Analysing the haemodynamics of flow in carotid artery disease (CAD) serves as a means to better understand the development and progression of associated complex diseases. Major adverse cardiovascular events (MACE) remain a challenge to predict using current clinical markers; however, CAD patients show a higher association with MACE occurrence and present as a key target population for cardiovascular screening [1,2]. The haemodynamics in heavily plaqued carotid arteries plays a key mechanistic role in disease initiation and progression, yet their translation into clinically usable biomarkers has been limited by imaging and modelling constraints. This study develops a computationally efficient means of modelling patient-specific flow, utilising 3D tomographic ultrasound (tUS) to generate anatomically faithful reconstructions including the luminal plaque, and computational fluid dynamics (CFD) to simulate flow in these arteries. Herein, a semi-automated framework integrates 3D tUS and CFD to extract haemodynamic metrics from carotid arteries and assesses their association with subsequent cardiovascular events. 75 cases were modelled, with clinical follow-up data being used to classify patients according to the occurrence of MACE or otherwise. The results demonstrate that flow vorticity and measures of flow disturbance, in the region of interest around the carotid bifurcation, show a stronger association with MACE than more conventionally calculated wall shear metrics, even in cohorts with comparable degrees of stenosis [3]. Sensitivity analyses indicate that these haemodynamic trends are robust to modelling assumptions and are captured reliably using steady-state simulations representative of peak systolic flow. The study highlights the potential of tUS-derived haemodynamic biomarkers as reproducible, scalable, and mechanistically informed predictors of cardiovascular risk. It provides a pathway for integrating patient-specific haemodynamics into clinical risk stratification and development of computational tools for cardiovascular diagnostic support.