Project description
Heart digital twins to predict arrhythmia
Myocardial infarction (MI) or heart attack often causes permanent scarring of cardiac tissue. This increases the risk of sudden cardiac death from arrhythmias. Currently, risk prediction relies on the heart’s ejection fraction, but it has many limitations, missing high-risk patients and leading to unnecessary defibrillator implants. With the support of the Marie Skłodowska-Curie Actions programme, the HEART-TWIN project proposes to develop cardiac digital twins to facilitate patient stratification. These cardiac models will integrate clinical data from non-invasive diagnostic methods such as MRI to simulate clinical outcomes. The ultimate goal of the project is to support precision cardiology and more efficient post-MI care.
Objective
Myocardial infarction (MI) leads to permanent myocardial scarring, significantly increasing the risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs). Current risk stratification methods using ejection fraction (EF) are inadequate, as many SCDs occur in patients with preserved EF, and many implanted defibrillators remain unused. Physics-based mechanistic cardiac digital twins (CDTs) can predict VA risk stratification (VARS). CDTs are computational models that integrate multimodal clinical data, such as MRI and CT, to predict VA risk by simulating cardiac properties like myocardial conduction speeds and activation sites, which cannot be easily measured with standard diagnostics.
The HEART-TWIN (Holistic Efficient Arrhythmic Risk Twinning for Post-MI Stratification) project aims to develop, validate, and apply efficient CDT strategies for post-MI arrhythmic risk prediction using non-invasive clinical data. The project is structured around three main research objectives (ROs).
RO1 will focus on characterising the largest multimodal dataset (~500 post-MI patients) using ECG and MRI data, enabling the creation of robust clinical phenotypes and providing data for CDT development.
RO2 will develop an open-source CDT framework validated through verification and validation with uncertainty quantification (VVUQ). Efficient electrophysiology models, such as the pseudo-diffusion reaction-Eikonal model, will be used to simulate ECGs and enhance the accuracy of VA risk predictions.
RO3 aims to link CDT simulations with clinical outcomes to achieve deep phenotyping of post-MI patients. By leveraging these data, the project will enable AI-driven discovery of phenotypic subgroups and improve VARS through personalised, mechanistic insights.
HEART-TWIN will produce the most advanced open-source digital twin framework for post-MI patients, optimising clinical decision-making and risk stratification, ultimately paving the way for precision cardiology.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2024-PF-01
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08002 Barcelona
Spain
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