Machine learning analysis of serum biomarkers for cardiovascular risk assessment in chronic kidney disease.

ABSTRACT:

Background

Chronic kidney disease (CKD) patients show an increased burden of atherosclerosis and high risk of cardiovascular events (CVEs). There are several biomarkers described as being associated with CVEs, but their combined effectiveness in cardiovascular risk stratification in CKD has not been tested. The objective of this work is to analyse the combined ability of 19 biomarkers associated with atheromatous disease in predicting CVEs after 4 years of follow-up in a subcohort of the NEFRONA study in individuals with different stages of CKD without previous CVEs.

Methods

Nineteen putative biomarkers were quantified in 1366 patients (73 CVEs) and their ability to predict CVEs was ranked by random survival forest (RSF) analysis. The factors associated with CVEs were tested

SUBMITTER: Forne C 

PROVIDER: S-EPMC7467598 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature