IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-49390-y.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-49390-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-49390-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ja Hyeon Ku & Myong Kim & Seok-Soo Byun & Hyeon Jeong & Cheol Kwak & Hyeon Hoe Kim & Sang Eun Lee, 2015. "External Validation of Models for Prediction of Lymph Node Metastasis in Urothelial Carcinoma of the Bladder," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
    2. Veer Sangha & Bobak J. Mortazavi & Adrian D. Haimovich & Antônio H. Ribeiro & Cynthia A. Brandt & Daniel L. Jacoby & Wade L. Schulz & Harlan M. Krumholz & Antonio Luiz P. Ribeiro & Rohan Khera, 2022. "Automated multilabel diagnosis on electrocardiographic images and signals," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Yiwang Zhou & Peter X.K. Song & Haoda Fu, 2021. "Net benefit index: Assessing the influence of a biomarker for individualized treatment rules," Biometrics, The International Biometric Society, vol. 77(4), pages 1254-1264, December.
    5. Konstantina Chalkou & Andrew J. Vickers & Fabio Pellegrini & Andrea Manca & Georgia Salanti, 2023. "Decision Curve Analysis for Personalized Treatment Choice between Multiple Options," Medical Decision Making, , vol. 43(3), pages 337-349, April.
    6. Dexin Chen & Meiting Fu & Liangjie Chi & Liyan Lin & Jiaxin Cheng & Weisong Xue & Chenyan Long & Wei Jiang & Xiaoyu Dong & Jian Sui & Dajia Lin & Jianping Lu & Shuangmu Zhuo & Side Liu & Guoxin Li & G, 2022. "Prognostic and predictive value of a pathomics signature in gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Jing Sun & Yue Liu & Jianhui Zhao & Bin Lu & Siyun Zhou & Wei Lu & Jingsun Wei & Yeting Hu & Xiangxing Kong & Junshun Gao & Hong Guan & Junli Gao & Qian Xiao & Xue Li, 2024. "Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    8. Shuaijian Yang & Jinhao Cheng & Jin Shang & Chen Hang & Jie Qi & Leni Zhong & Qingyan Rao & Lei He & Chenqi Liu & Li Ding & Mingming Zhang & Samit Chakrabarty & Xingyu Jiang, 2023. "Stretchable surface electromyography electrode array patch for tendon location and muscle injury prevention," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    9. Anirudh Tomer & Daan Nieboer & Monique J. Roobol & Ewout W. Steyerberg & Dimitris Rizopoulos, 2019. "Personalized schedules for surveillance of low‐risk prostate cancer patients," Biometrics, The International Biometric Society, vol. 75(1), pages 153-162, March.
    10. Bernd Lütkenhöner & Türker Basel, 2013. "Predictive Modeling for Diagnostic Tests with High Specificity, but Low Sensitivity: A Study of the Glycerol Test in Patients with Suspected Menière’s Disease," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-12, November.
    11. Shamil D. Cooray & Lihini A. Wijeyaratne & Georgia Soldatos & John Allotey & Jacqueline A. Boyle & Helena J. Teede, 2020. "The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal," IJERPH, MDPI, vol. 17(9), pages 1-20, April.
    12. Minta Thomas & Yu-Ru Su & Elisabeth A. Rosenthal & Lori C. Sakoda & Stephanie L. Schmit & Maria N. Timofeeva & Zhishan Chen & Ceres Fernandez-Rozadilla & Philip J. Law & Neil Murphy & Robert Carreras-, 2023. "Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    13. Khushal Arjan & Lui G Forni & Richard M Venn & David Hunt & Luke Eliot Hodgson, 2021. "Clinical decision-making in older adults following emergency admission to hospital. Derivation and validation of a risk stratification score: OPERA," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    14. Alex Thompson & Scott Devine & Mike Kattan & Andrew Muir, 2014. "Prediction of Treatment Week Eight Response & Sustained Virologic Response in Patients Treated with Boceprevir Plus Peginterferon Alfa and Ribavirin," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-8, August.
    15. Tracey L. Marsh & Holly Janes & Margaret S. Pepe, 2020. "Statistical inference for net benefit measures in biomarker validation studies," Biometrics, The International Biometric Society, vol. 76(3), pages 843-852, September.
    16. Chang Wook Jeong & Sangchul Lee & Jin-Woo Jung & Byung Ki Lee & Seong Jin Jeong & Sung Kyu Hong & Seok-Soo Byun & Sang Eun Lee, 2014. "Mobile Application-Based Seoul National University Prostate Cancer Risk Calculator: Development, Validation, and Comparative Analysis with Two Western Risk Calculators in Korean Men," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-7, April.
    17. Yanqing Wang & Yingqi Zhao & Yingye Zheng, 2022. "Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 564-581, December.
    18. Hormuzd A. Katki & Ionut Bebu, 2021. "A simple framework to identify optimal cost‐effective risk thresholds for a single screen: Comparison to Decision Curve Analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 887-903, July.
    19. Stuart G. Baker, 2024. "Evaluating Risk Prediction with Data Collection Costs: Novel Estimation of Test Tradeoff Curves," Medical Decision Making, , vol. 44(1), pages 53-63, January.
    20. Tae Yoon Lee & Paul Gustafson & Mohsen Sadatsafavi, 2023. "Closed-Form Solution of the Unit Normal Loss Integral in 2 Dimensions, with Application in Value-of-Information Analysis," Medical Decision Making, , vol. 43(5), pages 621-626, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49390-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.