Integrating Uncertainty Quantification into Computational Fluid Dynamics Models of Coronary Arteries Under Steady Flow.

Computational fluid dynamics (CFD) simulations are increasingly being integrated into clinical medicine, where they have the potential to support clinicians in disease diagnosis, prognosis, and treatment. However, these models frequently use deterministic approaches, neglecting inherent variability (or uncertainty) in input parameters, thereby undermining model credibility and limiting clinical adoption. Herein, we integrate modern and certifiable uncertainty quantification techniques to characterize and quantify the variability in coronary artery wall shear stress (WSS) under steady-flow conditions due to intrinsic uncertainty in model-dependent quantities. Univariate probability distributions were fitted to hemodynamic parameters (density, pressure, radius, velocity, and viscosity), and sampled parameter ensembles were applied to an analytical solution (Poiseuille flow) and a patient-specific coronary artery model. Results from the analytical solution demonstrated that variability in input parameters propagated into uncertainty in WSS values, with uncertainty in velocity accounting for the majority (∼81%) of WSS variability. In the patient-specific model, spatial medians in WSS varied by ∼50% due to input parameter uncertainties, with viscosity (∼59%) and velocity (∼40%) emerging as the dominant contributors to WSS variability. Across each use case, unary interactions dominated (i.e., first-order Sobol indices accounted for the majority of the variance), contributing to ∼93% and ∼99% of the total WSS variance in the analytical and patient-specific model, respectively. Collectively, this study establishes an uncertainty-aware framework to strengthen computational biomechanics model credibility, aligning with emerging regulatory guidance and enabling more trustworthy modeling-based decision support in the management of coronary artery disease.