Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering
Issued Date
2022-12-01
Resource Type
eISSN
20754426
Scopus ID
2-s2.0-85144642943
Journal Title
Journal of Personalized Medicine
Volume
12
Issue
12
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Personalized Medicine Vol.12 No.12 (2022)
Suggested Citation
Thongprayoon C., Radhakrishnan Y., Jadlowiec C.C., Mao S.A., Mao M.A., Vaitla P., Acharya P.C., Leeaphorn N., Kaewput W., Pattharanitima P., Tangpanithandee S., Krisanapan P., Nissaisorakarn P., Cooper M., Cheungpasitporn W. Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering. Journal of Personalized Medicine Vol.12 No.12 (2022). doi:10.3390/jpm12121992 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/87155
Title
Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering
Author's Affiliation
Mayo Clinic Scottsdale-Phoenix, Arizona
MedStar Georgetown University Hospital
Texas Tech University Health Sciences Center El Paso
Faculty of Medicine Ramathibodi Hospital, Mahidol University
Faculty of Medicine, 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
Texas Tech University Health Sciences Center El Paso
Faculty of Medicine Ramathibodi Hospital, Mahidol University
Faculty of Medicine, 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: Our study aimed to characterize kidney transplant recipients who received high kidney donor profile index (KDPI) kidneys using unsupervised machine learning approach. Methods: We used the OPTN/UNOS database from 2010 to 2019 to perform consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 8935 kidney transplant recipients from deceased donors with KDPI ≥ 85%. We identified each cluster’s key characteristics using the standardized mean difference of >0.3. We compared the posttransplant outcomes among the assigned clusters. Results: Consensus cluster analysis identified 6 clinically distinct clusters of kidney transplant recipients from donors with high KDPI. Cluster 1 was characterized by young, black, hypertensive, non-diabetic patients who were on dialysis for more than 3 years before receiving kidney transplant from black donors; cluster 2 by elderly, white, non-diabetic patients who had preemptive kidney transplant or were on dialysis less than 3 years before receiving kidney transplant from older white donors; cluster 3 by young, non-diabetic, retransplant patients; cluster 4 by young, non-obese, non-diabetic patients who received dual kidney transplant from pediatric, black, non-hypertensive non-ECD deceased donors; cluster 5 by low number of HLA mismatch; cluster 6 by diabetes mellitus. Cluster 4 had the best patient survival, whereas cluster 3 had the worst patient survival. Cluster 2 had the best death-censored graft survival, whereas cluster 4 and cluster 3 had the worst death-censored graft survival at 1 and 5 years, respectively. Cluster 2 and cluster 4 had the best overall graft survival at 1 and 5 years, respectively, whereas cluster 3 had the worst overall graft survival. Conclusions: Unsupervised machine learning approach kidney transplant recipients from donors with high KDPI based on their pattern of clinical characteristics into 6 clinically distinct clusters.