Cardiac Operative Risk in Latin America: A Comparison of Machine Learning Models vs EuroSCORE-II

Abstract

Background: Machine learning is a useful tool for predicting medical outcomes. This study aimed to develop a machine learning-based preoperative score to predict cardiac surgical operative mortality.

Methods: We developed various models to predict cardiac operative mortality using machine learning techniques and compared each model to European System for Cardiac Operative Risk Evaluation-II (EuroSCORE-II) using the area under the receiver operating characteristic (ROC) and precision-recall (PR) curves (ROC AUC and PR AUC) as performance metrics. The model calibration in our population was also reported with all models and in high-risk groups for gradient boosting and EuroSCORE-II. This study is a retrospective cohort based on a prospectively collected database from July 2008 to April 2018 from a single cardiac surgical center in Bogotá, Colombia.

Results: Model comparison consisted of hold-out validation: 80% of the data were used for model training, and the remaining 20% of the data were used to test each model and EuroSCORE-II. Operative mortality was 6.45% in the entire database and 6.59% in the test set. The performance metrics for the best machine learning model, gradient boosting (ROC: 0.755; PR: 0.292), were higher than those of EuroSCORE-II (ROC: 0.716, PR: 0.179), with a P value of .318 for the AUC of the ROC and .137 for the AUC of the PR.

Conclusions: The gradient boosting model was more precise than EuroSCORE-II in predicting mortality in our population based on ROC and PR analyses, although the difference was not statistically significant.