Alexandre Vallée1, Alexandre Cinaud2, Athanase Protogerou3, Yi Zhang4, Jirar Topouchian2, Michel E Safar2, Jacques Blacher2. 1. Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, Paris-Descartes University, AP-HP, Paris, France. alexandre.g.vallee@gmail.com. 2. Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, Paris-Descartes University, AP-HP, Paris, France. 3. Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National and Kapodistrian University of Athens, Athens, Greece. 4. Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Abstract
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.
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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