Dataset Information

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

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 CO2 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.

SUBMITTER: Zhang B

PROVIDER: S-EPMC7224026 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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

Zhang Binghao B Ratano Damian D Brochard Laurent J LJ Georgopoulos Dimitrios D Duffin James J Long Michael M Schepens Tom T Telias Irene I Slutsky Arthur S AS Goligher Ewan C EC Chan Timothy C Y TCY

Journal of clinical monitoring and computing 20200201 2

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 incorp ...[more]

PMID: 32008149

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Project description:BACKGROUND:Lung-protective ventilation (LPV) has been found to minimize the risk of ventilator-induced lung injury (VILI). However, whether LPV is able to diminish ventilator-induced diaphragm dysfunction (VIDD) remains unknown. This study was designed to test the hypothesis that LPV protects the diaphragm against VIDD. METHODS:Adult male Wistar rats received either conventional mechanical (tidal volume [VT]: 10 ml/kg, positive end-expiratory pressure [PEEP]: 2 cm H2O; CV group) or lung-protective (VT: 5 ml/kg, PEEP: 10 cm H2O; LPV group) ventilation for 12 h. Then, diaphragms and lungs were collected for biochemical and histological analyses. Transcriptome sequencing (RNA-seq) was performed to determine the differentially expressed genes in the diaphragms between groups. RESULTS:Our results suggested that LPV was associated with diminished pulmonary injuries and reduced oxidative stress compared with the effects of the CV strategy in rats. However, animals that received LPV showed increased protein degradation, decreased cross-sectional areas (CSAs) of myofibers, and reduced forces of the diaphragm compared with the same parameters in animals receiving CV (p < 0.05). In addition, the LPV group showed a higher level of oxidative stress in the diaphragm than the CV group (p < 0.05). Moreover, RNA-seq and western blots revealed that the peroxisome proliferator-activated receptor γ coactivator-1alpha (PGC-1α), a powerful reactive oxygen species (ROS) inhibitor, was significantly downregulated in the LPV group compared with its expression in the CV group (p < 0.05). CONCLUSIONS:Compared with the CV strategy, the LPV strategy did not protect the diaphragm against VIDD in rats. In contrast, the LPV strategy worsened VIDD by inducing oxidative stress together with the downregulation of PGC-1α in the diaphragm. However, further studies are required to determine the roles of PGC-1α in ventilator-induced diaphragmatic oxidative stress.

Project description:Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome.

Project description:A variety of pulmonary insults can prompt the need for life-saving mechanical ventilation; however, misuse, prolonged use, or an excessive inflammatory response, can result in ventilator-induced lung injury. Past research has observed an increased instance of respiratory distress in older patients and differences in the inflammatory response. To address this, we performed high pressure ventilation on young (2-3 months) and old (20-25 months) mice for 2 hours and collected data for macrophage phenotypes and lung tissue integrity. Large differences in macrophage activation at baseline and airspace enlargement after ventilation were observed in the old mice. The experimental data was used to determine plausible trajectories for a mathematical model of the inflammatory response to lung injury which includes variables for the innate inflammatory cells and mediators, epithelial cells in varying states, and repair mediators. Classification methods were used to identify influential parameters separating the parameter sets associated with the young or old data and separating the response to ventilation, which was measured by changes in the epithelial state variables. Classification methods ranked parameters involved in repair and damage to the epithelial cells and those associated with classically activated macrophages to be influential. Sensitivity results were used to determine candidate in-silico interventions and these interventions were most impact for transients associated with the old data, specifically those with poorer lung health prior to ventilation. Model results identified dynamics involved in M1 macrophages as a focus for further research, potentially driving the age-dependent differences in all macrophage phenotypes. The model also supported the pro-inflammatory response as a potential indicator of age-dependent differences in response to ventilation. This mathematical model can serve as a baseline model for incorporating other pulmonary injuries.

Dataset Information

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

Publications

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

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