Relationship between stroke volume and pulse wave velocity

Shun Kamoi, Christopher Pretty, Yeong Shiong Chiew, Shaun Davidson, Antoine Pironet, Thomas Desaive, Geoffrey M. Shaw, J. Geoffrey Chase

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

8 Citations (Scopus)


Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or noninvasive additional measurements. This study investigates the relationship between SV and Pulse Wave Velocity (PWV) to examine whether the value of PWV can capture the changes in SV. The analysis was performed using data from six porcine experiments (N=6 Pietrain Pigs, 20-29 kg) in which left ventricular volume, aortic arc pressure, and descending aortic pressure waveforms were measured simultaneously. From the measured data, correlation coefficients were determined between absolute value of aortic PWV, SV and trend value 'PWV - mean PWV', 'SV - mean SV' during periods when changes in SV were induced from preload changes, as well as infusion of dobutamine. The results showed good correlation (r = 0.59) for trend value, however, the correlation coefficient were poor with r = 0.028 for absolute value across all pigs. The analysis showed that value of PWV is reliable for capturing trend value of SV in preload changes. However, it is unreliable for capturing absolute value of SV or changes in SV made from dobutamine.

Original languageEnglish
Title of host publication9th IFAC Symposium on Biological and Medical Systems BMS 2015
Number of pages6
Publication statusPublished - 1 Sep 2015
Externally publishedYes
EventIFAC Symposium on Biological and Medical Systems 2015 - Berlin, Germany
Duration: 31 Aug 20152 Sep 2015
Conference number: 9th


ConferenceIFAC Symposium on Biological and Medical Systems 2015
Abbreviated titleBMS 2015


  • Biomedical systems
  • Cardiovascular systems
  • Decision support systems
  • Parameter ID
  • Physiological models

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