Uncertainty quantification for assessing variabilities in simulations of coronary artery aneurysms caused by Kawasaki disease

We apply an uncertainty quantification (UQ) framework to evaluate the reliability of patient-specific cardiovascular simulations of coronary artery aneurysms (CAAs) caused by Kawasaki Disease (KD). The study focuses on the impact of uncertain input parameters—cardiac output, inflow waveform, in-plane velocity distribution, and intramyocardial pressure—on key hemodynamic metrics: wall shear stress (WSS), residence time (RT), and fractional flow reserve (FFR). Three patient-specific aorto-coronary geometries were used in computational fluid dynamics (CFD) simulations, incorporating reduced-order sub-modeling to improve efficiency. Time-varying signals were perturbed via Karhunen–Loève expansion, generating 100 realizations per case. Results show that a 20% uncertainty in cardiac output and 7% waveform variance yield 8–35% variability in WSS and RT. Sensitivity analysis identified cardiac output as the dominant factor (>52% contribution), followed by inflow waveform (20–30%), with lesser influence from in-plane velocity distribution (∼10%) and negligible effect from intramyocardial pressure. This is the first UQ application to KD-related CAA simulations, providing insight into input-driven variability and emphasizing the importance of accurate clinical data for improving simulation-guided decision-making.