Hemodynamics derived from computational fluid dynamics based on magnetic resonance angiography is associated with functional outcomes in atherosclerotic middle cerebral artery stenosis

Abstract

Background: To investigate the relationship between fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH), 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 a modified Rankin Scale (mRS) at 3 months.Methods: A total of 120 patients with 50-99% atherosclerotic MCA stenosis were included. The training and internal validation groups were composed of 99 participants and 21 participants, respectively. Demographic, imaging data, and functional outcome (mRS at 3 months) were collected. Hemodynamic parameters were obtained from the CFD model. The FVH score was based on the number of territories where FVH is positive, according to the spatial distribution in the Alberta Stroke Program Early Computed Tomography Score (ASPECTS). The prediction models were constructed according to clinical and hemodynamic parameters using multivariate logistic analysis. The DeLong test compared areas under the curves (AUCs) of the models. Results:The multivariable logistic regression analysis showed that the National Institute of Health Stroke Scale (NIHSS) at admission, hypertension, hyperlipidemia, the ratio of wall shear stress before treatment (WSSR before ), and difference in the ratio of wall shear stress (WSSR) were independently associated with functional outcome (all P<0.05). In the training group before treatment, the AUC of model 1a (only clinical variables) and 2a (clinical variables with addition of WSSR before ) were 0.750 and 0.802. After treatment, the AUC of model 1b (only clinical variables) and 2b (clinical variables with addition of difference in WSSR) were 0.815 and 0.883, respectively. The AUC of models with hemodynamic parameters was significantly higher than the models based on clinical variables only (all P<0.05, DeLong test). In the internal validation group before treatment, the AUC of the model (clinical variables) was 0.782, and that of the model (clinical variables and WSSR before ) was 0.800. After treatment, the AUC of the model (clinical variables) was 0.833, and that of the model (clinical variables and difference in WSSR) was 0.861. There were no significant differences between the good and the poor functional outcome group concerning FVH before scores before treatment (0.30±0.81 vs. 0.26±0.97; P=0.321) and FVH after scores after treatment (0.08±0.39 vs.

Correlation of fluid-attenuated inversion recovery sequence vascular hyperintensity in magnetic resonance with collateral circulation and short-term clinical prognosis in acute ischemic stroke

“…This reverses the blood flow to the cerebral tissue in the ischemic area and causes different degrees of compensatory perfusion there ( 13 ). The degree of opening of the collateral vessels has an important effect on the prognosis of patients ( 14 ). Furthermore, good collateral access exerts a substantial protective effect on the ischemic cerebral tissue: it can improve its condition, maintain perfusion, increase the benefit rate of revascularization therapy, reduce the incidence of cerebrovascular events, reduce the number of cerebral infarcts and infarct size, facilitate the recovery of neurological function in the lesion area, and improve the clinical prognosis ( 15 ).…”

Clinical implications of haemodynamics in symptomatic intracranial atherosclerotic stenosis by computational fluid dynamics modelling: a systematic review

“…In patients with 50%-99% sICAS in MCA who mostly received intravenous thrombolysis and some received acute endovascular treatment, Wu et al associated the WSSR before treatment (OR:0.86, 95%CI:0.75 to 0.99, p=0.041) and the difference of WSSR before and after treatment (OR, 0.72; 95% CI, 0.52 to 1.03; p=0.043) with the functional outcome by modified Rankin Scale at 3 months. 27 In contrast to prediction models solely incorporating clinical variables, the inclusion of haemodynamic metrics may enhance the predictive capacity for functional outcomes in such patients (p<0.05).…”