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    Computational Fluid Dynamics (CFD) models of the whole heart can predict cardiovascular function. Gaussian Process Emulators (GPEs) accelerate whole heart CFD model fitting, identifying preload as a key factor influencing heart flow dynamics.

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    Area of Science:

    • Biomedical Engineering
    • Computational Science
    • Cardiovascular Physiology

    Background:

    • Computational Fluid Dynamics (CFD) aids in designing artificial heart valves and planning procedures by analyzing local flow dynamics.
    • Comprehensive whole heart CFD models are needed to assess cardiovascular function and predict long-term outcomes.
    • Fitting patient-specific whole heart CFD models is computationally intensive and requires specific clinical data, limiting clinical use.

    Purpose of the Study:

    • To develop and validate a patient-specific four-chamber heart CFD model using Navier-Stokes-Brinkman (NSB) equations.
    • To evaluate Gaussian Process Emulators (GPEs) as surrogate models for accelerating the fitting of whole heart CFD models.
    • To perform a variance-based global sensitivity analysis (GSA) to identify key parameters influencing heart function.

    Main Methods:

    • Creation of a validated patient-specific four-chamber heart CFD model.
    • Implementation of Gaussian Process Emulators (GPEs) as surrogate models.
    • Application of variance-based global sensitivity analysis (GSA) to the CFD model.

    Main Results:

    • Global sensitivity analysis identified preload as the primary determinant of blood flow in both the right and left sides of the heart.
    • Vascular outflow resistances showed left-right differences, with pulmonary artery resistance significantly impacting flow more than aortic resistance.
    • Gaussian Process Emulators effectively identified critical parameters within personalized whole heart CFD models.

    Conclusions:

    • Gaussian Process Emulators are valuable tools for parameter identification in personalized whole heart CFD models.
    • Accurate measurement of preload is crucial for reliable whole heart CFD modeling and cardiovascular function prediction.
    • This approach enhances the potential for clinical adoption of advanced CFD modeling in cardiology.