Fail to fill: an in silico investigation of complex cardiac filling dynamics

Heart failure with preserved ejection fraction (HFpEF) is a complex condition in which stiffening of the heart muscle limits the filling of the heart. This results in blood congestion, reduced atrial function, and shortness of breath during exertion. The significant increase in the number of heart failure patients, along with the nonspecific symptoms and the complex differential diagnosis, the urgency to researching the disease mechanisms and treatments is underscored. This thesis investigates, through a biophysical computer model, how abnormalities in the heart muscle, particularly ventricular stiffness and reduced atrial function, influence the clinical presentation of the patient. The model reveals the relationship between these abnormalities and the typical heart failure symptoms at rest and during exertion. These insights have led to the development of an algorithm that can simulate the individual patient's heart. This so-called Digital Twin provides unique insight into the filling function of the heart and, consequently, the severity of heart failure. The research has also contributed to the development of a novel pacemaker therapy in heart failure with preserved ejection fraction. This thesis has demonstrated that computer models make a valuable contribution to both the understanding of heart failure and its detection. Furthermore, these models provide a platform for testing the effectiveness of various treatment methods.