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 m(2), 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 m(2). 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.