Comparison of morphological and rheological conditions between conventional and eversion carotid endarterectomy using computational fluid dynamics

Abstract

Purpose:

To compare postoperative morphological and rheological conditions after eversion carotid endarterectomy versus conventional carotid endarterectomy using computational fluid dynamics.

Basic methods:

Hemodynamic metrics (velocity, wall shear stress, time-averaged wall shear stress and temporal gradient wall shear stress) in the carotid arteries were simulated in one patient after conventional carotid endarterectomy and one patient after eversion carotid endarterectomy by computational fluid dynamics analysis based on patient specific data.

Principal findings:

Systolic peak of the eversion carotid endarterectomy model showed a gradually decreased pressure along the stream path, the conventional carotid endarterectomy model revealed high pressure (about 180 Pa) at the carotid bulb. Regions of low wall shear stress in the conventional carotid endarterectomy model were much larger than that in the eversion carotid endarterectomy model and with lower time-averaged wall shear stress values (conventional carotid endarterectomy: 0.03–5.46 Pa vs. eversion carotid endarterectomy: 0.12–5.22 Pa).

Conclusions:

Computational fluid dynamics after conventional carotid endarterectomy and eversion carotid endarterectomy disclosed differences in hemodynamic patterns. Larger studies are necessary to assess whether these differences are consistent and might explain different rates of restenosis in both techniques.

Keywords

Computational fluid dynamics carotid endarterectomy eversion carotid endarterectomy conventional carotid endarterectomy rheology carotid artery stenosis

Introduction

Conventional carotid endarterectomy (C-CEA) with patch-angioplasty and eversion carotid endarterectomy (E-CEA) are reconstructive techniques in the surgical treatment of extracranial carotid artery stenosis. C-CEA with patch angioplasty is performed through a longitudinal arteriotomy of the internal and common carotid artery and is the most frequently employed endarterectomy technique. Whereas in E-CEA, the internal carotid artery (ICA) is completely transected at its origin at the carotid bifurcation, as initially described by DeBakey.^1,2 These differences in the surgical approach lead to changes in the postoperative morphology and possibly rheology of patients treated by C-CEA compared to E-CEA.

The incidence of restenosis after CEA due to extracranial carotid artery disease ranges from 1% to 41%,^3–10 depending on operative technique, severity of stenosis and the length of follow-up. The importance of restenosis as a cause of subsequent neurological events is not well established.^11–18 However, a subanalysis of the Carotid Revascularization Endarterectomy versus Stenting Trial (CREST) revealed that patients with carotid restenosis within 2 years had a higher risk of ipsilateral stroke than those without.¹⁹ Metaanalyses of restenosis rates after C-CEA and E-CEA report lower restenosis rates after E-CEA.^20,21

In recent years, computational fluid dynamics (CFD) have become an accepted technique for investigating flow alterations in anatomical correct models in carotid artery disease and assessment of rheological conditions comparing different operative techniques.^22–29 To the best of our knowledge, there has yet not been a CFD comparison between the postoperative morphologic and flow conditions in patients after C-CEA versus E-CEA.

The purpose of this pilot study was to gain insights into the mechanisms of initiation and formation of restenosis after C-CEA and E-CEA by investigating the technique-specific hemodynamic metrics with CFD.

Methods

Patient A, a 71-year-old male patient with asymptomatic carotid artery stenosis underwent C-CEA with patch-angioplasty. Patient B, a 75-year-old male patient with symptomatic carotid artery stenosis underwent E-CEA. An intraoperatively performed angiography of the reconstructed carotid artery showed an unremarkable morphological result without signs of residual stenosis, thrombus or dissection in both patients.

To validate the simulation, intraoperatively assessed flow measurements within the internal carotid (ICA) and common carotid artery (CCA) using intraoperative ultrasound (MediStim©, VeriQ©, Deisenhofen, Germany) were performed for both patients (Patient A and B). Within the clinical setting both patients received a multidetector computed tomography (MDCT) one day after surgery. Patient A who was treated by C-CEA was scanned with a Somatom Definition (Siemens Medical Systems, Erlangen, Germany). Scan and reconstruction parameters were as follows: 120 kV, 120 ref. mAs, reconstructed slice thickness 1 mm, reconstruction increment 0.7 mm, pixel spacing 0.5 × 0.5 mm and administration of 90 ml contrast medium (iomeprol with 350 mg iodine per ml, Imeron 350, Bracco Diagnostics, Princeton, NJ, USA) with 50 ml saline chaser. Patient B who was treated by E-CEA was scanned with a Somatom Definition (Siemens Medical Systems, Erlangen, Germany) as well. Scan and reconstruction parameters were as follows: 120 kV, 120 ref. mAs, reconstructed slice thickness 0.75 mm, reconstruction increment 0.7 mm, pixel spacing 0.4 × 0.4 mm and administration of 90 ml contrast medium (iomeprol with 400 mg iodine per ml, Imeron 400, Bracco Diagnostics, Princeton, NJ, USA) with 50 ml saline chaser (Figure 1). The segmentation and surface reconstruction of the carotid arteries were accomplished by a semi-automatic threshold-based segmentation tool (Amira 5.4, Visage Imaging Inc., USA). As shown in Figure 2(a and b), regions of the carotid arteries have been selected in each cross-sectional image of the datasets. Geometries of the vessels were reconstructed, detailed views of which are displayed in Figure 2(c and d).

Figure 1.

Maximum intensity projections (MIPs) in sagittal orientation of CTA datasets show the postoperative morphometry after C-CEA (A) and E-CEA (B), respectively.

Figure 2.

Segmentation (a and b) and surface reconstruction (c–e) of both datasets (a and c: C-CEA; b and d: E-CEA). The superior thyroid artery in the E-CEA model (e) has been artificially extended in order to reduce the pressure difference at the outlets of this artery and the external carotid artery (black arrow). ICA: internal carotid artery; ECA: external carotid artery; CCA: common carotid artery.

The reconstructed geometries were meshed in ICEM (ANSYS 12.1 Inc, Canonsburg, USA) with tetrahedral elements in the core region and prismatic cells in the boundary layer near the vessel wall (10 layers of prismatic cells define the near-wall region). The superior thyroid artery in the E-CEA model has been artificially extended in order to reduce the pressure difference at the outlets of this artery and the external carotid artery (ECA) (Figure 2e). The C-CEA model possessed 2,005,753 cells in total and the E-CEA model contained 2,708,971 cells totally. To confirm the insensitivity of the results to the spatial resolution, a grid independence analysis was conducted; solutions on finer grids (C-CEA: 4,106,211 cells; E-CEA: 4,872,533 cells) were compared to the baseline models. A point at the center of the carotid bulb was studied: the maximum discrepancies of the velocity magnitude at this point during a cardiac cycle between the two grids were 3.6% for the C-CEA model and 3.9% for the E-CEA model, and exact same trends of the variable variations were observed. Thus, for the purposes of our study the baseline resolutions are adequate.

Transit time flow measurement has been achieved during the examination (VeriQ™ Intraoperative TTFM, MEDISTIM). Mean flow rates at the middle of CCA and at the origin of ICA have been recorded. Intraoperative measurements at the common carotid artery (CCA) were used as the inlet flow boundary conditions for numerical simulations; while, the measured flow information of ICA were employed to validate the computational results.

Pulsatile flow information was derived from the waveforms described in older human subjects.³⁰ Parabolic velocity profile was applied at the inlet of CCA,³¹ and the maximum velocity at the inlet along the centerline of CCA was calculated based on the instantaneous volumetric flow rate, as represented in Figure 3. Vessel boundaries were assumed to be non-compliant. No-slip boundary conditions were imposed at the walls, and zero-pressure boundary conditions were applied at the outlets of ICA, ECA and their branches.

Figure 3.

Discrepancies of the velocity magnitude at the center of the carotid bulb. C-CEA: conventional carotid endarterectomy; E-CEA: eversion carotid endarterectomy.

The blood was treated as Newtonian and incompressible³² with a density of 1044 kg/m³ and a dynamic viscosity of 0.00365 kg·m⁻¹·s⁻¹. As shown in Figure 2(c and d), no obvious stenosis is presented in the postoperative carotid arteries; thus, a laminar model of the flow was applied during the computation.^29,33 The finite volume solver, CFD-ACE+ (ESI Group, France), was employed for the numerical solution of the transport equations – the Naviér-Stokes equations. A second-order accurate discretization (central differences) was used to solve the flow velocity. Algebraic MultiGrid acceleration³⁴ was employed and the SIMPLEC-type pressure correction³⁵ was used for pressure-velocity coupling. The simulation has been carried out for five cardiac cycles to achieve a periodic solution for each model. The results presented below are based on the output of the final cycle.

Results

Flow analysis and validations

Figure 4 shows flow patterns within the carotid arteries at systolic peak (a and b) and during mid diastole (b and d) based on the C-CEA (a and c) and E-CEA (c and d) models. At systolic peak, the flow was fairly organized throughout carotid arteries in both of the models. Relatively high velocities (0.55–0.69 m/s) occurred along the centerline of CCA and ICA, indicating stronger flow transports in those vessels. In the C-CEA model, flow with low velocity (0.063 m/s) and reversely directed flow could be observed in the carotid bulb. In the E-CEA model, no sudden reduction of the flow velocity was observed near that region. During diastole, flow velocities in both cases were reduced; the maximum velocity magnitude (0.219 m/s of the C-CEA model and 0.223 m/s for the E-CEA model) was about 1/3 of that at systolic peak. Highly turbulent flow pattern with low velocities (0.022 m/s) was displayed in the carotid bulb of the C-CEA model. In the E-CEA model, flow remained organized generally; slightly helical features presented in the carotid bulb and in the ECA. The C-CEA model markedly broadened the vessel diameter with subsequent increase of slow and turbulent blood flow, compared to the postoperative conditions after E-CEA where the bifurcation keeps its physiological shape (Figures 1 and 2).

Figure 4.

Flow patterns within the carotid arteries at systolic peak (a and b) and during mid diastole (c and d) based on the C-CEA (a and c) and E-CEA (b and d) models. Whereas flow is fairly organized in systole and diastole in the E-CEA model (b–d), flow in the aortic bulb of the C-CEA model (a–c) is turbulent with low velocities in both systole and especially diastole.

The intraoperatively measured mean flow rates near the origin of ICA for the C-CEA and E-CEA models were 239 ml/min and 257 ml/min, respectively, which were 63% and 71% of the inflow at CCA (379 ml/min and 360 ml/min for the C-CEA and E-CEA cases, respectively). To compare our results with the measured data, instantaneous flow rates were recorded at the proximal site of the ICA and ECA, as shown in Figure 2(c and e). The calculated mean flow rates at the ICA for the C-CEA and E-CEA models were 241.14 ml/min and 261.9 ml/min with discrepancies of 0.9% and 1.5% for the C-CEA and E-CEA models, respectively. The computational flow results could therefore be validated and further investigations regarding the loading distributions can be established.

Loading distributions

Figure 5 displays the loading distributions (pressure and wall shear stress (WSS)) of the models at systolic peak. Figure 5(a and b) presents the pressure distributions of the C-CEA and E-CEA models. Pressure in the E-CEA model gradually decreased along the stream path, from inlet at the CCA (about 240 Pa) to the outlets at ICA and ECA (zero pressure). However, in the C-CEA model, relatively high pressure (about 180 Pa) presented at the carotid bulb, which was due to the turbulent flow pattern occurring in that region, with low velocities but more stagnating times. The WSS at systolic peak in both of the models (Figure 5c and d) showed similar variation ranges (0.05–16.3 Pa for the C-CEA model and 0.03–15.9 Pa for the E-CEA model).

Figure 5.

Loading distributions (a and b: pressure/c and d: wall shear stress) of the models (a and c: C-CEA; b and d: E-CEA) at systolic peak. In the C-CEA model (a), the arrow indicates a relatively high pressure (about 180 Pa) at the carotid bulb.

Further investigation of the loading distribution of the models involved the study of two other parameters – the time-averaged WSS (TAWSS) and the maximum temporal gradient of WSS (TGWSS). The TAWSS was calculated as the magnitude of the time-averaged wall shear stress vector equation (1)

TAWSS = | | \frac{1}{T} \int_{0}^{T} τ d t | |

(1)

where T denotes a cardiac cycle and τ indicates the instantaneous WSS vector. Figure 6(a and b) displays the TAWSS distribution over the carotid arterial surfaces. The TAWSS ranges of the C-CEA and E-CEA cases were 0.03–5.46 Pa and 0.12–5.22 Pa, respectively. The color map of Figure 6(a and b) has been restricted to 0–1 Pa, in order to clearly display the low TAWSS regions. Both of the cases presented low TAWSS (<0.5 Pa) at the carotid bulb; however, the low shear stress region in the C-CEA model was much larger than that in the E-CEA model and with lower TAWSS values.

Figure 6.

Loading distributions (a and b: time-averaged wall shear stress (TAWSS)/c and d: maximum temporal gradient of wall shear stress (TGWSS) over the carotid arterial surfaces based on the C-CEA (a and c) and E-CEA (b and d) models. The low TAWSS region in the C-CEA model is much larger than that in the E-CEA model and with lower TAWSS values (black arrow a and b).

TGWSS quantifies the maximum value of the time derivative in WSS calculated between every two consecutive time points during the cycle. It can be outlined by equation (2)

TGWSS i = max (\frac{\partial | | τ | | i}{\partial t}) = max (\frac{| | τ | | i n + 1 - | | τ | | i n}{Δ})

(2)

where i is the spatial point and n is the time step. As shown in Figure 6(c and d), the distribution pattern of TGWSS was similar to that of WSS.

Discussion

This study investigates differences in hemodynamic metrics after C-CEA and E-CEA using physiological imaging by CFD that might explain different restenosis rates in both techniques.

Most recurrences occur within 2 years postoperatively due to neointimal hyperplasia caused by the “response to injury” reaction of the endarterectomised vessel wall, whereas atherosclerosis is the more frequent long-term cause.^36,37 In recent years, CFD analysis has become an important tool for providing insights into the spatial and temporal distribution of hemodynamical parameters that affect acute thrombogenesis or atheromatous plaque formation.

Low shear stress and turbulent flow is associated with atherosclerotic disease throughout the arterial system, observed in the carotid bifurcation amongst others.^38–45 Normally, in the arterial system shear stress ranges from 1 Pa to 7 Pa.⁴³ Low WSS (<1 Pa) directly affects genetic expression within endothelial tissue, encouraging the expression of genes involved in atherosclerosis.^46,47 On the other hand, high WSS (larger than 40 Pa) is believed to be able to induce platelet aggregation, disrupture of smooth muscle caveolae, leading to intimal injury or rupture of vulnerable plaques due to endothelial denudation.^48–51 Our computational results of patient data do not present WSS (at systolic peak) higher than 40 Pa, indicating the effectiveness of C-CEA and E-CEA in creating physiologic WSS for these specific patients. However, expansion of the vessel lumen by C-CEA with patch angioplasty increased the diameter of the carotid bulb resulting in a larger vessel region with low shear stress and increased slow and turbulent blood flow, which may stimulate thrombogenesis and recurrent atheromatous plaques.

Baan et al.⁵² analyzed the strain of the vessel wall by measuring the ratio of the change in outside radius of the carotid bulb during the systole and diastole using ultrasound. The authors showed a significantly lower strain after C-CEA, which is highly consistent with the WSS pattern of our C-CEA model. Hayase et al.²⁵ compared postoperative hemodynamics after carotid artery stenting (CAS) with those after C-CEA with patch-angioplasty by use of CFD. Similar to the present study the authors showed an increased low and turbulent flow at the carotid bulb after C-CEA that may cause thrombogenesis and recurrent atheromatous plaques.

Marshall et al.²⁷ studied the pulsatile flow in physiologically realistic models of a normal and a stenosed human carotid bifurcation phantom. In contrast to the stenosed model, flow in the bulb region of the non-stenosed “internal carotid artery” was concentrated along the inner wall with a low flow region near the outer wall of the bulb. TAWSS was also markedly different for the two phantoms, showing much higher values along the inner wall of the ICA in the normal phantom. Birchall et al.²² performed CFD-analysis of abnormal hemodynamics at the atheromatous carotid bifurcation compared to a healthy subject. Very close to our E-CEA model the sequential streamlines through the cardiac cycle in the healthy carotid bulb showed flow split into two relatively high-velocity streams attached to the inner walls of the ICA and CCA, with development in the early deceleration phase (corresponding to the mid diastole of the present model) of regions of slow, recirculating and weakly turbulent flow, most prominently within the lateral aspects of the bulb. Hence results of both studies regarding normal anatomy are very close to the dynamic pattern of the current E-CEA model, leading to the ascertainment that postoperative morphology and hemodynamics of the ICA operated by the eversion technique seems closer to the literature descriptions of a non-operated non-stenosed artery.

High values of TGWSS are related to the expression of atherogenesis-related genes in endothelial cells.⁵³ This may indicate the vulnerability of these regions to potentially suffer from atherosclerosis. Low TAWSS less than 0.5 Pa may stimulate atherogenic phenotype,⁵⁴ indicating a higher possibility of C-CEA for recurrent atheromatous plaques at the carotid bulb, especially near the adjoining wall of the external-common carotid artery.

Focusing on the carotid bulb alone, the E-CEA showed a more preferable loading distribution than the C-CEA model with regard to the potential of recurrent atheromatous plaques.

Limitations

Since not every patient routinely receives CTA directly after CEA and due to the complexity of the segmentation and simulation, number of patients is very limited. Additionally, due to the retrospective design of this pilot study the intraoperatively assessed flow measurements were not adjusted to blood pressure and heart rate. In this context, a different postoperative blood pressure response caused by transection of the sinus nerve during E-CEA, as shown in prior studies,^55,56 might have influence on rheological conditions that were therefore not accounted for. However, all measures were taken to perform realistic patient-specific simulation as shown by good agreement of the intraoperatively and experimentally measured data. Due to low discrepancies between the computational flow results and intraoperatively measured data, flow results could be validated and further investigations regarding the loading distributions could be established. Further, for the purpose of our study, base resolutions with the baseline time step were deemed adequate.

In conclusion, CFD of C-CEA and E-CEA were successfully calculated on the basis of patient-specific data and disclosed differences in the postoperative hemodynamic patterns between both operative techniques. However, further studies in larger populations are necessary to prove our results, potentially elucidating higher restenosis rates after patch angioplasty.

Footnotes

Acknowledgements

Matthias Müller-Eschner and Hendrik von Tengg-Kobligk received support from the German Research Foundation (DFG) within project R03, SFB/TRR 125 “Cognition-Guided Surgery”. Duanduan Chen is supported by the National Natural Science Foundation of China (31200704), and would like to acknowledge the ESI Group and Mr. Xiangde Zhu for the use of CFD-ACE+, ANSYS Inc. for the use of ICEM, and Visage Imaging Inc. for the use of Amira.

Conflict of interest

None declared.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

Etheredge

. A simple technic for carotid endarterectomy. Am J Surg 1970; 120: 275–278.

Fields

Crawford

Debakey

. Surgical considerations in cerebral arterial insufficiency. Neurology 1958; 8: 801–808.

AbuRahma

. Processes of care for carotid endarterectomy: surgical and anesthesia considerations. J Vasc Surg 2009; 50: 921–933.

Baracchini

Saladini

Lorenzetti

. Gender-based outcomes after eversion carotid endarterectomy from 1998 to 2009. J Vasc Surg 2012; 55: 338–345.

Cao

Giordano

De Rango

. Eversion versus conventional carotid endarterectomy: A prospective study. Eur J Vasc Endovasc Surg 1997; 14: 96–104.

Crawford

Chung

Hodgman

. Restenosis after eversion vs patch closure carotid endarterectomy. J Vasc Surg 2007; 46: 41–48.

Demirel

Attigah

Bruijnen

. Multicenter experience on eversion versus conventional carotid endarterectomy in symptomatic carotid artery stenosis: observations from the stent-protected angioplasty versus carotid endarterectomy (space-1) trial. Stroke 2012; 43: 1865–1871.

Radak

Davidovic

Tanaskovic

. Surgical treatment of carotid restenosis after eversion endarterectomy–serbian bicentric prospective study. Ann Vasc Surg 2012; 26: 783–789.

Radak

Tanaskovic

Matic

. Eversion carotid endarterectomy–our experience after 20 years of carotid surgery and 9897 carotid endarterectomy procedures. Ann Vasc Surg 2012; 26: 924–928.

10.

Radak

Tanaskovic

Ilijevski

. Eversion carotid endarterectomy versus best medical treatment in symptomatic patients with near total internal carotid occlusion: a prospective nonrandomized trial. Ann Vasc Surg 2010; 24: 185–189.

11.

Bonati

Ederle

McCabe

. Long-term risk of carotid restenosis in patients randomly assigned to endovascular treatment or endarterectomy in the carotid and vertebral artery transluminal angioplasty study (cavatas): long-term follow-up of a randomised trial. Lancet Neurol 2009; 8: 908–917.

12.

Eckstein

Ringleb

Allenberg

. Results of the stent-protected angioplasty versus carotid endarterectomy (space) study to treat symptomatic stenoses at 2 years: a multinational, prospective, randomised trial. Lancet Neurol 2008; 7: 893–902.

13.

Arquizan

Trinquart

Touboul

. Restenosis is more frequent after carotid stenting than after endarterectomy: The eva-3s study. Stroke 2011; 42: 1015–1020.

14.

DeGroote

Lynch

Jamil

. Carotid restenosis: long-term noninvasive follow-up after carotid endarterectomy. Stroke 1987; 18: 1031–1036.

15.

Keagy

Edrington

Poole

. Incidence of recurrent or residual stenosis after carotid endarterectomy. Am J Surg 1985; 149: 722–725.

16.

Lattimer

Burnand

. Recurrent carotid stenosis after carotid endarterectomy. Br J Surg 1997; 84: 1206–1219.

17.

Horrocks

When should i re-operate for recurrent carotid stenosis. In: Naylor

Mackey

(eds). Carotid artery surgery: a problem based approach, London: Harcourt, 2000, pp. 2018–2025.

18.

Moore

Kempczinski

Nelson

. Recurrent carotid stenosis: Results of the asymptomatic carotid atherosclerosis study. Stroke 1998; 29: 2018–2025.

19.

Lal

Beach

Roubin

. Restenosis after carotid artery stenting and endarterectomy: a secondary analysis of crest, a randomised controlled trial. Lancet Neurol 2012; 11: 755–763.

20.

Antonopoulos

Kakisis

Sergentanis

. Eversion versus conventional carotid endarterectomy: a meta-analysis of randomised and non-randomised studies. Eur J Vasc Endovasc Surg 2011; 42: 751–765.

21.

Cao

De Rango

Zannetti

. Eversion versus conventional carotid endarterectomy for preventing stroke. Cochrane Database Syst Rev 2001; 1: CD001921–CD001921.

22.

Birchall

Zaman

Hacker

. Analysis of haemodynamic disturbance in the atherosclerotic carotid artery using computational fluid dynamics. Eur Radiol 2006; 16: 1074–1083.

23.

Cebral

Yim

Lohner

. Blood flow modeling in carotid arteries with computational fluid dynamics and mr imaging. Acad Radiol 2002; 9: 1286–1299.

24.

Groen

Simons

van den Bouwhuijsen

. Mri-based quantification of outflow boundary conditions for computational fluid dynamics of stenosed human carotid arteries. J Biomech 2010; 43: 2332–2338.

25.

Hayase H, Tokunaga K, Nakayama T, et al. Computational fluid dynamics of carotid arteries after carotid endarterectomy or carotid artery stenting based on postoperative patient-specific computed tomography angiography and ultrasound flow data. Neurosurgery 68: 1096–1101; discussion 1101.

26.

LaDisa

Jr Bowers

Harmann

. Time-efficient patient-specific quantification of regional carotid artery fluid dynamics and spatial correlation with plaque burden. Med Phys 2010; 37: 784–792.

27.

Marshall

Zhao

Papathanasopoulou

. Mri and cfd studies of pulsatile flow in healthy and stenosed carotid bifurcation models. J Biomech 2004; 37: 679–687.

28.

Martin

Zaman

Hacker

. Analysis of haemodynamic factors involved in carotid atherosclerosis using computational fluid dynamics. Br J Radiol 2009; 82 Spec No 1: S33–S38.

29.

Xue

Gao

Duan

. Preliminary study of hemodynamic distribution in patient-specific stenotic carotid bifurcation by image-based computational fluid dynamics. Acta Radiol 2008; 49: 558–565.

30.

Hoi

Wasserman

Xie

. Characterization of volumetric flow rate waveforms at the carotid bifurcations of older adults. Physiol Meas 2010; 31: 291–302.

31.

Campbell

Ries

Dhawan

. Effect of inlet velocity profiles on patient-specific computational fluid dynamics simulations of the carotid bifurcation. J Biomech Eng 2012; 134: 051001–051001.

32.

Fung

. Biomechanicscirculation. Blood flow in arteries, New York: Springer Verlag, 1997.

33.

Younis

Kaazempur-Mofrad

Chan

. Hemodynamics and wall mechanics in human carotid bifurcation and its consequences for atherogenesis: Investigation of inter-individual variation. Biomech Model Mechanobiol 2004; 3: 17–32.

34.

Lonsdale

Schuller

. Multigrid efficiency for complex flow simulations on distributed memory machines. Parallel Comput 1993; 19: 23–32.

35.

Vandoormaal

Raithby

. Enhancements of the simple method for predicting incompressible fluid-flows. Numer Heat Transfer 1984; 7: 147–163.

36.

Hugl

Oldenburg

Neuhauser

. Effect of age and gender on restenosis after carotid endarterectomy. Ann Vasc Surg 2006; 20: 602–608.

37.

Ouriel

Green

. Clinical and technical factors influencing recurrent carotid stenosis and occlusion after endarterectomy. J Vasc Surg 1987; 5: 702–706.

38.

Asakura

Karino

. Flow patterns and spatial distribution of atherosclerotic lesions in human coronary arteries. Circ Res 1990; 66: 1045–1066.

39.

Friedman

Brinkman

Qin

. Relation between coronary artery geometry and the distribution of early sudanophilic lesions. Atherosclerosis 1993; 98: 193–199.

40.

Gnasso

Irace

Carallo

. In vivo association between low wall shear stress and plaque in subjects with asymmetrical carotid atherosclerosis. Stroke 1997; 28: 993–998.

41.

Motomiya

Karino

. Flow patterns in the human carotid artery bifurcation. Stroke 1984; 15: 50–56.

42.

Pedersen

Agerbaek

Kristensen

. Wall shear stress and early atherosclerotic lesions in the abdominal aorta in young adults. Eur J Vasc Endovasc Surg 1997; 13: 443–451.

43.

Zarins

Giddens

Bharadvaj

. Carotid bifurcation atherosclerosis. Quantitative correlation of plaque localization with flow velocity profiles and wall shear stress. Circ Res 1983; 53: 502–514.

44.

Bandyk

Kaebnick

Adams

. Turbulence occurring after carotid bifurcation endarterectomy: a harbinger of residual and recurrent carotid stenosis. J Vasc Surg 1988; 7: 261–274.

45.

Friedman

. Some atherosclerosis may be a consequence of the normal adaptive vascular response to shear. Atherosclerosis 1990; 82: 193–196.

46.

Dewey

Jr Bussolari

Gimbrone

. The dynamic response of vascular endothelial cells to fluid shear stress. J Biomech Eng 1981; 103: 177–185.

47.

Resnick

Gimbrone

Jr . Hemodynamic forces are complex regulators of endothelial gene expression. FASEB J 1995; 9: 874–882.

48.

Schirmer

Malek

. Prediction of complex flow patterns in intracranial atherosclerotic disease using computational fluid dynamics. Neurosurgery 2007; 61: 842–851. discussion 852.

49.

Stroud

Berger

Saloner

. Influence of stenosis morphology on flow through severely stenotic vessels: implications for plaque rupture. J Biomech 2000; 33: 443–455.

50.

Slager

Wentzel

Gijsen

. The role of shear stress in the generation of rupture-prone vulnerable plaques. Nat Clin Pract Cardiovasc Med 2005; 2: 401–407.

51.

Fry

. Certain histological and chemical responses of the vascular interface to acutely induced mechanical stress in the aorta of the dog. Circ Res 1969; 24: 93–108.

52.

Baan

Jr Thompson

Reul

. Vessel wall and flow characteristics after carotid endarterectomy: eversion endarterectomy compared with dacron patch plasty. Eur J Vasc Endovasc Surg 1997; 13: 583–591.

53.

Bao

Frangos

. Temporal gradient in shear but not steady shear stress induces pdgf-a and mcp-1 expression in endothelial cells: role of no, nf kappa b, and egr-1. Arterioscler Thromb Vasc Biol 1999; 19: 996–1003.

54.

Malek

Alper

Izumo

. Hemodynamic shear stress and its role in atherosclerosis. JAMA 1999; 282: 2035–2042.

55.

Demirel

Attigah

Bruijnen

. Perioperative blood pressure alterations after eversion and conventional carotid endarterectomy sustain in the midterm. Langenbecks Arch Surg 2013; 398: 303–312.

56.

Demirel

Bruijnen

Attigah

. The effect of eversion and conventional-patch technique in carotid surgery on postoperative hypertension. J Vasc Surg 2011; 54: 80–86.

Comparison of morphological and rheological conditions between conventional and eversion carotid endarterectomy using computational fluid dynamics – a pilot study

Abstract

Purpose:

Basic methods:

Principal findings:

Conclusions:

Keywords

Introduction

Methods

Results

Flow analysis and validations

Loading distributions

Discussion

Limitations

Footnotes

Acknowledgements

Conflict of interest

Funding

References