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

P. Vaitla¹, C. Thongprayoon², C. Jadlowiec³, N. Leeaphorn⁴, S. Mao², M. Mao², A. Prakrati⁵, M. Cooper⁶, W. Cheungpasitporn²

¹University of Mississippi Medical Center, Jackson, MS, ²Mayo Clinic, Rochestor, MN, ³Mayo Clinic, Phoenix, AZ, ⁴Saint Luke, Kansas City, MO, ⁵Texas Tech, El Paso, TX, ⁶Georgetown Transplant Institute, Washington, DC

Meeting: 2022 American Transplant Congress

Abstract number: 1734

Keywords: Ethics, Hispanic, Kidney transplantation, Outcome

Topic: Clinical Science » Kidney » 50 - Health Equity and Access

Session Information

Session Name: Health Equity and Access

Session Type: Poster Abstract

Date: Tuesday, June 7, 2022

Session Time: 7:00pm-8:00pm

Presentation Time: 7:00pm-8:00pm

Location: Hynes Halls C & D

*Purpose: Health care policies and coverage for non-US citizens vary significantly across the country. Kidney transplant outcomes in non-US citizens are at par or better compared to US citizens. However, the proportion of non-US citizens receiving a deceased donor organ is lower compared to the proportion of non-US citizen organ donors. Our study aimed to cluster non-US citizen kidney transplant recipients using unsupervised machine learning approach.

*Methods: Consensus cluster analysis was performed based on recipient, donor, and transplant-related characteristics in 11,300 non-US citizen kidney transplant recipients in United States from 2010 to 2019 in the OPTN/UNOS database. Each cluster’s key characteristics were identified using the standardized mean difference, and subsequently compared the post-transplant outcomes among the clusters

*Results: Consensus cluster analysis identified two distinct clusters that best represented clinical characteristics of non-US citizen kidney transplant recipients. Cluster 1 had 3,226 (29%) patients, while cluster 2 (71%) had 8074 patients. Cluster 1 patients were featured by young age, preemptive kidney transplant or dialysis duration less than 1 year, working income, private insurance, non-hypertensive Hispanic living donor with low number of HLA mismatch. In contrast, cluster 2 patients were characterized by non-ECD deceased donor with KDPI <85%. As such, cluster 1 patients had less cold ischemia time, lower proportion of machine-perfused kidneys, and lower incidence of delayed graft function after kidney transplant. Compared to cluster 1, 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 1-year acute rejection (4.7% vs. 4.9%; p=0.63). While the highest proportion of cluster 1 patients was highest in Regions 7 and 10, the highest proportion of cluster 2 patients was found in Regions 11, 6, and 5.

*Conclusions: Unsupervised machine learning approach identified two clinically distinct clusters of non-US citizen kidney transplant recipients with differing post-transplant outcomes. There are different cluster distributions among 11 geographic OPTN Regions that may help identify future strategies to improve outcomes of non-U.S. citizen kidney transplant recipients.

To cite this abstract in AMA style:

Vaitla P, Thongprayoon C, Jadlowiec C, Leeaphorn N, Mao S, Mao M, Prakrati A, Cooper M, Cheungpasitporn W. Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering [abstract]. Am J Transplant. 2022; 22 (suppl 3). https://atcmeetingabstracts.com/abstract/phenotypes-of-non-citizen-kidney-transplant-recipients-in-the-united-states-by-machine-learning-consensus-clustering/. Accessed May 7, 2025.

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