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Arterial Stiffness and Coronary Ischemia: New Aspects and Paradigms
Alexandre Vallée1, Alexandre Cinaud2, Athanase Protogerou3
1Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, Paris-Descartes University, AP-HP, Paris, France. alexandre.g.vallee@gmail.com.
Insights
Aortic stiffness, measured by pulse wave velocity (PWV), is a key predictor of coronary heart disease (CHD). Advanced AI models enhance the accuracy of PWV index calculations for improved CHD risk prediction.
Area of Science:
- Cardiovascular Medicine
- Biomedical Engineering
- Medical Statistics
Background:
- Aortic stiffness (AS) is linked to hypertension and is a significant predictor of coronary heart disease (CHD).
- Carotid-femoral pulse wave velocity (PWV) is the standard method for measuring AS.
- An index incorporating age, gender, heart rate, and mean blood pressure refines PWV measurements.
Purpose of the Study:
- To review the significance of measuring PWV and calculating individual PWV index for CHD prediction.
- To explore the utility of novel statistical nonlinear models for accurate AS assessment.
- To highlight the role of artificial intelligence in enhancing CHD risk prediction.
Main Methods:
- Review of current literature on aortic stiffness measurement and CHD prediction.
- Analysis of the application of PWV index in cardiovascular risk assessment.
- Investigation of artificial intelligence, including decision tree and artificial neural network models, for predictive medicine.
Main Results:
- PWV index serves as a crucial marker for large artery damage in CHD and is relevant for cerebrovascular and renal models.
- PWV index is particularly valuable in angiographic CHD decisions and for high-risk patients with vulnerable plaques.
- AI-driven models, such as decision trees and neural networks, demonstrate potential for accurate coronary prediction algorithms.
Conclusions:
- PWV index is a valuable tool for assessing cardiovascular risk and guiding clinical decisions in CHD.
- Despite current limitations in simple diagnostic approaches, advanced statistical and AI models offer improved accuracy for CHD prediction.
- Integrating PWV measurements with AI can lead to more precise and personalized predictive medicine strategies for coronary artery disease.
Purpose Of Review:
Aortic stiffness (AS) is widely associated with hypertension and considered as a major predictor of coronary heart disease (CHD). AS is measured using carotid-femoral pulse wave velocity (PWV), particularly when this parameter is associated with an index involving age, gender, heart rate, and mean blood pressure. The present review focuses on the interest of measurement of PWV and the calculation of individual PWV index for the prediction of CHD, in addition with the use of new statistical nonlinear models enabling results with very high levels of accuracy.
Recent Findings:
PWV index may so constitute a substantial marker of large arteries prediction and damage in CHD and may be also used in cerebrovascular and renal circulations models. PWV index determinations are particularly relevant to consider in angiographic CHD decisions and in the presence of vulnerable plaques with high cardiovascular risk. Due to the variability in symptoms and clinical characteristics of patients, together with some imperfections in results, there is no very simple adequate diagnosis approach enabling to improve the so defined CHD prediction in usual clinical practice. In recent works in relation to "artificial intelligence" and involving "decision tree" models and "artificial neural networks," it has been possible to determine consistent pathways introducing predictive medicine and enabling to obtain efficient algorithm classification models of coronary prediction.
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