SICVALVES - Multiscale Modeling of Valvular Heart Diseases

Background: Valvular Heart Diseases (VHD) are a detrimental health burden to the aging population where aortic valve stenosis alone has reached an incidence of more than 12% of patients >70y. It is a chronic-progressive disease that varies with gender and age. If le ft untreated, VHDs can cause malignant arrhythmias and severe heart failure. However, existing guidelines for treatment planning are using only rough function parameters that fail to account for inter -individual variability and are far away from the demands of precision medicine. In the age of digital medicine, computational modeling has the potential to unveil important pathophysiological mechanisms and to contribution towards personalized precision medicine. Objectives: To use advanced models of the cell, tissue, and organ level in VHD to gain mechanistic insights about triggers of ventricular arrhythmias, diastolic, and systolic dysfunction and myocardial metabolic alterations and how these processes reinforce each other. Gender differences will be systematically taken into account. Ultimately, models shall contribute to improve diagnostics, risk assessment, and treatment planning of VHDs. Methods: Based on our previous work, we will technologically advance, test, and validate existing computational models of biomechanics, electrophysiology, and hemodynamics. For model parameterization we use existing multidimensional clinical data (imaging, sensors, omics) from own previous research in patients with VHD (obtained before and after valve replacement). Valida tion of the model`s accuracy to predict mechanistic changes will be done with data obtained before and after aortic valve replacement. Innovation: Advanced computational models will be leveraged to build personalized models and use these for quantitative assessment of hypertrophic remodeling and propensity for arrhythmias. Novel methodologies will be developed to understand important mechanisms of adverse remodeling in VHDs, as well as to improve patient care by optimizing patient selection and precision tr eatment planning in valve disease.

The project is part of the consortial network ERA-CVD SICVALVES, involving the following research groups: Dr. Christoph Augustin (Medical University of Graz), Dr. Sarah Nordmeyer (Charité Berlin), and Dr. Jason Bayer (IHU LIRYC Bordeaux). The goal of the project is to apply and validate computer-based models to better understand disease-related changes in the heart muscle and to optimize patient-specific therapy planning for heart valve diseases. The models aim to: 1. Plan heart valve replacement therapy more individually, thereby improving treatment outcomes, 2. Predict the regression of disease-related heart muscle changes after valve replacement, and 3. Better understand the relationship between heart muscle changes and the development of cardiac arrhythmias. To achieve this, existing hemodynamic, biomechanical, and electrophysiological models were optimized using patient-specific data. Preoperative patient data were used to predict postoperative conditions with these models. These predictions are then compared with actual postoperative measurements for validation. The focus of the research group in Graz was on creating virtual, anatomical replicas of the patient's hearts, developing software for the electromechanical simulation of the heart muscle, and personalizing the computer model with clinical data. Data collected from previous studies at Charité Berlin were used to individually calibrate the computer-based models, enabling patient-specific therapy planning for heart valve replacement in patients with heart valve diseases.