Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients

Abstract

Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69-0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53-0.78, p = 0.021). At 1-year follow-up, the RF test found AUC 0.80 (95%CI 0.71-0.89) vs. LR 0.50 (95%CI 0.33-0.66, p < 0.001). The results of our study support the hypothesis that ML methods can improve both the identification of MACE risk patients and the prediction vs. traditional statistical techniques even in a small sample size. The application of ML techniques to focus the efforts on the detection of MACE in very young patients after coronary angiography could help tailor upfront follow-up strategies in such young patients according to their risk of MACE and to be used for proper assignment of health resources.

Keywords: acute coronary syndrome; coronary angiography; coronary artery disease; machine learning; major adverse cardiovascular events; prediction models; young patient.

Figure 1

Study flow chart.

Figure 2

(a) Areas under the receiver operating characteristic (ROC) and (b) precision/recall (PR) curves for machine-learning models. AUC, Area Under the Curve; LDA, Linear Discriminant Analysis; MLP, Multi-layer Perceptron; NB, Naive Bayes; RF, Random Forest; LR, Logistic Regression; SVM, Support Vector Machine.

Figure 3

(a) Relevance of the 15 most important clinical variables extracted from the Random Forest classifier to long-term follow-up. (b) SHAP analysis for those 15 most important clinical variables.