Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering

Charat Thongprayoon; Yeshwanter Radhakrishnan; Caroline C Jadlowiec; Shennen A Mao; Michael A Mao; Pradeep Vaitla; Prakrati C Acharya; Napat Leeaphorn; Wisit Kaewput; Pattharawin Pattharanitima; Supawit Tangpanithandee; Pajaree Krisanapan; Pitchaphon Nissaisorakarn; Matthew Cooper; Wisit Cheungpasitporn

doi:10.3390/jpm12121992

Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering

J Pers Med. 2022 Dec 1;12(12):1992. doi: 10.3390/jpm12121992.

Authors

Charat Thongprayoon¹, Yeshwanter Radhakrishnan¹, Caroline C Jadlowiec², Shennen A Mao³, Michael A Mao⁴, Pradeep Vaitla⁵, Prakrati C Acharya⁶, Napat Leeaphorn⁴, Wisit Kaewput⁷, Pattharawin Pattharanitima⁸, Supawit Tangpanithandee^{1

9}, Pajaree Krisanapan^{1

8}, Pitchaphon Nissaisorakarn¹⁰, Matthew Cooper¹¹, Wisit Cheungpasitporn¹

Affiliations

¹ Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
² Division of Transplant Surgery, Mayo Clinic, Phoenix, AZ 85054, USA.
³ Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
⁴ Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.
⁵ Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA.
⁶ Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA.
⁷ Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand.
⁸ Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand.
⁹ Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand.
¹⁰ Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
¹¹ Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 21042, USA.

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.

Keywords: clustering; kidney donor profile index; kidney transplant; kidney transplantation; transplantation.

Grants and funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.