文章基本信息

标题：IDENTIFYING IMPORTANT RISK FACTORS FOR SURVIVAL IN KIDNEY GRAFT FAILURE PATIENTS USING RANDOM SURVIVAL FORESTS
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作者：HAMIDI OMID ; POOROLAJAL JALAL ; FARHADIAN MARYAM 等
期刊名称：Iranian Journal of Public Health
印刷版ISSN：2251-6085
电子版ISSN：2251-6093
出版年度：2016
卷号：45
期号：1
页码：27-33
语种：English
出版社：Tehran University of Medical Sciences
摘要：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 re-sults. 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 im-portant 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.
关键词：RANDOM SURVIVAL FOREST; KIDNEY TRANSPLANTATION; COX PROPORTIONAL HAZARDS