title: A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.
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.
  
  
  RESULTS
  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 < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold.
  
  
  CONCLUSIONS
  According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.
authors.affiliation: Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France.|Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France.
authors.forename: Jérôme
authors.initials: J
authors.lastname: Allyn
authors.affiliation: Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France.|Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Allou
authors.affiliation: Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France.
authors.forename: Pascal
authors.initials: P
authors.lastname: Augustin
authors.affiliation: Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France.|Département d'Anesthésie Réanimation, Institut Mutualiste Montsouris, 42 boulevard Jourdan, Paris, France.
authors.forename: Ivan
authors.initials: I
authors.lastname: Philip
authors.affiliation: Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Martinet
authors.affiliation: Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France.
authors.forename: Myriem
authors.initials: M
authors.lastname: Belghiti
authors.affiliation: Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France.
authors.forename: Sophie
authors.initials: S
authors.lastname: Provenchere
authors.affiliation: Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France.|Université Denis Diderot, PRESS Sorbonne Cité, Paris, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Montravers
authors.affiliation: Unité de Soutien Méthodologique, CHU de La Réunion, Saint-Denis, France.|INSERM, CIC 1410, Saint-Pierre, France.
authors.forename: Cyril
authors.initials: C
authors.lastname: Ferdynus
mesh_terms: D000368:Aged; D006348:Cardiac Surgical Procedures; D002315:Cardiopulmonary Bypass / Q000009:adverse effects / Q000379:methods; D015897:Comorbidity; D003661:Decision Support Techniques; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012372:ROC Curve; D015203:Reproducibility of Results