One of the proposed mechanisms of ventilator-associated lung injury is cyclic recruitment of atelectasis. Collapse of dependent lung regions with every breath should lead to large oscillations in PaO2 as shunt varies throughout the respiratory cycle. We placed a fluorescence-quenching PO2 probe in the brachiocephalic artery of six anesthetized rabbits after saline lavage. Using pressure-controlled ventilation with oxygen, ventilator settings were varied in random order over three levels of positive end-expiratory pressure (PEEP), respiratory rate (RR), and plateau pressure minus PEEP (Δ). Dependence of the amplitude of PaO2 oscillations on PEEP, RR, and Δ was modeled by multiple linear regression. Before lavage, arterial PO2 oscillations varied from 3 to 22 mm Hg. After lavage, arterial PO2 oscillations varied from 5 to 439 mm Hg. Response surfaces showed markedly nonlinear dependence of amplitude on PEEP, RR, and Δ. The large PaO2 oscillations observed provide evidence for cyclic recruitment in this model of lung injury. The important effect of RR on the magnitude of PaO2 oscillations suggests that the static behavior of atelectasis cannot be accurately extrapolated to predict dynamic behavior at realistic breathing frequencies.
Ventilator-associated lung injury (VALI) in the acute respiratory distress syndrome (ARDS) is thought to damage the alveolar epithelium and capillary endothelium primarily by stretch injury. Overdistention of normal but functionally small parts of the lung with normal tidal volumes can result in mechanical injury and an inflammatory response that further damages the acutely injured lung (1–3). Recently a large multicenter trial demonstrated a significant reduction in mortality in ARDS by use of protective ventilation strategies that included reduced tidal volumes to minimize overdistention (4).
An additional mechanism of VALI is thought to be the stretch injury associated with repetitive collapse and re-expansion of atelectasis with each breath, often called cyclic recruitment. Protective ventilation strategies seek to minimize cyclic recruitment by use of high levels of extrinsic positive end-expiratory pressure (PEEP) to keep the lung maximally recruited throughout the respiratory cycle (1–3). Several experimental studies have suggested that increased extrinsic PEEP can reduce VALI (5–9). In contrast, preliminary results of the ARDS Network ALVEOLI study (http://hedwig.mgh.harvard.edu/ardsnet/ards04.html) showed no benefit to increased PEEP. Details of this study required for critical evaluation, however, have not been published to date.
Recently, de Durante and coworkers showed that the higher respiratory rate (RR) associated with the low tidal volume ventilation strategy in the ARDS Network study (4) elevates intrinsic PEEP, and suggested this may have been a factor in reducing VALI by reducing cyclic recruitment (10). The effects of extrinsic PEEP in reducing atelectasis and cyclic recruitment in ARDS at very low RRs have been extensively investigated (11–14). The effects of RR specifically on cyclic recruitment, however, have not been reported.
Williams and coworkers recently showed in saline-lavaged dogs that PaO2 oscillated at a frequency matching the RR, with large amplitude at certain ventilator settings. The large magnitude of these oscillations could only be explained by changes in shunt fraction within the respiratory cycle, a phenomenon consistent with cyclic recruitment (15). Their probe was not fast enough to accurately record the amplitude of Po2 oscillations at high RR, and there have been no reports to date of the effects of RR on PaO2 oscillations.
The aim of our study was to investigate the effects of RR, extrinsic PEEP, and plateau pressure minus PEEP (Δ) on the amplitude of PaO2 oscillations. We placed a fast Po2 probe in rabbits after saline lavage to accurately measure PaO2 oscillations over a range of RRs. We specifically tested the hypothesis that RR has a significant effect on PaO2 oscillations.
The study of the effects of several predictor variables is most efficiently approached by choice of several levels of each of the predictor variables and analysis of the response variable with multiple linear regression. Although this multivariable approach to experimental design has not been applied frequently in the physiology literature, it is well established in engineering analysis (16).
Some of the results of these studies have been reported previously in the form of an abstract (17).
We chose an experimental design of three levels for each of the three independent variables: RR, Δ, and extrinsic PEEP. The levels of RR chosen were 10, 20, and 30 breaths per minute. The levels of Δ chosen were 20, 30, and 40 cm H2O. Pilot studies showed unacceptable hypoxemia at PEEP of zero after lavage and unacceptable hemodynamic compromise at PEEP greater than 16 cm H2O. The levels of extrinsic PEEP chosen were therefore 4, 10, and 16 cm H2O. Pilot studies also showed an unacceptably high incidence of pneumothorax at high peak pressures, and the upper ranges of Δ and PEEP were therefore jointly modified at the highest levels to keep maximum airway pressure less than 46 cm H2O. A detailed description of the 27 ventilator settings and experimental design considerations that led to these choices can be found in the online data supplement.
The study protocol was approved by the Institutional Animal Care and Use Committee at the University of Pennsylvania. The experimental protocol was to induce general anesthesia, prepare the rabbits with tracheostomy, vascular catheters, and the Po2 probe, and calibrate the probe in vivo for each animal against standard blood gas analysis. PaO2 oscillations were then recorded for normal lungs at two ventilator settings, and mild to moderate lung injury was induced by saline lavage. Throughout the experimental study, the rabbits were ventilated (Servo 900C; Siemens, Germany) in pressure-controlled mode with a fraction of inspired oxygen of 1.0, and inspiratory:expiratory (I:E) ratio of 1:1. After lavage, the ventilator was adjusted to each of the 27 prechosen settings in random order, and the PaO2, respiratory mechanical, and hemodynamic data were recorded at each setting. Colloid boluses and IV epinephrine infusions were used as necessary for hemodynamic support.
The oxygen probe used for these studies was a fiber optic, fluorescence-quenching probe with an uncoated ruthenium complex at the probe tip (FOXY-AL300; Ocean Optics, Dunedin, FL). Details of the spectrometer, in vitro bench studies to test for possible signal artifacts, probe time response, and criteria for proper in vivo placement can be found in the online data supplement.
After digital filtering to reduce high-frequency noise components, the peak-to-peak amplitude of PaO2 was modeled as the predicted variable in a multiple linear regression with RR, Δ, and extrinsic PEEP as predictor variables (16). Solutions to the normal equations, residual plots and analysis, and calculations of sums of squares were performed in Mathcad 2000i (Mathsoft, Cambridge, MA). Details of the development of the model and analysis of residuals can be found in the online data supplement. The full model for the amplitude data was:
1 |
Unless otherwise noted, amplitude of the arterial PaO2 oscillations refers to peak-to-peak amplitude. Ventilator settings are abbreviated in the format of RR20 Δ20 PEEP10 to denote a RR of 20 breaths/minute, plateau airway pressure minus extrinsic PEEP (Δ) of 20 cm H2O, and extrinsic PEEP of 10 cm H2O. All data are presented in the format mean ± standard deviation.
Four animals received three lavages and two animals received four lavages. The lavages (26 ml/kg per lavage) induced a mild-to-moderate degree of acute lung injury, as demonstrated in Table 1
Before Lavage | After Lavage | p Value | |
---|---|---|---|
SBP, mm Hg | 61 ± 20 | 72 ± 10 | NS |
DBP, mm Hg | 34 ± 12 | 44 ± 8 | NS |
HR, bpm | 232 ± 23 | 230 ± 20 | NS |
SPAP, mm Hg* | 27 ± 0 | 30 ± 3 | NS |
PAD, mm Hg* | 15 ± 1 | 21 ± 5 | NS |
VT, ml | 69 ± 14 | 118 ± 19 | p < 0.002 |
VE, L/min | 1.2 ± 0.1 | 1.8 ± 0.3 | p < 0.004 |
Cdynamic, ml/kg/cm H2O | 0.772 ± 0.067 | 1.39 ± 0.16 | p < 0.001 |
EtCO2, mm Hg | 24 ± 4 | 22 ± 8 | NS |
SpO2, % | 100 ± 0 | 100 ± 0 | NS |
PaO2, mm Hg | 535 ± 22 | 351 ± 72 | p < 0.002 |
Before lavage, the amplitude of PaO2 oscillations ranged from 3.3 to 21.7 mm Hg. The average amplitude of the PaO2 oscillations at ventilator settings of RR20 Δ20 PEEP10 was 8.9 ± 5.8 mm Hg (n = 6). After lavage the peak-to-peak amplitude of the PaO2 oscillations ranged from 5.3 mm Hg at a setting of RR30 Δ20 PEEP16 to 439 mm Hg at a setting of RR10 Δ40 PEEP4. The average amplitude of the PaO2 oscillations at ventilator settings of RR20 Δ20 PEEP10, after lavage, was 130.8 ± 62.2 mm Hg (n = 6).
illustrate two representative examples of the effect of RR on the amplitude of oscillation and on mean PaO2, after saline lavage. Decreasing RR from 20 to 10 caused a large increase in amplitude, and increasing the RR from 10 to 30 caused a large decrease in amplitude in these examples. Figures 1A and 1B also illustrate the rapid establishment of a new steady state in PaO2 oscillations after a change in the RR, with other ventilator settings held constant. Similar plots showing rapid establishment of a new steady state after changes in PEEP and Δ are presented in the online data supplement. Figures 2A–2C illustrate a specific example of the effects of RR and PEEP on PaO2 oscillations. In this rabbit, similar mean PaO2 and amplitude were achieved by low RR and high PEEP in Figure 2A and lower PEEP and high RR in Figure 2B. Figure 2C illustrates the effect of reducing PEEP at the lower RR.Average data for all the rabbits is plotted in the three-dimensional response surfaces showing the effects of PEEP and RR on amplitude, at three fixed Δ values, in Figure 3
. These surfaces are the plots of the complete regression model (Equation 1) that fits the data from all rabbits that completed the protocol (n = 6). The response surfaces show highly nonlinear effects of both PEEP and RR on amplitude, with substantial interactions between PEEP and RR. Figure 4 presents the three-dimensional response surfaces showing the effects of PEEP and Δ on amplitude, for three fixed RR. These response surfaces also show a high degree of nonlinearity and of interaction between the predictor variables. For clarity, individual data points scattered around the response surface plots have been omitted. Variability between individuals is illustrated in Figure 5 , where the surface of Figure 3B has been expanded into three two-dimensional plots, with variability between individuals represented by the error bars around the plotted points. Peak values of PaO2 for each ventilator setting are presented in the online data supplement.Partial F testing for the amplitude model showed that the order of predictive value for the three independent variables is: RR greater than PEEP much greater than Δ (F values 86.4, 84.1, and 12.1). These F values are associated with p values of less than 10−15, less than 10−15, and 1.4 × 10−8, respectively.
In this model of acute lung injury, the peak-to-peak amplitude of PaO2 oscillations during the respiratory cycle was surprisingly large at low RRs, low extrinsic PEEP, and large Δ. The maximum amplitude for each of the six animals ranged from 334 to 439 mm Hg, with an average maximum amplitude of 390 ± 39 mm Hg. RR, PEEP, and Δ all had highly significant effects on the amplitude of PaO2 oscillations, but PEEP and RR were much more significant predictor variables (p < 10−15) than Δ (p = 1.4 × 10−8), within the range of values studied.
We measured PaO2 oscillations in a saline-lavage model of acute lung injury, and like any experimental model, the saline-lavage model has limitations. Lavage models simulate the surfactant depletion and dysfunction of acute lung injury and ARDS (18, 20–23) but are not as successful at reproducing the vascular and inflammatory pathology of early ARDS (21, 23). After lavage, static pressure–volume curves are right shifted (18, 19), similar to ARDS (24–27), and oxygenation after lavage is impaired. Lavage models of lung injury, however, are generally more recruitable than other models of lung injury, and the impaired oxygenation is more responsive to PEEP and increased airway pressure than in other lung injury models and in ARDS (18, 19, 28, 29). Although lavage is primarily aimed at depleting surfactant, it has been shown that mechanical ventilation with oxygen after lavage rapidly leads to neutrophil infiltration, cytokine expression, and capillary-alveolar protein leak (18, 19, 29).
An additional limitation of our model of lung injury is the known susceptibility of saline-lavaged rabbits to hemodynamic depression and barotrauma at high airway pressures (21, 30). Several animals did not complete the protocol, which included high airway pressures and high PEEP, because of pneumothorax. Also, several animals did not complete the protocol due to severe hemodynamic depression despite a prospectively defined protocol for fluid management and inotropic support (see online data supplement) that was similar to prior reported models (18). Although this presents an opportunity for selection bias that would limit conclusions about PaO2 oscillations to the experimental group that completed the protocol, there is no known mechanism that would link susceptibility to barotrauma with changes in dynamic behavior of atelectasis. It is also unlikely that animals with failed Po2 probe placement were systematically different from the experimental group. We did not measure cardiac output or venous blood gases at all 27 ventilator settings and cannot assess the effects of individual ventilator settings on hemodynamic depression. We also cannot calculate the average shunt fraction at each of the 27 ventilator settings.
PaCO2 was not controlled in these studies and varied widely between the ventilator settings, and this may have influenced the magnitude of PaO2 oscillations via the Bohr effect. However, the expected influence of the Bohr effect specifically on the amplitude of PaO2 oscillations can be shown by theoretic calculations to be minor. Consistent with this expectation, addition of end-tidal carbon dioxide as a predictor variable made no significant improvement in the linear regression model (see online data supplement), indicating that end-tidal carbon dioxide had no independent predictive value.
To avoid confounding effects of progression of the lung injury with time (18), the protocol of 27 ventilator settings was completed in less than 90 minutes for each animal, and the order of ventilator settings was randomized. Plots of residuals versus order number did show a slight linear trend, indicating a small progression of injury over time. Time effects were accounted for by inclusion of a linear term for order number in the regression model, and plots of residuals versus order number after inclusion of this linear term showed no further systematic time trend.
To minimize individual variability in injury due to lavage, the number of lavages was determined by fixed functional criteria. Adding a term to the regression model for individual variations made no significant improvement in the model, indicating that variability between individuals was of negligible importance in predicting the PaO2 oscillations. The relatively small variability between individuals at identical ventilator settings is reflected in the small error bars in Figure 5.
Although not specifically designed for use in vivo, the Po2 probe we used had minimal responses to changes in variables besides Po2 that would be expected to vary during the respiratory cycle, such as temperature, pH and PaCO2 (31, 32), and movement in the brachiocephalic artery with respiratory excursions. The probe calibration was affected by Pco2 and temperature changes larger than the changes within a single respiratory cycle, and we did not correct for the Pco2 or temperature at each ventilator setting. The main effect of these variables on calibration, however, was a shift in baseline with little change in sensitivity and therefore should have had little effect on the measured amplitude of the PaO2 oscillations.
There have been some prior reports of arterial Po2 oscillations with mechanical ventilation for comparison with our results. Krogh and Lindhard first suggested on theoretic grounds that PaO2 should oscillate (33), and cyclic changes in arterial saturation were first reported by Bergman (34). Folgering and coworkers used a specially designed polarographic Po2 sensor with a very fast time response to measure PaO2 oscillations in the carotid artery of healthy anesthetized cats (35). Their report of maximal oscillations, for a high mean PaO2, of about 20 mm Hg at a RR of 20 compares favorably with our value of 8.9 mm Hg before lavage. Williams and coworkers used a much slower Po2 probe to measure PaO2 oscillations in the aorta of anesthetized, saline-lavaged dogs that were ventilated with 70% inspired oxygen. They reported a maximal amplitude of the PaO2 oscillations of 23 mm Hg at a PEEP of 15 cm H2O. Although their oxygen probe was not fast enough to track these PaO2 oscillations accurately, they did estimate, based on an approximate correction of the probe time response, that the real oscillations in their model could have been as large as 69 mm Hg (15).
In contrast to the study by Williams and coworkers, we measured amplitudes of PaO2 oscillations of up to 439 mm Hg. Our data demonstrate that in a lavage model of lung injury, PaO2 oscillations can be over an order of magnitude larger than any prior reports. Although Williams and coworkers suggested that cyclic changes in shunt fraction could be a mechanism for the PaO2 oscillations they measured, other mechanisms for oscillations cannot be neglected when the amplitude of oscillation is less than 23 mm Hg (see online data supplement). In contrast, the large oscillations we measured at some ventilator settings provide compelling evidence for variations in shunt fraction throughout the respiratory cycle in our saline-lavage model of lung injury. In addition, no prior reports have examined the influence of RR or Δ on PaO2 oscillations in saline-lavage injury or the relative influence of PEEP, RR, and Δ.
The primary determinant of PaO2 while breathing 100% O2 is the shunt fraction (36). At high mean PaO2 and for large amplitudes of oscillation, therefore, the predominant mechanism for the PaO2 oscillations is variation in shunt fraction during the respiratory cycle. Several minor mechanisms that could result in small PaO2 oscillations are discussed in the online data supplement. The large PaO2 oscillations observed for some ventilator settings suggest significant cyclic recruitment of atelectasis in this surfactant depletion model. Other workers have presented evidence of cyclic recruitment in similar lung injury models based on PaO2 oscillations (15), dynamic computed tomography (37, 38), and vital microscopy (39).
Recently, the concept that dependent regions of injured lungs repetitively collapse with mechanical ventilation has been challenged (40). In oleic acid–injured dogs, measurements of regional lung volumes with the parenchymal marker technique have shown that the FRC of dependent lung regions is normal or increased (41, 42), suggesting that the increased dependent density observed on computed tomography at end-expiration (11, 12, 37, 38, 43, 44) is a result of alveolar flooding rather than collapse. Because flooded and collapsed alveoli would both result in shunt, gas exchange measurements are not able to distinguish between these alternatives. The parenchymal marker technique has also shown that tidal volume excursions in dependent lung regions are nearly in phase with other lung regions (41, 42), contrary to what would be expected for cyclic collapse (40). The large oscillations in PaO2 that we observed for some ventilator settings, however, suggest that a substantial fraction of collapsed or flooded alveoli are reopened with every respiratory cycle. These differing results may reflect a difference between oleic acid–injured dog lungs and lavage-injured rabbit lungs. Alternatively, this contrast could represent a fundamental gap in our understanding of the dynamics of aeration in injured lungs.
The substantial effect of RR on the magnitude of PaO2 oscillations, shown in Figures 1–5, suggests that the dynamics of mechanical events leading to recruitment and derecruitment are important determinants of the amount of lung that is cyclically recruited. The dynamic behavior of atelectasis (or flooding) in the injured lung has important implications for ventilator management in experimental studies and for the design of experimental studies of VALI.
Ventilator management in experimental studies and in clinical practice is often approached using static behavior to predict the dynamic behavior of the system. For example, a common goal to avoid cyclic recruitment is to set PEEP slightly above the static lower inflection point, the rationale being that this will ensure maximum recruitment and avoid cyclic collapse (45, 46). Recent experimental (13, 24, 43, 47, 48) and theoretic (26, 49) evidence suggests that recruitment is not so abrupt and occurs throughout the range of pressures in a pressure–volume curve. In either case, the amount of recruitment at any given pressure on the static pressure–volume curve is commonly thought of as representing the amount of recruitment at that same airway pressure during tidal ventilation. In our model, a fixed amount of atelectasis and shunt at a fixed, static airway pressure would be observed as a fixed PaO2 at that static airway pressure. Extrapolating this static information to dynamic changes in PaO2 during ventilation would predict that the minimum PaO2 during the respiratory cycle should be a function of PEEP alone and the maximum PaO2 should be a function of the plateau pressure alone. Figure 3 shows instead that the amplitude of PaO2 oscillations is very strongly influenced by RR, as confirmed by partial F testing with p values less than 10−15.
In this dynamic system, whether the lung actually reaches the amount of collapse or flooding associated with a fixed PEEP level depends not just on the extrinsic PEEP setting but also how recruited the lung was at the beginning of exhalation and the time available for collapse relative to how rapidly collapse takes place, which in turn is a function of regional alveolar tissue mechanics, regional compliance, regional airway resistance (a primary determinant of intrinsic PEEP), and gas/liquid interfacial mechanics. Similarly, whether the lung is opened to the amount of recruitment associated with a fixed plateau pressure depends not only on the plateau pressure but also on the amount of atelectasis at the beginning of inspiration, the time available for inspiration, and the time it takes to recruit atelectasis. Thus, in the behavior of this dynamic system, the amplitude of the PaO2 oscillations is fixed inside a window of limiting values, with the upper and lower limits determined by the plateau pressure and extrinsic PEEP. The static behavior, however, merely defines the limits of this window, and as RR increases, the amplitude of oscillations becomes progressively smaller within these static limiting values. This point is directly illustrated by the example data of Figure 1. Taking advantage of the dynamic system behavior offers the opportunity to avoid cyclic recruitment at higher RRs but at lower extrinsic PEEP, mean airway pressure, and peak airway pressure, as shown in Figure 2.
Rimensberger and coworkers showed on different grounds and in an animal model similar to ours that PEEP could be set below the static lower inflection point and still maintain recruitment. They were using RRs in the range of 24 to 32 (21). Neumann and coworkers also suggested that short expiratory times could allow expiration without collapse if the expiratory time was less than 0.6 seconds, based on dynamic computed tomography scanning in oleic acid injury in pigs (37), and in saline-lavage and endotoxin infusion models of lung injury in pigs (44). Similarly, Markstaller and coworkers reported a median expiratory time constant for collapse, as assessed by dynamic computed tomography, of 0.8 seconds in saline-lavaged pigs (38).
Similar opportunities to avoid cyclic recruitment at PEEP below the lower inflection point may exist in ventilator management in clinical practice. The dynamics of the recruitment of atelectasis in ARDS, however, are largely unexplored. Several investigators have presented computed tomography evidence for cyclic recruitment in patients with very low RRs (11–14), but the impact of more rapid RRs on cyclic recruitment in human ARDS is yet to be defined.
The dynamic nature of cyclic recruitment also has important implications for the design of experimental studies of VALI. Frequently in the design of these studies, tidal volume and PEEP are the main focus and are therefore the controlled experimental variables. RR is set according to study goals for PaCO2 management (28, 50–52). In this approach, RR is in general inversely related to tidal volume in volume-controlled ventilation or to Δ in pressure-controlled ventilation. However, RR is commonly not controlled as an experimental variable. The data of Figure 3 and the partial F testing of the predictor variables suggests that RR may in fact be a more important predictor of cyclic recruitment in some models of lung injury than tidal volume. In our model, RR was equally as important as extrinsic PEEP in determining the amplitude of the PaO2 oscillations, and far more important than the difference between plateau pressure and extrinsic PEEP.
The authors gratefully acknowledge the helpful general advice for management of the anesthetized rabbits, and especially specific advice concerning fluid management, from Karen Rosenthal, D.V.M. and Mathew Johnston, V.M.D.
1. | Dreyfuss D, Saumon G. Ventilator-induced lung injury: lessons from experimental studies. Am J Respir Crit Care Med 1998;157:294–323. |
2. | Slutsky AS, Tremblay LN. Multiple system organ failure: is mechanical ventilation a contributing factor? Am J Respir Crit Care Med 1998;157:1721–1725. |
3. | International Consensus Conferences in Intensive Care Medicine. Ventilator-associated lung injury in ARDS. Am J Respir Crit Care Med 1999;160:2118–2124. |
4. | The Acute Respiratory Distress Syndrome Network. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med 2000;342:1301–1308. |
5. | Sandhar BK, Niblett DJ, Argiras EP, Dunnill MS, Sykes MK. Effects of positive end-expiratory pressure on hyaline membrane formation in a rabbit model of the neonatal respiratory distress syndrome. Intensive Care Med 1988;14:538–546. |
6. | Muscedere JG, Mullen JB, Gan K, Slutsky AS. Tidal ventilation at low airway pressures can augment lung injury. Am J Respir Crit Care Med 1994;149:1327–1334. |
7. | Webb HH, Tierney DF. Experimental pulmonary edema due to intermittent positive pressure ventilation with high inflation pressures: protection by positive end-expiratory pressure. Am Rev Respir Dis 1974;110:556–565. |
8. | Tremblay L, Miatto D, Hamid Q, Govindarajan A, Slutsky AS. Injurious ventilation induces widespread pulmonary epithelial expression of tumor necrosis factor-alpha and interleukin-6 messenger RNA. Crit Care Med 2002;30:1693–1700. |
9. | Tremblay L, Valenza F, Ribeiro SP, Li J, Slutsky AS. Injurious ventilatory strategies increase cytokines and c-fos m-RNA expression in an isolated rat lung model. J Clin Invest 1997;99:944–952. |
10. | de Durante G, del Turco M, Rustichini L, Cosimini P, Giunta F, Hudson LD, Slutsky AS, Ranieri VM. ARDSNet lower tidal volume ventilatory strategy may generate intrinsic positive end-expiratory pressure in patients with acute respiratory distress syndrome. Am J Respir Crit Care Med 2002;165:1271–1274. |
11. | Gattinoni L, Pelosi P, Crotti S, Valenza F. Effects of positive end-expiratory pressure on regional distribution of tidal volume and recruitment in adult respiratory distress syndrome. Am J Respir Crit Care Med 1995;151:1807–1814. |
12. | Puybasset L, Cluzel P, Chao N, Slutsky AS, Coriat P, Rouby JJ. A computed tomography scan assessment of regional lung volume in acute lung injury: the CT Scan ARDS Study Group. Am J Respir Crit Care Med 1998;158:1644–1655. |
13. | Crotti S, Mascheroni D, Caironi P, Pelosi P, Ronzoni G, Mondino M, Marini JJ, Gattinoni L. Recruitment and derecruitment during acute respiratory failure: a clinical study. Am J Respir Crit Care Med 2001;164:131–140. |
14. | Puybasset L, Gusman P, Muller JC, Cluzel P, Coriat P, Rouby JJ. Regional distribution of gas and tissue in acute respiratory distress syndrome. III: Consequences for the effects of positive end-expiratory pressure. CT Scan ARDS Study Group. Adult Respiratory Distress Syndrome. Intensive Care Med 2000;26:1215–1227. |
15. | Williams EM, Viale JP, Hamilton RM, McPeak H, Sutton L, Hahn CE. Within-breath arterial PO2 oscillations in an experimental model of acute respiratory distress syndrome. Br J Anaesth 2000;85:456–459. |
16. | Draper NR, Smith H. Applied regression analysis. In: Bradley RA, Hunter JS, Kendall DG, Watson GS, editors. Wiley series in probability and mathematical statistics. New York: John Wiley and Sons; 1981. p. 78–280. |
17. | Baumgardner JE, Markstaller K, Pfeiffer B, Doebrich M, Otto CM. Effect of ventilator management on arterial PO2 oscillations [abstract]. Shock 2002;17(Suppl):68–69. |
18. | Rotta AT, Gunnarsson B, Fuhrman BP, Hernan LJ, Steinhorn DM. Comparison of lung protective ventilation strategies in a rabbit model of acute lung injury. Crit Care Med 2001;29:2176–2184. |
19. | Sugiura M, McCulloch PR, Wren S, Dawson R, Froese AB. Ventilator pattern influences neutrophil influx and activation in atelectasis-prone rabbit lung. J Appl Physiol 1994;77:1355–1365. |
20. | Lachmann B, Robertson B, Vogel J. In vivo lung lavage as an experimental model of the respiratory distress syndrome. Acta Anaesthesiol Scand 1980;24:231–236. |
21. | Rimensberger PC, Cox PN, Frndova H, Bryan C. The open lung during small tidal volume ventilation: concepts of recruitment and “optimal” positive end-expiratory pressure. Crit Care Med 1999;27:1946–1952. |
22. | Ito Y, Manwell SEE, Kerr CL, Veldhuizen RAW, Yao L-J, Bjarneson D, McCaig LA, Bartlett AJ, Lewis JF. Effects of ventilation strategies on the efficacy of exogenous surfactant therapy in a rabbit model of acute lung injury. Am J Respir Crit Care Med 1998;157:149–155. |
23. | Neumann P, Hedenstierna G. Ventilation-perfusion distributions in different porcine lung injury models. Acta Anaesthesiol Scand 2001;45:78–86. |
24. | Maggiore SM, Jonson B, Richard JC, Jaber S, Lemaire F, Brochard L. Alveolar derecruitment at decremental positive end-expiratory pressure levels in acute lung injury: comparison with the lower inflection point, oxygenation, and compliance. Am J Respir Crit Care Med 2001;164:795–801. |
25. | Amato MBP, Barbas CSV, Medeiros DM, Schettino GPP, Lorenzi-Filho G, Kairalla RA, Deheinzelin D, Morais C, Fernandes EO, Takagaki TY. Beneficial effects of the “open lung approach” with low distending pressures in acute respiratory distress syndrome. Am J Respir Crit Care Med 1995;152:1835–1846. |
26. | Hickling KG. The pressure-volume curve is greatly modified by recruitment: a mathematical model of ARDS lungs. Am J Respir Crit Care Med 1998;158:194–202. |
27. | Matamis D, Lemaire F, Harf A, Brun-Buisson C, Ansquer JC, Atlan G. Total respiratory pressure-volume curves in the adult respiratory distress syndrome. Chest 1984;86:58–66. |
28. | Kloot TE, Blanch L, Melynne Youngblood A, Weinert C, Adams AB, Marini JJ, Shapiro RS, Nahum A. Recruitment maneuvers in three experimental models of acute lung injury: effect on lung volume and gas exchange. Am J Respir Crit Care Med 2000;161:1485–1494. |
29. | Suh GY, Koh Y, Chung MP, An CH, Kim H, Jang WY, Han J, Kwon OJ. Repeated derecruitments accentuate lung injury during mechanical ventilation. Crit Care Med 2002;30:1848–1853. |
30. | McCulloch PR, Forkert PG, Froese AB. Lung volume maintenance prevents lung injury during high frequency oscillatory ventilation in surfactant-deficient rabbits. Am Rev Respir Dis 1988;137:1185–1192. |
31. | Hlastala MP. A model of fluctuating alveolar gas exchange during the respiratory cycle. Respir Physiol 1972;15:214–232. |
32. | Lin KH, Cumming G. A model of time-varying gas exchange in the human lung during a respiratory cycle at rest. Respir Physiol 1973;17:93–112. |
33. | Krogh A, Lindhard J. On the average composition of the alveolar air and its variation during the respiratory cycle. J Physiol 1914;47:431–445. |
34. | Bergman NA. Cyclic variations in blood oxygenation with the respiratory cycle. Anesthesiology 1961;22:900–908. |
35. | Folgering H, Smolders FDJ, Kreuzer F. Respiratory oscillations of the arterial PO2 and their effects on the ventilatory controlling system in the cat. Pflugers Arch 1978;375:1–7. |
36. | Hlastala M, Colley P, Cheney F. Pulmonary shunts: a comparison between oxygen and inert gas infusion methods. J Appl Physiol 1975;39:1048–1051. |
37. | Neumann P, Berglund JE, Mondejar EF, Magnusson A, Hedenstierna G. Effect of different pressure levels on the dynamics of lung collapse and recruitment in oleic-acid-induced lung injury. Am J Respir Crit Care Med 1998;158:1636–1643. |
38. | Markstaller K, Eberle B, Kauczor HU, Scholz A, Bink A, Thelen M, Heinrichs W, Weiler N. Temporal dynamics of lung aeration determined by dynamic CT in a porcine model of ARDS. Br J Anaesth 2001;87:459–468. |
39. | Schiller HJ, McCann UG, Carney DE, Gatto LA, Steinberg JM, Nieman GF. Altered alveolar mechanics in the acutely injured lung. Crit Care Med 2001;29:1049–1055. |
40. | Hubmayr RD. Perspective on lung injury and recruitment: a skeptical look at the opening and collapse story. Am J Respir Crit Care Med 2002;165:1647–1653. |
41. | Martynowicz MA, Minor TA, Walters BJ, Hubmayr RD. Regional expansion of oleic acid-injured lungs. Am J Respir Crit Care Med 1999;160:250–258. |
42. | Martynowicz MA, Walters BJ, Hubmayr RD. Mechanisms of recruitment in oleic acid-injured lungs. J Appl Physiol 2001;90:1744–1753. |
43. | Pelosi P, Goldner M, McKibben A, Adams A, Eccher G, Caironi P, Losappio S, Gattinoni L, Marini JJ. Recruitment and derecruitment during acute respiratory failure: an experimental study. Am J Respir Crit Care Med 2001;164:122–130. |
44. | Neumann P, Berglund JE, Mondejar EF, Magnusson A, Hedenstierna G. Dynamics of lung collapse and recruitment during prolonged breathing in porcine lung injury. J Appl Physiol 1998;85:1533–1543. |
45. | Dreyfuss D, Saumon G. Pressure-volume curves. Am J Respir Crit Care Med 2001;163:2–3. |
46. | Martin-Lefevre L, Ricard JD, Roupie E, Dreyfuss D, Saumon G. Significance of the changes in the respiratory system pressure-volume curve during acute lung injury in rats. Am J Respir Crit Care Med 2001;164:627–632. |
47. | Jonson B, Richard JC, Straus C, Mancebo J, Lemaire F, Brochard L. Pressure-volume curves and compliance in acute lung injury: evidence of recruitment above the lower inflection point. Am J Respir Crit Care Med 1999;159:1172–1178. |
48. | Richard JC, Maggiore SM, Jonson B, Mancebo J, Lemaire F, Brochard L. Influence of tidal volume on alveolar recruitment: respective role of PEEP and a recruitment maneuver. Am J Respir Crit Care Med 2001;163:1609–1613. |
49. | Wilson TA, Anafi RC, Hubmayr RD. Mechanics of edematous lungs. J Appl Physiol 2001;90:2088–2093. |
50. | Chiumello D, Pristine G, Slutsky AS. Mechanical ventilation affects local and systemic cytokines in an animal model of acute respiratory distress syndrome. Am J Respir Crit Care Med 1999;160:109–116. |
51. | Hickling KG, Wright T, Laubscher K, Town IG, Tie A, Graham P, Monteath J, A'Court G. Extreme hypoventilation reduces ventilator-induced lung injury during ventilation with low positive end-expiratory pressure in saline-lavaged rabbits. Crit Care Med 1998;26:1690–1697. |
52. | Imanaka H, Shimaoka M, Matsuura N, Nishimura M, Ohta N, Kiyonon H. Ventilator-induced lung injury is associated with neutrophil infiltration, macrophage activation, and TGF-B1 mRNA upregulation in rat lungs. Anesth Analg 2001;92:428–436. |