The EurValve project started in February 2016, and ran until January 2019.
The project proposal was a response to an EC call where they were looking for teams to develop potentially-useable (after regulatory approval) clinical ‘Decision-Support’ tools that could assist clinicians in difficult areas where extra data might assist the decision.
The main idea was developed by Sheffield’s in silico cardiovascular modelling group, in consultation with Berlin’s clinical experts.
The result was a proposal for a computational tool that would provide extra, derived, ‘biomarkers’ (quantifying aortic stenosis and mitral regurgitation) obtained from 3D analyses of the patient’s cardiac geometry.
These data will potentially be added to an existing commercial valve-sizing system that is currently in clinical use.
The project was in four parts:
A retrospective data analysis to identify possible new associations between clinical data and outcomes.
Construction of the simulation model (including new associations) into a prototype Decision Support System.
Comparison of model with trial results.
We processed retrospective data from thousands of patients across the three large hospitals included in the project (Berlin – Germany, Eindhoven – Netherlands, Sheffield – UK). We used sophisticated machine-learning techniques to seek possible currently-unknown associations between clinical data, disease characteristics and interventional outcomes.
The project was a large pan-European research project investigating the application of complex 3D modelling to the optimisation of interventions in Valvular Heart Disease.