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Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning

Author

Listed:
  • Christian Bock

    (ETH Zürich
    Swiss Institute for Bioinformatics)

  • Joan Elias Walter

    (University Hospital of Basel, University of Basel
    University Hospital of Basel, University of Basel
    University Hospital Zurich, University of Zurich)

  • Bastian Rieck

    (ETH Zürich
    Swiss Institute for Bioinformatics
    Helmholtz Munich and Technical University of Munich)

  • Ivo Strebel

    (University Hospital of Basel, University of Basel
    University Hospital of Basel, University of Basel)

  • Klara Rumora

    (University Hospital of Basel, University of Basel
    University Hospital of Basel, University of Basel)

  • Ibrahim Schaefer

    (University Hospital of Basel, University of Basel
    University Hospital of Basel, University of Basel)

  • Michael J. Zellweger

    (University Hospital of Basel, University of Basel
    University Hospital of Basel, University of Basel)

  • Karsten Borgwardt

    (ETH Zürich
    Swiss Institute for Bioinformatics
    Max Planck Institute of Biochemistry)

  • Christian Müller

    (University Hospital of Basel, University of Basel
    University Hospital of Basel, University of Basel)

Abstract

Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnostic accuracy or expose patients to possibly harmful radiation. Here we show how machine learning (ML) can outperform cardiologists in predicting the presence of stress-induced fCAD in terms of area under the receiver operating characteristic (AUROC: 0.71 vs. 0.64, p = 4.0E-13). We present two ML approaches, the first using eight static clinical variables, whereas the second leverages electrocardiogram signals from exercise stress testing. At a target post-test probability for fCAD of

Suggested Citation

  • Christian Bock & Joan Elias Walter & Bastian Rieck & Ivo Strebel & Klara Rumora & Ibrahim Schaefer & Michael J. Zellweger & Karsten Borgwardt & Christian Müller, 2024. "Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49390-y
    DOI: 10.1038/s41467-024-49390-y
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    References listed on IDEAS

    as
    1. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    2. Antônio H. Ribeiro & Manoel Horta Ribeiro & Gabriela M. M. Paixão & Derick M. Oliveira & Paulo R. Gomes & Jéssica A. Canazart & Milton P. S. Ferreira & Carl R. Andersson & Peter W. Macfarlane & Wagner, 2020. "Automatic diagnosis of the 12-lead ECG using a deep neural network," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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