Case Studies: Lessons Learned from~Interesting Cases

Evans, Corey H.; Pyhel, H. Jack; White, Russell D.

doi:10.1007/978-0-387-76597-6_20

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Table 1 summarizes the phases of the test. The indications for the terminating test were heart rate measurements bigger than the 85% of the maximum heart rate [HR; the highest value of HR that can be attained and measured during incremental exercise, usually predicted as 220-age (7,27)], inability to maintain the imposed pedaling rate, the subject's desire to stop, and the recommendation of the medical staff or at the end of the test.…”

Section: Exercise Testmentioning

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: Exercise Testmentioning

confidence: 99%

“…Regarding the exercise, it also integrates a mechanism for the identification of the metabolic rate ratio that relates the level of exercise regarding the MRTCO 2 and MRTO 2 values, which are necessary for the respiratory regulation (see Eqs. [25][26][27][28].…”

Section: Gas Exchange Systemmentioning

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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“…The time was considered as the accumulated difference between the ages in each record, assigning the value zero to the first fit of the model for each database. The AT values were used because they account for the metabolic response of the subject to cardiorespiratory changes [48,52]. Except for time, the values of the inputs of the previous record were considered as the equivalent values in the record to be predicted.…”

Section: Dynamic Parameter Modelingmentioning

confidence: 99%

Dynamic Fitting Strategy for Physiological Models: A Case Study of Cardiorespiratory Model for Simulation of Incremental Aerobic Exercise

Sarmiento¹,

Serna²,

Mañanas³

2023

Preprint

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The use of mathematical models of physiological systems in medicine has allowed the development of diagnostic, treatment, and medical educational tools, but their application for predictive, preventive, and personalized purposes is restricted by their complexity. Although there are strategies that reduce the complexity of applying models by fitting techniques, they focus on a single instant of time, neglecting the effect of the system's temporal evolution. The aim of this work is to propose a dynamic fitting strategy of physiological models with large number of parameters and a constrained amount of experimental data, focused on obtaining better predictions based on the system's temporal trend and useful to predict future states. It was applied in a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects under aerobic exercise were used for fitting and validation. The model predictions obtained at steady-state using the proposed strategy and the nominal values of the parameters were compared. The best results corresponded mostly to the proposed strategy, mainly regarding the overall prediction error. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

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Case Studies: Lessons Learned from~Interesting Cases

Cited by 2 publications

References 6 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

Dynamic Fitting Strategy for Physiological Models: A Case Study of Cardiorespiratory Model for Simulation of Incremental Aerobic Exercise

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