Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering

dc.contributor.authorTangpanithandee S.
dc.contributor.authorThongprayoon C.
dc.contributor.authorJadlowiec C.C.
dc.contributor.authorMao S.A.
dc.contributor.authorMao M.A.
dc.contributor.authorVaitla P.
dc.contributor.authorLeeaphorn N.
dc.contributor.authorKaewput W.
dc.contributor.authorPattharanitima P.
dc.contributor.authorKrisanapan P.
dc.contributor.authorNissaisorakarn P.
dc.contributor.authorCooper M.
dc.contributor.authorCheungpasitporn W.
dc.contributor.otherMahidol University
dc.date.accessioned2023-06-18T17:37:05Z
dc.date.available2023-06-18T17:37:05Z
dc.date.issued2022-12-01
dc.description.abstractBackground 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.
dc.identifier.citationMedicina (Lithuania) Vol.58 No.12 (2022)
dc.identifier.doi10.3390/medicina58121831
dc.identifier.eissn16489144
dc.identifier.issn1010660X
dc.identifier.pmid36557033
dc.identifier.scopus2-s2.0-85144491629
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/85187
dc.rights.holderSCOPUS
dc.subjectMedicine
dc.titleClinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering
dc.typeArticle
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85144491629&origin=inward
oaire.citation.issue12
oaire.citation.titleMedicina (Lithuania)
oaire.citation.volume58
oairecerif.author.affiliationMayo Clinic Scottsdale-Phoenix, Arizona
oairecerif.author.affiliationMedStar Georgetown University Hospital
oairecerif.author.affiliationFaculty of Medicine Ramathibodi Hospital, Mahidol University
oairecerif.author.affiliationThammasat University
oairecerif.author.affiliationPhramongkutklao College of Medicine
oairecerif.author.affiliationMayo Clinic
oairecerif.author.affiliationUniversity of Mississippi Medical Center
oairecerif.author.affiliationHarvard Medical School
oairecerif.author.affiliationMayo Clinic in Jacksonville, Florida

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