Predictability of Survival Models for Waiting List and Transplant Patients: Calculating LYFT
Predictability of Survival Models for Waiting List and Transplant Patients: Calculating LYFT
复制标题
DOI：
10.1111/j.1600-6143.2009.02708.x
复制
发表时间：
2009-07-01
影响因子：
8.8
通讯作者：
Leichtman, A. B.
中科院分区：
文献类型：
--
作者：
Wolfe, R. A.;McCullough, K. P.;Leichtman, A. B.
“移植后的生命年数”（LYFT）是指与从未接受过肾移植相比，候选人通过肾移植可以预期获得的额外寿命。LYFT组分生存模型（有和没有移植的患者寿命，以及移植物寿命）与目前用于器官分配的其他预测方程相比，是长期生存的可比或更好的预测因子。此外，这些模型在预测两个患者中的哪一个将随着他们的医学特征（以及因此预测的寿命）的不同而活得更长方面越来越成功。三个LYFT分量方程的C-统计量和相关性已经使用独立的、非重叠的半分随机样本进行了验证。基于这些生存模型的分配政策可能会导致从目前的捐赠者库中获得的生命年数大幅增加。
'Life years from transplant' (LYFT) is the extra years of life that a candidate can expect to achieve with a kidney transplant as compared to never receiving a kidney transplant at all. The LYFT component survival models (patient lifetimes with and without transplant, and graft lifetime) are comparable to or better predictors of long-term survival than are other predictive equations currently in use for organ allocation. Furthermore, these models are progressively more successful at predicting which of two patients will live longer as their medical characteristics (and thus predicted lifetimes) diverge. The C-statistics and the correlations for the three LYFT component equations have been validated using independent, nonoverlapping split-half random samples. Allocation policies based on these survival models could lead to substantial increases in the number of life years gained from the current donor pool.