Predicting kidney graft failure using time-dependent renal function covariates.

Chronic rejection and recurrent disease are the major causes of late graft failure in renal transplantation. To assess outcome, most researchers use Cox proportional hazard analysis with time-fixed covariates. We developed a model adding time-dependent renal function covariates to improve the prediction of late graft failure. We studied 692 kidney transplants at the Leiden University Medical Center that had functioned for at least 6 months. Graft failure from chronic rejection or recurrent disease occurred in 106 patients. The reciprocal of last recorded serum creatinine (RC), the ratio of RC and RC at 6 months (RC6), and the time elapsed since last observation (TEL) were used as time-dependent covariates. Cadaveric donor transplantation, a lower RC, and a lower ratio of RC/RC6 were independently associated with graft failure. The impact of the last recorded RC was dependent on its value, TEL, and the time since transplantation. Validation of the model confirmed much higher failure predictions in those with subsequent graft failure compared with nonfailures. This study illustrates that the prediction of late graft failure could be improved significantly by using time-dependent renal function covariates.