Association of atherosclerotic cardiovascular disease risk stratification with carotid wall shear stress as measured by vector flow mapping: a study of a Chinese population

Introduction

Atherosclerotic cardiovascular disease (ASCVD), including coronary heart disease, stroke, and peripheral arterial disease, is the leading cause of death globally, and is growing in prevalence both in low- and middle-income countries (1). Considering the rapid increase in the number of people exposed to ASCVD around the world, accurate risk prediction for cardiovascular events is critical for prevention and treatment (2). Several region-specific risk models that mainly involve traditional risk factors and disease history have recently been developed to evaluate the risk for ASCVD and provide evidence for prevention strategies in developed countries (3,4). In addition to the traditional risk factors of ASCVD, carotid intima-media thickness (CIMT), a noninvasive, relatively cost-effective, and widely available ultrasonic parameter is also associated with the ASCVD process (5).

Atherosclerosis (AS), a chronic inflammatory disease of the arteries, is the pathologic basis of ASCVD (6). Endothelial dysfunction is considered the initial step in AS, and it encompasses a constellation of nonadaptive alterations in functional phenotype, which have important implications for the regulation of thrombosis and hemostasis, redox balance, local vascular tone, and the orchestration of acute and chronic inflammatory reactions within the arterial wall (7,8). Wall shear stress (WSS), influenced by geometrical properties of arteries, blood flow velocity, and plasma viscosity, reflects the parallel shear stress that blood flow applies to the endoluminal surface of the vessel wall (9). Abnormal WSS leads to endothelial cell (EC) activation and expression of atherogenic molecules, including monocyte chemoattractant protein-1 and platelet-derived growth factors (10). Research indicates that WSS exerts a complex effect in atherosclerotic plaque since both low and high WSS has been associated with plaque development and destabilization (11,12). Consequently, the accurate measurement of arterial WSS has considerable clinical implications.

A variety of methods have been used to measure WSS in clinical practice. Magnetic resonance imaging (MRI), with the advantages of being noninvasive and having high accuracy, is a commonly used and reliable method for WSS measurement; however, it is also associated with certain disadvantages, such as its long examination time and limited spatial resolution (13). Computed tomography (CT) is another tool for measuring WSS, but it is not commonly used due to its high radiation exposure (14). With its ultrahigh resolution, intravascular ultrasound (IVUS) is used for WSS measurement of smaller vessels. However, IVUS is often undesirable due to its invasive nature (15). Meanwhile, conventional ultrasound methods for vascular examination, such as color Doppler flow imaging (CDFI) and pulse wave Doppler, entail challenges in quantitative examination and the precise detection of complex flow patterns and are thus restricted to the evaluation of laminar flow or the axial component (16).

Vector flow mapping (VFM) is a noninvasive ultrasonic imaging technique for observing and quantitatively evaluating the flow field and hemodynamics in vessels (17). As a combination of color Doppler imaging and speckle-tracking analysis, the VFM technique quantitatively measures the blood flow velocity vector and WSS, with the deviation between the measured results and the WSS value measured by optical particle image velocimetry being controlled within 8.1–16.3% (18).

Due to the significant differences in diet, genetic factors, and metabolic abnormalities, the risk factor profiles in China differ from those in Western countries. Hence, tools and equations for ASCVD risk stratification, such as the Framingham general cardiovascular disease equations and the pooled cohort equations (PCEs) are not applicable to the Chinese population (19). However, a novel, more suitable tool for the Chinese population has recently been reported (20). In this study, we used the VFM technique to measure carotid WSS and determined the correlation between carotid WSS measured via the VFM technique and ASCVD risk stratification. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1541/rc).

Methods

Study population

Individuals admitted to the Department of Cardiology at Qilu Hospital of Shandong University from November 2022 to February 2023 were recruited in this study. Individuals with cardiomyopathy, various arrhythmias, peripheral vascular disease, aortitis, or heart failure were excluded. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Qilu Hospital of Shandong University (No. QLCR20210027) and informed consents were obtained from all participants.

Data collected included the following: information from routine physical examinations, including gender, age, body mass index (BMI), heart rate (HR), and blood pressure; blood biochemical examinations, including levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and fasting glucose (GLU); and past medical history. Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg or the intake of antihypertensive drugs (21). Diabetes mellitus (DM) was diagnosed according to the 2010 guidelines for type 2 DM (22). Smoking was defined as a current or past smoking history.

ASCVD risk stratification assessment

In this study, we used a previously published ASCVD risk stratification method (20). In this framework, two categories are classified as secondary prevention and primary prevention based on the presence of ASCVD. Individuals with ASCVD who have had ≥2 severe ASCVD events or 1 severe ASCVD event coupled with ≥2 high-risk factors are classified as ultrahigh risk, and other individuals with ASCVD are classified as very high risk; individuals without ASCVD but meeting one of the following three conditions are directly classified as high-risk: (I) LDL-C ≥4.9 mmol/L (or TC ≥7.2 mmol/L), (II) patients with diabetes aged ≥40 years, and (III) chronic kidney disease stage 3–5. Individuals without the abovementioned three conditions should be evaluated according to the individual’s serum cholesterol level stratification (TC and LDL-C indices), the presence or absence of hypertension, and the cumulative number of additional risk factors.

Carotid artery ultrasound

Before ultrasonography, an electrocardiogram-synchronized examination was conducted. A 3- to 15-MHz linear transducer (L441) connected to an ultrasound system (ALOKA LISENDO 880, Fujifilm, Tokyo, Japan) was used to scan the bilateral common carotid artery (CCA). CIMT, internal diameter, and blood flow velocity were measured at the plaque-free far walls of the bilateral CCA. All ultrasound measurements were repeated three times and averaged to represent the final results.

In the conventional two-dimensional ultrasound, the long axis of the CCA was positioned parallel to the skin surface. For color Doppler, the color sampling frame angle between the carotid artery and the probe was adjusted from 0° to 30°. The echo gain was changed as necessary, and the dynamic and velocity ranges were minimized within the aliasing correction. Next, to optimize the crossbeam Doppler signal in the vessel, the crossbeam was automatically set and then changed in 5° increments (Figure 1A). Subsequently, with vessel wall motion and blood flow along the three beam lines serving as boundary conditions, speckle tracking automatically calculated wall motion and collected dynamic images for three complete cardiac cycles (Figure 1B). Following this, the color signals reversed by exceeding the aliasing were corrected frame by frame. The WSS of the vessel was then obtained in each frame of a cardiac cycle (Figure 1C,1D). WSS was automatically calculated via an analytical formula embedded in the software as follows:

WSS=μ(dvdy)μ=4.0×10−3(N⋅s⋅m−2)[1]

Figure 1 Visualization and quantitative calculation of carotid WSS via the VFM technique. (A) The blood flow on the three crossbeam lines used as boundary condition. (B) Use of two-dimensional speckle tracking technology to trace wall motion. (C) Different color lines on the vessel wall indicate WSS levels. (D) Visualization of carotid WSS of each point of a complete cardiac cycle. VFM, vector flow mapping; WSS, wall shear stress.

where dv/dy: wall shear rate, µ is the blood viscosity coefficient (23).

Analysis of carotid WSS

WSS parameters were recorded via the collection of the blood flow vector. The mean value of WSS on vessel wall for each frame of a cardiac cycle was analyzed, after which the maximum WSS of a cardiac cycle (WSS_max), minimum WSS of a cardiac cycle (WSS_min), mean WSS of a cardiac cycle (WSS_mean), and the difference between the WSS_max and WSS_min (WSS_△) were obtained. The bilateral WSS parameters were averaged to represent the final WSS value.

Statistical analysis

Continuous variables are reported as the mean and standard deviations and were analyzed with the Student t-test or Mann-Whitney test. Categorical data are expressed as numbers with percentages and were analyzed with the χ² test. The correlation between traditional risk factors or biochemical indicators and ultrasonic parameters were analyzed by Spearman analysis and linear regression. Receiver operating characteristic (ROC) analysis was used to assess relations between ultrasonic parameters and a very high or ultrahigh risk of ASCVD. P<0.05 indicated a statistically significant difference. Statistical analysis was performed with SPSS 25.0 (IBM Corp., Armonk, NY, USA).

Results

Baseline characteristics

There were 165 individuals initially included in the study. Among them, three were excluded due to refusal to participate in the VFM examination, five due to severe cardiac arrhythmia, and two due to ambiguous past medical history (Figure 2). A total of 155 individuals were enrolled in this study, 39 (25.2%) were assessed as low risk, 15 (9.7%) as moderate risk, 11 (7%) as high risk, 18 (11.6%) as very high risk, and 72 (46.5%) as ultrahigh risk. Age, gender, BMI, SBP, TC, TG, and LDL-C differed significantly across the risk levels of ASCVD; meanwhile, DBP, HR, HDL-C, and GLU were similar (Table 1).

Figure 2 Flowchart of participant enrolment. ASCVD, atherosclerotic cardiovascular disease; VFM, vector flow mapping.

Ultrasound parameters of different ASCVD risk levels

CIMT was measured via B-mode ultrasound, and WSS parameters including WSS_max, WSS_min, WSS_mean, and WSS_△ were calculated with the VFM technique. With a higher ASCVD risk, CIMT was thicker (P<0.001) and values of WSS_max, WSS_min, WSS_mean, and WSS_△ were lower (all P values <0.001; Table 2).

Correlation between CIMT, ASCVD risk stratification, and WSS parameters

The Spearman coefficient (r) was used to analyze the correlations between CIMT and WSS parameters or ASCVD risk stratification and WSS parameters. CIMT was negatively correlated with WSS_max (r=−0.419; P<0.001), WSS_min (r=−0.448, P<0.001), WSS_mean (r=−0.433; P<0.001), and WSS_△ (r=−0.297; P<0.001) (Figure 3). ASCVD risk stratification was also negatively correlated with WSS_max (r=−0.627; P<0.001), WSS_min (r=−0.678; P<0.001), WSS_mean (r=−0.678; P<0.001), and WSS_△ (r=−0.430; P<0.001) (Figure 4).

Figure 3 Correlation between carotid WSS and CIMT. WSS_max (A), WSS_min (B), WSS_mean (C), and WSS_△ (D) were negatively correlated with CIMT. CIMT, carotid intima-media thickness; WSS, wall shear stress; WSS_△, difference between WSS_max and WSS_min; WSS_max, maximum wall shear stress of a complete cardiac cycle; WSS_mean, mean wall shear stress of a cardiac cycle; WSS_min, minimum wall shear stress of a complete cardiac cycle.

Figure 4 Correlation between carotid WSS and ASCVD risk stratification. WSS_max (A), WSS_min (B), WSS_mean (C), and WSS_△ (D) were negatively correlated with ASCVD risk stratification. ASCVD, atherosclerotic cardiovascular disease; WSS, wall shear stress; WSS_△, difference between WSS_max and WSS_min; WSS_max, maximum wall shear stress of a complete cardiac cycle; WSS_mean, mean wall shear stress of a cardiac cycle; WSS_min, minimum wall shear stress of a complete cardiac cycle.

Relationship between biochemical biomarkers, traditional risk factors, and WSS parameters

Linear regression analysis was performed to examine factors affecting carotid WSS. Univariate linear regression analysis indicated that WSS_max was negatively correlated with age, BMI, SBP, and GLU but positively correlated with HR, LDL-C, and TC; WSS_min was negatively correlated with age, BMI, and SBP but positively correlated with HR, LDL-C, and TC; WSS_mean was negatively correlated with age, BMI, and SBP but positively correlated with HR, LDL-C, and TC; and finally, WSS_△ was negatively correlated with age and BMI but positively correlated with HR, LDL-C, and TC (Table 3).

Multivariate linear regression analysis indicated that WSS_max was correlated with age, BMI, and HR (R²=0.460; P<0.001); WSS_min was correlated with age, BMI, SBP, HR, HDL-C and LDL-C (R²=0.472; P<0.001); WSS_mean was correlated with age, BMI, HR and LDL-C (R²=0.454; P<0.001); and WSS_△ was correlated with age, BMI, and SBP (R²=0.316; P<0.001) (Table 4).

Values of carotid WSS and CIMT in predicting very high or ultrahigh risk of ASCVD

ROC analysis was conducted in the overall cohort to determine the associations between carotid WSS, CIMT, and very high or ultrahigh risk of ASCVD (Figure 5). The areas under the curve (AUCs) of WSS_max, WSS_min, WSS_mean, WSS_△, and CIMT for assessing very high or ultrahigh risk of ASCVD were 0.854, 0.853, 0.866, 0.763, and 0.756, respectively. The AUC of CIMT differed significantly from that of WSS_max (P=0.018), WSS_min (P=0.017), and WSS_mean (P=0.001), indicating that WSS parameters had a higher significance than CIMT in predicting very high or ultrahigh risk of ASCVD.

Figure 5 ROC curves for carotid WSS and CIMT in predicting very high or ultrahigh risk of ASCVD. The AUCs of WSS_max (B), WSS_min (C), WSS_mean (D), and WSS_△ (E) were 0.854, 0.853, 0.866, and 0.763, respectively, while that of CIMT (A) was 0.756. WSS_max, WSS_min, and WSS_mean yielded larger AUCs than did CIMT (WSS_max: P=0.018; WSS_min: P=0.017; WSS_mean: P=0.001). ASCVD, atherosclerotic cardiovascular disease; AUC, area under the curve; CIMT, carotid intima-media thickness; ROC, receiver operating characteristic; WSS, wall shear stress; WSS_△, difference between WSS_max and WSS_min; WSS_max, maximum wall shear stress of a complete cardiac cycle; WSS_mean, mean wall shear stress of a cardiac cycle; WSS_min, minimum wall shear stress of a complete cardiac cycle.

Discussion

In this study, the VFM technique was used to measure and visualize the carotid WSS parameters of a Chinese population. We found that carotid WSS was negatively correlated with basic parameters (such as age, BMI, and HR), CIMT and ASCVD risk stratification. Furthermore, carotid WSS could better predict very high or ultrahigh risk of ASCVD as compared to CIMT.

It is not possible to directly measure WSS under noninvasive conditions. At present, the measurement of WSS is mainly based on various medical imaging techniques, and the calculation results of different methods diverge substantially. The majority of recent studies on WSS have used phase-contrast MRI to measure WSS (24). This involves certain advantages, such as vascular background suppression and blood flow velocity direction acquisition (25), but is also restricted by its long duration and poor spatial resolution (26,27). In one study, a three-dimensional model of the coronary artery was established via CT angiography examination, from which a blood flow pattern was obtained via the Navier-Stokes equation (14). Finally, the WSS was calculated in a computational fluid dynamics model (28). However, CT measurement of WSS requires iodinated contrast media and radiation exposure. In our study, the VFM technique was used to quantify carotid WSS. Combining color Doppler with two-dimensional speckle-tracking technology, the VFM technique is able to calculate blood velocity information, and values of WSS are then determined point by point and frame by frame, with good spatial and temporal resolution (18). Recently, the VFM technique has been used to quantitatively assess carotid WSS in patients with hypertension (23) and to determine the association between carotid WSS and cerebral small vessel disease (29) and that between carotid WSS and brachial-ankle pulse wave velocity (30). An abundance of other work has sought to extend the clinical value of WSS as measured by the VFM technique.

WSS occupies a prominent role in AS development. Disturbances of WSS, either an increase or a decrease, contribute to AS development in a variety of ways (31). It is widely known that WSS is directly affected by blood flow velocity, viscosity, vessel radius, and carotid geometry (32,33). Moreover, the correlations between WSS and traditional risk factors have become increasingly clear due to numerous studies being conducted on this topic. Research suggests that the WSS value decreases as age increases (34) and that an increase in the triglyceride glucose index is inversely proportional to WSS (35). In this study, the carotid WSS was compared across individuals with different ASCVD risk levels. It was found that with greater ASCVD risk, value of carotid WSS decreases, which is in line with other work (30).

In our study, WSS was measured at a plaque-free area of the CCA, not only due to the ease of CCA measurements but because the deviation of WSS measured at this location is smaller. WSS may represent a “systemic” characteristic in addition to being a regional parameter (30). WSS promotes AS through endothelial mechanotransduction (36), but the degree to which it does may vary across individuals. However, the effect exerted by WSS on ECs occurs throughout the arterial system. Carotid WSS was significantly and independently associated with pulse wave velocity, which is a gold standard for measuring arterial stiffness (30). Another argument in favor of WSS as a “systemic” characteristic is that WSS is associated with various ASCVD risk factors such as age (37), which was also the case in our study.

Intriguingly, we found that WSS parameters had superior value compared to CIMT in predicting a very high or ultrahigh risk of ASCVD. Although CIMT is a clinically recognized and consistently used indicator for reflecting ASCVD, its value has been scrutinized because it contains carotid media thickness, which is easily affected by blood pressure and age (38). Measurement error might have also caused this result. Value of CIMT measured at CCA are closed to the spatial resolution of ultrasound, where measuring error may represent a substantially large fraction of the quantity (39), but the quantity measured of carotid WSS are large enough to overcome errors in measurement.

This study involved certain limitations that should be addressed. First, selection bias might have been introduced, as individuals exposed to a risk of ASCVD were enrolled from a single center. Therefore, it is necessary to carry out WSS measurement at multiple centers to generate more generalizable results. Second, the study lacked an assessment of interrater reliability, which should be explored in future studies. In addition, deviation of WSS from blood viscosity changes were not examined, which also might have produced bias. Finally, predicted value of WSS for very high or ultrahigh risk of ASCVD may not be accurate due the cross-sectional design of the study. Nonetheless, in view of the advantages of the VFM technique—including its noninvasiveness, convenience, and excellent repeatability—it is advised that the VFM measurement of carotid WSS be considered a routine examination for individuals with a risk of ASCVD in order to establish a longitudinal study for individuals and further verify value of this assessment tool.

Conclusions

This is the first study to use the VFM technique to obtain carotid WSS and to assess the associations between carotid WSS and ASCVD risk stratification for a Chinese population. Carotid WSS was negatively correlated with CIMT and ASCVD risk. Moreover, the value of carotid WSS in predicting very high or ultrahigh risk of ASCVD was higher than that of CIMT. Carotid WSS measured by the VFM technique can effectively facilitate the diagnosis and prevention of ASCVD.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1541/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1541/dss

Funding: This work was supported by the National Key Research and Development Program of China (No. 2022YFC3602400, 2022YFC3602403), the grants of the National Natural Science Foundation of China (Nos. 81970377, 82001834) and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (No. 2023-PT320-06).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1541/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by Ethics Committee of Qilu Hospital of Shandong University (No. QLCR20210027) and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Ray KK, Ference BA, Séverin T, Blom D, Nicholls SJ, Shiba MH, et al. World Heart Federation Cholesterol Roadmap 2022. Glob Heart 2022;17:75. [Crossref] [PubMed]
2. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, Himmelfarb CD, Khera A, Lloyd-Jones D, McEvoy JW, Michos ED, Miedema MD, Muñoz D, Smith SC Jr, Virani SS, Williams KA Sr, Yeboah J, Ziaeian B. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2019;140:e596-646. [Crossref] [PubMed]
3. DAWBER TR. KANNEL WB, REVOTSKIE N, STOKES J 3rd, KAGAN A, GORDON T. Some factors associated with the development of coronary heart disease: six years' follow-up experience in the Framingham study. Am J Public Health Nations Health 1959;49:1349-56. [Crossref] [PubMed]
4. Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;129:S49-73. [Crossref] [PubMed]
5. Zhou A, Chen K, Wei Y, Ye Q, Xiao Y, Shi R, Wang J, Li WD. Machine learning-based prediction of carotid intima-media thickness progression: a three-year prospective cohort study. Front Med (Lausanne) 2025;12:1593662. [Crossref] [PubMed]
6. Frostegård J. Immunity, atherosclerosis and cardiovascular disease. BMC Med 2013;11:117. [Crossref] [PubMed]
7. Gimbrone MA Jr, García-Cardeña G. Endothelial Cell Dysfunction and the Pathobiology of Atherosclerosis. Circ Res 2016;118:620-36. [Crossref] [PubMed]
8. Bu LL, Yuan HH, Xie LL, Guo MH, Liao DF, Zheng XL. New Dawn for Atherosclerosis: Vascular Endothelial Cell Senescence and Death. Int J Mol Sci 2023;24:15160. [Crossref] [PubMed]
9. Chiu JJ, Chien S. Effects of disturbed flow on vascular endothelium: pathophysiological basis and clinical perspectives. Physiol Rev 2011;91:327-87. [Crossref] [PubMed]
10. Peng Z, Shu B, Zhang Y, Wang M. Endothelial Response to Pathophysiological Stress. Arterioscler Thromb Vasc Biol 2019;39:e233-43. [Crossref] [PubMed]
11. Samady H, Eshtehardi P, McDaniel MC, Suo J, Dhawan SS, Maynard C, Timmins LH, Quyyumi AA, Giddens DP. Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease. Circulation 2011;124:779-88. [Crossref] [PubMed]
12. Koskinas KC, Chatzizisis YS, Baker AB, Edelman ER, Stone PH, Feldman CL. The role of low endothelial shear stress in the conversion of atherosclerotic lesions from stable to unstable plaque. Curr Opin Cardiol 2009;24:580-90. [Crossref] [PubMed]
13. Efstathopoulos EP, Patatoukas G, Pantos I, Benekos O, Katritsis D, Kelekis NL. Wall shear stress calculation in ascending aorta using phase contrast magnetic resonance imaging. Investigating effective ways to calculate it in clinical practice. Phys Med 2008;24:175-81. [Crossref] [PubMed]
14. Han D, Starikov A, Ó Hartaigh B, Gransar H, Kolli KK, Lee JH, Rizvi A, Baskaran L, Schulman-Marcus J, Lin FY, Min JK. Relationship Between Endothelial Wall Shear Stress and High-Risk Atherosclerotic Plaque Characteristics for Identification of Coronary Lesions That Cause Ischemia: A Direct Comparison With Fractional Flow Reserve. J Am Heart Assoc 2016;5:e004186. [Crossref] [PubMed]
15. Slager CJ, Wentzel JJ, Schuurbiers JC, Oomen JA, Kloet J, Krams R, von Birgelen C, van der Giessen WJ, Serruys PW, de Feyter PJ. True 3-dimensional reconstruction of coronary arteries in patients by fusion of angiography and IVUS (ANGUS) and its quantitative validation. Circulation 2000;102:511-6. [Crossref] [PubMed]
16. Liu M, Song D, Hong S, Dong Y, Gao W, Du Y, Zhu L, Xu J, Dong F. Characteristics and Correlations of Wall Shear Stress and Flow Turbulence in the Carotid Bifurcation Evaluated Using an Ultrasound Vector Flow Imaging. J Vasc Res 2024;61:38-49. [Crossref] [PubMed]
17. Saito K, Abe S, Kumamoto M, Uchihara Y, Tanaka A, Sugie K, Ihara M, Koga M, Yamagami H. Blood Flow Visualization and Wall Shear Stress Measurement of Carotid Arteries Using Vascular Vector Flow Mapping. Ultrasound Med Biol 2020;46:2692-9. [Crossref] [PubMed]
18. Asami R, Tanaka T, Shimizu M, Seki Y, Nishiyama T, Sakashita H, Okada T. Ultrasonic Vascular Vector Flow Mapping for 2-D Flow Estimation. Ultrasound Med Biol 2019;45:1663-74. [Crossref] [PubMed]
19. Yang XL, Chen JC, Li JX, Cao J, Lu XF, Liu FC, Hu DS, Liu XQ, Shen C, Yu L, Lu FH, Wu XP, Zhao LC, Huang JF, Li Y, Wu XG, Gu DF. Risk stratification of atherosclerotic cardiovascular disease in Chinese adults. Chronic Dis Transl Med 2016;2:102-9. [Crossref] [PubMed]
20. Li JJ, Zhao SP, Zhao D, Lu GP, Peng DQ, Liu J, Chen ZY, Guo YL, Wu NQ, Yan SK, Wang ZW, Gao RL. 2023 Chinese guideline for lipid management. Front Pharmacol 2023;14:1190934. [Crossref] [PubMed]
21. Bakris G, Ali W, Parati G. ACC/AHA Versus ESC/ESH on Hypertension Guidelines: JACC Guideline Comparison. J Am Coll Cardiol 2019;73:3018-26. [Crossref] [PubMed]
22. Diagnosis and classification of diabetes mellitus. Diabetes Care 2010;33:S62-9. [Crossref] [PubMed]
23. He L, Cai Y, Feng Y, Wang W, Feng T, Shen E, Yang S. Utility of vector flow mapping technology in quantitative assessment of carotid wall shear stress in hypertensive patients: A preliminary study. Front Cardiovasc Med 2022;9:967763. [Crossref] [PubMed]
24. Potters WV, van Ooij P, Marquering H, vanBavel E, Nederveen AJ. Volumetric arterial wall shear stress calculation based on cine phase contrast MRI. J Magn Reson Imaging 2015;41:505-16. [Crossref] [PubMed]
25. Stamm AC, Wright CL, Knopp MV, Schmalbrock P, Heverhagen JT. Phase contrast and time-of-flight magnetic resonance angiography of the intracerebral arteries at 1.5, 3 and 7 T. Magn Reson Imaging 2013;31:545-9. [Crossref] [PubMed]
26. Pantos I, Patatoukas G, Efstathopoulos EP, Katritsis D. In vivo wall shear stress measurements using phase-contrast MRI. Expert Rev Cardiovasc Ther 2007;5:927-38. [Crossref] [PubMed]
27. Reneman RS, Arts T, Hoeks AP. Wall shear stress--an important determinant of endothelial cell function and structure--in the arterial system in vivo. Discrepancies with theory. J Vasc Res 2006;43:251-69. [Crossref] [PubMed]
28. Min JK, Koo BK, Erglis A, Doh JH, Daniels DV, Jegere S, Kim HS, Dunning A, Defrance T, Leipsic J. Effect of image quality on diagnostic accuracy of noninvasive fractional flow reserve: results from the prospective multicenter international DISCOVER-FLOW study. J Cardiovasc Comput Tomogr 2012;6:191-9. [Crossref] [PubMed]
29. Kamimura T, Aoki S, Nezu T, Eto F, Shiga Y, Nakamori M, Imamura E, Mizoue T, Wakabayashi S, Maruyama H. Association between Carotid Wall Shear Stress-Based Vascular Vector Flow Mapping and Cerebral Small Vessel Disease. J Atheroscler Thromb 2023;30:1165-75. [Crossref] [PubMed]
30. Cheng Y, Chen J, Zhao Q, Zhang J, Gao J. Association of carotid wall shear stress measured by vector flow mapping technique with ba-PWV: a pilot study. Front Cardiovasc Med 2023;10:1293106. [Crossref] [PubMed]
31. Zhou M, Yu Y, Chen R, Liu X, Hu Y, Ma Z, Gao L, Jian W, Wang L. Wall shear stress and its role in atherosclerosis. Front Cardiovasc Med 2023;10:1083547. [Crossref] [PubMed]
32. Han N, Wang J, Ma Y, Ma L, Zheng Y, Fan F, Wu C, Yue S, Li J, Liang J, Zhang H, Zhou Y, Yang T, Zhang J. The hemodynamic and geometric characteristics of carotid artery atherosclerotic plaque formation. Quant Imaging Med Surg 2024;14:4348-61. [Crossref] [PubMed]
33. Bartolo MA, Qureshi MU, Colebank MJ, Chesler NC, Olufsen MS. Numerical predictions of shear stress and cyclic stretch in pulmonary hypertension due to left heart failure. Biomech Model Mechanobiol 2022;21:363-81. [Crossref] [PubMed]
34. Bapir M, Untracht GR, Hunt JEA, McVey JH, Harris J, Skene SS, Campagnolo P, Dikaios N, Rodriguez-Mateos A, Sampson DD, Sampson DM, Heiss C. Age-Dependent Decline in Common Femoral Artery Flow-Mediated Dilation and Wall Shear Stress in Healthy Subjects. Life (Basel) 2022;12:2023. [Crossref] [PubMed]
35. Tripolino C, Irace C, Scavelli FB, de Franceschi MS, Esposito T, Carallo C, Gnasso A. Triglyceride glucose index and common carotid wall shear stress. J Investig Med 2014;62:340-4. [Crossref] [PubMed]
36. Zócalo Y, Bia D. Sex- and Age-Related Physiological Profiles for Brachial, Vertebral, Carotid, and Femoral Arteries Blood Flow Velocity Parameters During Growth and Aging (4-76 Years): Comparison With Clinical Cut-Off Levels. Front Physiol 2021;12:729309. [Crossref] [PubMed]
37. He L, Cai Y, Feng Y, Feng T, Cademartiri F, Shen E. Age-related wall shear stress changes assessed by vascular vector flow mapping in the carotid arteries of healthy adults: a cross-sectional study. Cardiovasc Diagn Ther 2024;14:668-78. [Crossref] [PubMed]
38. Carvalho-Romano LFRS, Bonafé RP, Paim LR, Marques ER, Vegian CFL, Pio-Magalhães JA, Mello DSS, de Rossi G, Coelho-Filho OR, Schreiber R, Sposito AC, Matos-Souza JR, Nadruz W Jr. Association of carotid wall layers with atherosclerotic plaques and cardiac hypertrophy in hypertensive subjects. J Hum Hypertens 2022;36:732-7. [Crossref] [PubMed]
39. Azarpazhooh MR, Mathiesen E, Rundek T, Romanens M, Adams A, Armando L, Perez H, Villafañe H, Garcia NH, Ibañez B, Bogiatzi C, Tabrizi R, Fuster V, Spence JD. Reliability, Reproducibility, and Advantages of Measuring Carotid Total Plaque Area. J Am Soc Echocardiogr 2022;35:530-2. [Crossref] [PubMed]