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Fig 1.

Features of the pulse wave in ascending aorta.

Superposition of forward and reflected pressure waves results in certain amount of augmented pressure, with a characteristic inflection point located to the left of pressure peak in elderly subjects. In younger subjects inflection point is typically found to the right of pressure peak, what results in negative values of augmented pressure.

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Fig 2.

Schematic representation of the study workflow.

(1) Radial pressure waveforms were recorded in 20 healthy subjects using applanation tonometer. (2) Recorded pressure waveforms were used to estimate parameters of the blood flow model in the system of 55 compliant arteries. (3) Pulse wave analysis using SphygmoCor software was performed on model-predicted radial, carotid, and aortic pressure waveforms separately.

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Table 1.

Participant characteristics.

Shown are means ± standard deviations and ranges.

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Table 2.

Arterial tree structure definition based on papers by Stergiopulos et al. [26] and Olufsen et al. [25].

Parameters rin and rout are the proximal and distal radii of the artery, respectively. Resistance (RT 104g/s/cm4) and compliance (CT 10-6g/s/cm4) are defined only for terminal arteries, see Eq (7). R and L denote right and left, respectively.

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Fig 3.

Assumed aortic inflow.

Dependence of the heart ejection profile on parameter τ (τ = 0.08, 0.1 and 0.12 seconds) for 5 L/min cardiac output and heart rate of 75 beats/min, see Eq (6).

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Fig 4.

Comparison of pressure waveforms in radial artery recorded using SphygmoCor (circles) with the fitted model curves (solid lines) for 20 healthy volunteers.

Shown are also mean absolute percentage errors for each subject separately. Summary of parameters used in the simulations is presented in Table 3.

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Table 3.

Fixed and estimated model parameters.

For the parameters that were estimated means ± standard deviations are shown for Men and Women separately.

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Table 4.

Pearson correlation coefficients between model-estimated parameters (see Table 3) and demographic/clinical participants characteristics (see Table 1).

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Fig 5.

Comparison of the central pulse wave characteristics estimated using SphygmoCor software using different input pressure waveforms, i.e. recorded radial (RR), simulated radial (SR), simulated carotid (SCa), and simulated aortic (SA) waveforms.

(A) Boxplots comparing pressure indices together with augmentation index and sub-endocardial viability ratio (SEVR). Symbols *, **, and *** denote p-value < 0.05, < 0.01 and < 0.001, respectively. (B) Pearson correlation coefficient matrices between different central waveform characteristics. Shown is a graphical representation of the correlation coefficient where size and color of the circle indicates correlation strength and direction (upper diagonal) together with correlation coefficients values (lower diagonal). Correlation coefficients with p-value ≥ 0.05 are crossed out. (C) Scatter plot and linear regression models comparing augmentation index estimated using recorded radial and simulated aortic pressure waveforms. Shown is also regression equation and Pearson correlation coefficient for the subgroup of patients for which both aortic waves were classified as the same type.

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Fig 6.

Dependence of most clinically relevant pulse wave indices on model parameters.

(A)Resulting stepwise regression model for predicting central augmentation index. The steeper is the heart ejection profile, i.e. the smaller is the parameter τ, the smaller is the central augmentation index (AI) estimated using pulse wave analysis method. (B) Correlation between the model-predicted pulse wave velocity (PWV) and the model-estimated stiffness of large arteries described by parameter k3 (compare Eqs (2) and (3)). PWV was calculated by dividing the distance between aortic arch and femoral artery by the time needed by the wave to travel that path.

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