Identification of a Non-Linear Model as a New Method to Detect Expiratory Airflow Limitation in Mechanically Ventilated Patients View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-12

AUTHORS

S. Khirani, L. Biot, P. Lavagne, A. Duguet, T. Similowski, P. Baconnier

ABSTRACT

Expiratory flow limitation (EFL) can occur in mechanically ventilated patients with chronic obstructive pulmonary disease and other disorders. It leads to dynamic hyperinflation with ensuing deleterious consequences. Detecting EFL is thus clinically relevant. Easily applicable methods however lack this detection being routinely made in intensive care. Using a simple mathematical model, we propose a new method to detect EFL that does not require any intervention or modification of the ongoing therapeutic. The model consists in a monoalveolar representation of the respiratory system, including a collapsible airway that is submitted to periodic changes in pressure at the airway opening: EFL provokes a sharp expiratory increase in the resistance Rc of the collapsible airway. The model parameters were identified via the Levenberg-Marquardt method by fitting simulated data on the airway pressure and the flow signals recorded in 10 mechanically ventilated patients. A sensitivity study demonstrated that only 8/11 parameters needed to be identified, the remaining three being given reasonable physiological values. Flow-volume curves built at different levels of positive expiratory pressure, PEEP, during "PEEP trials" (stepwise increases in positive end-expiratory pressure to optimize ventilator settings) have shown evidence of EFL in three cases. This was concordant with parameter identification (high Rc during expiration for EFL patients). We conclude from these preliminary results that our model is a potential tool for the non-invasive detection of EFL in mechanically ventilated patients. More... »

PAGES

241-254

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/b:acbi.0000046596.43503.36

DOI

http://dx.doi.org/10.1023/b:acbi.0000046596.43503.36

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1000854054

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15520532


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