About: A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection

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About: A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection
Creator	Chan, Timothy Slutsky, Arthur Goligher, Ewan Long, Michael Telias, Irene Brochard, J Duffin, James Georgopoulos, Dimitrios Laurent, · Ratano, Damian Schepens, Tom Zhang, Binghao
Source	PMC
abstract	Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid–base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patient’s transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO(2) respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10877-020-00479-x) contains supplementary material, which is available to authorized users.
has issue date	2020-02-01(xsd:dateTime)
bibo:doi	10.1007/s10877-020-00479-x
bibo:pmid	32008149
has license	no-cc
sha1sum (hex)	bb8fb14886cd53bd4c90185edf05eccc87a6afeb
schema:url	https://doi.org/10.1007/s10877-020-00479-x
resource representing a document's title	A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection
has PubMed Central identifier	PMC7224026
has PubMed identifier	32008149
schema:publication	J Clin Monit Comput
resource representing a document's body	covid:bb8fb14886cd53bd4c90185edf05eccc87a6afeb#body_text
is schema:about of	named entity 'Lung' named entity 'reflect' named entity 'patient' named entity 'model' named entity 'model' named entity 'incorporates' named entity 'physiological' named entity 'respiratory' named entity 'model' named entity 'evaluate' named entity 'safety' named entity 'response' named entity 'respiratory drive' named entity 'sedation' named entity 'global optimization' named entity 'Mechanical ventilation' named entity 'physiological' named entity 'sedation' named entity 'ventilator' named entity 'lung' named entity 'ventilator' named entity 'acute respiratory failure' named entity 'response curve' named entity 'tidal volume' named entity 'lung' named entity 'ventilator support' named entity 'tidal volume' named entity 'cerebral blood flow' named entity 'ventilatory support' named entity 'oxygen' named entity 'diaphragm' named entity 'physiologically-based' named entity 'steady-state' named entity 'physiological' named entity 'receptor' named entity '4.2' named entity 'CSF' named entity 'respiratory drive' named entity 'respiratory muscle' named entity 'alveolar' named entity 'central respiratory drive' named entity 'Lung' named entity 'bicarbonate' named entity 'Respiratory drive' named entity 'metabolic' named entity 'dead space' named entity 'PSV' named entity 'respiratory frequency' named entity 'receptor' named entity 'chemoreceptors' named entity 'tidal volume' named entity 'linear regression' named entity 'pharmacokinetic' named entity 'hyperbola' named entity 'minute ventilation' named entity 'hypoxia' named entity 'assisted ventilation' named entity 'dead space' named entity 'physiology' named entity 'bounding box' named entity 'propofol' named entity 'CO2' named entity 'carbonate' named entity 'oxygen consumption' named entity 'respiratory frequency' named entity 'ventilator settings' named entity 'Industrial Engineering' named entity 'chemoreflex' named entity '1, 2' named entity 'acid-base'

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