Key result
Personalized physics-based flow models yield hemodynamic biomarkers to improve cardiovascular decision-making and treatment planning.
Physics-based flow models offer valuable hemodynamic insights that complement traditional imaging and may improve cardiovascular disease management and treatment planning.
May enhance treatment planning; leaves open prospective validation before clinical adoption.
Personalized physics-based flow models are becoming increasingly important in cardiovascular medicine. They are a powerful complement to traditional methods of clinical decision-making and offer a wealth of physiological information beyond conventional anatomic viewing using medical imaging data. These models have been used to identify key hemodynamic biomarkers, such as pressure gradient and wall shear stress, which are associated with determining the functional severity of cardiovascular diseases. Importantly, simulation-driven diagnostics can help researchers understand the complex interplay between geometric and fluid dynamic parameters, which can ultimately improve patient outcomes and treatment planning. The possibility to compute and predict diagnostic variables and hemodynamics biomarkers can therefore play a pivotal role in reducing adverse treatment outcomes and accelerate development of novel strategies for cardiovascular disease management.
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Vardhan et al. (2021) conducted a review in Cardiovascular diseases. Physics-based flow models was evaluated. Personalized physics-based flow models provide key hemodynamic biomarkers to complement traditional clinical decision-making and improve treatment planning in cardiovascular medicine.
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