Practical identifiability and uncertainty quantification of a pulsatile cardiovascular model.

Autor: Marquis AD; University of Michigan, Ann Arbor, MI, USA; NC State University, Raleigh, NC, USA., Arnold A; NC State University, Raleigh, NC, USA; Worcester Polytechnic Institute, Worcester, MA, USA., Dean-Bernhoft C; Medical College of Wisconsin, Milwaukee, WI, USA., Carlson BE; University of Michigan, Ann Arbor, MI, USA., Olufsen MS; NC State University, Raleigh, NC, USA. Electronic address: msolufse@ncsu.edu.
Jazyk: angličtina
Zdroj: Mathematical biosciences [Math Biosci] 2018 Oct; Vol. 304, pp. 9-24. Date of Electronic Publication: 2018 Jul 11.
DOI: 10.1016/j.mbs.2018.07.001
Abstrakt: 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 workflow to calibrate a lumped-parameter model to left ventricular pressure and volume time series data. 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.
(Copyright © 2018. Published by Elsevier Inc.)
Databáze: MEDLINE
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