Abstract

This study aimed to derive a novel classification of blood flow pattern in abdominal aortic aneurysms (AAA) based on computational fluid dynamics (CFDs), and to determine the predictive value of flow patterns in AAA rupture. This was an age and sex matched case control study. Cases were identified as patients who underwent emergency endovascular or open repair due to ruptured or AAA at risk of impending rupture. Controls were age and sex matched with patients with an AAA who were asymptomatic and had a confirmed unruptured AAA from computed tomography angiography images from the same period. Classification of blood flow pattern (type I: non-helical main flow channel with multiple vortices; type II: non-helical main flow channel with single vortices; and type III, helical main flow channel with helical vortices) and haemodynamic parameters (areas of low wall shear stress [AlowWSS], aneurysm pressure drop [Δ pressure], etc) were derived from CFD analyses. Multivariable regression was used to determine independent AAA rupture risk factors. The incremental discriminant and reclassification abilities for AAA rupture were compared among different models. Fifty-three ruptured and 53 intact AAA patients were included. Ruptured AAA showed a higher prevalence of type III flow pattern than intact AAA (60.4% vs. 15.1%; p < .001). Type III flow pattern was associated with a significantly increased risk of aneurysm rupture (odds ratio 10.22, 95% confidence interval 3.43 – 30.49). Among all predicting models, the combination of AAA diameter, haemodynamic parameters (AlowWSS or Δ pressure), and flow pattern showed highest discriminant abilities in both the overall population (c-index = 0.862) and subgroup patients with AAAs < 55 mm (c-index = 0.972). Compared with AAA diameter, adding the flow pattern could significantly improve the reclassification abilities in both the overall population (net reclassification index [NRI] = 0.321; p < .001) and the subgroup of AAAs < 55 mm (NRI = 0.732; p < .001). Type III flow pattern was associated with a significantly increased risk of AAA rupture. The integration of blood flow pattern may improve the identification of high risk aneurysms in both overall population and in those with AAAs < 55 mm.

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