EurValve: Personalised Decision Support for Heart Valve Disease

Valvular heart disease

Valvular heart disease currently affects 2.5% of the population. It is overwhelmingly a disease of the elderly and as a consequence is on the rise. It is dominated by two conditions, aortic stenosis and mitral regurgitation, both of which are associated with significant morbidity and mortality, yet which pose a truly demanding challenge for treatment optimisation.

The timing and nature of interventional treatment is crucial in valve disease, but it remains a major challenge in current clinical practice. According to databases, in the EU more than 70,000 valves are replaced or repaired per year (EACTS.org).

The EurValve project

EurValve will implement, test and validate a modelling based decision support system (DSS) for aortic and mitral valve diseases that allows simulating, comparing and understanding the effects (outcomes) and risks of different treatment strategies.

In addition, the DSS will improve knowledge of disease mechanisms by applying a holistic assessment of cardiovascular function. This includes haemodynamic data at all cardiovascular compartments (ventricle, valve, vessels), and multiscale components that couple organ with cell function.

By combining multiple complex modelling components developed in recent EC-funded research projects, a comprehensive, clinically-compliant decision-support system will be developed to meet this challenge.

This will involve quantifying individualised disease severity and patient impairment, predicting disease progression, ranking the effectiveness of alternative candidate procedures, and optimising the patient-specific intervention plan.

The outcome of EurValve

The automated valvular heart disease workflow will provide an assessment of an individual patient to guide a clinician through the established clinical guidelines for their condition. The workflow will provide data on pre-operative and predicted post-operative cardiac performance, in an easily-assessable form, to enable the clinician to make the best-informed judgement possible.