High frequency percussive ventilation (HFPV) is a non-conventional ventilatory modality which has provenvery effective and safe in patients with acute respiratory failure. HFPV ventilator measures airway pressure that representsthe sum of the endotracheal tube pressure drop and thetracheal pressure dissipated to inflate a lung. The estimation of the difference between the peak airway and tracheal pressure ΔPp may be very useful to the clinician to avoid lung injury. The aim of this study is to provide a comprehensive solutionfor estimation of ΔPp in adult endotracheal tubes, by developinga flow-independent model, based on endotracheal tube size, ventilator set parameters (i.e. peak pressures, pulsatile frequencies) and patient’s respiratory system resistance andcompliance. The model for the estimation of ΔPp was determinedby using the Least Absolute Shrinkage and SelectionOperator (LASSO) regularized least-squares regression technique.The identified model was successively assessed on testdata set.

Estimation of the Endotracheal Tube Pressure Drop during HFPV: A Flow-Independent Model

AJCEVIC, MILOŠ;LUCANGELO, UMBERTO;ACCARDO, AGOSTINO
2016

Abstract

High frequency percussive ventilation (HFPV) is a non-conventional ventilatory modality which has provenvery effective and safe in patients with acute respiratory failure. HFPV ventilator measures airway pressure that representsthe sum of the endotracheal tube pressure drop and thetracheal pressure dissipated to inflate a lung. The estimation of the difference between the peak airway and tracheal pressure ΔPp may be very useful to the clinician to avoid lung injury. The aim of this study is to provide a comprehensive solutionfor estimation of ΔPp in adult endotracheal tubes, by developinga flow-independent model, based on endotracheal tube size, ventilator set parameters (i.e. peak pressures, pulsatile frequencies) and patient’s respiratory system resistance andcompliance. The model for the estimation of ΔPp was determinedby using the Least Absolute Shrinkage and SelectionOperator (LASSO) regularized least-squares regression technique.The identified model was successively assessed on testdata set.
978-3-319-32701-3
978-3-319-32703-7
http://link.springer.com/chapter/10.1007%2F978-3-319-32703-7_27
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2870846
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