Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.
Nat Commun
; 15(1): 5034, 2024 Jun 12.
Article
en En
| MEDLINE
| ID: mdl-38866791
ABSTRACT
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Enfermedad de la Arteria Coronaria
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Curva ROC
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Electrocardiografía
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Prueba de Esfuerzo
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Aprendizaje Automático
Límite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Nat Commun
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Nature communications
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Suiza