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Prognostic assessment of valvular aortic disease treatment coupling Immunological and biomechanical profiles

Project description

Predictive modelling for aortic valve disease treatment

Aortic valve diseases affect the aortic valve in the heart, which controls blood flow from the left ventricle to the aorta, and hence the distribution of oxygen-rich blood throughout the body. Procedural complications and high hospitalisation rates highlight a need for improved treatment prediction. The ERC-funded PROTEGO project aims to develop a predictive methodology that considers immunological and biomechanical patient profiles. The research team will create digital twins for patients and high-fidelity treatment models for optimal clinical outcome. Overall, the study is expected to improve our understanding of the factors that contribute to increase in inflammation and the interaction between biomarkers and negative post-treatment prognosis in patients with aortic valve diseases.

Objective

Aortic valve diseases are degenerative conditions that develop progressively and insidiously. Once symptoms become evident, life expectancy is significantly reduced. While treatments for these pathologies are widely available, there remains a remarkably high rate of procedural complications. These complications have been shown to have a negative impact on cardiac mortality and the likelihood of rehospitalization for heart failure. This underscores the need for further technological advancements. Protego's objective is to determine whether a combination of immunological and biomechanical profiles in patients with aortic valve diseases can effectively predict post-treatment prognosis. My goal is to develop an innovative, validated, and clinically applicable methodology that can identify the best treatment options and predict post-procedural outcomes while minimizing complications. This methodology will serve to determine the timing of treatment for patients with valvular aortic diseases and assess whether the proposed treatment is likely to be beneficial preoperatively, while also minimizing the risk of post-procedural complications. I will achieve this by combining imaging analysis, deep learning algorithms, in silico models, and in vitro tests. My approach involves the following key objectives: (i) creating a multi-physics digital twin of patients with aortic valve diseases, (ii) developing a validated, high-fidelity model for treatment with quantification of post-treatment outcomes and (iii) generating a proof of concept for a clinically applicable predictive model trained using both immunological profiles and biomechanical features of patients. This innovative approach will provide a deeper understanding of how clinical and biomechanical outcomes correlate with the amplification of inflammation, helping us comprehend the interaction between biomarkers and negative post-treatment prognosis in patients with aortic valve diseases.

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Topic(s)

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-STG

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Host institution

POLITECNICO DI MILANO
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 198 295,00
Address
PIAZZA LEONARDO DA VINCI 32
20133 Milano
Italy

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Region
Nord-Ovest Lombardia Milano
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 198 295,00

Beneficiaries (2)

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