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Linear and nonlinear analysis of respiratory resistance in ARDS patients


We analyzed during the whole respiratory cycle the resistance, elastance, and flow dependence of resistance in ARDS patients, at three levels of PEEP, using two different models: linear modeling, and nonlinear modeling for flow dependence of resistance.


Airway opening pressure (P), flow (V') and volume (V) signals were recorded in 18 patients with ARDS and in 15 patients without known respiratory disorder (control group), all artificially ventilated, at three levels of PEEP (0, 5, and 10 hPa). Data were analyzed using multiple linear regression analysis according to the equations: P = Ers*V + Rrs*V' + Po, and (b) P = Ers*V + (k1 + k2*Vα')*V' + Po, where Ers and Rrs represent the respiratory system elastance and resistance, k1 the linear coefficient of resistance, k2 the flow-dependent coefficient of resistance, and Po the end expiratory pressure.


The respiratory resistance (controls: P = 0.01, ARDS: P = 0.001), the linear coefficient of resistance (controls: P = 0.05, ARDS P = 0.02) and the flow-dependent coefficient of resistance (controls: P = 0.04, ARDS: P = 0.04) were diminished significantly with the application of PEEP in both groups of patients, especially at a high level of PEEP (10 hPa). All the measured mechanical parameters were significantly higher in the ARDS group compared with the controls.


Mechanical ventilation with PEEP application in ARDS patients is related to a decline of linear and nonlinear coefficients of respiratory resistance, during the respiratory cycle. More complex models accounting for the flow dependence of resistance may improve the accuracy of measurements of respiratory mechanics and offer more efficient mechanical ventilation in ARDS patients.

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Frantzeskaki, F., Betrosian, A., Amygdalou, A. et al. Linear and nonlinear analysis of respiratory resistance in ARDS patients. Crit Care 9, P101 (2005).

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  • Mechanical Ventilation
  • Multiple Linear Regression
  • Respiratory System
  • Nonlinear Analysis
  • Multiple Linear Regression Analysis