Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty.

Area of Science:

• Cardiovascular Physiology
• Computational Modeling
• Biomedical Engineering

Background:

• Lumped parameter network (LPN) models offer a computationally efficient method to simulate cardiovascular physiology.
• Accurate patient-specific parameter identification is crucial for maximizing the clinical utility of complex LPN models.
• Current methods for parameter tuning often require manual adjustments and may not fully account for clinical data variability.

Purpose of the Study:

• To develop and validate a framework for automated parameter identification in 0D lumped cardiovascular models.
• To match model parameters with non-invasively obtained clinical data for improved patient-specific simulations.
• To assess the framework's performance in a cohort of pediatric patients with single ventricle physiology and Norwood procedure.

Main Methods:

• A framework combining local identifiability analysis, Bayesian estimation, and maximum a posteriori simplex optimization was developed.
• The automated tuning framework was applied to virtual patient data and subsequently to clinical data from four single ventricle patients.
• Multi-level estimation, involving sub-model analysis to update parameter prior information, was investigated to reduce uncertainty.

Main Results:

• The framework successfully determined physiologically consistent point estimates for LPN model parameters.
• Quantification of parameter uncertainty was achieved, reflecting errors and assumptions in clinical data.
• Multi-level estimation significantly reduced the parameter marginal posterior variance, indicating improved model accuracy.

Conclusions:

• Automated parameter identification enhances the clinical applicability of cardiovascular LPN models.
• The developed framework provides a robust method for tuning patient-specific parameters using clinical data.
• This approach holds promise for improving treatment planning and decision-making in pediatric cardiovascular medicine, particularly for single ventricle patients.