Published in:
Open Access 01-12-2014 | Technical advance
Visualisation of time-varying respiratory system elastance in experimental ARDS animal models
Published in: BMC Pulmonary Medicine | Issue 1/2014
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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, E
drs
, 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 E
drs
was mapped across each breathing cycle for each subject.
Results
Six time-varying, breath-specific E
drs
maps were generated, one for each subject. Each E
drs
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 E
drs
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.