Innovative personalised risk stratification in aortopathy: an engineering approach

Project ID: 2531ad1542

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UCL Lead department: Institute of Cardiovascular Science

Project Summary:

Aortopathies are a group of progressive diseases characterised by dilation of the aorta that can ultimately lead to life-threatening aortic wall dissection and rupture. Aorthopaties have several known causes, syndromic or non-syndromic, familial, or sporadic, with an incidence of aortic aneurysms of 5.9 cases per 100,000 person-years. Underlying causes of progression of aortic dilation are multifactorial, depending on genetics, anatomy, arterial wall biomechanics and hemodynamic conditions. Prognosis varies greatly according to the underlying disorder and management, and from patient to patient. In some cases aortic dilation is slow and mild, whilst in others the course can be rapid and lead to extreme dilation with high risk of spontaneous dissection and mortality rate up to 50%. Although prognosis in aortopathy patients depends on the underlying disease, current international guidelines all indicate aortic diameter as the only validated predictor for risks of dissection/rupture, but also recognise that diameter alone is suboptimal to stratify patients and define timing for prophylactic surgery. In this project, we propose to improve the management and treatment of aortopathy patients by introducing innovative risk stratification tools based on the integration of personalised, morphological and biomechanical parameters to the clinical data. The overall hypothesis for the project is that novel, combined statistical, biomechanical and hemodynamic models can help describe different pathways of aortic dilation progression above somatic growth in patients, to refine risk stratification. The project will: i) set up longitudinal 3D statistical shape models to predict different shape modes of aortic dilation in aortopathy patients; ii) carry out patient-specific computational simulations to model aortic deformations and hemodynamics during the cardiac cycle; and, iii) derive biomechanics and hemodynamic parameters to explain different aortic dilation modes. The findings will contribute to a comprehensive metric to capture the phenotypical, biomechanical and hemodynamics features of aortic dilation in aortopathy disorders.