Download PDF
CC BY 4.0 · TH Open 2021; 05(02): e155-e162
DOI: 10.1055/s-0041-1728790
Original Article

Thrombin–Fibrinogen In Vitro Flow Model of Thrombus Growth in Cerebral Aneurysms

Malebogo N. Ngoepe
1   Department of Mechanical Engineering, University of Cape Town, Cape Town, South Africa
2   Stellenbosch Institute for Advanced Study, Wallenberg Research Centre, Stellenbosch University, Stellenbosch, South Africa
,
Etheresia Pretorius 
3   Department of Physiological Sciences, Stellenbosch University, Stellenbosch, South Africa
,
Ilunga J. Tshimanga
4   Department of Mechanical Engineering, University of South Africa, Johannesburg, South Africa
,
Zahra Shaikh
4   Department of Mechanical Engineering, University of South Africa, Johannesburg, South Africa
,
Yiannis Ventikos
5   Department of Mechanical Engineering, University College London, London, United Kingdom
,
Wei Hua Ho
4   Department of Mechanical Engineering, University of South Africa, Johannesburg, South Africa
6   School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Johannesburg, South Africa
› Author Affiliations Funding National Research Foundation South Africa: NFSG180502325333.
› Further Information
   Permissions and Reprints

Abstract

Cerebral aneurysms are balloon-like structures that develop on weakened areas of cerebral artery walls, with a significant risk of rupture. Thrombi formation is closely associated with cerebral aneurysms and has been observed both before and after intervention, leading to a wide variability of outcomes in patients with the condition. The attempt to manage the outcomes has led to the development of various computational models of cerebral aneurysm thrombosis. In the current study, we developed a simplified thrombin–fibrinogen flow system, based on commercially available purified human-derived plasma proteins, which enables thrombus growth and tracking in an idealized cerebral aneurysm geometry. A three-dimensional printed geometry of an idealized cerebral aneurysm and parent vessel configuration was developed. An unexpected outcome was that this phantom-based flow model allowed us to track clot growth over a period of time, by using optical imaging to record the progression of the growing clot into the flow field. Image processing techniques were subsequently used to extract important quantitative metrics from the imaging dataset, such as end point intracranial thrombus volume. The model clearly demonstrates that clot formation, in cerebral aneurysms, is a complex interplay between mechanics and biochemistry. This system is beneficial for verifying computational models of cerebral aneurysm thrombosis, particularly those focusing on initial angiographic occlusion outcomes, and will also assist manufacturers in optimizing interventional device designs.

Keywords

cerebral aneurysms - thrombosis - in vitro - thrombin–fibrinogen


Publication History

Received: 18 June 2020

Accepted: 01 February 2021

Article published online:
12 May 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Lawton MT, Quiñones-Hinojosa A, Chang EF, Yu T. Thrombotic intracranial aneurysms: classification scheme and management strategies in 68 patients. Neurosurgery 2005; 56 (03) 441-454 , discussion 441–454
    CrossrefPubMedGoogle Scholar
  • 2 Steiner T, Juvela S, Unterberg A, Jung C, Forsting M, Rinkel G. European Stroke Organization. European Stroke Organization guidelines for the management of intracranial aneurysms and subarachnoid haemorrhage. Cerebrovasc Dis 2013; 35 (02) 93-112
    CrossrefPubMedGoogle Scholar
  • 3 Thompson BG, Brown Jr RD, Amin-Hanjani S. et al; American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, and Council on Epidemiology and Prevention, American Heart Association, American Stroke Association. Guidelines for the management of patients with unruptured intracranial aneurysms: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2015; 46 (08) 2368-2400
    CrossrefPubMedGoogle Scholar
  • 4 Whittle IR, Dorsch NW, Besser M. Spontaneous thrombosis in giant intracranial aneurysms. J Neurol Neurosurg Psychiatry 1982; 45 (11) 1040-1047
    CrossrefPubMedGoogle Scholar
  • 5 Cohen JE, Itshayek E, Gomori JM. et al. Spontaneous thrombosis of cerebral aneurysms presenting with ischemic stroke. J Neurol Sci 2007; 254 (1-2): 95-98
    CrossrefPubMedGoogle Scholar
  • 6 Byrne JV, Beltechi R, Yarnold JA, Birks J, Kamran M. Early experience in the treatment of intra-cranial aneurysms by endovascular flow diversion: a multicentre prospective study. PLoS One 2010; 5 (09) 1-8
    CrossrefPubMedGoogle Scholar
  • 7 Tähtinen OI, Manninen HI, Vanninen RL. et al. The silk flow-diverting stent in the endovascular treatment of complex intracranial aneurysms: technical aspects and midterm results in 24 consecutive patients. Neurosurgery 2012; 70 (03) 617-623 , discussion 623–624
    CrossrefPubMedGoogle Scholar
  • 8 Petr O, Brinjikji W, Cloft H, Kallmes DF, Lanzino G. Current trends and results of endovascular treatment of unruptured intracranial aneurysms at a single institution in the flow-diverter era. AJNR Am J Neuroradiol 2016; 37 (06) 1106-1113
    CrossrefPubMedGoogle Scholar
  • 9 Boulouis G, Rodriguez-Régent C, Rasolonjatovo EC. et al. Unruptured intracranial aneurysms: an updated review of current concepts for risk factors, detection and management. Rev Neurol (Paris) 2017; 173 (09) 542-551
    CrossrefPubMedGoogle Scholar
  • 10 Kulcsár Z, Houdart E, Bonafé A. et al. Intra-aneurysmal thrombosis as a possible cause of delayed aneurysm rupture after flow-diversion treatment. AJNR Am J Neuroradiol 2011; 32 (01) 20-25
    CrossrefPubMedGoogle Scholar
  • 11 Guglielmi G, Viñuela F, Sepetka I, Macellari V. Electrothrombosis of saccular aneurysms via endovascular approach. Part 1: electrochemical basis, technique, and experimental results. J Neurosurg 1991; 75 (01) 1-7
    CrossrefPubMedGoogle Scholar
  • 12 Turjman F, Levrier O, Combaz X. et al. EVIDENCE trial: design of a phase 2, randomized, controlled, multicenter study comparing flow diversion and traditional endovascular strategy in unruptured saccular wide-necked intracranial aneurysms. Neuroradiology 2015; 57 (01) 49-54
    CrossrefPubMedGoogle Scholar
  • 13 Lee D, Yuki I, Murayama Y. et al. Thrombus organization and healing in the swine experimental aneurysm model. Part I. A histological and molecular analysis. J Neurosurg 2007; 107 (01) 94-108
    CrossrefPubMedGoogle Scholar
  • 14 Yuki I, Lee D, Murayama Y. et al. Thrombus organization and healing in an experimental aneurysm model. Part II. The effect of various types of bioactive bioabsorbable polymeric coils. J Neurosurg 2007; 107 (01) 109-120
    CrossrefPubMedGoogle Scholar
  • 15 Bouzeghrane F, Naggara O, Kallmes DF, Berenstein A, Raymond J. International Consortium of Neuroendovascular Centres. In vivo experimental intracranial aneurysm models: a systematic review. AJNR Am J Neuroradiol 2010; 31 (03) 418-423
    CrossrefPubMedGoogle Scholar
  • 16 Kadirvel R, Ding Y-H, Dai D, Rezek I, Lewis DA, Kallmes DF. Cellular mechanisms of aneurysm occlusion after treatment with a flow diverter. Radiology 2014; 270 (02) 394-399
    CrossrefPubMedGoogle Scholar
  • 17 Cullen JM, Lu G, Shannon AH. et al. A novel swine model of abdominal aortic aneurysm. J Vasc Surg 2019; 70 (01) 252-260.e2
    CrossrefPubMedGoogle Scholar
  • 18 Gester K, Lüchtefeld I, Büsen M. et al. In vitro evaluation of intra-aneurysmal, flow-diverter-induced thrombus formation: a feasibility study. AJNR Am J Neuroradiol 2016; 37 (03) 490-496
    CrossrefPubMedGoogle Scholar
  • 19 Clauser J, Knieps MS, Büsen M. et al. A novel plasma-based fluid for particle image velocimetry (PIV): in-vitro feasibility study of flow diverter effects in aneurysm model. Ann Biomed Eng 2018; 46 (06) 841-848
    CrossrefPubMedGoogle Scholar
  • 20 Ouared R, Chopard B, Stahl B. et al. Thrombosis modeling in intracranial aneurysms: a lattice Boltzmann numerical algorithm. Comput Phys Commun 2008; 179: 128-131
    CrossrefPubMedGoogle Scholar
  • 21 Rayz VL, Boussel L, Ge L. et al. Flow residence time and regions of intraluminal thrombus deposition in intracranial aneurysms. Ann Biomed Eng 2010; 38 (10) 3058-3069
    CrossrefPubMedGoogle Scholar
  • 22 Peach TW, Ngoepe M, Spranger K, Zajarias-Fainsod D, Ventikos Y. Personalizing flow-diverter intervention for cerebral aneurysms: from computational hemodynamics to biochemical modeling. Int J Numer Methods Biomed Eng 2014; 30 (11) 1387-1407
    CrossrefPubMedGoogle Scholar
  • 23 Ngoepe MN, Ventikos Y. Computational modelling of clot development in patient-specific cerebral aneurysm cases. J Thromb Haemost 2016; 14 (02) 262-272
    CrossrefPubMedGoogle Scholar
  • 24 Ou C, Huang W, Yuen MMF. A computational model based on fibrin accumulation for the prediction of stasis thrombosis following flow-diverting treatment in cerebral aneurysms. Med Biol Eng Comput 2017; 55 (01) 89-99
    CrossrefPubMedGoogle Scholar
  • 25 Sarrami-Foroushani A, Lassila T, Hejazi SM, Nagaraja S, Bacon A, Frangi AF. A computational model for prediction of clot platelet content in flow-diverted intracranial aneurysms. J Biomech 2019; 91: 7-13
    CrossrefPubMedGoogle Scholar
  • 26 Tsuji M, Ishida F, Kishimoto T. et al. Double porous media modeling in computational fluid dynamics for hemodynamics of stent-assisted coiling of intracranial aneurysms: a technical case report. Brain Hemorrhages 2020; 1 (01) 85-88
    CrossrefPubMedGoogle Scholar
  • 27 Chung B, Cebral JR. CFD for evaluation and treatment planning of aneurysms: review of proposed clinical uses and their challenges. Ann Biomed Eng 2015; 43 (01) 122-138
    CrossrefPubMedGoogle Scholar
  • 28 Kessler U, Grau T, Gronchi F. et al. Comparison of porcine and human coagulation by thrombelastometry. Thromb Res 2011; 128 (05) 477-482
    CrossrefPubMedGoogle Scholar
  • 29 Gertz SD, Mintz Y, Beeri R. et al. Lessons from animal models of arterial aneurysm. Aorta (Stamford) 2013; 1 (05) 244-254
    Article in Thieme ConnectPubMedGoogle Scholar
  • 30 Namba K, Mashio K, Kawamura Y, Higaki A, Nemoto S. Swine hybrid aneurysm model for endovascular surgery training. Interv Neuroradiol 2013; 19 (02) 153-158
    CrossrefPubMedGoogle Scholar
  • 31 Thompson JW, Elwardany O, McCarthy DJ. et al. In vivo cerebral aneurysm models. Neurosurg Focus 2019; 47 (01) E20
    CrossrefPubMedGoogle Scholar
  • 32 Ngoepe MN, Frangi AF, Byrne JV, Ventikos Y. Thrombosis in cerebral aneurysms and the computational modeling thereof: a review. Front Physiol 2018; 9: 306
    CrossrefPubMedGoogle Scholar
  • 33 Mulder G, Bogaerds ACB, Rongen P. et al. On automated analysis of flow patterns in cerebral aneurysms based on vortex identification. J Eng Math 2009; 64: 391-401
    CrossrefPubMedGoogle Scholar
  • 34 Ho WH, Tshimanga IJ, Ngoepe MN. et al. Evaluation of a desktop 3D printed rigid refractive-indexed-matched flow phantom for PIV measurements on cerebral aneurysms. Cardiovasc Eng Technol 2020; 11 (01) 14-23
    CrossrefPubMedGoogle Scholar
  • 35 Cebral JR, Putman CM, Alley MT, Hope T, Bammer R, Calamante F. Hemodynamics in normal cerebral arteries: qualitative comparison of 4D phase-contrast magnetic resonance and image-based computational fluid dynamics. J Eng Math 2009; 64 (04) 367-378
    CrossrefPubMedGoogle Scholar
  • 36 Faeder JR, Morel PA. Reductionism is dead: long live reductionism! systems modeling needs reductionist experiments. Biophys J 2016; 110 (08) 1681-1683
    CrossrefPubMedGoogle Scholar
  • 37 Link KG, Stobb MT, Sorrells MG. et al. A mathematical model of coagulation under flow identifies factor V as a modifier of thrombin generation in hemophilia A. J Thromb Haemost 2020; 18 (02) 306-317
    CrossrefPubMedGoogle Scholar
  • 38 Potgieter M, Bester J, Kell DB, Pretorius E. The dormant blood microbiome in chronic, inflammatory diseases. FEMS Microbiol Rev 2015; 39 (04) 567-591
    CrossrefPubMedGoogle Scholar
  • 39 Bester J, Pretorius E. Effects of IL-1β, IL-6 and IL-8 on erythrocytes, platelets and clot viscoelasticity. Sci Rep 2016; 6: 1-10
    Google Scholar
  • 40 Chalouhi N, Ali MS, Jabbour PM. et al. Biology of intracranial aneurysms: role of inflammation. J Cereb Blood Flow Metab 2012; 32 (09) 1659-1676
    Google Scholar
  • 41 Chen J, Diamond SL. Reduced model to predict thrombin and fibrin during thrombosis on collagen/tissue factor under venous flow: roles of γ′-fibrin and factor XIa. PLOS Comput Biol 2019; 15 (08) e1007266
    CrossrefPubMedGoogle Scholar
  • 42 Mangin PH, Gardiner EE, Nesbitt WS. et al; Subcommittee on Biorheology. In vitro flow based systems to study platelet function and thrombus formation: recommendations for standardization: communication from the SSC on Biorheology of the ISTH. J Thromb Haemost 2020; 18 (03) 748-752
    CrossrefPubMedGoogle Scholar