Respiratory Control: A Modeling Perspective
Mark Ansermino. Author information Article notes Copyright and License information Disclaimer. Specialty section: This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Bioengineering and Biotechnology. Received Aug 10; Accepted Jan The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.
No use, distribution or reproduction is permitted which does not comply with these terms. This article has been cited by other articles in PMC. Abstract The goal of this study is to derive a minimally complex but credible model of respiratory CO 2 gas exchange that may be used in systematic design and pilot testing of closed-loop end-tidal CO 2 controllers in mechanical ventilation. Keywords: respiratory CO 2 gas exchange, data-based modeling, closed-loop mechanical ventilation control.
Introduction It is anticipated that autonomous closed-loop controlled mechanical ventilators will be increasingly used in the future to enhance the safety of mechanical ventilation. Table 1 Preclinical testing of closed-loop mechanical ventilation controllers reported in the literature. Open in a separate window. Respiratory Co 2 Gas Exchange Model Model Structure Selection Respiratory CO 2 gas exchange process during mechanical ventilation involves the lungs, arteries, and veins as well as body tissues Grodins et al.
Figure 1. Model Identification Since Eq. Comparative Model Analysis The three candidate models were compared in terms of predictive capability and reliability. Results and Discussion A parsimonious and credible model of respiratory mechanics and physiology may expedite the design, analysis, testing, and deployment of closed-loop mechanical ventilation controllers.
Data On the average, Figure 2. Predictive Capability and Reliability Overall, the two-compartment models M2 and M3 exhibited superior predictive accuracy than their one-compartment counterpart M1. Figure 3. Table 4 Parameters identified from the data-based modeling analysis [median IQR ]. Limitation and Future Perspectives This study has a number of limitations that need to be addressed in follow-up studies. Conclusion and Future Work In our effort to derive a minimally complex, high-fidelity, and transparent data-based respiratory physiology model applicable to the design and pilot testing of closed-loop end-tidal CO 2 controllers, we conducted a comparative data-based modeling analysis of the respiratory CO 2 gas exchange process.
Author Contributions All authors listed have made substantial, direct, and intellectual contribution to the work and approved it for publication. Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Footnotes 1 The weights of the pediatric subjects were not available in the anonymized data. References Anderson J. Modeling soluble gas exchange in the airways and alveoli. Current and projected workforce requirements for care of the critically ill and patients with pulmonary disease: can we meet the requirements of an aging population?
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Respiratory Control - A Modeling Perspective | F.S. Grodins | Springer
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Cost-utility of a disease management program for patients with asthma. Download references. YYV collected, analyzed, and interpreted the data regarding economic evaluation and was a major contributor in writing the manuscript. AAS provided guidance in the whole process of economic evaluation, reviewed and edited the manuscript. Both authors read and approved the final manuscript. Professor Henry Glick of University of Pennsylvania provided support and guidance in calculating transition probabilities for composite asthma control and medication adherence Health states.
Professor David Price of Observational and Pragmatic Research Institute provided invaluable support in providing resources for some of the data input model. We would like to thank the Director General of Health Malaysia for his permission to publish this article. These findings are the result of work supported by the Ministry of Health Malaysia. The views expressed in this paper are those of the authors, and no official endorsement by the Ministry of Health is intended or should be inferred.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Correspondence to Asrul Akmal Shafie. Reprints and Permissions. Search all BMC articles Search. Methods A lifetime horizon dynamic adherence Markov model with monthly cycle was developed, for quality-adjusted life year QALY gained and hospitalization averted outcomes.
Background Asthma is a heterogeneous chronic respiratory disease that is usually characterized by chronic airway inflammation. Model structure It was decided to develop a new model although there were a few existing Markov models in the literature. Full size image. References 1. Article Google Scholar 2. Article Google Scholar 3. Article Google Scholar 4. Article Google Scholar 6.
Article Google Scholar 8. Google Scholar 9. Article Google Scholar CAS Google Scholar Google Scholar Acknowledgements Professor Henry Glick of University of Pennsylvania provided support and guidance in calculating transition probabilities for composite asthma control and medication adherence Health states. Competing interests The authors declare that they have no competing interests. Consent for publication We would like to thank the Director General of Health Malaysia for his permission to publish this article.
Buy Hardcover. Buy Softcover. FAQ Policy. About this book Breathing is performed by the rhythmic contraction of respiratory muscles. Show all. Pages Ishiguro, Makio et al. Pages Ono, Kenichi et al. Richard et al. Pages Cathcart, Andrew J.