Share:
Share this content in WeChat
X
Clinical Article
Computational fluid dynamics and functional outcome in atherosclerotic middle cerebral artery stenosis: A correlation study
WU Jiahua  CHEN Guozhong  WANG Peng  MAO Cunnan  MIAO Zhengfei  SU Wen  YIN Xindao 

Cite this article as: Wu JH, Chen GZ, Wang P, et al. Computational fluid dynamics and functional outcome in atherosclerotic middle cerebral artery stenosis: A correlation study[J]. Chin J Magn Reson Imaging, 2021, 12(6): 10-15. DOI:10.12015/issn.1674-8034.2021.06.003.


[Abstract] Objective To investigate the correlation between hemodynamics and functional outcome in atherosclerotic middle cerebral artery (MCA) stenosis using a computational fluid dynamics (CFD) model based on magnetic resonance angiography (MRA), according to modified Rankin Scale (mRS) at 3 months. Materials andMethods Fifty patients with 50%-99% atherosclerotic MCA stenosis were included. Demographic, imaging data and functional outcome (mRS at 3 months) were collected. Hemodynamic parameters were obtained from CFD models: wall shear stress ratio (WSSR), pressure ratio (PR), velocity ratio (VR), area ratio (AR) and mass flow ratio (MFR). The patients were divided into good functional outcome group and poor functional outcome group based on mRS at 3 months. We compared the clinical variables and hemodynamic parameters between the two groups, and correlation analysis were carried out.Results A total of 50 patients were included. The patients were divided into good functional outcome group (mRS=0-2, n=28) and poor functional outcome group (mRS=3-6, n=22). Compared with good functional outcome group, more patients had a history of hypertension (81.82% vs. 50%, P=0.020), hyperlipidemia (54.55% vs. 25%, P=0.033) and diabetes mellitus (59.09% vs. 28.57%, P=0.030), higher NIHSS at admission (8.82±4.69 vs. 5.96±3.42; P=0.038) and NIHSS at 24 hours (8.00±5.38 vs. 4.39±3.43; P=0.027) in poor functional outcome group. WSSR (3.04±1.56 vs. 8.68±9.67, P=0.002), VR (2.02±0.59 vs. 3.65±2.16, P<0.001), AR (1.30±0.13 vs. 1.62±0.34, P<0.001) and MFR (1.42±0.31 vs. 1.94±0.84, P=0.008) in poor functional outcome group were significantly lower than those in good functional outcome group. PR (0.99±0.01 vs. 0.97±0.04, P=0.011) in poor functional outcome group were significantly higher than those in good functional outcome group. Correlation analysis showed that hypertension (r=0.334, P=0.018), diabetes mellitus (r=0.295, P=0.037), NIHSS at admission (r=0.425, P=0.002), NIHSS at 24 hours (r=0.472, P=0.001), WSSR (r=-0.299, P=0.035), VR (r=-0.384, P=0.006) and AR (r=-0.472, P=0.001) had statistically difference with functional outcome.Conclusions Low WSSR, VR, AR, MFR and high PR were more likely to lead to poor functional outcome in atherosclerotic middle cerebral artery stenosis. WSSR, VR and AR had negative correlation with functional outcome in atherosclerotic middle cerebral artery stenosis.
[Keywords] middle cerebral artery;acute ischemic stroke;computational fluid dynamics;magnetic resonance imaging

WU Jiahua   CHEN Guozhong   WANG Peng   MAO Cunnan   MIAO Zhengfei   SU Wen   YIN Xindao*  

Department of Medical Imaging, Nanjing Hospital Affiliated to Nanjing Medical University (Nanjing First Hospital), Nanjing 210006, China

Yin XD, E-mail: y.163yy@163.com

Conflicts of interest   None.

This work was part of Natural Science of Jiangsu Province (No. BK20201118); Science and Technology Development Fund of Nanjing Medical University (No. NMUB2019170).
Received  2020-12-01
Accepted  2021-01-28
DOI: 10.12015/issn.1674-8034.2021.06.003
Cite this article as: Wu JH, Chen GZ, Wang P, et al. Computational fluid dynamics and functional outcome in atherosclerotic middle cerebral artery stenosis: A correlation study[J]. Chin J Magn Reson Imaging, 2021, 12(6): 10-15. DOI:10.12015/issn.1674-8034.2021.06.003.

1
GBD 2016 Stroke Collaborators. Global, regional, and national burden of stroke, 1990-2016: a systematic analysis for the global burden of disease study 2016[J]. Lancet Neurol, 2019, 18(5): 439-458. DOI: 10.1016/S1474-4422(19)30034-1.
2
Wang YJ, Zhao XQ, Liu LP, et al. Prevalence and outcomes of symptomatic intracranial large artery stenoses and occlusions in China The Chinese intracranial atherosclerosis (CICAS) study[J]. Stroke, 2014, 45(3): 663-669. DOI: 10.1161/STROKEAHA.113.003508.
3
Leng X, Lan L, Ip HL, et al. Translesional pressure gradient and leptomeningeal collateral status in symptomatic middle cerebral artery stenosis[J]. Eur J Neurol, 2018, 25(2): 404-410. DOI: 10.1111/ene.13521.
4
Leng X, Lan L, Ip HL, et al. Hemodynamics and stroke risk in intracranial atherosclerotic disease[J]. Annal Neurol, 2019, 85(5): 752-764. DOI: 10.1002/ana.25456.
5
Liebeskind DS, Cotsonis GA, Saver JL, et al. Collaterals dramatically alter stroke risk in intracranial atherosclerosis[J]. Ann Neurol, 2011, 69(6): 963-974. DOI: 10.1002/ana.22354.
6
Leng XY, Scalzo F, Ip HL, et al. Computational fluid dynamics modeling of symptomatic intracranial atherosclerosis may predict risk of stroke recurrence[J]. PLoS One, 2014, 9(5): e97531. DOI: 10.1371/journal.pone.0097531.
7
Chen ZM, Qin HQ, Liu J, et al. Characteristics of wall shear stress and pressure of intracranial atherosclerosis analyzed by a computational fluid dynamics model: A pilot study[J]. Front Neurol, 2020, 10: 1372. DOI: 10.3389/fneur.2019.01372.
8
Taylor CA, Fonte TA, Min JK, et al. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: Scientific basis[J]. J Am Coll Cardiol, 2013, 61(22): 2233-2241. DOI: 10.1016/j.jacc.2012.11.083.
9
Xiang J, Tutino VM, Snyder KV, et al. CFD: Computational fluid dynamics or confounding factor dissemination? The role of hemodynamics in intracranial aneurysm rupture risk assessment[J]. AJNR Am J Neuroradiol, 2014, 35(10): 1849-1857. DOI: 10.3174/ajnr.A3710.
10
Alloubani A, Saleh A, Abdelhafiz I, et al. Hypertension and diabetes mellitus as a predictive risk factors for stroke[J]. Diabetes Metab Syndr, 2018, 12(4): 577-584. DOI: 10.1016/j.dsx.2018.03.009.
11
Brooks DC, Schindler JL. Management of hyperlipidemia after stroke[J]. Curr Treat Options Cardiovasc Med, 2019, 21(12): 93. DOI: 10.1007/s11936-019-0774-8.
12
Wu ZM, Zeng MY, Li C. Time-dependence of NIHSS in predicting functional outcome of patients with acute ischemic stroke treated with intravenous thrombolysis[J]. Postgrad Med J, 2019, 95(1122): 181-186. DOI: 10.1136/postgradmedj-2019-136398.
13
Dankbaar JW, Horsch AD, van den Hoven AF, et al. Prediction of clinical outcome after acute ischemic stroke: The value of repeated noncontrast computed tomography, computed tomographic angiography, and computed tomographic perfusion[J]. Stroke, 2017, 48(9): 2593-2596. DOI: 10.1161/STROKEAHA.117.017835.
14
Suh CH, Jung SC, Cho SJ, et al. Perfusion CT for prediction of hemorrhagic transformation in acute ischemic stroke: a systematic review and meta-analysis[J]. Eur Radiol, 2019, 29(8): 4077-4087. DOI: 10.1007/s00330-018-5936-7.
15
Kudo K, Sasaki M, Yamada K, et al. Differences in CT perfusion maps generated by different commercial software: quantitative analysis by using identical source data of acute stroke patients[J]. Radiology, 2010, 254(1): 200-209. DOI: 10.1148/radiol.254082000.
16
Sun H, Ma J, Liu Y, et al. CT perfusion for identification of patients at risk for delayed cerebral ischemia during the acute phase after aneurysmal subarachnoid hemorrhage: A meta-analysis[J]. Neurol India, 2019, 67(5): 1235-1239. DOI: 10.4103/0028-3886.271235.
17
Leng XY, Wong KS, Liebeskind DS. Evaluating intracranial atherosclerosis rather than intracranial stenosis[J]. Stroke, 2014, 45(2): 645-651. DOI: 10.1161/STROKEAHA.113.002491.
18
Leng X, Lan L, Ip HL, et al. Translesional pressure gradient and leptomeningeal collateral status in symptomatic middle cerebral artery stenosis[J]. Eur J Neurol, 2018, 25(2): 404-410. DOI: 10.1111/ene.13521.
19
Tang J, Chen GZ, Mao CN, et al. Computational fluid dynamics analysis of hemorrhagic transformation after reperfusion therapy in acute ischemic stroke patients with middle cerebral artery occlusion[J]. Chin J Magn Reson Imaging, 2020, 11(3): 161-165. DOI: 10.12015/issn.1674-8034.2020.03.001.
20
Dhawan SS, Nanjundappa RP, Branch JR, et al. Shear stress and plaque development[J]. Expert Rev Cardiovasc Ther, 2010, 8(4): 545-556. DOI: 10.1586/erc.10.28.
21
Samady H, Eshtehardi P, McDaniel MC, et al. Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease[J]. Circulation, 2011, 124(7): 779-788. DOI: 10.1161/CIRCULATIONAHA.111.021824.
22
Himburg HA, Grzybowski DM, Hazel AL, et al. Spatial comparison between wall shear stress measures and porcine arterial endothelial permeability[J]. Am J Physiol Heart Circ Physiol, 2004, 286(5): H1916-H1922. DOI: 10.1152/ajpheart.00897.2003.

PREV Relationship between FVH sign and brain oxygen metabolism of SWI sequence and clinical state in patients with acute/subacute cerebral infarction
NEXT Contrast study using the resting-state fractional amplitude of low-frequency fluctuations between treatment-naive patients with schizophrenia and obsessive-compulsive disorder
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn