The euHeart project integrates multiple types of functional and anatomical data into a consistent framework using multi-scale computational modelling to personalize treatments for cardiovascular diseases.
Can multi-scale computational modelling integrate multiple types of functional data into a consistent framework for personalized cardiac care?
Multi-scale computational modelling can integrate diverse functional and imaging data to create patient-specific cardiovascular models for personalized cardiac care.
The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.
Smith et al. (Fri,) conducted a review in Cardiovascular disease. Patient-specific cardiovascular modelling was evaluated. The euHeart project integrates multiple types of functional and anatomical data into a consistent framework using multi-scale computational modelling to personalize treatments for cardiovascular diseases.