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预测儿科等待名单上的候选者进行肝移植的可能性。

Predicting chance of liver transplantation for pediatric wait-list candidates.

作者信息

Luo Xun, Mogul Douglas B, Massie Allan B, Ishaque Tanveen, Bridges John F P, Segev Dorry L

机构信息

Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland.

出版信息

Pediatr Transplant. 2019 Nov;23(7):e13542. doi: 10.1111/petr.13542. Epub 2019 Jul 16.

Abstract

Information about wait-list time has been reported as one of the single most frequently asked questions by individuals awaiting a transplant but data regarding wait-list time have not been processed in a useful way for pediatric candidates. To predict chance of receiving a DDLT, we identified 6471 pediatric (<18 years), non status-1A, liver-only transplant candidates between 2006 and 2017 from the SRTR. Cox regression with shared frailty for DSA level effect was used to model the association of blood type, weight, allocation PELD and MELD, and DSA with chance of DDLT. Jackknife technique was used for validation. Median (interquartile range) wait-list time was 100 (34-309) days. Non-O Blood type, higher PELD/MELD score at listing, and DSA were associated with increased chance of DDLT, while age 1-5 years and 10-18 years was associated with lower chance of DDLT (P < 0.001 for all variables). Our model accurately predicted chance of transplant (C-statistic = 0.68) and was able to predict DDLT at specific follow-up times (eg, 3 months). This model can serve as the basis for an online tool that would provide useful information for pediatric wait-list candidates.

摘要

等待名单时间的信息已被报告为等待移植的个人最常问到的单一问题之一,但关于等待名单时间的数据尚未以对儿科候选者有用的方式进行处理。为了预测接受尸体供肝肝移植(DDLT)的可能性,我们从器官获取与移植网络(SRTR)中确定了2006年至2017年间6471名年龄小于18岁、非1A状态、仅接受肝脏移植的儿科候选者。采用具有供体特异性抗体(DSA)水平效应共享脆弱性的Cox回归模型来模拟血型、体重、分配的小儿终末期肝病评分(PELD)和终末期肝病模型(MELD)以及DSA与DDLT可能性之间的关联。采用刀切法进行验证。等待名单时间的中位数(四分位间距)为100(34 - 309)天。非O血型、登记时较高的PELD/MELD评分以及DSA与DDLT可能性增加相关,而1 - 5岁和10 - 18岁年龄组与DDLT可能性降低相关(所有变量P < 0.001)。我们的模型准确预测了移植可能性(C统计量 = 0.68),并且能够预测特定随访时间(如3个月)的DDLT。该模型可作为在线工具的基础,为儿科等待名单候选者提供有用信息。

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本文引用的文献

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OPTN/SRTR 2016 Annual Data Report: Liver.OPTN/SRTR 2016 年度数据报告:肝脏。
Am J Transplant. 2018 Jan;18 Suppl 1:172-253. doi: 10.1111/ajt.14559.
5
Prediction versus aetiology: common pitfalls and how to avoid them.预测与病因学:常见陷阱及规避方法。
Nephrol Dial Transplant. 2017 Apr 1;32(suppl_2):ii1-ii5. doi: 10.1093/ndt/gfw459.

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