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等待名单患者和移植患者生存模型的可预测性:计算LYFT

Predictability of survival models for waiting list and transplant patients: calculating LYFT.

作者信息

Wolfe R A, McCullough K P, Leichtman A B

机构信息

Scientific Registry of Transplant Recipients, Arbor Research Collaborative for Health, Ann Arbor, MI, USA.

出版信息

Am J Transplant. 2009 Jul;9(7):1523-7. doi: 10.1111/j.1600-6143.2009.02708.x.

Abstract

'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.

摘要

“移植后的生命年数”(LYFT)是指与完全未接受肾移植相比,肾移植候选人有望获得的额外生命年数。LYFT组件生存模型(有移植和无移植情况下的患者寿命,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,在预测长期生存方面具有可比性或更好的预测能力。此外,随着两名患者的医学特征(以及由此预测的寿命)差异增大,这些模型在预测哪名患者寿命更长方面越来越成功。三个LYFT组件方程的C统计量和相关性已使用独立、不重叠的对半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使从当前供体库中获得的生命年数大幅增加。

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