Blood pressure waveform morphology assessed using a transmission line model and harmonic distortion analysis

A major determinant of blood pressure waveform (BPW) morphology is vascular impedance, governed by material properties of the arterial wall and hemodynamics of blood flow. Analysis of BPW morphology can be an effective means of assessing cardiovascular health. A transmission line model of the mechan...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 9076 - 13
Hlavní autoři: Milkovich, Nicholas, Mitchell, Gary F., Suki, Bela, Zhang, Yanhang
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 17.03.2025
Nature Publishing Group
Nature Portfolio
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ISSN:2045-2322, 2045-2322
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Shrnutí:A major determinant of blood pressure waveform (BPW) morphology is vascular impedance, governed by material properties of the arterial wall and hemodynamics of blood flow. Analysis of BPW morphology can be an effective means of assessing cardiovascular health. A transmission line model of the mechanical impedance of the arterial tree was implemented to recreate physiologically realistic BPWs. We then examined the sensitivity of existing vascular measures, including augmentation index (AI), pulse wave velocity (PWV), and the recently proposed harmonic distortion (HD), to structural and mechanical parameters of vessel walls and blood flow. All three measures are primarily sensitive to structural stiffness while HD and AI also correlate strongly with geometric parameters. Further, in a simulated set of randomly constructed arterial trees using model parameters within a physiological range, the indexes are strongly correlated with stiffness. When controlling for all confounding factors, HD demonstrates a stronger correlation with arterial stiffness than AI for stiffness values higher than the average. Our study provides a mechanistic understanding of the determinants of AI and HD, with the latter being a promising measure of cardiovascular risk due to its ease of calculation and access, meeting key limitations set by AI and PWV.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-93129-8