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Arterial stiffness, as measured by pulse wave velocity, for the early non-invasive screening of cardiovascular disease is becoming ever more widely used and is an independent prognostic indicator for a variety of pathologies including arteriosclerosis. Carotid-femoral pulse wave velocity (cfPWV) is regarded as the gold standard for aortic stiffness. Existing algorithms for cfPWV estimation have been shown to have good repeatability and accuracy, however, further assessment is needed, especially when signal quality is compromised. We propose a method for calculating cfPWV based on a simplified tube-load model, which allows for the propagation and reflection of the pulse wave. In-vivo cfPWV measurements from 57 subjects and numerical cfPWV data based on a one-dimensional model were used to assess the method and its performance was compared to three other existing approaches (waveform matching, intersecting tangent, and cross-correlation). The cfPWV calculated using the simplified tube-load model had better repeatability than the other methods (Intra-group Correlation Coefficient, ICC = 0.985). The model was also more accurate than other methods (deviation, 0.13 ms−1) and was more robust when dealing with noisy signals. We conclude that the determination of cfPWV based on the proposed model can accurately and robustly evaluate arterial stiffness. [ABSTRACT FROM AUTHOR]

Carotid-femoral pulse transit time (cfPTT) is a widely accepted measure of central arterial stiffness. The cfPTT is commonly calculated from two synchronized pressure waves. However, measurement of synchronized pressure waves is technically challenging. In this paper, a method of decomposing the radial pressure wave is proposed for estimating cfPTT. From the radial pressure wave alone, the pressure wave can be decomposed into forward and backward waves by fitting a double triangular flow wave. The first zero point of the second derivative of the radial pressure wave and the peak of the dicrotic segment of radial pressure wave are used as the peaks of the fitted double triangular flow wave. The correlation coefficient between the measured wave and the estimated forward and backward waves based on the decomposition of the radial pressure wave was 0.98 and 0.75, respectively. Then from the backward wave, cfPTT can be estimated. Because it has been verified that the time lag estimation based on of backward wave has strong correlation with the measured cfPTT. The corresponding regression function between the time lag estimation of backward wave and measured cfPTT is y = 0.96x + 5.50 (r = 0.77; p < 0.001). The estimated cfPTT using radial pressure wave decomposition based on the proposed double triangular flow wave is more accurate and convenient than the decomposition of the aortic pressure wave based on the triangular flow wave. The significance of this study is that arterial stiffness can be directly estimated from a noninvasively measured radial pressure wave. [ABSTRACT FROM AUTHOR]

Objective. Aortic stiffness is associated with risk of cardiovascular events. Carotid-femoral pulse wave velocity (cfPWV) is the current noninvasive gold standard for assessing aortic stiffness. However, the cfPWV measurement is challenging, requiring simultaneous signals at the carotid and femoral sites. Approach. In this study, the aortic PWV is estimated using a single radial pressure waveform and compared with cfPWV. 111 subjects’ aortic PWVs are estimated from the decomposition of the derived central aortic pressure waveform based on three types of reconstructed flow waveform: the peak of triangular flow waveform based on 30% ejection time (Q 30%tri), the peak of triangular flow waveform based on inflection point (Q tri), and averaged flow waveform (Q avg). The central aortic pressure waveform is derived from a radial pressure waveform via a validated transfer function. Main results. The Q avg is used for estimating aortic PWV without the determination of the peak point of the triangular flow waveforms. The estimated aortic PWV shows good agreement with cfPWV. The mean difference ± SD is 0.29 ± 1.50 m sâˆ'1 (r 2 = 0.29, p < 0.001) for the Q 30%tri; 0.27 ± 1.40 m sâˆ'1 (r 2 = 0.38, p  < 0.001) for the Q tri; 0.23 ± 1.39 m sâˆ'1 (r 2 = 0.40, p  < 0.001) for the Q avg. The correlation between estimated aortic PWV based on Q 30%tri and measured cfPWV is weak. The results of Q tri and Q avg show no obvious difference. Significance. The proposed method can be used as a less complex way than conventional measurement of cfPWV to further assess arterial stiffness and predict cardiovascular risks or events. [ABSTRACT FROM AUTHOR]

Background: Exercise is accepted as an important contribution to the rehabilitation of patients with cardiovascular disease (CVD). This study aims to better understand the possible causes for lack of consensus and reviews the effects of three exercise modalities (aerobic, resistance and combined exercise) on central hemodynamics, arterial stiffness and cardiac function for better rehabilitation strategies in CVD. Methods: The electronic data sources, Cochrane Library, MEDLINE, Web of Science, EBSCO (CINAHL), and ScienceDirect from inception to July 2017 were searched for randomized controlled trials (RCTs) investigating the effect of exercise modalities in adult patients with CVD. The effect size was estimated as mean differences (MD) with 95% confidence intervals (CI). Subgroup analysis and meta-regression were used to study potential moderating factors. Results: Thirty-eight articles describing RCTs with a total of 2089 patients with CVD were included. The pooling revealed that aerobic exercise [MD(95%CI) = -5.87 (-8.85, -2.88), P = 0.0001] and resistance exercise [MD(95%CI) = -7.62 (-10.69, -4.54), P<0.00001] significantly decreased aortic systolic pressure (ASP). Resistance exercise significantly decreased aortic diastolic pressure [MD(95%CI) = -4(-5.63, -2.37), P<0.00001]. Aerobic exercise significantly decreased augmentation index (AIx) based on 24-week exercise duration and patients aged 50–60 years. Meanwhile, aerobic exercise significantly improved carotid-femoral pulse wave velocity (cf-PWV) [MD(95%CI) = -0.42 (-0.83, -0.01), P = 0.04], cardiac output (CO) [MD(95% CI) = 0.36(0.08, 0.64), P = 0.01] and left ventricular ejection fraction (LVEF) [MD(95%CI) = 3.02 (2.11, 3.93), P<0.00001]. Combined exercise significantly improved cf-PWV [MD(95%CI) = -1.15 (-1.95, -0.36), P = 0.004] and CO [MD(95% CI) = 0.9 (0.39, 1.41), P = 0.0006]. Conclusions: Aerobic and resistance exercise significantly decreased ASP, and long-term aerobic exercise reduced AIx. Meanwhile, aerobic and combined exercise significantly improved central arterial stiffness and cardiac function in patients with CVD. These findings suggest that a well-planned regime could optimize the beneficial effects of exercise and can provide some evidence-based guidance for those involved in cardiovascular rehabilitation of patients with CVD. [ABSTRACT FROM AUTHOR]

The physiological processes and mechanisms of an arterial system are complex and subtle. Physics-based models have been proven to be a very useful tool to simulate actual physiological behavior of the arteries. The current physics-based models include high-dimensional models (2D and 3D models) and low-dimensional models (0D, 1D and tube-load models). High-dimensional models can describe the local hemodynamic information of arteries in detail. With regard to an exact model of the whole arterial system, a high-dimensional model is computationally impracticable since the complex geometry, viscosity or elastic properties and complex vectorial output need to be provided. For low-dimensional models, the structure, centerline and viscosity or elastic properties only need to be provided. Therefore, low-dimensional modeling with lower computational costs might be a more applicable approach to represent hemodynamic properties of the entire arterial system and these three types of low-dimensional models have been extensively used in the study of cardiovascular dynamics. In recent decades, application of physics-based models to estimate central aortic pressure has attracted increasing interest. However, to our best knowledge, there has been few review paper about reconstruction of central aortic pressure using these physics-based models. In this paper, three types of low-dimensional physical models (0D, 1D and tube-load models) of systemic arteries are reviewed, the application of three types of models on estimation of central aortic pressure is taken as an example to discuss their advantages and disadvantages, and the proper choice of models for specific researches and applications are advised. [ABSTRACT FROM AUTHOR]

1 This is the first in vivo study to validate the use of machine learning techniques to improve the accuracy for estimating carotid-femoral pulse wave velocity from a single radial pulse waveform. 2 We found a novel radial pulse feature that correlates with arterial stiffness. 3 We provided a theoretical explanation for this correlation through an in silico study. Background and Objective: Arterial stiffness, commonly assessed by carotid-femoral pulse wave velocity (cfPWV), is an independent biomarker for cardiovascular disease. The measurement of cfPWV, however, has been considered impractical for routine clinical application. Pulse wave analysis using a single pulse wave measurement in the radial artery is a convenient alternative. This study aims to identify pulse wave features for a more accurate estimation of cfPWV from a single radial pulse wave measurement. Methods: From a dataset of 140 subjects, cfPWV was measured and the radial pulse waveform was recorded for 30 s twice in succession. Features were extracted from the waveforms in the time and frequency domains, as well as by wave separation analysis. All-possible regressions with bootstrapping, McHenry's select algorithm, and support vector regression were applied to compute models for cfPWV estimation. Results: The correlation coefficients between the measured and estimated cfPWV were r = 0.81, r = 0.81, and r = 0.8 for all-possible regressions, McHenry's select algorithm, and support vector regression, respectively. The features selected by all-possible regressions are physiologically interpretable. In particular, the amplitude ratio of the diastolic peak to the notch of the radial pulse waveform (R n , d r , P) is shown to be correlated with cfPWV. This correlation was further evaluated and found to be independent of wave reflections using a dataset (n = 3,325) of simulated pulse waves. Conclusions: The proposed method may serve as a convenient surrogate for the measurement of cfPWV. R n , d r , P is associated with aortic pulse wave velocity and this association may not be dependent on wave reflection. [ABSTRACT FROM AUTHOR]