  Computational Fluid Dynamics (CFD) is used to assist in designing artificial
valves and planning procedures, focusing on local flow features. However,
assessing the impact on overall cardiovascular function or predicting
longer-term outcomes may require more comprehensive whole heart CFD models.
Fitting such models to patient data requires numerous computationally expensive
simulations, and depends on specific clinical measurements to constrain model
parameters, hampering clinical adoption. Surrogate models can help to
accelerate the fitting process while accounting for the added uncertainty. We
create a validated patient-specific four-chamber heart CFD model based on the
Navier-Stokes-Brinkman (NSB) equations and test Gaussian Process Emulators
(GPEs) as a surrogate model for performing a variance-based global sensitivity
analysis (GSA). GSA identified preload as the dominant driver of flow in both
the right and left side of the heart, respectively. Left-right differences were
seen in terms of vascular outflow resistances, with pulmonary artery resistance
having a much larger impact on flow than aortic resistance. Our results suggest
that GPEs can be used to identify parameters in personalized whole heart CFD
models, and highlight the importance of accurate preload measurements.
