Molina RS, Molina-Rodríguez MA, Rincón FM, Maldonado JD. 6/3/2021

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.

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