Summary
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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| Web resources: | https://cordis.europa.eu/project/id/101162753 |
| Start date: | 01-10-2024 |
| End date: | 30-09-2029 |
| Total budget - Public funding: | 1 498 295,00 Euro - 1 498 295,00 Euro |
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Original description
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.Status
SIGNEDCall topic
ERC-2024-STGUpdate Date
23-11-2024
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