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

Journal: Clinical kidney journal
Published Date: Aug 5, 2019

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.

Authors

  • Carles Forné
    Biostatistics Unit, Institute for Biomedical Research Dr. Pifarré Foundation, IRBLleida, Lleida, Spain.
  • Serafi Cambray
    Vascular and Renal Translational Research Group, Institute for Biomedical Research Dr. Pifarré Foundation, IRBLleida and RedinRen RETIC, ISCIII, Lleida, Spain.
  • Marcelino Bermudez-Lopez
    Vascular and Renal Translational Research Group, Institute for Biomedical Research Dr. Pifarré Foundation, IRBLleida and RedinRen RETIC, ISCIII, Lleida, Spain.
  • Elvira Fernandez
    Vascular and Renal Translational Research Group, Institute for Biomedical Research Dr. Pifarré Foundation, IRBLleida and RedinRen RETIC, ISCIII, Lleida, Spain.
  • Milica Bozic
    Vascular and Renal Translational Research Group, Institute for Biomedical Research Dr. Pifarré Foundation, IRBLleida and RedinRen RETIC, ISCIII, Lleida, Spain.
  • Jose M Valdivielso
    Vascular and Renal Translational Research Group and UDETMA, Institut de Recerca Biomèdica de Lleida, Lleida, Spain.

Keywords

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