Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome

Abstract

The European Society of Cardiology guidelines recommend risk stratification with limited clinical parameters such as left ventricular (LV) function in patients with chronic coronary syndrome (CCS). Machine learning (ML) methods enable analysis of complex datasets including transthoracic echocardiography (TTE) studies. We aimed to evaluate the accuracy of ML using clinical and TTE data to predict all-cause five-year mortality in patients with CCS and to compare its performance with traditional risk stratification scores. Data of consecutive patients with CCS were retrospectively collected if they attended the outpatient clinic of Amsterdam UMC location AMC between 2015 and 2017 and had TTE assessment of the LV function. An eXtreme Gradient Boosting (XGBoost) model was trained to predict all-cause five-year mortality. The performance of this ML model was evaluated using data of the Amsterdam UMC location VUmc and compared to the reference standard of traditional risk scores. A total of 1253 patients (775 training set, 478 testing set) were included, of which 176 patients (105 training set, 71 testing set) died during the five-year follow-up period. The ML model demonstrated a superior performance (area under the curve [AUC] 0.79) compared to traditional risk stratification tools (AUC 0.62-0.76), and showed good external performance. The most important TTE risk predictors included in the ML model were LV dysfunction and significant tricuspid regurgitation. This study demonstrates that an explainable ML model using TTE and clinical data can accurately identify high-risk CCS patients, with a prognostic value superior to traditional risk scores.