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, Machine Learning, mortalité post opératoire, score prédictif RÉSUMÉ Introduction En chirurgie cardiaque, la morbi-mortalité est importante et la décision d'opérer un patient est complexe. L'objectif de notre étude était de comparer l'EuroSCORE II au Machine Learning pour prédire la mortalité dans les suites d, Mortality prediction after elective cardiac surgery: new Machine Anesthésie Réanimation Mots clefs : Chirurgie cardiaque, Decision Curve Analysis

, au sein de l'unité de chirurgie cardiaque d'un Centre Hospitalo-Universitaire parisien. Les patients admis pour chirurgie cardiaque non programmée étaient exclus. Les différents modèles de prédiction de la mortalité hospitalière, incluant l'EuroSCORE II, le modèle de régression logistique et le modèle Machine Learning, Matériel et Méthodes Nous avons réalisé une étude rétrospective, monocentrique, entre décembre 2005 et décembre 2012, à partir d'une base de données prospective

, La mortalité hospitalière était de 6,3%. La moyenne d'âge était de 63,4 ans et l'EuroSCORE II moyen de 3,7 %. L'aire sous la courbe ROC (IC 95%) pour le modèle Machine Learning 0,795 (0,755-0,834) était significativement plus élevée que celle de l'EuroSCORE II et du modèle de Régression Logistique

D. Conclusion, L. Dca, and . Modèle, Machine Learning était supérieur à l'EuroSCORE II pour la prédiction du risque de mortalité intra hospitalière dans les suites d'une chirurgie cardiaque programmée. Ce résultat confirme l'intérêt grandissant du Machine Learning pour l'établissement de modèles prédictifs en médecine