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.

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 < 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.

Fig 1. Receiver operating characteristic curves showing the performance of EuroSCORE I, EuroSCORE II, and the ML model in predicting post-operative mortality.

Areas under curves (95% CI) are 0.719 (0.674–0.763), 0.737 (0.691–0.783), and 0.795 (0.755–0.834), respectively.

Fig 2. Decision curves showing the clinical usefulness of EuroSCORE I, EuroSCORE II, and the ML model in predicting post-operative mortality.

The blue line represents the net benefit of providing surgery for all patients, assuming that all patients would survive. The red line represents the net benefit of surgery to none patients, assuming that all would die after surgery. The green, purple and turquoise lines represent the net benefit of applying surgery to patients according to EuroSCORE I, EuroSCORE II, and ML model, respectively. The selected probability threshold (i.e., the degree of certitude of postoperative mortality over which the patient's decision is not to operate) is plotted on the abscissa.