Proof of Concept of Model Based Cardiovascular Prediction

Aortic Coarctation, Aortic Valve Disease, Cardiovascular Modeling, Cardiovascular MRI

cardiovascular modeling, aortic coarctation, aortic valve disease

CARDIOPROOF is a proof-of-concept project that consolidates the outcomes of previous virtual physiological human (VPH) projects and checks the applicability and effectiveness of available predictive modelling and simulation tools, validating them in interrelated clinical trials conducted in three European centres of excellence in cardiac treatment (from Germany, Italy and the UK). CARDIOPROOF focuses on patients with aortic valve disease and aortic coarctation, which, if left untreated, can ensue irreversible heart failure. As a result treatment becomes mandatory, but optimum timing and the best type of treatment still remain difficult to determine. With more than 50.000 interventions per year within the EU, the diseases addressed by CARDIOPROOF have a significant socio-economic impact. Present clinical guidelines are highly complex and rely mostly on imaging diagnostics and clinical parameters, without benefiting, as yet, from patient-specific disease modelling based prediction. CARDIOPROOF goes beyond the current state of the art by conducting validation trials aimed at covering and comparing the complete spectrum of cardiovascular treatment, predicting the evolution of the disease and the immediate and mid-term outcome of treatment. Operational clustering is going to provide a seamless clinical solution that applies different modeling methods to realize the potential of personalised medicine taking into account user-friendliness as a key component of clinical usability. CARDIOPROOF's goal is to provide first-hand data on comparative cost-effectiveness and clinical efficacy of the most advanced VPH approaches compared to conventional diagnostics and treatment algorithms, thus accelerating the deployment of VPH methods in clinical environments, and bring to maturity holistic patient-specific computer-based predictive models and simulations.