Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.

Autor: Bock, Christian, Walter, Joan Elias, Rieck, Bastian, Strebel, Ivo, Rumora, Klara, Schaefer, Ibrahim, Zellweger, Michael J., Borgwardt, Karsten, Müller, Christian
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Zdroj: Nature Communications; 6/12/2024, Vol. 15 Issue 1, p1-16, 16p
Abstrakt: Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnostic accuracy or expose patients to possibly harmful radiation. Here we show how machine learning (ML) can outperform cardiologists in predicting the presence of stress-induced fCAD in terms of area under the receiver operating characteristic (AUROC: 0.71 vs. 0.64, p = 4.0E-13). We present two ML approaches, the first using eight static clinical variables, whereas the second leverages electrocardiogram signals from exercise stress testing. At a target post-test probability for fCAD of <15%, ML facilitates a potential reduction of imaging procedures by 15–17% compared to the cardiologist's judgement. Predictive performance is validated on an internal temporal data split as well as externally. We also show that combining clinical judgement with conventional ML and deep learning using logistic regression results in a mean AUROC of 0.74. Early detection of coronary artery disease is crucial to mitigate cardiac complications. Here, the authors show how machine learning can enhance risk stratification, and potentially reduce unwarranted invasive testing by integrating widely accessible clinical data and stress test ECG signals. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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