Butala Neel M, King Marissa D, Reitsma William, Formica Richard N, Abt Peter L, Reese Peter P, Parikh Chirag R
1 Department of Medicine, Massachusetts General Hospital, Boston, MA. 2 Yale School of Management, New Haven, CT. 3 NJ Sharing Network, New Providence, NJ. 4 Section of Nephrology, Department of Medicine, Yale School of Medicine, New Haven, CT. 5 Division of Transplant Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, PA. 6 Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA. 7 Renal Division, Department of Medicine, University of Pennsylvania, Philadelphia, PA. 8 Program of Applied Translational Research, Department of Medicine, Yale School of Medicine, New Haven, CT. 9 Clinical Epidemiology Research Center, Veterans Affairs Medical Center, West Haven, CT.
Transplantation. 2015 Dec;99(12):2617-24. doi: 10.1097/TP.0000000000000773.
Given growth in kidney transplant waitlists and discard rates, donor kidney acceptance is an important problem. We used network analysis to examine whether organ procurement organization (OPO) network centrality affects discard and outcomes.
We identified 106,160 deceased donor kidneys recovered for transplant from 2000 to 2010 in Scientific Registry of Transplant Recipients. We constructed the transplant network by year with each OPO representing a node and each kidney-sharing relationship between OPOs representing a directed tie between nodes. Primary exposures were the number of different OPOs to which an OPO has given a kidney or from which an OPO has received a kidney in year preceding procurement year. Primary outcomes were discard, cold-ischemia time, delayed graft function, and 1-year graft loss. We used multivariable regression, restricting analysis to the 50% of OPOs with highest discard and stratifying remaining OPOs by kidney volume. Models controlled for kidney donor risk index, waitlist time, and kidney pumping.
An increase in one additional OPO to which a kidney was given by a procuring OPO in a year was associated with 1.4% lower likelihood of discard for a given kidney (odds ratio, 0.986; 95% confidence interval, 0.974-0.998) among OPOs procuring high kidney volume, but 2% higher likelihood of discard (odds ratio, 1.021; 95% confidence interval, 1.006-1.037) among OPOs procuring low kidney volume, with mixed associations with recipient outcomes.
Our study highlights the value of network analysis in revealing how broader kidney sharing is associated with levels of organ acceptance. We conclude interventions to promote broader inter-OPO sharing could be developed to reduce discard for a subset of OPOs.
鉴于肾移植等待名单的增长和废弃率,供肾的接受是一个重要问题。我们使用网络分析来研究器官获取组织(OPO)网络中心性是否会影响废弃率和移植结果。
我们在移植受者科学登记处识别了2000年至2010年期间回收用于移植的106,160个已故供肾。我们逐年构建移植网络,每个OPO代表一个节点,OPO之间的每个肾脏共享关系代表节点之间的一条有向边。主要暴露因素是在获取年份前一年,一个OPO向其提供过肾脏或从其接收过肾脏的不同OPO的数量。主要结局包括废弃、冷缺血时间、移植肾功能延迟恢复和1年移植肾丢失。我们使用多变量回归,将分析限制在废弃率最高的50%的OPO,并按肾脏体积对其余OPO进行分层。模型控制了供肾者风险指数、等待时间和肾脏灌注。
在获取高肾脏体积的OPO中,采购OPO每年向其提供肾脏的OPO数量每增加一个,给定肾脏的废弃可能性降低1.4%(优势比,0.986;95%置信区间,0.974 - 0.998),但在获取低肾脏体积的OPO中,废弃可能性增加2%(优势比,1.021;95%置信区间,1.006 - 1.037),与受者结局存在混合关联。
我们的研究突出了网络分析在揭示更广泛的肾脏共享如何与器官接受水平相关联方面的价值。我们得出结论,可以制定促进更广泛的OPO间共享的干预措施,以减少一部分OPO的废弃情况。