Journal Article

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

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

2017

Overview

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. 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. 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Aged / Artificial intelligence / Biology and Life Sciences / Cancer / Cardiac Surgical Procedures / Cardiopulmonary Bypass - adverse effects / Cardiopulmonary Bypass - methods / Classification / Colleges & universities / Comorbidity / Computer and Information Sciences / Coronary artery bypass / Decision analysis / Decision making / Decision Support Techniques / Diagnostic tests / Female / France / Health aspects / Heart / Heart diseases / Heart surgery / Hospital Mortality / Humans / Internet / Learning algorithms / Logistic Models / Machine Learning / Male / Medicine and Health Sciences / Methods / Middle Aged / Models, Statistical / Mortality / Patient outcomes / Patients / Physical Sciences / Prediction models / Probability / Regression analysis / Regression models / Reproducibility of Results / Research and Analysis Methods / ROC Curve / Statistical analysis / Studies / Surgery