Use of consensus clustering to identify distinct subtypes of chronic kidney disease and associated mortality risk.

ABSTRACT:

Background

Chronic kidney disease (CKD) is a complex condition with diverse etiology and outcomes. Utilizing a data-driven clustering approach holds promise in identifying distinct CKD subgroups associated with specific risk profiles for death.

Methods

Unsupervised consensus clustering was utilized to classify chronic kidney disease (CKD) into subtypes based on 45 baseline characteristics in a cohort of 6,526 participants from the US National Health and Nutrition Examination Survey (NHANES) spanning the years 1999-2000 to 2017-2018.We examined the associations between CKD subgroups and clinical endpoints related to mortality, including all-cause mortality, cardiovascular disease mortality, cancer mortality, and mortality due to other causes.

Results

A total of 6,526 i

SUBMITTER: Qin Y 

PROVIDER: S-EPMC11611901 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature