donor, and recipient characteristics. Long-term survival prediction models may provide an efficient method for assessing the impact of novel pharmaceutical agents and clinical management protocols.