Valvular Heart Disease (VHD) currently affects 2.5% of the population, but is overwhelmingly a disease of the elderly and consequently on the rise. The prevalence is 13% in those over the age of 75, and the population beyond the age of 85 is set nearly to double by 2028. VHD 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.
Clinical Motivation: The timing and nature of interventional treatment is crucial in valve disease, but optimisation remains a major challenge in current clinical practice. Operating on patients too late carries the risk of development of irreversible heart failure. Operating too early exposes patients to unnecessary risks, conceivably causing short (e.g. valve thrombosis) or long term sequelae (e.g. early valve degeneration).
EurValve will implement, test and validate a modelling-based decision support system for aortic and mitral valve diseases that allows simulating, comparing and understanding the effects (outcomes) and risks of different treatment strategies. This decision support system will allow for in-silico simulation of different treatment options and thus allow comparison of their immediate haemodynamic outcome.
Objectives: The primary objectives of EurValve will be to apply and test, in clinical studies, modelling tools for:
- Virtual surgery: In-silico testing of valve replacement or repair
- Personalised simulation of the acute haemodynamic effects of different valve surgeries.
- Accounting for multi-scale effects of myocardial remodelling using proteomics techniques
- Validation of the parameters derived from the models against those in the current gold standards.
The target user of this system will be the healthcare professional, the surgeon or cardiologist, who will make the decision on the nature and timing of the intervention. The major advance of this system over current practice is that it integrates and interprets all heterogeneous data available about the patient, integrates population data where needed, and provides a consistent, repeatable, quantitative and auditable record of the information that contributes to the decision process.
Summary: 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 the challenge of treatment optimisation of valvular heart disease. This process will dramatically improve outcomes and consistency across Europe, maximising individual, societal and economic outcomes