Global Sensitivity Analysis of Four Chamber Heart Hemodynamics Using Surrogate Models

TL;DR

This study develops a patient-specific four-chamber heart CFD model and uses Gaussian Process Emulators to perform global sensitivity analysis, identifying key parameters influencing heart flow dynamics.

Contribution

The paper introduces the use of GPEs for efficient sensitivity analysis in personalized whole heart CFD models, enabling better parameter identification.

Findings

Preload is the main driver of flow in both heart sides.

Pulmonary artery resistance impacts flow more than aortic resistance.

GPEs effectively identify influential parameters in complex models.

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 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.