Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data

Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular disease.

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Institutions

Publication Details

Journal
European Heart Journal - Digital Health
Published
2024-08-09
DOI
https://doi.org/10.1093/ehjdh/ztae049
Citations
16
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
6.47

Funders

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article

Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data

Winfried März, Jos A. Bosch, Costas Papaloukas, Dimitrios I. Fotiadis et al.
16 citations
European Heart Journal - Digital Health
Cardiac Imaging and Diagnostics
6.47
article

Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data

Winfried März, Jos A. Bosch, Costas Papaloukas, Dimitrios I. Fotiadis, Pashupati P. Mishra, Olli T. Raitakari, Rick Quax, Antonis I. Sakellarios, Terho J. Lehtimäki, Vasileios S. Loukas, Cillian Hourican, Angela Koloi
article en
16 citations

Abstract

Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular disease.

European Heart Journal - Digital HealthVol. 5(5)
University of Turku (FI), Tampere University (FI), University of Patras (GR), University of Mannheim (DE), University of Ioannina (GR), Medical University of Graz (AT), Heidelberg University (DE), Turku University Hospital (FI), FORTH Institute of Molecular Biology and Biotechnology (GR), Synlab (Germany) (DE), Fimlab (Finland) (FI), University of Amsterdam (NL)
Foundation for Cardiovascular Research, Yrjö Jahnssonin Säätiö, European Commission, Kela, Suomen Kulttuurirahasto, Juho Vainion Säätiö, Signe ja Ane Gyllenbergin Säätiö, Emil Aaltosen Säätiö, Sydäntutkimussäätiö, Tampereen Tuberkuloosisäätiö, Diabetesliitto, Paavo Nurmen Säätiö, Tays, Syöpäsäätiö
Good health and well-being
Openalex Percentile: Top 3%
Cardiac Imaging and Diagnostics
6.47
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