identifying important risk factors for survival in kidney graft failure patients using random survival forests

background: kidney transplantation is the best alternative treatment for end-stage renal disease. several studies have been devoted to investigate predisposing factors of graft rejection. however, there is inconsistency between the results. the objective of the present study was to utilize an intuitive and robust approach for variable selection, random survival forests (rsf), and to identify important risk factors in kidney transplantation patients. methods: the data set included 378 patients with kidney transplantation obtained through a historical cohort study in hamadan, western iran, from 1994 to 2011. the event of interest was chronic nonreversible graft rejection and the duration between kidney transplantation and rejection was considered as the survival time. rsf method was used to identify important risk factors for survival of the patients among the potential predictors of graft rejection. results: the mean survival time was 7.35±4.62 yr. thirty-seven episodes of rejection were occurred. the most important predictors of survival were cold ischemic time, recipient's age, creatinine level at discharge, donors’ age and duration of hospitalization. rsf method predicted survival better than the conventional cox-proportional hazards model (out-of-bag c-index of 0.965 for rsf vs. 0.766 for cox model and integrated brier score of 0.081 for rsf vs. 0.088 for cox model). conclusion: a rsf model in the kidney transplantation patients outperformed traditional cox-proportional hazard model. rsf is a promising method that may serve as a more intuitive approach to identify important risk factors for graft rejection.