Global Sensitivity Analysis of Four Chamber Heart Hemodynamics Using Surrogate Models

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Abstract

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 requires 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.
Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
Publication statusAccepted/In press - 17 Mar 2022

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