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

Peter H Charlton1, Jorge Mariscal Harana1, Samuel Vennin1,2

  • 1Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.

Insights

A new computer-simulated pulse wave (PW) database aids cardiovascular health research. This resource helps understand PW determinants and develop analysis algorithms for better cardiovascular assessments.

Area of Science:

  • Cardiovascular physiology
  • Biomedical engineering
  • Computational modeling

Background:

  • Arterial pulse waves (PWs) offer insights into cardiovascular (CV) health but their physical determinants are not fully understood.
  • Current development of PW analysis algorithms is hindered by a lack of comprehensive PW datasets with reference CV measurements.

Purpose of the Study:

  • To create a computational database of simulated PWs representing a range of CV conditions in healthy adults aged 25-75.
  • To provide a resource for understanding PW determinants and advancing PW analysis algorithm development.

Main Methods:

  • A literature review identified typical CV properties across age decades (25-75 years).
  • A computational model simulated PWs (pressure, flow velocity, luminal area, photoplethysmographic) using identified CV properties.
  • A database of 4,374 virtual subjects' PWs was generated and validated against in vivo data.

Main Results:

  • Simulated PWs and derived indexes showed good agreement with in vivo data, accurately reproducing age-related hemodynamic and morphological changes.
  • The database facilitated novel hemodynamic insights and in silico assessment of PW algorithms.
  • Case studies demonstrated the database's utility in understanding PW index determinants.

Conclusions:

  • The publicly available PW database is a valuable resource for CV research.
  • It supports the understanding of PW determinants and the development and preclinical assessment of PW analysis algorithms.
  • The known CV properties for each simulated PW enhance its utility for algorithm development and validation.

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