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Model and Application to Support the Coronary Artery Diseases (CAD): Development and Testing

  • Lina Teresa Gaudio
  • Pierangelo Veltri
  • Salvatore De Rosa
  • Ciro Indolfi
  • Gionata Fragomeni
Original Research Article
  • 17 Downloads

Abstract

Cardiovascular diseases are among the main causes of morbidity, disability, and mortality. Most of them occur because of an atherosclerotic plaque developing within a coronary artery, which can cause a narrowing of the vessel lumen (coronary stenosis) or even break it. It is, therefore, useful to evaluate the role of the stress state of the endothelial layer of the arterial tissue, both for the maintenance of the blood circulation and for the implications in presence of a pathology that can lead to thromboembolic complications. The aim of the following study was to develop and test an application that is able to evaluate specific hemodynamic shear stress indicators in coronary arteries at different percentages of stenosis and in different patients’ specific conditions. The application, based on Java, allows users to view the results of simulations performed on a coronary anatomy that can be customized with a stenosis of different degrees and positions. Being in possession of a predictive tool for disturbed flow factors may be important for the location and development of atherosclerotic plaque. Moreover, the application can be a valid tool to help in the evaluation of the condition and in the follow-up of the coronary affected by pathology.

Keywords

Coronary artery diseases Application Shear stress indices CFD analysis 

References

  1. 1.
    Wilkins E et al (2017) European cardiovascular disease statistics 2017. European Heart Network, BrusselsGoogle Scholar
  2. 2.
    Sans S, Kesteloot H, Kromhout D (1997) The burden of cardiovascular diseases mortality in Europe: task force of the European Society of cardiology on cardiovascular mortality and morbidity statistics in Europe. Eur Heart J 18(8 (1997):1231–1248CrossRefPubMedGoogle Scholar
  3. 3.
    Hansson GK (2005) Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med 352:1685–1695.  https://doi.org/10.1056/NEJMra043430 CrossRefPubMedGoogle Scholar
  4. 4.
    Heitzer T et al (2001) Endothelial dysfunction, oxidative stress, and risk of cardiovascular events in patients with coronary artery disease. Circulation 104(22):2673–2678.  https://doi.org/10.1161/hc4601.099485 CrossRefPubMedGoogle Scholar
  5. 5.
    Harris PJ et al (1980) The prognostic significance of 50% coronary stenosis in medically treated patients with coronary artery disease. Circulation 62(2):240–248CrossRefPubMedGoogle Scholar
  6. 6.
    Chang K, Prasad E, Olsen R (2000) Textbook of angiology. Springer, BerlinCrossRefGoogle Scholar
  7. 7.
    Parisi AF, Edward DF, Hartigan OP (1992) A comparison of angioplasty with medical therapy in the treatment of single-vessel coronary artery disease. N Engl J Med 326(1):10–16CrossRefPubMedGoogle Scholar
  8. 8.
    Gibson CM et al (1993) Relation of vessel wall shear stress to atherosclerosis progression in human coronary arteries. Arterioscler Thromb Vasc Biol 13(2 (1993):310–315CrossRefGoogle Scholar
  9. 9.
    Samady H et al (2011) Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease. Circulation.  https://doi.org/10.1161/CIRCULATIONAHA.111.021824 CrossRefPubMedGoogle Scholar
  10. 10.
    Caruso MV et al (2016) Computational analysis of aortic hemodynamics during total and partial extracorporeal membrane oxygenation and intra-aortic balloon pump support. Acta Bioeng Biomech 18(3):3–9PubMedGoogle Scholar
  11. 11.
    Caruso MV et al (2017) Influence of IABP-Induced abdominal occlusions on aortic hemodynamics: a patient-specific computational evaluation. ASAIO J 63(2):161–167.  https://doi.org/10.1097/MAT.0000000000000479 CrossRefPubMedGoogle Scholar
  12. 12.
    Fragomeni G et al (2013) Apicoaortic conduit and cerebral perfusion in mixed aortic valve disease: a computational analysis. Interact Cardiovasc Thorac Surg 17(6): 950–955.  https://doi.org/10.1093/icvts/ivt379 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Hilgenstock A, Ernst R (1996) Analysis of installation effects by means of computational fluid dynamics—CFD vs experiments? Flow Meas Instrum 7(3–4): 161–171CrossRefGoogle Scholar
  14. 14.
    Knight J, Olgac U, Saur SC, Poulikakos D, Marshall W, Kurtcuoglu V (2010) Choosing the optimal wall shear parameter for the prediction of plaque location—a patient-specific computational study in human right coronary arteries. Atherosclerosis 211(2):445–450CrossRefPubMedGoogle Scholar
  15. 15.
    Chaichana T, Sun Z, Jewkes J (2011) Computation of hemodynamics in the left coronary artery with variable angulations. J Biomech 44(10):1869–1878.  https://doi.org/10.1016/j.jbiomech.2011.04.033 CrossRefPubMedGoogle Scholar
  16. 16.
    Chaichana T, Sun Z, Jewkes J (2012), Computational fluid dynamics analysis of the effect of plaques in the left coronary artery. Comput Math Methods Med.  https://doi.org/10.1155/2012/504367 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Lee J, Smith NP (2012) The multi-scale modelling of coronary blood flow. Ann Biomed Eng 40(11):2399–2413.  https://doi.org/10.1007/s10439-012-0583-7 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Zhong L et al (2018) Application of patient-specific computational fluid dynamics in coronary and intra-cardiac flow simulations: challenges and opportunities. Front Physiol.  https://doi.org/10.3389/fphys.2018.00742 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Zhang J-M et al (2014) Perspective on CFD studies of coronary artery disease lesions and hemodynamics: a review. Int J Numer Methods Biomed Eng 30(6):659–680.  https://doi.org/10.1002/cnm.2625 CrossRefGoogle Scholar
  20. 20.
    Zhang J-M et al (2015) Hemodynamic analysis of patient-specific coronary artery tree. Int J Numer Methods Biomed Eng 31(4): e02708.  https://doi.org/10.1002/cnm.2708 CrossRefGoogle Scholar
  21. 21.
    Zhang J-M et al (2016) Simplified models of non-invasive fractional flow reserve based on CT images. PLoS One 11(5): e0153070.  https://doi.org/10.1371/journal.pone.0153070 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Tan X, Wei et al (2017) Combined diagnostic performance of coronary computed tomography angiography and computed tomography derived fractional flow reserve for the evaluation of myocardial ischemia: a meta analysis. Int J Cardiol 236:100–106.  https://doi.org/10.1016/j.ijcard.2017.02.053 CrossRefPubMedGoogle Scholar
  23. 23.
    Zhang J-M et al (2018) Advanced analyses of computed tomography coronary angiography can help discriminate ischemic lesions. Int J Cardiol.  https://doi.org/10.1016/j.ijcard.2018.04.020 CrossRefPubMedGoogle Scholar
  24. 24.
    Ku DN et al (1985) Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress. Arterioscler Thromb Vasc Biol 5(3): 293–302Google Scholar
  25. 25.
    Glagov S et al (1988) Hemodynamics and atherosclerosis. Arch Pathol Lab Med 112(10):1018–1031PubMedPubMedCentralGoogle Scholar
  26. 26.
    De Franciscis S, Fragomeni G, Caruso MV, Serra R (2014) A new photographic computerized measurement system for chronic wound assessment. Acta Phlebol 15(1):13–18Google Scholar
  27. 27.
    Mazzitelli R et al (2016) Numerical prediction of the effect of aortic left ventricular assist device outflow-graft anastomosis location. Biocybern Biomed Eng 36(2):327–343.  https://doi.org/10.1016/j.bbe.2016.01.005 CrossRefGoogle Scholar
  28. 28.
    Martínez-Pérez B, De La Torre-Díez I, López-Coronado M, Herreros-González J (2013) Mobile apps in cardiology. JMIR mHealth and uHealth 1(2):e15CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Oster J, Behar J, Colloca R, Li Q, Clifford GD (2013) Open source Java-based ECG analysis software and Android app for atrial fibrillation screening. In Computing in Cardiology Conference (CinC), 2013 (pp. 731–734). IEEEGoogle Scholar
  30. 30.
    Plante TB et al (2016) Validation of the instant blood pressure smartphone app. JAMA Intern Med 176(5):700–702.  https://doi.org/10.1001/jamainternmed.2016.0157 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Favaloro RG (1968) Saphenous vein autograft replacement of severe segmental coronary artery occlusion: operative technique. Ann Thor Surg 5(4):334–339CrossRefGoogle Scholar
  32. 32.
    Yilmaz F, Gundogdu MY (2008) A critical review on blood flow in large arteries; relevance to blood rheology, viscosity models, and physiologic conditions. Korea–Australia Rheol J 20(4):197–211Google Scholar
  33. 33.
    Dolan JM, Kolega J (2013) High wall shear stress and spatial gradients in vascular pathology: a review. Ann Biomed Eng 41(7):1411–1427.  https://doi.org/10.1007/s10439-012-0695-0 2013.CrossRefPubMedGoogle Scholar
  34. 34.
    Malek AM, Alper SL (1999) Hemodynamic shear stress and its role in atherosclerosis. Jama 282(21):2035–2042.  https://doi.org/10.1001/jama.282.21.2035 CrossRefPubMedGoogle Scholar
  35. 35.
    Asakura T, Karino T (1990) Flow patterns and spatial distribution of atherosclerotic lesions in human coronary arteries. Circ Res 66(4):1045–1066. 1990CrossRefPubMedGoogle Scholar
  36. 36.
    Soulis JV, Lampri OP, Fytanidis (2011) Relative residence time and oscillatory shear index of non-Newtonian flow models in aorta. In Biomedical Engineering, 2011 10th International Workshop on (pp. 1–4). IEEE.  https://doi.org/10.1109/IWBE.2011.6079011

Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2018

Authors and Affiliations

  1. 1.Bioengineering Unit, Department of Medical and Surgical SciencesMagna Graecia UniversityCatanzaroItaly
  2. 2.Cardiology Unit, Department of Medical and Surgical SciencesMagna Graecia UniversityCatanzaroItaly

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