Use of 12-month renal function and baseline clinical factors to predict long-term graft survival: Application to BENEFIT and BENEFIT-EXT trials

Mark A. Schnitzler, Krista L. Lentine, David Axelrod, Adrian Gheorghian, Min You, Anupama Kalsekar, Gilbert L'Italien

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

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, 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. 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.

Original languageEnglish (US)
Pages (from-to)172-181
Number of pages10
JournalTransplantation
Volume93
Issue number2
DOIs
StatePublished - Jan 27 2012

Keywords

  • Glomerular filtration rate
  • Graft failure
  • Kidney transplantation
  • Mortality
  • Regression analysis
  • Validation

ASJC Scopus subject areas

  • Transplantation

Fingerprint

Dive into the research topics of 'Use of 12-month renal function and baseline clinical factors to predict long-term graft survival: Application to BENEFIT and BENEFIT-EXT trials'. Together they form a unique fingerprint.

Cite this