Identifying When Steady-State Flow Simulations In Patient-Specific Coronaries Recapitulate Pulsatile Flow Dynamics.

Computational models have emerged as a powerful tool to non-invasively monitor vital biomarkers in coronary artery disease, providing a viable alternative to conventional invasive methods and improving clinical decision-making. The precision of these in silico models, however, is often counter-balanced by their substantial computational cost. Pulsatile flow conditions, which closely mimic physiological conditions, are typically employed in computational fluid dynamics (CFD) simulations to capture dynamic changes in hemodynamic variables, but come at a significant computational cost. This study addresses the critical question of the necessity for complex pulsatile models versus the adequacy of simpler steady-state models to capture hemodynamic metrics such as fractional flow reserve, velocity, vorticity, and wall shear stress within the cardiac cycle. By comparing steady-state and pulsatile flow simulations in 12 patients with stenosed coronary arteries, our research evaluates whether steady-state simulations can accurately reflect patient-specific hemodynamic profiles at key clinical moments. The results indicate a comparability of metric magnitudes and distributions between the two types of simulation, particularly during diastole, with minimal influence from the choice of inlet waveform. This suggests that for metrics such as fractional flow reserve (FFR), steady-state simulations are sufficiently accurate and markedly reduce computational load, whereas pulsatile simulations may be necessary for capturing complex dynamics at systole. This distinction underscores the potential for selective application of steady-state models in clinical practice, facilitating the integration of computational fluid dynamics modeling by identifying when the reduced complexity of steady-state simulations can effectively support patient care in coronary artery disease.

Duke Scholars

Author Amanda Randles Biomedical Engineering