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

TLDR

Cardiac surgery outcomes are difficult to predict, and recent methods such as machine learning and decision‑curve analysis have been developed to improve risk prediction. The authors performed a retrospective cohort study using a prospectively collected database from 2005 to 2012 at a university hospital, comparing EuroSCORE II, a logistic regression model, and a machine‑learning model by ROC and decision‑curve analysis. In a cohort of 6,520 elective cardiac surgery patients (6.3% in‑hospital mortality), the machine‑learning model achieved a higher AUC (0.795) than EuroSCORE II (0.737) and logistic regression (0.742) and, per decision‑curve analysis, offered greater net benefit across all probability thresholds, confirming its superior accuracy for mortality prediction.

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.