Clinical Impact

In all these diseases, the timing and type of treatment (surgery, catheter/based intervention or medication/follow-up) are crucial to :

  • prevent potentially life threatening sequelae (e.g. heart failure)
  • avoid too early procedures which might in turn increase the risk for re-operation during the life time of a patient

Improving such decision-making can have a significant impact on:

  • patient management guidelines
  • outcomes
  • socio-economic costs

Current costs per patient per (re)intervention in the EU range:

  • between 10,000-20,000 € for valve surgery
  • between 8,000-10,000 € per catheter intervention in CoA


Significant reduction of costs through the use of VPH technologies applicable to early diagnosis, prediction of disease and treatments outcomes

  • CARDIOPROOF will thus not only inform on the best treatment strategies to allow patients a good health status, compatible with a working life, but it will also show that modelling is more cost effective than traditional guidelines based decision making.
  • Impact on the device industry: currently, the designs and testing of heart valves underlay a very costly and time consuming process. The modelling tools of CARDIOPROOF would provide new options for in-silico testing of such devices in realistic human, but full non-invasive, conditions.

The main clinical question addressed by CARDIOPROOF in the presence of significant aortic valve disease is when and how to intervene. The timing issue arises because if done too early, the lifetime risk of multiple operations increases. However, when correction of the disease is performed too late, there is a risk of permanent left ventricular impairment (leading to overt heart failure) and ultimately death. In addition, the type of surgical treatment (valve reconstruction vs. mechanical or biological valve substitute) has a direct impact on short–term haemodynamic performance and longterm outcome.

CARDIOPROOF will address this question by modelling the response to aortic valve repair both in terms of local haemodynamics and left ventricular load. The response will be modelled for virtual implantation of different mechanical and biological valve substitutes and for virtual valve reconstruction. This should allow better understanding of optimum time and type for aortic valve interventions and thus patient management. For better guiding therapy in AVD, CARDIOPROOF is going to apply and validate modelling methods that predict the immediate outcome of intervention on cardiac pump and intrinsic myocardial function.

Furthermore, CARDIOPROOF will develop methods by which the effect of stenting on aortic pressure and LV load can be predicted using a combination of pre-intervention imaging and modelling techniques. These methods should allow identifying those patients who can be expected to have a haemodynamic improvement after the intervention, and should thus improve clinical decision-making in aortic coarctation. By the integration of retrospective data, CARDIOPROOF also attempts to predict the mid-term outcome (2-3 years follow-up) in these patients. Therefore from the point of view of CoA, CARDIOPROOF has 2 main objectives: The validation of models that enable assessing the midterm outcome, and the use of already existing expertise and data for assessing the comparative effectiveness of modelling in CoA: how do the existing predictive models affect the medical management (i.e. conservative approach vs. interventional) and their potential implications in terms of cost-to-treatment effectiveness?

Both AVD and CoA are strictly connected to the morphology of the aortic root (in AVD) and aortic

arch (in AVD and CoA). CARDIOPROOF’s activities will have as outcome computer-based modelling merging all information on the ascending aortic disease into a single “patient-specific” virtual model, inclusive of aortic root geometry, direction of the aortic blood flow, aortic wall stress and aortic valve morphology, and providing the expected effect on aortic dilation from intervention in both AVD and COA. Eventually, aortic dilation is also a significant predictor of dissection and rupture (acute cardiovascular events possibly leading to death), thus the ability to predict the evolution of aortic arch dilation is clinically relevant. This will support the way towards the definition of clear and reliable criteria for predicting the effect of aortic root disease on AVD and CoA and identifying the best timing for surgical intervention.