Aim: Long-term survival of renal allografts has improved over the last 20 years. However, less is known about current expectations for long-term allograft function as determined by estimated glomerular filtration rate (eGFR). The aim of this study was to investigate factors which affect graft function at 5 years' post-renal transplantation. The statistically significant factors were then used to construct a predictive model for expected eGFR at five years' post-transplant.
Methods: We retrospectively reviewed all adult patients who received a renal transplant in the Republic of Ireland between 1990 and 2004. Data collected included era of transplantation (1990-1994, 1995-1999, 2000-2004), donor and recipient age and gender, number of human leucocyte antigen mismatches, cold ischemia time (CIT), number of prior renal transplants, immunosuppressive regimen used and acute rejection episodes. Estimated GFR was calculated at 5 years after transplantation from patient data using the Modified Diet in Renal Disease (MDRD) equation. Consecutive sampling was used to divide the study population into two equal unbiased groups of 489 patients. The first group (derivation cohort) was used to construct a predictive model for eGFR five years' post-transplantation, the second (validation cohort) to test this model.
Results: Nine hundred and seventy eight patients were analyzed. The median age at transplantation was 43 years (range 18-78) and 620 (63.4%) were male. One hundred and seventy five patients (17.9%) had received a prior renal transplant. Improved eGFR at five years' post-transplantation was associated with tacrolimus-based combination immunosuppression, younger donor age, male recipient, absence of cytomegalovirus disease and absence of acute rejection episodes as independently significant factors (p < 0.05). The predictive model developed using these factors showed good correlation between predicted and actual median eGFR at five years. The model explained 20% of eGFR variability. The validation model findings were consistent with the derivation model (21% variability of eGFR explained by model using same covariates on new data).
Conclusion: The predictive model we have developed shows good correlation between predicted and actual median eGFR at five years' post-transplant. Applications of this model include comparison of current and future therapy options such as new immunosuppressive regimens.
Keywords: Allograft function; deceased donor; eGFR; kidney transplantation; predictive model.