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European Radiology

, Volume 29, Issue 2, pp 689–698 | Cite as

The role of wall shear stress in the parent artery as an independent variable in the formation status of anterior communicating artery aneurysms

  • Xin Zhang
  • Zhi-Qiang Yao
  • Tamrakar Karuna
  • Xu-Ying He
  • Xue-Min Wang
  • Xi-Feng Li
  • Wen-Chao Liu
  • Ran Li
  • Shen-Quan Guo
  • Yun-Chang Chen
  • Gan-Cheng Li
  • Chuan-Zhi DuanEmail author
Vascular-Interventional
  • 133 Downloads

Abstract

Objectives

The study aimed to determine which hemodynamic parameters independently characterize anterior communicating artery (AcomA) aneurysm formation and explore the threshold of wall shear stress (WSS) of the parent artery to better illustrate the correlation between the magnitude of WSS and AcomA aneurysm formation.

Methods

Eighty-one patients with AcomA aneurysms and 118 patients without intracranial aneurysms (control population), as confirmed by digital subtraction angiography (DSA) from January 2014 to May 2017, were included in this cross-sectional study. Three-dimensional-DSA was performed to evaluate the morphologic characteristics of AcomA aneurysms. Local hemodynamic parameters were obtained using transcranial color-coded duplex (TCCD). Multivariate logistic regression and a two-piecewise linear regression model were used to determine which hemodynamic parameters are independent predictors of AcomA aneurysm formation and identify the threshold effect of WSS of the parent artery with respect to AcomA aneurysm formation.

Results

Univariate analyses showed that the WSS (p < 0.0001), angle between the A1 and A2 segments of the anterior cerebral artery (ACA) (p < 0.001), hypertension (grade II) (p = 0.007), fasting blood glucose (FBG; > 6.0 mmol/L) (p = 0.005), and dominant A1 (p < 0.001) were the significant parameters. Multivariate analyses showed a significant association between WSS of the parent artery and AcomA aneurysm formation (p = 0.0001). WSS of the parent artery (7.8-12.3 dyne/cm2) had a significant association between WSS and aneurysm formation (HR 2.0, 95% CI 1.3-2.8, p < 0.001).

Conclusions

WSS ranging between 7.8 and 12.3 dyne/cm2 independently characterizes AcomA aneurysm formation. With each additional unit of WSS, there was a one-fold increase in the risk of AcomA aneurysm formation.

Key Points

• Multivariate analyses and a two-piecewise linear regression model were used to evaluate the risk factors for AcomA aneurysm formation and the threshold effect of WSS on AcomA aneurysm formation.

• WSS ranging between 7.8 and 12.3 dyne/cm 2 was shown to be a reliable hemodynamic parameter in the formation of AcomA aneurysms. The probability of AcomA aneurysm formation increased one-fold for each additional unit of WSS.

• An ultrasound-based TCCD technique is a simple and accessible noninvasive method for detecting WSS in vivo; thus, it can be applied as a screening tool for evaluating the probability of aneurysm formation in primary care facilities and community hospitals because of the relatively low resource intensity.

Keywords

Anterior communicating artery aneurysm Hemodynamics Risk 

Abbreviations and acronyms

ACA

Anterior cerebral artery

AcomA

Anterior communicating artery aneurysm

CAD

Coronary artery disease

CFD

Computational fluid dynamics

CTA

Computed tomography angiography

CWT

Circumferential wall tension

DBP

Diastolic blood pressure

DSA

Digital subtraction angiography

FBG

Fasting blood glucose;

ID

Internal diameter

MRA

Magnetic resonance angiography

SBP

Systolic blood pressure

TCCD

Transcranial color-coded duplex

WSS

Wall shear stress

Notes

Acknowledgements

We acknowledge further polishing of the article to improve the language and the rationality of the content provided by Dr. Tamrakar Karuna (CMS-Teaching Hospital, Bharatpur, Chitwan, Nepal) and the helpful comments on this article received from the reviewers. We also appreciate Prof. Chi Chen (Department of Health Statistics of Guizhou University of TCM, Guiyang City, China) for his important contribution to the professional statistical analysis in the study.

Funding

This study received funding from the Science and Technology Project Foundation of Guangdong Province (grant no. 2016A020215098), Key Project of Clinical Research of Southern Medical University (grant no. LC2016ZD024), and National Key Research Development Program (grant no. 2016YFC1300804, 2016YFC1300800).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Chuan-Zhi Duan who works in the Department of Neurosurgery, Zhujiang Hospital, Southern Medical University.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Prof. Chi Chen, who has significant statistical expertise (Department of Health Statistics of Guizhou University of TCM, Guiyang City, China), kindly provided statistical advice for this manuscript.

Informed consent

This is a retrospective and cross-sectional study. The data are anonymous, and the requirement for written informed consent was waived by the Institutional Review Board. However, the informed consent concerning the DSA procedure was signed by each patient before the operation.

Ethical approval

Approval for this study was obtained from the local Institutional Review Board of the participating centers. Ethics approval was obtained from the Institutional Review Board of Southern Medical University Zhujiang Hospital and the First Affiliated Hospital of Zhengzhou University.

Methodology

• retrospective

• cross-sectional study

• multicenter study

References

  1. 1.
    Horiuchi T, Tanaka Y, Hongo K (2005) Surgical treatment for aneurysmal subarachnoid hemorrhage in the 8th and 9th decades of life. Neurosurgery 56:469–475 discussion 469-475CrossRefGoogle Scholar
  2. 2.
    Leipzig TJ, Morgan J, Horner TG, Payner T, Redelman K, Johnson CS (2005) Analysis of intraoperative rupture in the surgical treatment of 1694 saccular aneurysms. Neurosurgery 56:455–468 discussion 455-468CrossRefGoogle Scholar
  3. 3.
    Qiu T, Jin G, Xing H, Lu H (2017) Association between hemodynamics, morphology, and rupture risk of intracranial aneurysms: a computational fluid modeling study. Neurol Sci 38:1009–1018CrossRefGoogle Scholar
  4. 4.
    Meng H, Tutino VM, Xiang J, Siddiqui A (2014) High WSS or low WSS? Complex interactions of hemodynamics with intracranial aneurysm initiation, growth, and rupture: toward a unifying hypothesis. AJNR Am J Neuroradiol 35:1254–1262CrossRefGoogle Scholar
  5. 5.
    Karmonik C, Yen C, Grossman RG, Klucznik R, Benndorf G (2009) Intra-aneurysmal flow patterns and wall shear stresses calculated with computational flow dynamics in an anterior communicating artery aneurysm depend on knowledge of patient-specific inflow rates. Acta Neurochir (Wien) 151:479–485 discussion 485CrossRefGoogle Scholar
  6. 6.
    Malek AM, Alper SL, Izumo S (1999) Hemodynamic shear stress and its role in atherosclerosis. JAMA 282:2035–2042CrossRefGoogle Scholar
  7. 7.
    Meng H, Wang Z, Hoi Y et al (2007) Complex hemodynamics at the apex of an arterial bifurcation induces vascular remodeling resembling cerebral aneurysm initiation. Stroke 38:1924–1931CrossRefGoogle Scholar
  8. 8.
    Can A, Du R (2016) Association of hemodynamic factors with intracranial aneurysm formation and rupture: systematic review and meta-analysis. Neurosurgery 78:510–520CrossRefGoogle Scholar
  9. 9.
    Skodvin TØ, Evju Ø, Helland CA, Isaksen JG (2017) Rupture prediction of intracranial aneurysms: a nationwide matched case-control study of hemodynamics at the time of diagnosis. J Neurosurg:1–7Google Scholar
  10. 10.
    Fukazawa K, Ishida F, Umeda Y et al (2015) Using computational fluid dynamics analysis to characterize local hemodynamic features of middle cerebral artery aneurysm rupture points. World Neurosurg 83:80–86CrossRefGoogle Scholar
  11. 11.
    Kaspera W, Ładziński P, Larysz P et al (2014) Morphological, hemodynamic, and clinical independent risk factors for anterior communicating artery aneurysms. Stroke 45:2906–2911CrossRefGoogle Scholar
  12. 12.
    Ye J, Zheng P, Hassan M, Jiang S, Zheng J (2017) Relationship of the angle between the A1 and A2 segments of the anterior cerebral artery with formation and rupture of anterior communicating artery aneurysm. J Neurol Sci 375:170–174CrossRefGoogle Scholar
  13. 13.
    Krejza J, Mariak Z, Walecki J, Szydlik P, Lewko J, Ustymowicz A (1999) Transcranial color Doppler sonography of basal cerebral arteries in 182 healthy subjects: age and sex variability and normal reference values for blood flow parameters. AJR Am J Roentgenol 172:213–218CrossRefGoogle Scholar
  14. 14.
    Irace C, Carallo C, De Franceschi MS et al (2012) Human common carotid wall shear stress as a function of age and gender: a 12-year follow-up study. Age (Dordr) 34:1553–1562CrossRefGoogle Scholar
  15. 15.
    Stolz E, Kaps M, Kern A, Dorndorf W (1999) Frontal bone windows for transcranial color-coded duplex sonography. Stroke 30:814–820CrossRefGoogle Scholar
  16. 16.
    Velcheva I, Antonova N, Damianov P, Dimitrov N (2010) Common carotid artery hemodynamic factors in patients with cerebral infarctions. Clin Hemorheol Microcirc 45:233–238Google Scholar
  17. 17.
    Carallo C, Irace C, Pujia A et al (1999) Evaluation of common carotid hemodynamic forces. Relations with wall thickening. Hypertension 34:217–221CrossRefGoogle Scholar
  18. 18.
    Longo M, Granata F, Racchiusa S et al (2017) Role of hemodynamic forces in unruptured intracranial aneurysms: an overview of a complex scenario. World Neurosurg 105:632–642CrossRefGoogle Scholar
  19. 19.
    Kawaguchi T, Nishimura S, Kanamori M et al (2012) Distinctive flow pattern of wall shear stress and oscillatory shear index: similarity and dissimilarity in ruptured and unruptured cerebral aneurysm blebs. J Neurosurg 117:774–780CrossRefGoogle Scholar
  20. 20.
    Liu J, Xiang J, Zhang Y et al (2014) Morphologic and hemodynamic analysis of paraclinoid aneurysms: ruptured versus unruptured. J Neurointerv Surg 6:658–663CrossRefGoogle Scholar
  21. 21.
    Shojima M, Oshima M, Takagi K et al (2004) Magnitude and role of wall shear stress on cerebral aneurysm: computational fluid dynamic study of 20 middle cerebral artery aneurysms. Stroke 35:2500–2505CrossRefGoogle Scholar
  22. 22.
    Sugiyama S, Meng H, Funamoto K et al (2012) Hemodynamic analysis of growing intracranial aneurysms arising from a posterior inferior cerebellar artery. World Neurosurg 78:462–468CrossRefGoogle Scholar
  23. 23.
    Chien A, Tateshima S, Sayre J, Castro M, Cebral J, Viñuela F (2009) Patient-specific hemodynamic analysis of small internal carotid artery-ophthalmic artery aneurysms. Surg Neurol 72:444–450 discussion 450CrossRefGoogle Scholar
  24. 24.
    Frösen J (2016) Flow dynamics of aneurysm growth and rupture: challenges for the development of computational flow dynamics as a diagnostic tool to detect rupture-prone aneurysms. Acta Neurochir Suppl 123:89–95CrossRefGoogle Scholar
  25. 25.
    Cheng C, Helderman F, Tempel D et al (2007) Large variations in absolute wall shear stress levels within one species and between species. Atherosclerosis 195:225–235CrossRefGoogle Scholar
  26. 26.
    Galizia MS, Barker A, Liao Y et al (2014) Wall morphology, blood flow and wall shear stress: MR findings in patients with peripheral artery disease. Eur Radiol 24:850–856CrossRefGoogle Scholar
  27. 27.
    Gnasso A, Carallo C, Irace C et al (1996) Association between intima-media thickness and wall shear stress in common carotid arteries in healthy male subjects. Circulation 94:3257–3262CrossRefGoogle Scholar
  28. 28.
    Samijo SK, Barkhuysen R, Willigers JM et al (2002) Wall shear stress assessment in the common carotid artery of end-stage renal failure patients. Nephron 92:557–563CrossRefGoogle Scholar
  29. 29.
    Katritsis D, Kaiktsis L, Chaniotis A, Pantos J, Efstathopoulos EP, Marmarelis V (2007) Wall shear stress: theoretical considerations and methods of measurement. Prog Cardiovasc Dis 49:307–329CrossRefGoogle Scholar
  30. 30.
    Liu Z, Zhao Y, Wang X et al (2016) Low carotid artery wall shear stress is independently associated with brain white-matter hyperintensities and cognitive impairment in older patients. Atherosclerosis 247:78–86CrossRefGoogle Scholar
  31. 31.
    Sui B, Gao P, Lin Y, Gao B, Liu L, An J (2008) Assessment of wall shear stress in the common carotid artery of healthy subjects using 3.0-tesla magnetic resonance. Acta Radiol 49:442–449CrossRefGoogle Scholar
  32. 32.
    Mynard JP, Wasserman BA, Steinman DA (2013) Errors in the estimation of wall shear stress by maximum Doppler velocity. Atherosclerosis 227:259–266CrossRefGoogle Scholar
  33. 33.
    Nixon AM, Gunel M, Sumpio BE (2010) The critical role of hemodynamics in the development of cerebral vascular disease. J Neurosurg 112:1240–1253CrossRefGoogle Scholar
  34. 34.
    Shakur SF, Alaraj A, Mendoza-Elias N, Osama M, Charbel FT (2018) Hemodynamic characteristics associated with cerebral aneurysm formation in patients with carotid occlusion. J Neurosurg :1-6Google Scholar
  35. 35.
    Jing L, Zhong J, Liu J et al (2016) Hemodynamic effect of flow diverter and coils in treatment of large and giant intracranial aneurysms. World Neurosurg 89:199–207CrossRefGoogle Scholar
  36. 36.
    Box FM, van der Grond J, de Craen AJ et al (2007) Pravastatin decreases wall shear stress and blood velocity in the internal carotid artery without affecting flow volume: results from the PROSPER MRI study. Stroke 38:1374–1376CrossRefGoogle Scholar
  37. 37.
    Box FM, van der Geest RJ, Rutten MC, Reiber JH (2005) The influence of flow, vessel diameter, and non-Newtonian blood viscosity on the wall shear stress in a carotid bifurcation model for unsteady flow. Invest Radiol 40:277–294CrossRefGoogle Scholar
  38. 38.
    Dormandy JA (1974) Medical and engineering problems of blood viscosity. Biomed Eng 9:284–289Google Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Xin Zhang
    • 1
  • Zhi-Qiang Yao
    • 1
    • 2
  • Tamrakar Karuna
    • 3
  • Xu-Ying He
    • 1
  • Xue-Min Wang
    • 4
  • Xi-Feng Li
    • 1
  • Wen-Chao Liu
    • 1
  • Ran Li
    • 1
  • Shen-Quan Guo
    • 1
  • Yun-Chang Chen
    • 1
  • Gan-Cheng Li
    • 1
  • Chuan-Zhi Duan
    • 1
    Email author
  1. 1.National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Neurosurgery Institute, Department of Neurosurgery, Zhujiang HospitalSouthern Medical UniversityGuangzhouChina
  2. 2.Department of Interventional Neuroradiologythe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
  3. 3.Department of NeurosurgeryCMS-Teaching HospitalBharatpurNepal
  4. 4.Key Laboratory of Psychiatric Disorders of Guangdong Province, Department of Neurobiology, School of Basic Medical ScienceSouthern Medical UniversityGuangzhouChina

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