Using Unsupervised Clustering to Characterize Phenotypes Among Older Kidney Transplant Recipients: A Cohort Study.

ABSTRACT:

Background

Older kidney transplant recipients have inferior outcomes compared to younger recipients, and this risk may be compounded by donor characteristics.

Objective

We applied an unsupervised machine learning clustering approach to group older recipients into similar phenotypes. We evaluated the association between each cluster and graft failure, and the impact of donor quality on outcomes.

Design

This is a nationally representative retrospective cohort study.

Setting and patients

Kidney transplant recipients aged ≥65 years identified from the Scientific Registry of Transplant Recipients (2000-2017).

Measurements and methods

We used unsupervised clustering to generate phenotypes using 16 recipient factors. Donor quality was evaluated using 2 approach

SUBMITTER: Singh S 

PROVIDER: S-EPMC11909662 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature