Literature DB >> 9575944

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

N Stergiopulos1, B E Westerhof, N Westerhof.   

Abstract

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.

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Mesh:

Year:  1998        PMID: 9575944     DOI: 10.1152/ajpheart.1998.274.4.H1386

Source DB:  PubMed          Journal:  Am J Physiol        ISSN: 0002-9513


  7 in total

1.  Increasing pulse wave velocity in a realistic cardiovascular model does not increase pulse pressure with age.

Authors:  Mohammad W Mohiuddin; Ryan J Rihani; Glen A Laine; Christopher M Quick
Journal:  Am J Physiol Heart Circ Physiol       Date:  2012-05-04       Impact factor: 4.733

2.  Subject-specific estimation of central aortic blood pressure via system identification: preliminary in-human experimental study.

Authors:  Nima Fazeli; Chang-Sei Kim; Mohammad Rashedi; Alyssa Chappell; Shaohua Wang; Roderick MacArthur; M Sean McMurtry; Barry Finegan; Jin-Oh Hahn
Journal:  Med Biol Eng Comput       Date:  2014-09-03       Impact factor: 2.602

Review 3.  Age-related changes in venticular-arterial coupling: pathophysiologic implications.

Authors:  David A Kass
Journal:  Heart Fail Rev       Date:  2002-01       Impact factor: 4.214

Review 4.  Generic and patient-specific models of the arterial tree.

Authors:  Philippe Reymond; Orestis Vardoulis; Nikos Stergiopulos
Journal:  J Clin Monit Comput       Date:  2012-07-29       Impact factor: 2.502

5.  Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes.

Authors:  Peter H Charlton; Jorge Mariscal Harana; Samuel Vennin; Ye Li; Phil Chowienczyk; Jordi Alastruey
Journal:  Am J Physiol Heart Circ Physiol       Date:  2019-08-23       Impact factor: 4.733

6.  Clinical Assessment of Central Blood Pressure.

Authors:  Hiroshi Miyashita
Journal:  Curr Hypertens Rev       Date:  2012-05

7.  Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning.

Authors:  Vasiliki Bikia; Theodore G Papaioannou; Stamatia Pagoulatou; Georgios Rovas; Evangelos Oikonomou; Gerasimos Siasos; Dimitris Tousoulis; Nikolaos Stergiopulos
Journal:  Sci Rep       Date:  2020-09-14       Impact factor: 4.379

  7 in total

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