On the importance of the nonuniform aortic stiffening in the hemodynamics of physiological aging

Front. Bioeng. Biotechnol.

Rovas

Pagoulatou

et al. 2021

Self Cite

In-vivo assessment of aortic characteristic impedance (Zao) and total arterial compliance (CT) has been hampered by the need for either invasive or inconvenient and expensive methods to access simultaneous recordings of aortic pressure and flow, wall thickness, and cross-sectional area. In contrast, regional pulse wave velocity (PWV) measurements are non-invasive and clinically available. In this study, we present a non-invasive method for estimating Zao and CT using cuff pressure, carotid-femoral PWV (cfPWV), and carotid-radial PWV (crPWV). Regression analysis is employed for both Zao and CT. The regressors are trained and tested using a pool of virtual subjects (n = 3,818) generated from a previously validated in-silico model. Predictions achieved an accuracy of 7.40%, r = 0.90, and 6.26%, r = 0.95, for Zao, and CT, respectively. The proposed approach constitutes a step forward to non-invasive screening of elastic vascular properties in humans by exploiting easily obtained measurements. This study could introduce a valuable tool for assessing arterial stiffness reducing the cost and the complexity of the required measuring techniques. Further clinical studies are required to validate the method in-vivo.

Section: Methodsmentioning

confidence: 99%

Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study

Front. Bioeng. Biotechnol.

Rovas

Pagoulatou

et al. 2021

Self Cite

“…The selected distributions of the input model parameters are summarized in Table 2 . The parameters of arterial distensibility and terminal compliance were altered simultaneously, while nonuniform aortic stiffening was considered for the elderly and hypertensive virtual subjects, following the methodology described in our previous work ( Bikia et al, 2019 ; Pagoulatou et al, 2019 ). Peripheral resistances were modified uniformly in order to achieve the specific value of total peripheral resistance in the selected range.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure Waveform via Deep Learning

Front. Bioeng. Biotechnol.

Lazaroska

Scherrer

et al. 2021

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Determination of left ventricular (LV) end-systolic elastance (Ees) is of utmost importance for assessing the cardiac systolic function and hemodynamical state in humans. Yet, the clinical use of Ees is not established due to the invasive nature and high costs of the existing measuring techniques. The objective of this study is to introduce a method to assess cardiac contractility, using as a sole measurement an arterial blood pressure (BP) waveform. Particularly, we aim to provide evidence on the potential in using the morphology of the brachial BP waveform and its time derivative for predicting LV Eesvia convolution neural networks (CNNs). The requirement of a broad training dataset is addressed by the use of an in silico dataset (n = 3,748) which is generated by a validated one-dimensional mathematical model of the cardiovasculature. We evaluated two CNN configurations: 1) a one-channel CNN (CNN1) with only the raw brachial BP signal as an input, and 2) a two-channel CNN (CNN2) using as inputs both the brachial BP wave and its time derivative. Accurate predictions were yielded using both CNN configurations. For CNN1, Pearson’s correlation coefficient (r) and RMSE were equal to 0.86 and 0.27 mmHg/ml, respectively. The performance was found to be greatly improved for CNN2 (r = 0.97 and RMSE = 0.13 mmHg/ml). Moreover, all absolute errors from CNN2 were found to be less than 0.5 mmHg/ml. Importantly, the brachial BP wave appeared to be a promising source of information for estimating Ees. Predictions were found to be in good agreement with the reference Ees values over an extensive range of LV contractility values and loading conditions. Therefore, the proposed methodology could be easily transferred to the bedside and potentially facilitate the clinical use of Ees for monitoring the contractile state of the heart in the real-life setting.

“…Especially, variation of arterial distensibility of all arteries was performed in a uniform manner with respect to the variation of the aortic distensibility. In specific cases, the model accounted for non-uniform and more pronounced aortic stiffening, as previously described in related works 17 , 18 , to simulate older or hypertensive individuals. By inputting a specific set of parameters, the model generated analytical solutions for pressure and flow at each arterial segment.…”

Section: Methodsmentioning

confidence: 99%

On the similarity between aortic and carotid pressure diastolic decay: a mathematical modelling study

Rovas

Anagnostopoulos

et al. 2023

Sci Rep

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Aortic diastolic pressure decay (DPD) has been shown to have considerable pathophysiological relevance in the assessment of vascular health, as it is significantly affected by arterial stiffening. Nonetheless, the aortic pressure waveform is rarely available and hence the utility of the aortic DPD is limited. On the other hand, carotid blood pressure is often used as a surrogate of central (aortic) blood pressure in cardiovascular monitoring. Although the two waveforms are inherently different, it is unknown whether the aortic DPD shares a common pattern with the carotid DPD. In this study, we compared the DPD time constant of the aorta (aortic RC) and the DPD time constant of the carotid artery (carotid RC) using an in-silico-generated healthy population from a previously validated one-dimensional numerical model of the arterial tree. Our results demonstrated that there is near-absolute agreement between the aortic RC and the carotid RC. In particular, a correlation of ~ 1 was reported for a distribution of aortic/carotid RC values equal to 1.76 ± 0.94 s/1.74 ± 0.87 s. To the best of our knowledge, this is the first study to compare the DPD of the aortic and the carotid pressure waveform. The findings indicate a strong correlation between carotid DPD and aortic DPD, supported by the examination of curve shape and the diastolic decay time constant across a wide range of simulated cardiovascular conditions. Additional investigation is required to validate these results in human subjects and assess their applicability in vivo.