Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.
Christian BockJoan Elias WalterBastian RieckIvo StrebelKlara RumoraIbrahim SchaeferMichael J ZellwegerKarsten M BorgwardtChristian E MuellerPublished in: Nature communications (2024)
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.
Keyphrases
- machine learning
- stress induced
- coronary artery disease
- deep learning
- acute myocardial infarction
- percutaneous coronary intervention
- big data
- artificial intelligence
- newly diagnosed
- end stage renal disease
- ejection fraction
- coronary artery bypass grafting
- prognostic factors
- cardiovascular events
- physical activity
- left ventricular
- acute coronary syndrome
- heart failure
- electronic health record
- radiation induced
- body composition
- patient reported outcomes
- aortic stenosis
- resistance training
- climate change
- real time pcr
- label free
- risk assessment