OBJECTIVE: We tested whether the continuous monitoring of dynamic compliance could become a useful bedside tool for detecting the beginning of collapse of a fully recruited lung. DESIGN: Prospective laboratory animal investigation. SETTING: Clinical physiology research laboratory, University of Uppsala, Sweden. SUBJECTS: Eight pigs submitted to repeated lung lavages. INTERVENTIONS: Lung recruitment maneuver, the effect of which was confirmed by predefined oxygenation, lung mechanics, and computed tomography scan criteria, was followed by a positive end-expiratory pressure (PEEP) reduction trial in a volume control mode with a tidal volume of 6 mL/kg. Every 10 mins, PEEP was reduced in steps of 2 cm H2O starting from 24 cm H2O. During PEEP reduction, lung collapse was defined by the maximum dynamic compliance value after which a first measurable decrease occurred. Open lung PEEP according to dynamic compliance was then defined as the level of PEEP before the point of collapse. This value was compared with oxygenation (Pao2) and CT scans. MEASUREMENTS AND MAIN RESULTS: Pao2 and dynamic compliance were monitored continuously, whereas computed tomography scans were obtained at the end of each pressure step. Collapse defined by dynamic compliance occurred at a PEEP of 14 cm H2O. This level coincided with the oxygenation-based collapse point when also shunt started to increase and occurred one step before the percentage of nonaerated tissue on the computed tomography exceeded 5%. Open lung PEEP was thus at 16 cm H2O, the level at which oxygenation and computed tomography scan confirmed a fully open, not yet collapsed lung condition. CONCLUSIONS: In this experimental model, the continuous monitoring of dynamic compliance identified the beginning of collapse after lung recruitment. These findings were confirmed by oxygenation and computed tomography scans. This method might become a valuable bedside tool for identifying the level of PEEP that prevents end-expiratory collapse.
OBJECTIVE: We tested whether the continuous monitoring of dynamic compliance could become a useful bedside tool for detecting the beginning of collapse of a fully recruited lung. DESIGN: Prospective laboratory animal investigation. SETTING: Clinical physiology research laboratory, University of Uppsala, Sweden. SUBJECTS: Eight pigs submitted to repeated lung lavages. INTERVENTIONS: Lung recruitment maneuver, the effect of which was confirmed by predefined oxygenation, lung mechanics, and computed tomography scan criteria, was followed by a positive end-expiratory pressure (PEEP) reduction trial in a volume control mode with a tidal volume of 6 mL/kg. Every 10 mins, PEEP was reduced in steps of 2 cm H2O starting from 24 cm H2O. During PEEP reduction, lung collapse was defined by the maximum dynamic compliance value after which a first measurable decrease occurred. Open lung PEEP according to dynamic compliance was then defined as the level of PEEP before the point of collapse. This value was compared with oxygenation (Pao2) and CT scans. MEASUREMENTS AND MAIN RESULTS:Pao2 and dynamic compliance were monitored continuously, whereas computed tomography scans were obtained at the end of each pressure step. Collapse defined by dynamic compliance occurred at a PEEP of 14 cm H2O. This level coincided with the oxygenation-based collapse point when also shunt started to increase and occurred one step before the percentage of nonaerated tissue on the computed tomography exceeded 5%. Open lung PEEP was thus at 16 cm H2O, the level at which oxygenation and computed tomography scan confirmed a fully open, not yet collapsed lung condition. CONCLUSIONS: In this experimental model, the continuous monitoring of dynamic compliance identified the beginning of collapse after lung recruitment. These findings were confirmed by oxygenation and computed tomography scans. This method might become a valuable bedside tool for identifying the level of PEEP that prevents end-expiratory collapse.
Authors: Salvatore Grasso; Pierpaolo Terragni; Alberto Birocco; Rosario Urbino; Lorenzo Del Sorbo; Claudia Filippini; Luciana Mascia; Antonio Pesenti; Alberto Zangrillo; Luciano Gattinoni; V Marco Ranieri Journal: Intensive Care Med Date: 2012-02-10 Impact factor: 17.440
Authors: Raffaele L Dellaca; Marie Andersson Olerud; Emanuela Zannin; Peter Kostic; Pasquale P Pompilio; Göran Hedenstierna; Antonio Pedotti; Peter Frykholm Journal: Intensive Care Med Date: 2009-09-30 Impact factor: 17.440
Authors: Raffaele L Dellacà; Emanuela Zannin; Peter Kostic; Marie Andersson Olerud; Pasquale P Pompilio; Goran Hedenstierna; Antonio Pedotti; Peter Frykholm Journal: Intensive Care Med Date: 2011-04-01 Impact factor: 17.440
Authors: Maria Paula Caramez; Robert M Kacmarek; Mohamed Helmy; Eriko Miyoshi; Atul Malhotra; Marcelo B P Amato; R Scott Harris Journal: Intensive Care Med Date: 2009-01-31 Impact factor: 17.440
Authors: Alysson R Carvalho; Peter M Spieth; Paolo Pelosi; Marcos F Vidal Melo; Thea Koch; Frederico C Jandre; Antonio Giannella-Neto; Marcelo Gama de Abreu Journal: Intensive Care Med Date: 2008-09-30 Impact factor: 17.440