Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States.

ABSTRACT:

Background

This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach.

Methods

Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m². Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters.

Results

Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m². In this cohort,

SUBMITTER: Thongprayoon C 

PROVIDER: S-EPMC9224965 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature