Literature DB >> 12108836

Model-based assessment of cardiovascular health from noninvasive measurements.

Xinshu Xiao1, Edwin T Ozawa, Yaqi Huang, Roger D Kamm.   

Abstract

Cardiovascular health is currently assessed through a variety of hemodynamic parameters, many of which can only be determined by invasive measurement often requiring hospitalization. A noninvasive method of evaluating several of these parameters such as systemic vascular resistance (SVR), maximum left ventricular elasticity (E(LV)), end diastolic volume (VED), and cardiac output, is presented. The method has three elements: (1) a distributed model of the human cardiovascular system (Ozawa et aL, Ann. Biomed. Eng. 29:284-297, 2001) to generate a solution library that spans the anticipated range of parameter values, (2) a method for establishing the multidimensional relationship between features computed from the arterial blood pressure and/or flow traces (e.g., mean arterial pressure, pulse amplitude, mean flow velocity) and the critical hemodynamic parameters, and (3) a parameter estimation method that yields the best fit between measured and computed data. Sensitivity analyses were used to determine the critical parameters, and the influence of fixed model parameters. Using computer-generated brachial pressure and velocity profiles (which can be measured noninvasively), the error associated with this method was found to be less than 3% for SVR, and less than 10% for E(LV) and V(ED). Simulations were also performed to test the ability of the approach to predict changes in SVR and E(LV) from an initial base line state.

Entities:  

Mesh:

Year:  2002        PMID: 12108836     DOI: 10.1114/1.1484217

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  5 in total

Review 1.  Continuous and less invasive central hemodynamic monitoring by blood pressure waveform analysis.

Authors:  Ramakrishna Mukkamala; Da Xu
Journal:  Am J Physiol Heart Circ Physiol       Date:  2010-07-09       Impact factor: 4.733

2.  Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty.

Authors:  Daniele E Schiavazzi; Alessia Baretta; Giancarlo Pennati; Tain-Yen Hsia; Alison L Marsden
Journal:  Int J Numer Method Biomed Eng       Date:  2016-06-08       Impact factor: 2.747

3.  Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research.

Authors:  Vasiliki Bikia; Terence Fong; Rachel E Climie; Rosa-Maria Bruno; Bernhard Hametner; Christopher Mayer; Dimitrios Terentes-Printzios; Peter H Charlton
Journal:  Eur Heart J Digit Health       Date:  2021-10-18

4.  On a sparse pressure-flow rate condensation of rigid circulation models.

Authors:  D E Schiavazzi; T Y Hsia; A L Marsden
Journal:  J Biomech       Date:  2015-11-28       Impact factor: 2.712

5.  Subject-specific pulse wave propagation modeling: Towards enhancement of cardiovascular assessment methods.

Authors:  Jan Poleszczuk; Malgorzata Debowska; Wojciech Dabrowski; Alicja Wojcik-Zaluska; Wojciech Zaluska; Jacek Waniewski
Journal:  PLoS One       Date:  2018-01-11       Impact factor: 3.240

  5 in total

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