Computational modelling provides valuable insights into the haemodynamics of valvular heart disease, facilitating the prediction of disease progression and assessment of therapeutic interventions.
An aging population and an increasing incidence of cardiovascular risk factors form the basis for a global rising prevalence of valvular heart disease (VHD). Research to further our understanding of the pathophysiology of VHD is often confined to the clinical setting. However, in recent years, sophisticated computational models of the cardiovascular system have been increasingly used to investigate a variety of VHD states. Computational modelling provides new opportunities to gain insights into pathophysiological processes that may otherwise be difficult, or even impossible, to attain in human or animal studies. Simulations of co-existing cardiac pathologies, such as heart failure, atrial fibrillation, and mixed valvular disease, have unveiled new insights that can inform clinical research and practice. More recently, advancements have been made in using models for making patient-specific diagnostic predictions. This review showcases valuable insights gained from computational studies on VHD and their clinical implications.
Šeman et al. (Tue,) conducted a review in Valvular heart disease. Computational modelling was evaluated. Computational modelling provides valuable insights into the haemodynamics of valvular heart disease, facilitating the prediction of disease progression and assessment of therapeutic interventions.