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Visualisation of time-varying respiratory system elastance in experimental ARDS animal models.

van Drunen EJ, Chiew YS, Pretty C, Shaw GM, Lambermont B, Janssen N, Chase JG, Desaive T - BMC Pulm Med (2014)

Bottom Line: These are the first such maps generated and they thus show unique results in high resolution.The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases.However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Liège, Liège, Belgium. tdesaive@ulg.ac.be.

ABSTRACT

Background: Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection.

Methods: The single compartment lung model was extended to monitor dynamic time-varying respiratory system elastance, Edrs, within each breathing cycle. Two separate animal models were considered, each consisting of three fully sedated pure pietrain piglets (oleic acid ARDS and lavage ARDS). A staircase recruitment manoeuvre was performed on all six subjects after ARDS was induced. The Edrs was mapped across each breathing cycle for each subject.

Results: Six time-varying, breath-specific Edrs maps were generated, one for each subject. Each Edrs map shows the subject-specific response to mechanical ventilation (MV), indicating the need for a model-based approach to guide MV. This method of visualisation provides high resolution insight into the time-varying respiratory mechanics to aid clinical decision making. Using the Edrs maps, minimal time-varying elastance was identified, which can be used to select optimal PEEP.

Conclusions: Real-time continuous monitoring of in-breath mechanics provides further insight into lung physiology. Therefore, there is potential for this new monitoring method to aid clinicians in guiding MV treatment. These are the first such maps generated and they thus show unique results in high resolution. The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases. However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability.

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Related in: MedlinePlus

Variation in Edrsacross a normalised breath during a RM for Subject 1 (PaO2/FiO2 = 126.6 mmHg). The change in airway pressure for each normalised breathing cycle is shown in grey.
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Figure 1: Variation in Edrsacross a normalised breath during a RM for Subject 1 (PaO2/FiO2 = 126.6 mmHg). The change in airway pressure for each normalised breathing cycle is shown in grey.

Mentions: Each subject has approximately 160 to 360 breathing cycles over the course of the RM. All breathing cycles are normalised to their total inspiratory time to provide clarity and to ensure consistency between breaths with different inspiratory times. Thus, each breath effectively begins at 0% and ends at 100% of the total inspiration time. The time-varying, breath-specific Edrs map of the RM for Subjects 1-6 are shown in Figures 1, 2, 3, 4, 5 and 6 respectively, where blue indicates low Edrs and red indicates high Edrs. The corresponding airway pressure and PEEP are shown in grey. The PF ratio for each subject is stated in the corresponding figure caption. Each figure is also provided with a MATLAB figure in a compressed zip file to permit rotation. The Edrs and Rrs for each subject over the course of the RM is also shown within each MATLAB figure file (see Additional file 1). The top view of all Edrs maps are shown in Additional file 2.


Visualisation of time-varying respiratory system elastance in experimental ARDS animal models.

van Drunen EJ, Chiew YS, Pretty C, Shaw GM, Lambermont B, Janssen N, Chase JG, Desaive T - BMC Pulm Med (2014)

Variation in Edrsacross a normalised breath during a RM for Subject 1 (PaO2/FiO2 = 126.6 mmHg). The change in airway pressure for each normalised breathing cycle is shown in grey.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016000&req=5

Figure 1: Variation in Edrsacross a normalised breath during a RM for Subject 1 (PaO2/FiO2 = 126.6 mmHg). The change in airway pressure for each normalised breathing cycle is shown in grey.
Mentions: Each subject has approximately 160 to 360 breathing cycles over the course of the RM. All breathing cycles are normalised to their total inspiratory time to provide clarity and to ensure consistency between breaths with different inspiratory times. Thus, each breath effectively begins at 0% and ends at 100% of the total inspiration time. The time-varying, breath-specific Edrs map of the RM for Subjects 1-6 are shown in Figures 1, 2, 3, 4, 5 and 6 respectively, where blue indicates low Edrs and red indicates high Edrs. The corresponding airway pressure and PEEP are shown in grey. The PF ratio for each subject is stated in the corresponding figure caption. Each figure is also provided with a MATLAB figure in a compressed zip file to permit rotation. The Edrs and Rrs for each subject over the course of the RM is also shown within each MATLAB figure file (see Additional file 1). The top view of all Edrs maps are shown in Additional file 2.

Bottom Line: These are the first such maps generated and they thus show unique results in high resolution.The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases.However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Liège, Liège, Belgium. tdesaive@ulg.ac.be.

ABSTRACT

Background: Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection.

Methods: The single compartment lung model was extended to monitor dynamic time-varying respiratory system elastance, Edrs, within each breathing cycle. Two separate animal models were considered, each consisting of three fully sedated pure pietrain piglets (oleic acid ARDS and lavage ARDS). A staircase recruitment manoeuvre was performed on all six subjects after ARDS was induced. The Edrs was mapped across each breathing cycle for each subject.

Results: Six time-varying, breath-specific Edrs maps were generated, one for each subject. Each Edrs map shows the subject-specific response to mechanical ventilation (MV), indicating the need for a model-based approach to guide MV. This method of visualisation provides high resolution insight into the time-varying respiratory mechanics to aid clinical decision making. Using the Edrs maps, minimal time-varying elastance was identified, which can be used to select optimal PEEP.

Conclusions: Real-time continuous monitoring of in-breath mechanics provides further insight into lung physiology. Therefore, there is potential for this new monitoring method to aid clinicians in guiding MV treatment. These are the first such maps generated and they thus show unique results in high resolution. The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases. However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability.

Show MeSH
Related in: MedlinePlus