Practical Identifiability and Uncertainty Quantification of a Pulsatile Cardiovascular Model

TL;DR

This study develops a methodology for calibrating a cardiovascular model, identifying key parameters, and quantifying uncertainty to improve prediction accuracy using experimental data from rats.

Contribution

The paper introduces a novel approach combining sensitivity analysis, optimization, and UQ for parameter estimation in cardiovascular models.

Findings

Identified 5 key model parameters from rat data.

UQ intervals effectively capture measurement and model errors.

Method improves model calibration and predictive capability.

Abstract

Mathematical models are essential tools to study how the cardiovascular system maintains homeostasis. The utility of such models is limited by the accuracy of their predictions, which can be determined by uncertainty quantification (UQ). A challenge associated with the use of UQ is that many published methods assume that the underlying model is identifiable (e.g. that a one-to-one mapping exists from the parameter space to the model output). In this study we present a novel methodology that is used here to calibrate a lumped-parameter model to left ventricular pressure and volume time series data sets. Key steps include using (1) literature and available data to determine nominal parameter values; (2) sensitivity analysis and subset selection to determine a set of identifiable parameters; (3) optimization to find a point estimate for identifiable parameters; and (4) frequentist and Bayesian UQ calculations to assess the predictive capability of the model. Our results show that it is possible to determine 5 identifiable model parameters that can be estimated to our experimental data from three rats, and that computed UQ intervals capture the measurement and model error.