Toward a noninvasive subject-specific estimation of abdominal aortic pressure

2008 ◽  
Vol 295 (3) ◽  
pp. H1156-H1164 ◽  
Author(s):  
Carl-Johan Thore ◽  
Jonas Stålhand ◽  
Matts Karlsson
Keyword(s):  
Aortic Pressure ◽  
Processing Technique ◽  
Distributed Model ◽  
Specific Information ◽  
Peripheral Artery ◽  
Tree Model ◽  
Signal Processing Technique ◽  
Arterial Tree ◽  
One Dimensional ◽  
Abdominal Aortic

A method for estimation of central arterial pressure based on linear one-dimensional wave propagation theory is presented in this paper. The equations are applied to a distributed model of the arterial tree, truncated by three-element windkessels. To reflect individual differences in the properties of the arterial trees, we pose a minimization problem from which individual parameters are identified. The idea is to take a measured waveform in a peripheral artery and use it as input to the model. The model subsequently predicts the corresponding waveform in another peripheral artery in which a measurement has also been made, and the arterial tree model is then calibrated in such a way that the computed waveform matches its measured counterpart. For the purpose of validation, invasively recorded abdominal aortic, brachial, and femoral pressures in nine healthy subjects are used. The results show that the proposed method estimates the abdominal aortic pressure wave with good accuracy. The root mean square error (RMSE) of the estimated waveforms was 1.61 ± 0.73 mmHg, whereas the errors in systolic and pulse pressure were 2.32 ± 1.74 and 3.73 ± 2.04 mmHg, respectively. These results are compared with another recently proposed method based on a signal processing technique, and it is shown that our method yields a significantly ( P < 0.01) lower RMSE. With more extensive validation, the method may eventually be used in clinical practice to provide detailed, almost individual, specific information as a valuable basis for decision making.

scholarly journals Validation of a one-dimensional model of the systemic arterial tree

2009 ◽  
Vol 297 (1) ◽  
pp. H208-H222 ◽  
Author(s):  
Philippe Reymond ◽  
Fabrice Merenda ◽  
Fabienne Perren ◽  
Daniel Rüfenacht ◽  
Nikos Stergiopulos
Keyword(s):  
Constitutive Law ◽  
Distributed Model ◽  
Arterial Tree ◽  
One Dimensional ◽  
Continuity Equations ◽  
Nonlinear Viscoelastic ◽  
Model Predictions ◽  
The One ◽  
D Model ◽  
Systemic Arterial Tree

A distributed model of the human arterial tree including all main systemic arteries coupled to a heart model is developed. The one-dimensional (1-D) form of the momentum and continuity equations is solved numerically to obtain pressures and flows throughout the systemic arterial tree. Intimal shear is modeled using the Witzig-Womersley theory. A nonlinear viscoelastic constitutive law for the arterial wall is considered. The left ventricle is modeled using the varying elastance model. Distal vessels are terminated with three-element windkessels. Coronaries are modeled assuming a systolic flow impediment proportional to ventricular varying elastance. Arterial dimensions were taken from previous 1-D models and were extended to include a detailed description of cerebral vasculature. Elastic properties were taken from the literature. To validate model predictions, noninvasive measurements of pressure and flow were performed in young volunteers. Flow in large arteries was measured with MRI, cerebral flow with ultrasound Doppler, and pressure with tonometry. The resulting 1-D model is the most complete, because it encompasses all major segments of the arterial tree, accounts for ventricular-vascular interaction, and includes an improved description of shear stress and wall viscoelasticity. Model predictions at different arterial locations compared well with measured flow and pressure waves at the same anatomical points, reflecting the agreement in the general characteristics of the “generic 1-D model” and the “average subject” of our volunteer population. The study constitutes a first validation of the complete 1-D model using human pressure and flow data and supports the applicability of the 1-D model in the human circulation.

One Dimensional Model of the Systemic Arterial Tree Including Cerebral Circulation

2007 ◽  
Author(s):  
Philippe Reymond ◽  
Fabrice Merenda ◽  
Fabienne Perren ◽  
Daniel Rüfenacht ◽  
Nikos Stergiopulos
Keyword(s):  
Cerebral Circulation ◽  
Cerebral Arteries ◽  
Distributed Model ◽  
Dimensional Model ◽  
Heart Model ◽  
Arterial Tree ◽  
One Dimensional ◽  
Distributed Models ◽  
The Past ◽  
Systemic Arterial Tree

The aim of this study is to develop a distributed model of the entire systemic arterial tree, coupled to a heart model and including a detailed description of the cerebral arteries. Distributed models of the arterial tree have been studied extensively in the past (Avolio [1]; Cassot et al [2]; Meister [3]; Schaaf and Abbrecht [4]; Stergiopulos et al [5]; Westerhof et al [6]; Zagzoule and Marc-Vergnes [7]), however, no model has been developed so far that offers a physiologically relevant coupling to the heart and includes the entire cerebral artery network.

Validation of a patient-specific one-dimensional model of the systemic arterial tree

2011 ◽  
Vol 301 (3) ◽  
pp. H1173-H1182 ◽  
Author(s):  
Philippe Reymond ◽  
Yvette Bohraus ◽  
Fabienne Perren ◽  
Francois Lazeyras ◽  
Nikos Stergiopulos
Keyword(s):  
Arterial Wall ◽  
Specific Model ◽  
Constitutive Law ◽  
Distributed Model ◽  
Patient Specific ◽  
Dimensional Model ◽  
Arterial Tree ◽  
One Dimensional ◽  
Flow Waveforms ◽  
Systemic Arterial Tree

The aim of this study is to develop and validate a patient-specific distributed model of the systemic arterial tree. This model is built using geometric and hemodynamic data measured on a specific person and validated with noninvasive measurements of flow and pressure on the same person, providing thus a patient-specific model and validation. The systemic arterial tree geometry was obtained from MR angiographic measurements. A nonlinear viscoelastic constitutive law for the arterial wall is considered. Arterial wall distensibility is based on literature data and adapted to match the wave propagation velocity of the main arteries of the specific subject, which were estimated by pressure waves traveling time. The intimal shear stress is modeled using the Witzig-Womersley theory. Blood pressure is measured using applanation tonometry and flow rate using transcranial ultrasound and phase-contrast-MRI. The model predicts pressure and flow waveforms in good qualitative and quantitative agreement with the in vivo measurements, in terms of wave shape and specific wave features. Comparison with a generic one-dimensional model shows that the patient-specific model better predicts pressure and flow at specific arterial sites. These results obtained let us conclude that a patient-specific one-dimensional model of the arterial tree is able to predict well pressure and flow waveforms in the main systemic circulation, whereas this is not always the case for a generic one-dimensional model.

Blind identification of the aortic pressure waveform from multiple peripheral artery pressure waveforms

2007 ◽  
Vol 292 (5) ◽  
pp. H2257-H2264 ◽  
Author(s):  
Gokul Swamy ◽  
Qi Ling ◽  
Tongtong Li ◽  
Ramakrishna Mukkamala
Keyword(s):  
Mean Squared Error ◽  
Systolic Pressure ◽  
Aortic Pressure ◽  
Training Data ◽  
Absolute Pressure ◽  
Pressure Waveform ◽  
Peripheral Artery ◽  
Arterial Tree ◽  
Artery Pressure ◽  
Time Specific

We have developed a new technique to estimate the clinically relevant aortic pressure waveform from multiple, less invasively measured peripheral artery pressure waveforms. The technique is based on multichannel blind system identification in which two or more measured outputs (peripheral artery pressure waveforms) of a single-input, multi-output system (arterial tree) are mathematically analyzed so as to reconstruct the common unobserved input (aortic pressure waveform) to within an arbitrary scale factor. The technique then invokes Poiseuille's law to calibrate the reconstructed waveform to absolute pressure. Consequently, in contrast to previous related efforts, the technique does not utilize a generalized transfer function or any training data and is therefore entirely patient and time specific. To demonstrate proof of concept, we have evaluated the technique with respect to four swine in which peripheral artery pressure waveforms from the femoral and radial arteries and a reference aortic pressure waveform from the descending thoracic aorta were simultaneously measured during diverse hemodynamic interventions. We report that the technique reliably estimated the entire aortic pressure waveform with an overall root mean squared error (RMSE) of 4.6 mmHg. For comparison, the average overall RMSE between the peripheral artery pressure and reference aortic pressure waveforms was 8.6 mmHg. Thus the technique reduced the RMSE by 47%. As a result, the technique also provided similar improvements in the estimation of systolic pressure, pulse pressure, and the ejection interval. With further successful testing, the technique may ultimately be employed for more precise monitoring and titration of therapy in, for example, critically ill and hypertension patients.

Physical basis of pressure transfer from periphery to aorta: a model-based study

1998 ◽  
Vol 274 (4) ◽  
pp. H1386-H1392 ◽  
Author(s):  
Nikos Stergiopulos ◽  
Berend E. Westerhof ◽  
Nico Westerhof
Keyword(s):  
Transfer Function ◽  
Delay Time ◽  
Wave Speed ◽  
Diastolic Pressure ◽  
Arterial Compliance ◽  
Aortic Pressure ◽  
Considerable Effect ◽  
Distributed Model ◽  
Arterial Tree ◽  
Central Aortic Pressure

We propose a new method to derive aortic pressure from peripheral pressure and velocity by using a time domain approach. Peripheral pressure is separated into its forward and backward components, and these components are then shifted with a delay time, which is the ratio of wave speed and distance, and added again to reconstruct aortic pressure. We tested the method on a distributed model of the human systemic arterial tree. From carotid and brachial artery pressure and velocity, aortic systolic and diastolic pressure could be predicted within 0.3 and 0.1 mmHg and 0.4 and 1.0 mmHg, respectively. The central aortic pressure wave shape was also predicted accurately from carotid and brachial pressure and velocity (root mean square error: 1.07 and 1.56 mmHg, respectively). The pressure transfer function depends on the reflection coefficient at the site of peripheral measurement and the delay time. A 50% decrease in arterial compliance had a considerable effect on reconstructed pressure when the control transfer function was used. A 70% decrease in arm resistance did not affect the reconstructed pressure. The transfer function thus depends on wave speed but has little dependence on vasoactive state. We conclude that central aortic pressure and the transfer function can be derived from peripheral pressure and velocity.

An adaptive transfer function for deriving the aortic pressure waveform from a peripheral artery pressure waveform

2009 ◽  
Vol 297 (5) ◽  
pp. H1956-H1963 ◽  
Author(s):  
Gokul Swamy ◽  
Da Xu ◽  
N. Bari Olivier ◽  
Ramakrishna Mukkamala
Keyword(s):  
Transfer Function ◽  
Transfer Functions ◽  
Systolic Pressure ◽  
Aortic Pressure ◽  
Function Technique ◽  
Pressure Waveform ◽  
Peripheral Artery ◽  
Arterial Tree ◽  
Artery Pressure ◽  
Mean Square Errors

We developed a new technique to mathematically transform a peripheral artery pressure (PAP) waveform distorted by wave reflections into the physiologically more relevant aortic pressure (AP) waveform. First, a transfer function relating PAP to AP is defined in terms of the unknown parameters of a parallel tube model of pressure and flow in the arterial tree. The parameters are then estimated from the measured PAP waveform along with a one-time measurement of the wave propagation delay time between the aorta and peripheral artery measurement site (which may be accomplished noninvasively) by exploiting preknowledge of aortic flow. Finally, the transfer function with its estimated parameters is applied to the measured waveform so as to derive the AP waveform. Thus, in contrast to the conventional generalized transfer function, the transfer function is able to adapt to the intersubject and temporal variability of the arterial tree. To demonstrate the feasibility of this adaptive transfer function technique, we performed experiments in 6 healthy dogs in which PAP and reference AP waveforms were simultaneously recorded during 12 different hemodynamic interventions. The AP waveforms derived by the technique showed agreement with the measured AP waveforms (overall total waveform, systolic pressure, and pulse pressure root mean square errors of 3.7, 4.3, and 3.4 mmHg, respectively) statistically superior to the unprocessed PAP waveforms (corresponding errors of 8.6, 17.1, and 20.3 mmHg) and the AP waveforms derived by two previously proposed transfer functions developed with a subset of the same canine data (corresponding errors of, on average, 5.0, 6.3, and 6.7 mmHg).

Associations of coronary and peripheral artery disease with presence, expansion, and rupture of abdominal aortic aneurysm – a grin without a cat!

VASA ◽  
2017 ◽  
Vol 46 (3) ◽  
pp. 151-158 ◽  
Author(s):  
Hisato Takagi ◽  
Takuya Umemoto
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Peripheral Artery Disease ◽  
Literature Search ◽  
Systematic Literature Search ◽  
Peripheral Artery ◽  
Epidemiological Evidence ◽  
Abdominal Aortic ◽  
Artery Disease ◽  
Meta Analyses

Abstract. Both coronary and peripheral artery disease are representative atherosclerotic diseases, which are also known to be positively associated with presence of abdominal aortic aneurysm. It is still controversial, however, whether coronary and peripheral artery disease are positively associated with expansion and rupture as well as presence of abdominal aortic aneurysm. In the present article, we overviewed epidemiological evidence, i. e. meta-analyses, regarding the associations of coronary and peripheral artery disease with presence, expansion, and rupture of abdominal aortic aneurysm through a systematic literature search. Our exhaustive search identified seven meta-analyses, which suggest that both coronary and peripheral artery disease are positively associated with presence of abdominal aortic aneurysm, may be negatively associated with expansion of abdominal aortic aneurysm, and might be unassociated with rupture of abdominal aortic aneurysm.

Determinants of central aortic pressure and pulse pressure amplification changes in patients with aortic aneurysm: the role of aortic diameter and aortic aneurysm location level

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Gurevich ◽  
I Emelyanov ◽  
N Zherdev ◽  
D Chernova ◽  
A Chernov ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Aortic Aneurysm ◽  
Pulse Pressure ◽  
Pulse Wave ◽  
Augmentation Index ◽  
Aortic Pressure ◽  
Aortic Diameter ◽  
Maximum Diameter ◽  
Central Aortic Pressure ◽  
Abdominal Aortic

Abstract Background The presence of aortic aneurysm can alters pulse wave propagation and reflection, causing changes in central aortic pressure and pulse pressure amplification (PPA) between the aorta and the brachial artery that might be associated with unfavorable hemodynamic effects for the central arteries and the heart. However, the impact of the location of the aneurysm and increase of the aortic diameter on central blood pressure (CBP) is not fully understood. Objective To investigate central aortic pressure and PPA regarding to association with arterial stiffness and aortic diameter in patients with ascending aortic aneurysm (AA), descending thoracic and abdominal aortic aneurysm (TAA and AAA). Methods 122 patients (96 males, 65±11 years) with aortic aneurysm were enrolled before aortic repair. The parameters of the aorta were evaluated by MSCT angiography: 44 patients (30 males, 55±13 years) had AA (the maximum diameter: 59.9±14.2 mm), 13 patients (11 males, 62±11 years) had TAA (the maximum diameter: 62.8±8.0 mm) and 65 patients (54 males, 69±8 years) had AAA (the maximum diameter: 52.3±17.2 mm). Brachial blood pressure (BBP) was measured by OMRON. CBP, augmentation index (AIx), carotid-femoral pulse wave velocity (PWV) were assessed by SphygmoCor. PPA was calculated as a difference between the values of central and brachial pulse pressure (CPP and BPP). Results Patients of the three groups did not differ in BPP (AA: 59.2±17.6; TAA 56.8±12.8; AAA: 59.3±11.4 mm Hg; P=0.5). Intergroup comparison revealed a difference in CPP between the three patients groups: CPP was higher in patients with AA and AAA, lower in patients with TAA (AA: 50.3±16.2; TAA 43.8±10.8; AAA: 50.0±11.2 mm Hg; P=0.05). PPA was lower in patients with AA and AAA than in patients with TAA (9.6±6.7 and 9.3±4.2 vs. 13.0±6.5 mm Hg; P=0.05 and P=0.04, respectively). IAx was higher in patients with AA and AAA than in patients with TAA (25.2±8.1 and 27.6±8.2 vs. 17.2±8.2 mm Hg; P=0.008 and P=0.001, respectively). A decrease of PPA across all patients correlated with an increase of IAx (r = - 0.268; P=0.003). CPP decreased with an increase of the aortic diameter for each level of the aneurysm (AA: r = - 0.460, P=0.016; TAA: r = - 0.833, P=0.003; AAA: r = - 0.275, P=0.05). PWV decreased with the expansion of the maximum aortic diameter at the level of the AA, TAA and AAA: (r = - 0.389, P=0.03; r = - 0.827, P=0.02 and r = - 0.350, P=0.01, respectively). Conclusion In patients with aortic aneurysm measurements of lower central pulse pressure and reduced PWV indicate an association with increased diameter of the aneurysm. An increase in augmentation index, early return of reflected waves, thus smaller PP amplification and higher CPP were identified in patients with ascending and abdominal aortic aneurysm compared by patients with descending thoracic aortic aneurysm. Funding Acknowledgement Type of funding source: None

Sign in / Sign up

Export Citation Format

Share Document