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Risk score for cardiac surgery in active left-sided infective endocarditis
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  • Published on:
    Machine learning to predict death after cardiac surgery in patients with infective endacarditis: Why not! But, where are the (big) data please?
    • Jerome Allyn, Intensivist Réanimation Polyvalente Site Félix Guyon, Centre Hospitalier Universitaire La Réunion, Saint-Denis, France
    • Other Contributors:
      • Nicolas Allou, Intensivist
      • Cyril Ferdynus, Biostatistician

    The study of Olmos et al., on prediction of in-hospital mortality in patients with active infective endocarditis undergoing cardiac surgery, is of great interest.1
    Indeed, this topic is fascinating because it is complicated to make a choice in so dramatic and not so rare situation.
    To help with this decision-making, the authors proposed a model for predicting hospital mortality: a classic multivariate logistic regression model.
    However, the editorial published with this article evokes in the title a new method: machine learning.2 Machine learning, which is a field of artificial intelligence, has already been used for predicting hospital mortality after elective cardiac surgery.3 This study aimed at comparing a machine learning model, a classic logistic regression model and EuroSCORE II on a cohort including 6,520 patients. The comparison of these models was based on ROC curves and decision curve analysis (DCA).4 Whatever the method of comparison, machine learning model was more accurate than other models.
    Our experience in this area probably allows us to make some comments on this editorial. Considering, the increase of studies comparing machine learning with logistic regression, it is now known that supervised machine learning algorithms improve the prediction of post-operative mortality. However, the size of the cohort used in the present study makes it difficult to apply machine learning algorithms. Indeed, this cohort comprised 671 patients who...

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    Conflict of Interest:
    None declared.