title: Linear and nonlinear analysis of pressure and flow during mechanical ventilation.
abstract: OBJECTIVE
  Linear modeling as a method of exploring respiratory mechanics during mechanical ventilation, was compared to nonlinear modeling for flow dependence of resistance in three distinct groups of patients, those with: (a) normal respiratory function (NRF), (b) chronic obstructive pulmonary disease (COPD), or (c) adult respiratory distress syndrome (ARDS).
  
  
  METHODS
  Airways opening pressure (Pao), flow (V'), and volume (V) signals were recorded in 32 ICU mechanically ventilated patients, under sedation and muscle relaxation (10 NRF, 11 COPD, 11 ARDS). All patients were ventilated with controlled mandatory ventilation mode at three levels of end-expiratory pressure (PEEPe): 0, 5, and 10 hPa. Data were analyzed according to: (a) Pao = PE + Ers V + Rrs V' and (b) Pao = PE + Ers V + k1V' + k2¿V'¿V'; where Ers and Rrs represent the intubated respiratory system (RS) elastance and resistance, k1 and k2 the linear and the nonlinear RS resistive coefficients, and PE the end-expiratory pressure. The model's goodness of fit to the data was evaluated by the root mean square difference of predicted minus measured Pao values.
  
  
  RESULTS
  NRF data fit both models well at all PEEPe levels. ARDS and particularly COPD data fit the nonlinear model better. Values of k2 were often negative in COPD and ARDS groups, and they increased in parallel with PEEPe. A gradual increase in PEEPe resulted in better fit of ARDS and COPD data to both models.
  
  
  CONCLUSIONS
  The model of V' dependence of resistance is more suitable for the ARDS and particularly the COPD groups. PEEP tends to diminish the V' dependence of respiratory resistance during the respiratory cycle, particularly in the COPD group, probably through an indirect effect of the increased lung volume.
authors.affiliation: Lung Function Department, St. Savas Hospital, Athens, Greece.
authors.forename: M P
authors.initials: MP
authors.lastname: Vassiliou
authors.forename: L
authors.initials: L
authors.lastname: Petri
authors.forename: A
authors.initials: A
authors.lastname: Amygdalou
authors.forename: M
authors.initials: M
authors.lastname: Patrani
authors.forename: C
authors.initials: C
authors.lastname: Psarakis
authors.forename: D
authors.initials: D
authors.lastname: Nikolaki
authors.forename: G
authors.initials: G
authors.lastname: Georgiadis
authors.forename: P K
authors.initials: PK
authors.lastname: Behrakis
mesh_terms: D005260:Female; D006801:Humans; D016014:Linear Models; D008173:Lung Diseases, Obstructive / Q000503:physiopathology / Q000628:therapy; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D011175:Positive-Pressure Respiration; D012121:Respiration, Artificial / Q000706:statistics & numerical data*; D012128:Respiratory Distress Syndrome / Q000503:physiopathology / Q000628:therapy; D012129:Respiratory Function Tests / Q000706:statistics & numerical data*; D015656:Respiratory Mechanics