Journal article
Use of 12-Month Renal Function and Baseline Clinical Factors to Predict Long-Term Graft Survival: Application to BENEFIT and BENEFIT-EXT Trials
Transplantation, Vol.93(2), pp.172-181
01/27/2012
DOI: 10.1097/TP.0b013e31823ec02a
PMID: 22198496
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(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.
Details
- Title: Subtitle
- Use of 12-Month Renal Function and Baseline Clinical Factors to Predict Long-Term Graft Survival: Application to BENEFIT and BENEFIT-EXT Trials
- Creators
- Mark A. Schnitzler - Saint Louis UniversityKrista L. Lentine - Saint Louis UniversityDavid Axelrod - Dartmouth–Hitchcock Medical CenterAdrian GheorghianMin You - Bristol Myers Squibb Hlth Serv, Plainsboro, NJ USAAnupama Kalsekar - Bristol Myers Squibb Hlth Serv, Plainsboro, NJ USAGilbert L'Italien - Bristol Myers Squibb Hlth Serv, Plainsboro, NJ USA
- Resource Type
- Journal article
- Publication Details
- Transplantation, Vol.93(2), pp.172-181
- Publisher
- Lippincott Williams & Wilkins
- DOI
- 10.1097/TP.0b013e31823ec02a
- PMID
- 22198496
- ISSN
- 0041-1337
- eISSN
- 1534-6080
- Number of pages
- 10
- Grant note
- Bristol-Myers Squibb
- Language
- English
- Date published
- 01/27/2012
- Academic Unit
- Surgery
- Record Identifier
- 9984322829502771
Metrics
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