Thongprayoon Charat, Mao Shennen A, Jadlowiec Caroline C, Mao Michael A, Leeaphorn Napat, Kaewput Wisit, Vaitla Pradeep, Pattharanitima Pattharawin, Tangpanithandee Supawit, Krisanapan Pajaree, Qureshi Fawad, Nissaisorakarn Pitchaphon, Cooper Matthew, Cheungpasitporn Wisit
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA.
Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
J Clin Med. 2022 Jun 8;11(12):3288. doi: 10.3390/jcm11123288.
Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.
本研究旨在通过使用无监督机器学习方法，更好地描述病态肥胖肾移植受者及其临床特征和结局。方法：基于2010年至2019年OPTN/UNOS数据，对移植前BMI≥40kg/m²的肾移植受者的受者、供者和移植特征进行共识聚类分析。使用标准化平均差确定关键聚类特征。比较各聚类之间的移植后结局，包括死亡删失的移植物失败、患者死亡和急性移植物排斥反应。结果：共识聚类分析确定了3204例BMI≥40kg/m²的肾移植受者。在该队列中，识别出五个临床上不同的聚类。聚类1的受者主要为白人且未致敏，透析时间短或为抢先移植，更有可能接受活体供肾移植。聚类2的受者年龄较大且患有糖尿病。他们可能已透析超过3年，并接受标准KDPI的已故供肾。聚类3的受者年轻、为黑人，患有高血压或肾小球疾病继发的肾病。聚类3的受者透析超过3年，接受KDPI<85%的非扩展标准供体、年轻已故供肾移植。聚类4的受者患有糖尿病，透析时间可变，接受非扩展标准KDPI肾脏或活体供肾移植。聚类5的受者是致敏的年轻再次移植者。聚类1、2、3、4和5的1年患者生存率分别为98.0%、94.4%、98.5%、98.7%和97%，1年死亡删失的移植物生存率分别为98.1%、93.0%、96.1%、98.8%和93.0%。聚类2的1年患者生存率最差。聚类2和5的1年死亡删失的移植物生存率最差。结论：通过应用无监督机器学习，在病态肥胖肾移植受者中观察到不同的移植后结局。较早接受移植和活体供肾的受者显示出更好的结局。出乎意料的是，聚类3受者的移植物生存率降低可能突出了社会经济因素对移植后支持的影响，以及少数族裔在获得抢先和活体供肾移植方面处于不利地位。尽管存在与肥胖相关的担忧，但所有聚类的1年患者和移植物生存率都较好，肥胖本身应重新被视为肾移植的一个硬障碍。
