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      Modeling individual differences in cardiovascular response to gravitational stress using a sensitivity analysis

      1 , 1 , 2
      Journal of Applied Physiology
      American Physiological Society

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          Abstract

          Computational modeling is used to predict cardiovascular responses to altered gravitational environments. However, considerable variation between subjects and model complexity makes accurate parameter assignment for individuals challenging. This computational effort studies sensitivity in cardiovascular model outcomes due to varying parameters across a normal physiological range. This allows determination of which parameters have the largest influence on outcomes, i.e., which parameters must be most carefully selected to give accurate predictions of individual responses.

          Abstract

          The human cardiovascular (CV) system elicits a physiological response to gravitational environments, with significant variation between different individuals. Computational modeling can predict CV response, however model complexity and variation of physiological parameters in a normal population makes it challenging to capture individual responses. We conducted a sensitivity analysis on an existing 21-compartment lumped-parameter hemodynamic model in a range of gravitational conditions to 1) investigate the influence of model parameters on a tilt test CV response and 2) to determine the subset of those parameters with the most influence on systemic physiological outcomes. A supine virtual subject was tilted to upright under the influence of a constant gravitational field ranging from 0 g to 1 g. The sensitivity analysis was conducted using a Latin hypercube sampling/partial rank correlation coefficient methodology with subsets of model parameters varied across a normal physiological range. Sensitivity was determined by variation in outcome measures including heart rate, stroke volume, central venous pressure, systemic blood pressures, and cardiac output. Results showed that model parameters related to the length, resistance, and compliance of the large veins and parameters related to right ventricular function have the most influence on model outcomes. For most outcome measures considered, parameters related to the heart are dominant. Results highlight which model parameters to accurately value in simulations of individual subjects’ CV response to gravitational stress, improving the accuracy of predictions. Influential parameters remain largely similar across gravity levels, highlighting that accurate model fitting in 1 g can increase the accuracy of predictive responses in reduced gravity.

          NEW & NOTEWORTHY Computational modeling is used to predict cardiovascular responses to altered gravitational environments. However, considerable variation between subjects and model complexity makes accurate parameter assignment for individuals challenging. This computational effort studies sensitivity in cardiovascular model outcomes due to varying parameters across a normal physiological range. This allows determination of which parameters have the largest influence on outcomes, i.e., which parameters must be most carefully selected to give accurate predictions of individual responses.

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          A methodology for performing global uncertainty and sensitivity analysis in systems biology.

          Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.
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            Body size and metabolism

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              Interpretation of the Correlation Coefficient: A Basic Review

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Journal of Applied Physiology
                Journal of Applied Physiology
                American Physiological Society
                8750-7587
                1522-1601
                June 01 2021
                June 01 2021
                : 130
                : 6
                : 1983-2001
                Affiliations
                [1 ]Department of Aerospace Engineering, Texas A&M University, College Station, Texas
                [2 ]Department of Health and Kinesiology, Texas A&M University, College Station, Texas
                Article
                10.1152/japplphysiol.00727.2020
                33914657
                51a536cc-eaaf-4669-bdf3-ba1dfd753fe4
                © 2021
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