ArticleClinical kidney journal2020
Machine learning analysis of serum biomarkers for cardiovascular risk assessment in chronic kidney disease.
Article in Clinical kidney journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review.Medicina (Kaunas, Lithuania) · 2021Pooled it
- Associations of skeletal muscle volume and density with cardiovascular events in maintenance hemodialysis: a cross-sectional study.BMC nephrology · 2025Article
- Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.Cardiovascular diabetology · 2025Article
- Associations between plasma osteopontin, sex, and 2-year global and cardiorenal outcomes in older outpatients screened for CKD: a secondary analysis of the SCOPE study.Clinical kidney journal · 2024Article
- A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease.Kidney diseases (Basel, Switzerland) · 2024Article
- Matrix Gla protein polymorphism rs1800802 is associated with atheroma plaque progression and with cardiovascular events in a chronic kidney disease cohort.Clinical kidney journal · 2024Article
- Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field.International journal of molecular sciences · 2022Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic 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.
methodsNineteen 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 in Fine and Gray (FG) regression models, with non-cardiovascular death and kidney transplant as competing events.
resultsRSF analysis detected several biomarkers as relevant for predicting CVEs. Inclusion of those biomarkers in an FG model showed that high levels of osteopontin, osteoprotegerin, matrix metalloproteinase-9 and vascular endothelial growth factor increased the risk for CVEs, but only marginally improved the discrimination obtained with classical clinical parameters: concordance index 0.744 (95% confidence interval 0.609-0.878) versus 0.723 (0.592-0.854), respectively. However, in individuals with diabetes treated with antihypertensives and lipid-lowering drugs, the determination of these biomarkers could help to improve cardiovascular risk estimates.
conclusionsWe conclude that the determination of four biomarkers in the serum of CKD patients could improve cardiovascular risk prediction in high-risk individuals.
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