Differences between kidney retransplant recipients as identified by machine learning consensus clustering

Author:

Thongprayoon Charat1,Vaitla Pradeep2,Jadlowiec Caroline C.3ORCID,Mao Shennen A.4,Mao Michael A.5,Acharya Prakrati C.6,Leeaphorn Napat7,Kaewput Wisit8,Pattharanitima Pattharawin9,Tangpanithandee Supawit1ORCID,Krisanapan Pajaree19ORCID,Nissaisorakarn Pitchaphon10ORCID,Cooper Matthew11,Cheungpasitporn Wisit1ORCID

Affiliation:

1. Division of Nephrology and Hypertension, Department of Medicine Mayo Clinic Rochester Minnesota USA

2. Division of Nephrology University of Mississippi Medical Center Jackson Mississippi USA

3. Division of Transplant Surgery Mayo Clinic Phoenix Arizona USA

4. Division of Transplant Surgery Mayo Clinic Jacksonville Florida USA

5. Division of Nephrology and Hypertension, Department of Medicine Mayo Clinic Jacksonville Florida USA

6. Division of Nephrology Texas Tech Health Sciences Center El Paso El Paso Texas USA

7. Renal Transplant Program University of Missouri‐Kansas City School of Medicine/Saint Luke's Health System Kansas City Missouri USA

8. Department of Military and Community Medicine Phramongkutklao College of Medicine Bangkok Thailand

9. Department of Internal Medicine Faculty of Medicine Thammasat University Pathum Thani Thailand

10. Department of Medicine Division of Nephrology, Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA

11. Department of Surgery Medical College of Wisconsin Milwaukee Wisconsin USA

Abstract

AbstractBackgroundOur study aimed to characterize kidney retransplant recipients using an unsupervised machine‐learning approach.MethodsWe performed consensus cluster analysis based on the recipient‐, donor‐, and transplant‐related characteristics in 17 443 kidney retransplant recipients in the OPTN/UNOS database from 2010 to 2019. We identified each cluster's key characteristics using the standardized mean difference of >.3. We compared the posttransplant outcomes, including death‐censored graft failure and patient death among the assigned clustersResultsConsensus cluster analysis identified three distinct clusters of kidney retransplant recipients. Cluster 1 recipients were predominantly white and were less sensitized. They were most likely to receive a living donor kidney transplant and more likely to be preemptive (30%) or need ≤1 year of dialysis (32%). In contrast, cluster 2 recipients were the most sensitized (median PRA 95%). They were more likely to have been on dialysis >1 year, and receive a nationally allocated, low HLA mismatch, standard KDPI deceased donor kidney. Recipients in cluster 3 were more likely to be minorities (37% Black; 15% Hispanic). They were moderately sensitized with a median PRA of 87% and were also most likely to have been on dialysis >1 year. They received locally allocated high HLA mismatch kidneys from standard KDPI deceased donors. Thymoglobulin was the most commonly used induction agent for all three clusters. Cluster 1 had the most favorable patient and graft survival, while cluster 3 had the worst patient and graft survival.ConclusionThe use of an unsupervised machine learning approach characterized kidney retransplant recipients into three clinically distinct clusters with differing posttransplant outcomes. Recipients with moderate allosensitization, such as those represented in cluster 3, are perhaps more disadvantaged in the kidney retransplantation process. Potential opportunities for improvement specific to these re‐transplant recipients include working to improve opportunities to improve access to living donor kidney transplantation, living donor paired exchange and identifying strategies for better HLA matching.

Publisher

Wiley

Subject

Transplantation

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