Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering
Issued Date
2022-12-01
Resource Type
ISSN
1010660X
eISSN
16489144
Scopus ID
2-s2.0-85144491629
Pubmed ID
36557033
Journal Title
Medicina (Lithuania)
Volume
58
Issue
12
Rights Holder(s)
SCOPUS
Bibliographic Citation
Medicina (Lithuania) Vol.58 No.12 (2022)
Suggested Citation
Tangpanithandee S., Thongprayoon C., Jadlowiec C.C., Mao S.A., Mao M.A., Vaitla P., Leeaphorn N., Kaewput W., Pattharanitima P., Krisanapan P., Nissaisorakarn P., Cooper M., Cheungpasitporn W. Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering. Medicina (Lithuania) Vol.58 No.12 (2022). doi:10.3390/medicina58121831 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/85187
Title
Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering
Author's Affiliation
Mayo Clinic Scottsdale-Phoenix, Arizona
MedStar Georgetown University Hospital
Faculty of Medicine Ramathibodi Hospital, Mahidol University
Thammasat University
Phramongkutklao College of Medicine
Mayo Clinic
University of Mississippi Medical Center
Harvard Medical School
Mayo Clinic in Jacksonville, Florida
MedStar Georgetown University Hospital
Faculty of Medicine Ramathibodi Hospital, Mahidol University
Thammasat University
Phramongkutklao College of Medicine
Mayo Clinic
University of Mississippi Medical Center
Harvard Medical School
Mayo Clinic in Jacksonville, Florida
Other Contributor(s)
Abstract
Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 ± 13 years) who received dual kidney transplant from pediatric (mean donor age 3 ± 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 ± 9 years) who received dual kidney transplant from adult (mean donor age 59 ± 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI ≥ 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage.