Uncertainty quantification for simulating coronary artery hemodynamics in aneurysms caused by kawasaki disease.

This study applies an Uncertainty Quantification (UQ) framework to assess the reliability of cardiovascular simulation about coronary artery aneurysms (CAAs) caused by Kawasaki Disease (KD) for advancing clinical decision-making. The objective is to evaluate the impact of uncertainties in hemodynamic metrics, including Wall Shear Stress (WSS), Residence Time (RT), and Fractional Flow Reserve (FFR). Three patient-specific aorto-coronary anatomic models were used to perform computational fluid dynamics (CFD) simulations. A reduced-order sub-modeling approach was utilized to reduce computational costs. Uncertainties were introduced to input parameters: cardiac output, inflow waveform, in-plane velocity distribution, and intramyocardial pressure. Time-varying signals were perturbed using the Karhunen-Loève expansion. 100 samples per each patient were obtained, assuming standard distributions for input parameters. Sensitivity analysis was conducted to determine the contribution of each parameter to output variability. A 20 % uncertainty in cardiac output and a perturbed inflow waveform with a 7 % process variance caused variability in WSS and RT of 8 % to 35 %. Sensitivity analysis revealed that cardiac output had the most significant impact, contributing over 52 % to output variability, while the inflow waveform contributed 20-30 %. The in-plane velocity distribution influenced WSS and RT by around 10 % but showed varying contributions to FFR -3 % to 27 %. Intramyocardial pressure had a negligible effect. This study is the first to apply UQ to KD-related CAA simulations, driven by clinical needs, with extensive investigations into the uncertain input parameters. The findings highlight cardiac output as the key factor in hemodynamic variability. It emphasizes the need for precise clinical data to enhance simulation-based predictions, particularly in managing CAAs in KD patients.