通过机器学习共识聚类分析美国非公民肾移植受者的不同表型。

Charat Thongprayoon, Pradeep Vaitla, Caroline C Jadlowiec, Napat Leeaphorn, Shennen A Mao, Michael A Mao, Fahad Qureshi, Wisit Kaewput, Fawad Qureshi, Supawit Tangpanithandee, Pajaree Krisanapan, Pattharawin Pattharanitima, Prakrati C Acharya, Pitchaphon Nissaisorakarn, Matthew Cooper, Wisit Cheungpasitporn
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引用次数: 1

摘要

背景:更好地了解非美国人的不同表型/亚群。公民肾移植受者可以帮助移植社区确定改善非美国肾移植患者预后的策略。接受肾脏移植的市民。这项研究的目的是聚集非美国。使用无监督机器学习方法的公民肾移植受者;方法:我们对非美国患者的受体、供体和移植相关特征进行了一致的聚类分析。使用受体、供体和移植相关特征,在OPTN/UNOS数据库中检索2010年至2019年美国公民肾移植受者。每个聚类的关键特征使用标准化平均差来确定。比较各组移植后的预后;结果:对11,300例非美国患者进行了一致聚类分析。公民肾移植受者,并确定了两个不同的集群最能代表临床特征。第1组患者的显著特征是年龄小、提前肾移植或透析持续时间小于1年、工作收入、私人保险、非高血压供者和HLA不匹配数量少的西班牙裔活体供者。相比之下,第2组患者的特征是非ecd死亡供者(KDPI p < 0.001),患者死亡(3.4% vs. 11.4%;P < 0.001),但相似的一年急性排斥反应(4.7% vs. 4.9%;P = 0.63);结论:机器学习聚类方法成功地识别了非美国的两个聚类。具有不同表型的公民肾移植受者与不同的结果相关,包括同种异体移植物损失和患者生存。这些发现强调了对非美国患者进行个体化治疗的必要性。接受肾脏移植的市民。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.

Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.

Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.

Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.

Background: Better understanding of the different phenotypes/subgroups of non-U.S. citizen kidney transplant recipients may help the transplant community to identify strategies that improve outcomes among non-U.S. citizen kidney transplant recipients. This study aimed to cluster non-U.S. citizen kidney transplant recipients using an unsupervised machine learning approach; Methods: We conducted a consensus cluster analysis based on recipient-, donor-, and transplant- related characteristics in non-U.S. citizen kidney transplant recipients in the United States from 2010 to 2019 in the OPTN/UNOS database using recipient, donor, and transplant-related characteristics. Each cluster's key characteristics were identified using the standardized mean difference. Post-transplant outcomes were compared among the clusters; Results: Consensus cluster analysis was performed in 11,300 non-U.S. citizen kidney transplant recipients and identified two distinct clusters best representing clinical characteristics. Cluster 1 patients were notable for young age, preemptive kidney transplant or dialysis duration of less than 1 year, working income, private insurance, non-hypertensive donors, and Hispanic living donors with a low number of HLA mismatch. In contrast, cluster 2 patients were characterized by non-ECD deceased donors with KDPI <85%. Consequently, cluster 1 patients had reduced cold ischemia time, lower proportion of machine-perfused kidneys, and lower incidence of delayed graft function after kidney transplant. Cluster 2 had higher 5-year death-censored graft failure (5.2% vs. 9.8%; p < 0.001), patient death (3.4% vs. 11.4%; p < 0.001), but similar one-year acute rejection (4.7% vs. 4.9%; p = 0.63), compared to cluster 1; Conclusions: Machine learning clustering approach successfully identified two clusters among non-U.S. citizen kidney transplant recipients with distinct phenotypes that were associated with different outcomes, including allograft loss and patient survival. These findings underscore the need for individualized care for non-U.S. citizen kidney transplant recipients.

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