Thongprayoon Charat, Vaitla Pradeep, Jadlowiec Caroline C, Leeaphorn Napat, Mao Shennen A, Mao Michael A, Qureshi Fahad, Kaewput Wisit, Qureshi Fawad, Tangpanithandee Supawit, Krisanapan Pajaree, Pattharanitima Pattharawin, Acharya Prakrati C, Nissaisorakarn Pitchaphon, Cooper Matthew, Cheungpasitporn Wisit
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA.
Medicines (Basel). 2023 Mar 27;10(4):25. doi: 10.3390/medicines10040025.
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%; < 0.001), patient death (3.4% vs. 11.4%; < 0.001), but similar one-year acute rejection (4.7% vs. 4.9%; = 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.
更好地了解非美国公民肾移植受者的不同表型/亚组,可能有助于移植界确定改善非美国公民肾移植受者结局的策略。本研究旨在采用无监督机器学习方法对非美国公民肾移植受者进行聚类;方法:我们利用2010年至2019年美国OPTN/UNOS数据库中与受者、供者和移植相关的特征,对非美国公民肾移植受者进行了共识聚类分析。使用标准化平均差确定每个聚类的关键特征。比较各聚类之间的移植后结局;结果:对11300例非美国公民肾移植受者进行了共识聚类分析,确定了两个最能代表临床特征的不同聚类。聚类1患者的特点是年龄小、先发肾移植或透析时间少于1年、有工作收入、有私人保险、供者无高血压、HLA错配数少的西班牙裔活体供者。相比之下,聚类2患者的特征是KDPI<85%的非扩展标准供者死亡。因此,聚类1患者肾移植后冷缺血时间缩短,机器灌注肾的比例较低,移植肾功能延迟发生率较低。与聚类1相比,聚类2的5年死亡截尾移植失败率更高(5.2%对9.8%;<0.001),患者死亡率更高(3.4%对11.4%;<0.001),但1年急性排斥反应相似(4.7%对4.9%;=;0.63);结论:机器学习聚类方法成功地在非美国公民肾移植受者中识别出两个具有不同表型的聚类,这些表型与不同的结局相关,包括移植肾丢失和患者生存。这些发现强调了对非美国公民肾移植受者进行个体化护理的必要性。