Estimation of arterial from a lung model during ramp exercise in healthy young subjects

Thomas, Vincent; Costes, Frédéric; Busso, Thierry

doi:10.1016/j.resp.2006.11.004

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…Equation 1 allows for estimating PACO 2 in mmHg from the value of PETCO 2 in mmHg and VT in ml. This expression is the result of a study focused on the evaluation of the difference between PETO 2 and PACO 2 under an exercise test (32) and has been applied in different works (33).…”

Section: Estimation Of Partial Pressures Of Alveolar Gassesmentioning

confidence: 99%

“…It is highlighted that both equations provide approximations to physiological variables that are difficult to measure experimentally, that have been implemented in different works related to exercise (7,28,31,33,34), and were selected as a result of a comprehensive review and comparison regarding the expected for the experimental conditions.…”

Section: Estimation Of Partial Pressures Of Alveolar Gassesmentioning

confidence: 99%

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An integrated mathematical model of the cardiovascular and respiratory response to exercise: model-building and comparison with reported models

Sarmiento

Serna

Mañanas

2021

American Journal of Physiology-Heart and Circulatory Physiology

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The use of physiological models in medicine allows the evaluation of new hypotheses, development of diagnosis and clinical treatment applications, development of training and medical education tools, as well as medical device design. Although several mathematical models of physiological systems have been presented in the literature, few of them are able to predict the human cardiorespiratory response under physical exercise stimulus adequately. This paper aims to present the building and comparison of an integrated cardiorespiratory model focused on the prediction of the healthy human response under rest and aerobic exercise. The model comprises cardiovascular circulation, respiratory mechanics, gas exchange system, as well as cardiovascular and respiratory controllers. Every system is based on previously reported physiological models and incorporates reported mechanisms related to the aerobic exercise dynamics. Experimental data of thirty healthy male volunteers undergoing a cardiopulmonary exercise test and simulated data from two of the most current and complete cardiorespiratory models were used to evaluate the performance of the presented model. Experimental design, processing, and exploratory analysis are described in detail. The simulation results were compared against the experimental data in steady-state and in transient regime. The predictions of the proposed model closely mimic the experimental data, showing in overall the lowest prediction error (10.35%), the lowest settling times for cardiovascular and respiratory variables, and in general, the fastest and similar responses in transient regime. These results suggest that the proposed model is suitable to predict the cardiorespiratory response of healthy adult humans under rest and aerobic exercise conditions.

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Section: Estimation Of Partial Pressures Of Alveolar Gassesmentioning

confidence: 99%

Section: Estimation Of Partial Pressures Of Alveolar Gassesmentioning

confidence: 99%

An integrated mathematical model of the cardiovascular and respiratory response to exercise: model-building and comparison with reported models

Sarmiento

Serna

Mañanas

2021

American Journal of Physiology-Heart and Circulatory Physiology

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“…Partial pressure in the alveolar compartments were obtained from values suggested by [39, 40]. These values reflect magnitudes that are between values in inspired air and standard values reported in the lung.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Patient-specific modeling of cardiovascular and respiratory dynamics during hypercapnia

Ellwein

Pope

Xie

et al. 2013

Mathematical Biosciences

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This study develops a lumped cardiovascular-respiratory system-level model that incorporates patient-specific data to predict cardiorespiratory response to hyper-capnia (increased CO2 partial pressure) for a patient with congestive heart failure (CHF). In particular, the study focuses on predicting cerebral CO2 reactivity, which can be defined as the ability of vessels in the cerebral vasculature to expand or contract in response CO2 induced challenges. It is difficult to characterize cerebral CO2 reactivity directly from measurements, since no methods exist to dynamically measure vasomotion of vessels in the cerebral vasculature. In this study we show how mathematical modeling can be combined with available data to predict cerebral CO2 reactivity via dynamic predictions of cerebral vascular resistance, which can be directly related to vasomotion of vessels in the cerebral vasculature. To this end we have developed a coupled cardiovascular and respiratory model that predicts blood pressure, flow, and concentration of gasses (CO2 and O2) in the systemic, cerebral, and pulmonary arteries and veins. Cerebral vascular resistance is incorporated via a model parameter separating cerebral arteries and veins. The model was adapted to a specific patient using parameter estimation combined with sensitivity analysis and subset selection. These techniques allowed estimation of cerebral vascular resistance along with other cardiovascular and respiratory parameters. Parameter estimation was carried out during eucapnia (breathing room air), first for the cardiovascular model and then for the respiratory model. Then, hypercapnia was introduced by increasing inspired CO2 partial pressure. During eucapnia, 7 cardiovascular parameters and 4 respiratory parameters was be identified and estimated, including cerebral and systemic resistance. During the transition from eucapnia to hypercapnia, the model predicted a drop in cerebral vascular resistance consistent with cerebral vasodilation.

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“…In [23] a two-compartment model of lung is coupled to a reconstruction of alveolar gas composition: this allows to estimate non-invasively arterial carbon dioxide partial pressure, this quantity being useful to appreciate the efficiency of breathing and the control of breathing during exercise.…”

Section: Gas Exchange Modellingmentioning

confidence: 99%

Impact of respiratory mechanics model parameters on gas exchange efficiency

et al. 2008

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In this article, we are interested in the impact of the mechanical characteristics of the bronchial tree and lung parenchyma on the gas exchange capacity of the human lungs. * The authors would like to thank C. Grandmont for the many interesting discussions on the subject. * * This work has been partially supported by the ANR project "Transport et Transfert via le Système Pulmonaire Humain" and by the ACINIM project "LePoumonVousDisJe".

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Estimation of arterial from a lung model during ramp exercise in healthy young subjects

Cited by 6 publications

References 15 publications

An integrated mathematical model of the cardiovascular and respiratory response to exercise: model-building and comparison with reported models

An integrated mathematical model of the cardiovascular and respiratory response to exercise: model-building and comparison with reported models

Patient-specific modeling of cardiovascular and respiratory dynamics during hypercapnia

Impact of respiratory mechanics model parameters on gas exchange efficiency

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