Patient asynchrony modelling during controlled mechanical ventilation therapy
Background and objective: Mechanical ventilation therapy of respiratory failure patients can be guided by monitoring patient-specific respiratory mechanics. However, the patient’s spontaneous breathing effort during controlled ventilation changes airway pressure waveform and thus affects the model...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English English |
| Published: |
Elsevier Ireland Ltd
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/1/Computer%20methods%20and%20programme%20in%20biomedicine_asynchrony_2019.pdf http://irep.iium.edu.my/76234/8/76234_Patient%20asynchrony%20modelling%20during%20controlled_scopus.pdf http://irep.iium.edu.my/76234/7/76234_Patient%20asynchrony%20modelling%20during%20controlled_wos.pdf |
| Summary: | Background and objective: Mechanical ventilation therapy of respiratory failure patients can be guided by
monitoring patient-specific respiratory mechanics. However, the patient’s spontaneous breathing effort
during controlled ventilation changes airway pressure waveform and thus affects the model-based identification of patient-specific respiratory mechanics parameters. This study develops a model to estimate
respiratory mechanics in the presence of patient effort.
Methods: Gaussian effort model (GEM) is a derivative of the single-compartment model with basis function. GEM model uses a linear combination of basis functions to model the nonlinear pressure waveform
of spontaneous breathing patients. The GEM model estimates respiratory mechanics such as Elastance and
Resistance along with the magnitudes of basis functions, which accounts for patient inspiratory effort.
Results and discussion: The GEM model was tested using both simulated data and a retrospective observational clinical trial patient data. GEM model fitting to the original airway pressure waveform is better
than any existing models when reverse triggering asynchrony is present. The fitting error of GEM model
was less than 10% for both simulated data and clinical trial patient data.
Conclusion: GEM can capture the respiratory mechanics in the presence of patient effect in volume control ventilation mode and also can be used to assess patient-ventilator interaction. This model determines
basis functions magnitudes, which can be used to simulate any waveform of patient effort pressure for
future studies. The estimation of parameter identification GEM model can further be improved by constraining the parameters within a physiologically plausible range during least-square nonlinear regression. |
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