  Numerical models are increasingly used for non-invasive diagnosis and
treatment planning in coronary artery disease, where service-based technologies
have proven successful in identifying hemodynamically significant and hence
potentially dangerous vascular anomalies. Despite recent progress towards
clinical adoption, many results in the field are still based on a deterministic
characterization of blood flow, with no quantitative assessment of the
variability of simulation outputs due to uncertainty from multiple sources. In
this study, we focus on parameters that are essential to construct accurate
patient-specific representations of the coronary circulation, such as aortic
pressure waveform, intramyocardial pressure and quantify how their uncertainty
affects clinically relevant model outputs. We construct a deformable model of
the left coronary artery subject to a prescribed inlet pressure and with
open-loop outlet boundary conditions, treating fluid-structure interaction
through an Arbitrary-Lagrangian-Eulerian frame of reference. Random input
uncertainty is estimated directly from repeated clinical measurements from
intra-coronary catheterization and complemented by literature data. We also
achieve significant computational cost reductions in uncertainty propagation
thanks to multifidelity Monte Carlo estimators of the outputs of interest,
leveraging the ability to generate, at practically no cost, one- and
zero-dimensional low-fidelity representations of left coronary artery flow,
with appropriate boundary conditions. The results demonstrate how the use of
multi-fidelity control variate estimators leads to significant reductions in
variance and accuracy improvements with respect to traditional Monte-Carlo.
