Patient-specific model of aortic valve stenosis: advancing precision medicine in valvular heart diseases - Finanziato dall’Unione Europea – Next Generation EU

Background: Aortic stenosis (AS) has a progressive and insidious development, with an important inflammatory substrate, and is associated with reduced life expectancy. Transcatheter aortic valve implantation (TAVI) has evolved as the treatment of choice for patients with severe aortic stenosis to include younger and lower-risk cohorts. The prolonged life expectancy of these patients further highlights the critical relevance of material durability and of long-term impact of procedural valve complications. Advances in computational modeling, such as Fluid-Structure Interaction (FSI) models, allows to simulate patient-specific valve implantation and predict outcomes. We have developed an FSI-based methodology to simulate both the valve implantation and its behavior during the cardiac cycle. Our preliminary findings showed a robust prediction of procedural success. A perspective validation of our model and its integration with an inflammatory characterization has the potential to advance precision medicine optimizing the ability to implement patient-specific strategies, including device selection and procedural planning, with a positive impact on clinical outcomes. Moreover, we believe that our model will facilitate health technology assessment and development. Aims: We aim to validate our FSI-model and integrate it with data on inflammatory biomarkers of patients with severe aortic stenosis, in order to develop a tool for patient-level decision-making and for in-silico trials. Methods: Using cardiovascular imaging techniques, detailed anatomical and physiological data will be obtained and subsequently analyzed using patient-specific FSI modeling. Pre-procedural structural and hemodynamic data will be used to simulate and optimize procedural outcomes. Validation of the model will be done using post-procedural imaging with echocardiography and cardiac MRI. Blood tests and biobank storage will be used to analyze relevant inflammatory biomarkers. Machine learning will be used to optimize the FSI-modeling by integrating data on inflammatory biomarkers. Expected outcomes and relevance: Computation FSI modeling will allow to guide patient-specific therapeutic strategies to improve clinical outcomes while optimizing health technology assessment. Strategies tailored on individual patients’ anatomical and biological characteristics will allow to improve risk stratification and procedural planning. Moreover, FSI models will allow performing in-silico trials with virtual patients, with the potential to limit the need for large-scale clinical trials. This could significantly reduce costs and time for health technology assessment, while providing potentially unlimited data, facilitating the development and implementation of new biomedical and medical therapies. Overall, the potential and versatility of our model, integrated with granular inflammatory data, allows to advance precision medicine for the treatment of patients with severe aortic stenosis