IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0169772.html
   My bibliography  Save this article

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

Author

Listed:
  • Jérôme Allyn
  • Nicolas Allou
  • Pascal Augustin
  • Ivan Philip
  • Olivier Martinet
  • Myriem Belghiti
  • Sophie Provenchere
  • Philippe Montravers
  • Cyril Ferdynus

Abstract

Background: The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding: We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p

Suggested Citation

  • Jérôme Allyn & Nicolas Allou & Pascal Augustin & Ivan Philip & Olivier Martinet & Myriem Belghiti & Sophie Provenchere & Philippe Montravers & Cyril Ferdynus, 2017. "A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-12, January.
    • Handle: RePEc:plo:pone00:0169772
    DOI: 10.1371/journal.pone.0169772
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169772
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0169772&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0169772?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mieke Deschepper & Willem Waegeman & Dirk Vogelaers & Kristof Eeckloo, 2020. "Using structured pathology data to predict hospital-wide mortality at admission," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-11, June.
    2. Raydonal Ospina & Jaciele Oliveira & Cristiano Ferraz & André Leite & João Gondim, 2023. "Ensemble Algorithms to Improve COVID-19 Growth Curve Estimates," Stats, MDPI, vol. 6(4), pages 1-18, September.
    3. Jun Young Kim & Muhammad Sohail & Heung Soo Kim, 2023. "Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty," Mathematics, MDPI, vol. 11(16), pages 1-15, August.
    4. Varios Autores, 2020. "Víctimas del conflicto armado colombiano," Books, Universidad Externado de Colombia, Facultad de Derecho, number 1173, htpr_v3_i.

    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:plo:pone00:0169772. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.