Head orientation should be considered in ultrasound studies on carotid artery distensibility

Holtackers, Robert J.; Spronck, Bart; Heusinkveld, Maarten H. G.; Crombag, Geneviève; t’Roodt, Jos Op; Delhaas, Tammo; Kooi, M. Eline; Reesink, Koen D.; Hermeling, Evelien

doi:10.1097/hjh.0000000000000985

Cited by 14 publications

(18 citation statements)

References 9 publications

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“…The measurement protocol and data used in the present study are elaborated by Holtackers et al. ( 2016 ). Briefly, twelve apparently healthy volunteers ( , 6 males, 6 females) were recruited.…”

Section: Methodsmentioning

confidence: 99%

“…For these purposes, a large set of virtual pressure ( P ), diameter ( D ), and intima–media thickness (IMT) measurements will be generated by sampling the measurement distributions of previously published P , D , and IMT measurements in healthy volunteers (Holtackers et al. 2016 ). For each D - P sample, we will obtain continuous D - P curves over the diastolic–systolic pressure range, using the single-exponential function introduced by Hayashi et al.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty quantification and sensitivity analysis of an arterial wall mechanics model for evaluation of vascular drug therapies

Heusinkveld

Quicken

Holtackers

et al. 2017

Biomech Model Mechanobiol

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Quantification of the uncertainty in constitutive model predictions describing arterial wall mechanics is vital towards non-invasive assessment of vascular drug therapies. Therefore, we perform uncertainty quantification to determine uncertainty in mechanical characteristics describing the vessel wall response upon loading. Furthermore, a global variance-based sensitivity analysis is performed to pinpoint measurements that are most rewarding to be measured more precisely. We used previously published carotid diameter-pressure and intima-media thickness (IMT) data (measured in triplicate), and Holzapfel-Gasser-Ogden models. A virtual data set containing 5000 diastolic and systolic diameter-pressure points, and IMT values was generated by adding measurement error to the average of the measured data. The model was fitted to single-exponential curves calculated from the data, obtaining distributions of constitutive parameters and constituent load bearing parameters. Additionally, we (1) simulated vascular drug treatment to assess the relevance of model uncertainty and (2) Department of Radiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands ated how increasing the number of measurement repetitions influences model uncertainty. We found substantial uncertainty in constitutive parameters. Simulating vascular drug treatment predicted a 6% point reduction in collagen load bearing (L coll ), approximately 50% of its uncertainty. Sensitivity analysis indicated that the uncertainty in L coll was primarily caused by noise in distension and IMT measurements. Spread in L coll could be decreased by 50% when increasing the number of measurement repetitions from 3 to 10. Model uncertainty, notably that in L coll , could conceal effects of vascular drug therapy. However, this uncertainty could be reduced by increasing the number of measurement repetitions of distension and wall thickness measurements used for model parameterisation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification and sensitivity analysis of an arterial wall mechanics model for evaluation of vascular drug therapies

Heusinkveld

Quicken

Holtackers

et al. 2017

Biomech Model Mechanobiol

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“…There is a direct coupling between axial and circumferential behavior, implying that nonphysiological axial behavior will also affect circumferential behavior and thus the overall modeling outcome (59). This coupling was illustrated by Holtackers et al (52), who showed that in vivo in volunteers, a change in carotid axial stretch induced by rotation of the head is detectible in the circumferential pressure-diameter relationship. From ex vivo biomechanical experiments, we know that axial force remains approximately constant over a wide range of transmural pressures (17,146,152).…”

Section: H702mentioning

confidence: 99%

Constitutive interpretation of arterial stiffness in clinical studies: a methodological review

Reesink

Spronck

2019

American Journal of Physiology-Heart and Circulatory Physiology

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Clinical assessment of arterial stiffness relies on noninvasive measurements of regional pulse wave velocity or local distensibility. However, arterial stiffness measures do not discriminate underlying changes in arterial wall constituent properties (e.g., in collagen, elastin, or smooth muscle), which is highly relevant for development and monitoring of treatment. In arterial stiffness in recent clinical-epidemiological studies, we systematically review clinical-epidemiological studies (2012–) that interpreted arterial stiffness changes in terms of changes in arterial wall constituent properties (63 studies included of 514 studies found). Most studies that did so were association studies (52 of 63 studies) providing limited causal evidence. Intervention studies (11 of 63 studies) addressed changes in arterial stiffness through the modulation of extracellular matrix integrity (5 of 11 studies) or smooth muscle tone (6 of 11 studies). A handful of studies (3 of 63 studies) used mathematical modeling to discriminate between extracellular matrix components. Overall, there exists a notable gap in the mechanistic interpretation of stiffness findings. In constitutive model-based interpretation, we first introduce constitutive-based modeling and use it to illustrate the relationship between constituent properties and stiffness measurements (“forward” approach). We then review all literature on modeling approaches for the constitutive interpretation of clinical arterial stiffness data (“inverse” approach), which are aimed at estimation of constitutive properties from arterial stiffness measurements to benefit treatment development and monitoring. Importantly, any modeling approach requires a tradeoff between model complexity and measurable data. Therefore, the feasibility of changing in vivo the biaxial mechanics and/or vascular smooth muscle tone should be explored. The effectiveness of modeling approaches should be confirmed using uncertainty quantification and sensitivity analysis. Taken together, constitutive modeling can significantly improve clinical interpretation of arterial stiffness findings.

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“…We recently set out to quantitatively assess the physiological confounders of PWV [1], investigating the magnitude of their effect and ways to correct for them. These confounders included blood pressure [6,7,8], heart rate [9,10], and axial stretch [11]. This mini-review will concentrate on blood pressure as a confounder of PWV, and will discuss several options to deal with the confounding effect.…”

Section: Arterial Stiffnessmentioning

confidence: 99%

Options for Dealing with Pressure Dependence of Pulse Wave Velocity as a Measure of Arterial Stiffness: An Update of Cardio-Ankle Vascular Index (CAVI) and CAVI0

Spronck

Delhaas

Butlin

et al. 2017

Pulse

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Pulse wave velocity (PWV), a marker of arterial stiffness, is known to change instantaneously with changes in blood pressure. In this mini-review, we discuss two main approaches for handling the blood pressure dependence of PWV: (1) converting PWV into a pressure-independent index, and (2) correcting PWV per se for the pressure dependence. Under option 1, we focus on cardio-ankle vascular index (CAVI). CAVI is essentially a form of stiffness index β - CAVI is estimated for a (heart-to-ankle) trajectory, whereas β is estimated for a single artery from pressure and diameter measurements. Stiffness index β, and therefore also CAVI, have been shown to theoretically exhibit a slight residual blood pressure dependence due to the use of diastolic blood pressure instead of a fixed reference blood pressure. Additionally, CAVI exhibits pressure dependence due to the use of an estimated derivative of the pressure-diameter relationship. In this mini-review, we will address CAVI's blood pressure dependence theoretically, but also statistically. Furthermore, we review corrected indices (CAVI₀ and β₀) that theoretically do not show a residual blood pressure dependence. Under option 2, three ways of correcting PWV are reviewed: (1) using an exponential relationship between pressure and cross-sectional area, (2) by statistical model adjustment, and (3) through reference values or rule of thumb. Method 2 requires a population to be studied to characterise the statistical model, and method 3 requires a representative reference study. Given these limitations, method 1 seems preferable for correcting PWV per se for its blood pressure dependence. In summary, several options are available to handle the blood pressure dependence of PWV. If a blood pressure-independent index is sought, CAVI₀ is theoretically preferable over CAVI. If correcting PWV per se is required, using an exponential pressure-area relationship provides the user with a method to correct PWV on an individual basis.

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Head orientation should be considered in ultrasound studies on carotid artery distensibility

Cited by 14 publications

References 9 publications

Uncertainty quantification and sensitivity analysis of an arterial wall mechanics model for evaluation of vascular drug therapies

Uncertainty quantification and sensitivity analysis of an arterial wall mechanics model for evaluation of vascular drug therapies

Constitutive interpretation of arterial stiffness in clinical studies: a methodological review

Options for Dealing with Pressure Dependence of Pulse Wave Velocity as a Measure of Arterial Stiffness: An Update of Cardio-Ankle Vascular Index (CAVI) and CAVI0

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