Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Black Kidney Transplant Recipients and Associated Outcomes
2
Issued Date
2022-07-01
Resource Type
ISSN
21686254
eISSN
21686262
Scopus ID
2-s2.0-85129844712
Pubmed ID
35507356
Journal Title
JAMA Surgery
Volume
157
Issue
7
Rights Holder(s)
SCOPUS
Bibliographic Citation
JAMA Surgery Vol.157 No.7 (2022) , E221286
Suggested Citation
Thongprayoon C., Vaitla P., Jadlowiec C.C., Leeaphorn N., Mao S.A., Mao M.A., Pattharanitima P., Bruminhent J., Khoury N.J., Garovic V.D., Cooper M., Cheungpasitporn W. Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Black Kidney Transplant Recipients and Associated Outcomes. JAMA Surgery Vol.157 No.7 (2022) , E221286. doi:10.1001/jamasurg.2022.1286 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/85773
Title
Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Black Kidney Transplant Recipients and Associated Outcomes
Author's Affiliation
Mayo Clinic Scottsdale-Phoenix, Arizona
MedStar Georgetown University Hospital
UMKC School of Medicine
Faculty of Medicine Ramathibodi Hospital, Mahidol University
Faculty of Medicine, Thammasat University
Henry Ford Hospital
Mayo Clinic
University of Mississippi Medical Center
Mayo Clinic in Jacksonville, Florida
MedStar Georgetown University Hospital
UMKC School of Medicine
Faculty of Medicine Ramathibodi Hospital, Mahidol University
Faculty of Medicine, Thammasat University
Henry Ford Hospital
Mayo Clinic
University of Mississippi Medical Center
Mayo Clinic in Jacksonville, Florida
Other Contributor(s)
Abstract
Importance: Among kidney transplant recipients, Black patients continue to have worse graft function and reduced patient and graft survival. Better understanding of different phenotypes and subgroups of Black kidney transplant recipients may help the transplant community to identify individualized strategies to improve outcomes among these vulnerable groups. Objective: To cluster Black kidney transplant recipients in the US using an unsupervised machine learning approach. Design, Setting, and Participants: This cohort study performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in Black kidney transplant recipients in the US from January 1, 2015, to December 31, 2019, in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database. Each cluster's key characteristics were identified using the standardized mean difference, and subsequently the posttransplant outcomes were compared among the clusters. Data were analyzed from June 9 to July 17, 2021. Exposure: Machine learning consensus clustering approach. Main Outcomes and Measures: Death-censored graft failure, patient death within 3 years after kidney transplant, and allograft rejection within 1 year after kidney transplant. Results: Consensus cluster analysis was performed for 22687 Black kidney transplant recipients (mean [SD] age, 51.4 [12.6] years; 13 635 men [60%]), and 4 distinct clusters that best represented their clinical characteristics were identified. Cluster 1 was characterized by highly sensitized recipients of deceased donor kidney retransplants; cluster 2, by recipients of living donor kidney transplants with no or short prior dialysis; cluster 3, by young recipients with hypertension and without diabetes who received young deceased donor transplants with low kidney donor profile index scores; and cluster 4, by older recipients with diabetes who received kidneys from older donors with high kidney donor profile index scores and extended criteria donors. Cluster 2 had the most favorable outcomes in terms of death-censored graft failure, patient death, and allograft rejection. Compared with cluster 2, all other clusters had a higher risk of death-censored graft failure and death. Higher risk for rejection was found in clusters 1 and 3, but not cluster 4. Conclusions and Relevance: In this cohort study using an unsupervised machine learning approach, the identification of clinically distinct clusters among Black kidney transplant recipients underscores the need for individualized care strategies to improve outcomes among vulnerable patient groups.