Hemodynamics comparison of an hour-long rest and activity state data in a human coronary digital twin

Although it is well established that hemodynamics can significantly influence the location and progression of cardiovascular disease (CVD), and 3D blood flow metrics are recognized as potential diagnostic indicators of these diseases, the dynamics of these metrics over time and their variation at different activity levels are not well understood. A relevant example is the impact of exercise on the vascular system over extended periods. Although exercise is widely recognized as a preventive measure of heart disease, the specifics of how activity levels and subsequent alterations in blood flow contribute to the mechanisms that drive CVD remain unclear. In this study, we used a digital coronary twin to establish a longitudinal hemodynamic map (LHM) of the rest and exercise states. An hour-long dataset for both the rest and exercise states, acquired from a wearable device for a single patient, was used to drive a complex 3D fluid dynamics simulation. Hemodynamic metrics such as maximum velocity, average velocity, maximum wall shear stress, average wall shear stress, time-averaged wall shear stress, and pressure gradient were compared between the two states. This analysis represents an initial step toward understanding how long-term exercise regimens can influence hemodynamic changes and potentially reduce the risk of cardiovascular disease. Our findings revealed that the maximum wall shear stress exhibited the highest sensitivity to changes in activity level, while the pressure gradient showed the least variability. This study contributes significantly to quantifying how 3D blood flow metrics differ between rest and active states, providing valuable insight regarding exercise-induced hemodynamic alterations and their potential role in mitigating CVD risk.