BACKGROUND: The volume-dependent single compartment model (VDSCM) has been applied for identification of overdistension in mechanically ventilated patients with acute lung injury. In this observational study we evaluated the use of the VDSCM to identify tidal recruitment/overdistension induced by tidal volume (VT) and positive end-expiratory pressure (PEEP) in lung-healthy anesthetized subjects.
METHODS: Fifteen patients (ASA physical status I–II) undergoing general anesthesia for elective plastic breast reconstruction surgery were mechanically ventilated in volume-controlled ventilation (VCV), with VT of 8 mL•kg−1 and PEEP of 0 cm H2O. With these settings, ventilatory mode was randomly adjusted in VCV or pressure-controlled ventilation (PCV) and PEEP was sequentially increased from 0 to 5 and 10 cm H2O, 5 min per step. Thereafter, PEEP was decreased to 0 cm H2O, VT increased to 10 mL•kg−1 and, keeping minute ventilation constant, PEEP was similarly increased to 5 and 10 cm H2O. Airway pressure and flow were continuously recorded and fitted to the VDSCM with or without considering flow-dependencies. A “distension index” (%E2) derived from the VDSCM was used to assess VT and PEEP-induced recruitment/overdistension. Positive and negative values of %E2 suggest tidal overdistension or tidal recruitment, respectively. In addition, the linear respiratory system elastance was calculated. Comparisons among variables at each PEEP value, VT setting, ventilatory mode, and regression model considering or not considering flow-dependencies were performed with the Wilcoxon-sign rank test for paired samples (P < 0.05). Multiple comparisons were corrected with the Bonferroni method. The relative change in the estimated noisy variance was used as an index of the goodness of fit of the models.
RESULTS: VDSCM including the flow-dependent parameter significantly improved estimated noisy variance in almost all experimental conditions (11.2 to 71.4, smallest of the lower and highest of the upper 95% confidence intervals). No differences in %E2 were observed between VCV and PCV, at comparable VT and PEEP levels, when flow-dependencies were included in the regression model. The negligence of the flow-dependent parameter systematically led to an underestimation of %E2 in PCV compared to VCV mode (all P < 0.02). At a given VT, %E2 was negative at a PEEP of 0 cm H2O and significantly increased with PEEP, being almost 0 at a PEEP of 5 cm H2O. At a given level of PEEP, %E2 significantly increased with VT.
CONCLUSIONS: The distension index %E2, derived from the VDSCM considering flow-dependencies, seems able to identify tidal recruitment/overdistension induced by VT and PEEP independent of flow waveform in healthy lung-anesthetized patients.
From *Laboratory of Pulmonary Engineering, Biomedical Engineering Program/COPPE, Federal University of Rio de Janeiro and National Institute of Cancer III, Rio de Janeiro, Brazil; and †Laboratory of Respiration Physiology, Carlos Chagas Filho Institute of Biophysics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Accepted for publication March 5, 2012.
Supported by grants from the Brazilian Council for Scientific and Technological Development (CNPq, No 470917/2010-5) and Carlos Chagas Filho Rio de Janeiro State Research Supporting Foundation (FAPERJ, No E-26/110.385/2010).
The authors declare no conflicts of interest.
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Address correspondence to Alysson Roncally Carvalho, PhD, Federal University of Rio de Janeiro, Carlos Chagas Filho Institute of Biophysics – –CCS, Laboratory of Respiratory Physiology, Ilha do Fundão, 22210-030 -, Rio de Janeiro, RJ, Brazil. Address email to email@example.com; firstname.lastname@example.org.
Pulmonary atelectasis is a common complication during general anesthesia.1 Anesthesia-induced atelectasis causes an increase in intrapulmonary shunt2 and is well correlated with the progressive reduction in lung compliance and oxygenation impairment during surgery.1
Recruitment maneuvers (RM) have been used to reexpand atelectatic areas.3,4 However, lung instability in anesthetized and paralyzed conditions may require several RM throughout surgery3,5 and atelectasis rapidly recurs if no positive end-expiratory pressure (PEEP) is applied.6,7 Thus, the use of PEEP for the prevention of the anesthesia-induced atelectasis seems reasonable.5,6,8 However, no evidence has been shown as to whether intraoperative PEEP alters respiratory complications in an unselected surgical population.9 Moreover, PEEP is associated with several adverse effects such as decreased venous return,10,11 increased pulmonary vascular resistance,12 decreased left ventricular compliance,12,13 cardiac output and systemic oxygen delivery,12,14 and still may result in lung overdistension with increased deadspace ventilation14,15 and lung rupture.15–17 Hence, the optimal level of PEEP is still controversial and difficult to predict.
In this observational study we evaluated the usefulness of continuous monitoring of respiratory system mechanical properties using the volume-dependent single compartment model (VDSCM) to identify tidal volume (VT) and PEEP-induced tidal recruitment/overdistension in lung-healthy anesthetized subjects. This model partitions respiratory system elastance (Ers) into a volume-independent elastance (E1) and a volume-dependent component (E2.V)18,19 and allows the calculation of a distension index (%E2) that quantifies the concavity of the dynamic pressure-volume (PV) curve yielding the identification of tidal recruitment/overdistension induced by VT and PEEP. Additionally we included in the VDSCM a flow-dependent resistance to evaluate the disturbances in %E2 due to nonlinearities mainly related to the endotracheal tube (ETT) mechanical properties and to compare %E2 in pressure (PCV)- and volume-controlled ventilation (VCV), with exponential and constant inspiratory flow waveforms, respectively.
The hospital IRB of the National Institute of Cancer III approved the study protocol (CEP no.47/05). Fifteen women having plastic breast reconstruction surgery were enrolled in the study. All patients were scheduled to have general anesthesia with tracheal intubation and controlled mechanical ventilation. The exclusion criteria were patients with respiratory diseases and ASA physical status more than II.20 All patients signed the free-informed consent form 24 hours before the surgical procedure.
After venous access implantation, anesthesia was induced by fentanyl (5 μg·kg−1), xylocaine (40 mg) and propofol (2 to 2.5 mg·kg−1) and muscle relaxation was achieved with rocuronium (0.8 mg·kg−1). Thereafter, mechanical ventilation with a facemask was applied until complete neuromuscular blockade, followed by tracheal intubation with an ETT of 7.5 mm inner diameter. Anesthesia was maintained with oxygen/nitrous oxide (40%–60%, respectively), plus sevoflurane or isoflurane titrated to maintain end-tidal concentrations 1.0 to 1.5 times the minimal alveolar concentration. Noninvasive arterial blood pressure, electrocardiogram, neuromuscular transmission, pulse oximetry, capnometry, minute ventilation, anesthetic gases, oxygen inspiratory fraction, and mechanical respiratory variables were continuously monitored (S/5, Datex-Ohmeda) throughout surgery.
All patients were initially mechanically ventilated (Aestiva/5, Datex-Ohmeda, EUA) in VCV with VT of 8 mL · kg−1; PEEP of 0 cm H2O; inspiratory to expiratory time ratio of 1:2 and respiratory rate ranging between 8 and 12 bpm, sufficient to keep end-tidal arterial pressure of carbon dioxide lower than 35 mm Hg. After anesthesia stabilization, PEEP was sequentially increased to 5 and 10 cm H2O. Thereafter, PEEP was reduced to 0 cm H2O, VT increased to 10 mL•kg−1, respiratory rate decreased to keep minute ventilation constant, and PEEP sequentially increased as previously described. For each combination of VT and PEEP, ventilatory mode was randomly switched between VCV and PCV and a 3-m in stabilization period was allowed before any data acquisition. After this period, airway opening pressure (Paw) and airflow (F) were continuously acquired for 3 m in yielding a 6-m in period at each ventilatory mode, PEEP and VT combination. Thereafter, the remaining ventilatory mode was adjusted, with the same combination of PEEP and VT, and a 3-m in stabilization period followed by 3-m in acquisition period was allowed.
Signal Acquisition and Processing
Paw was monitored through a lateral port distal to the antibacterial filter attached to the ETT, in which a pressure transducer 163PCO1D48 (Honeywell, Des Plaines, IL) was connected. F was monitored through a variable-orifice pneumotachometer (Hamilton Medical, Rhäzüns, Switzerland) connected to a differential pressure transducer 176PCO7HD2 (Honeywell). Paw and F signals were amplified, low-pass filtered (33 Hz, 4 poles), and recorded with a routine written in Labview 6 (National Instruments, Texas) with a U12 analog-to-digital converter (Labjack, Colorado) at a sampling rate of 200 Hz per channel. Volume (V) was computed as the numerical integration of flow.
Paw, F, and V were used for the calculation of the mechanical parameters of 3 mathematical models of the respiratory system by the least squares method applied to the respective motion equation on a breath-by-breath basis. The first model is the VDSCM18 represented by Equation 1:
The second model, described by Equation 2, presents the elastic properties coincident with the first includes a nonlinear flow-dependence21 that represents the resistive pressure decrease in a tube with a transitional laminar-turbulent airflow profile:
where Rrs and K1 are linear resistances, K2 ·F(t) is the flow-dependent resistance, E1 and E2·V(t) are, respectively, the volume-independent and volume-dependent elastances, PEEP is the airway pressure when V and F are zero.
The distension index (%E2) was then obtained according to Equation 3, considering E1 and E2 estimated with Equations 1 and 2:
Accordingly, a %E2 equal to zero indicates a straight PV line, which corresponds to a constant Ers during inspiration. Values of %E2 > 0% describe a concave shape in inspiratory PV suggestive of tidal overdistension.18 Negative values of %E2 indicate a convex shape in inspiratory PV curve, which suggests tidal recruitment.22 Figure 1 provides a schematic representation of this phenomena.
In addition, a third model was used to calculate the linear Ers by fitting Paw, F, and V to the linear single-compartment model (LSCM) as:
Mechanical parameters identification was performed with data acquired during 3-minute periods at the end of each combination of VT, PEEP, and ventilatory mode. In addition, respiratory cycles were discarded if (1.5. interquartile range [IQR]) • Q1 < %E2 < (1.5 • IQR) • Q3; where Q1 and Q3 are the first and third quartiles of %E2, respectively.
After testing for normality of the distribution of all calculated variables by the Shapiro-Wilk test (all P < 0.001), all variables are presented as median and range values. Comparisons among variables at different PEEP levels (0, 5, and 10 cm H2O), at each ventilatory mode (VCV versus PCV), VT values (6 vs 10 mL·kg-1) and with the regression models including or excluding the flowdependent component (K2·F) were performed with the Wilcoxon signrank test considering a probability value (P) < 0.05. Multiple comparisons were corrected by the Bonferroni procedure and in all cases the corrected P value is provided. Additionally, median and 95% confidence intervals of median differences at each comparison (CI, calculated by the HodgesLehmann estimator with the algorithm described by Bauer23) are provided (see Supplementary Tables I to IX, Supplemental Digital Content 1, http://links.lww.com/AA/A391).
We compared the effects on the calculation of the %E2 derived from the inclusion or exclusion of the flow-dependent parameter in the VDSCM in terms of the percentage differences between the %E2 estimated by these models. Additionally, the estimated noise variance (ENV) was used as an index of the goodness fit of each model used (LSCM, VDSCM, and VDSCM plus K2|F|):
where Pi is the measured airway pressure, P◯ is the airway pressure predicted by the regression model, n is the sample size, and m is the number of model parameters. The lower the value of ENV, the better the model fits the data. When 2 different models are fit to the same set of data, the model of higher order invariably produces a lower value of ENV. To decide if the reduction in ENV is statistically significant, we calculated the percent improvement in ENV as:
where ENV1 is the larger of the 2 values of ENV. Using the appropriate F distribution, we assume that if ΔENV is higher than 30%, then it is <5% likely that ENV1 and ENV2 are estimates of the same population variance. Thus, when ΔENV > 30% we concluded that model 2 gave a significantly better fit to the data compared to model 1.24,25
Online supplemental data (see Supplemental Digital Content 1, http://links.lww.com/AA/A391) provides additional results summarizing all statistical comparisons performed at each experimental condition.
Table 1 presents anthropometric variables as well as surgery duration of all patients. Ventilatory variables measured at each experimental condition are presented in Table 2. Of interest, note that Ers calculated by the LSCM, often used to set PEEP, suggests the combination of highest VT and PEEP levels as the optimal adjustment because a minimal value was reached when PEEP or VT increased (Table 2).
The VDSCM fitted Paw yielding a coefficient of determination always higher than 0.99 at all experimental conditions. Figure 2 shows how the addition of the volume-dependent elastance (E2VT) and the flow-dependent parameter (K2F) in the LSCM improve the goodness of the fit and how it affects the calculation of %E2 in the VDSCM and VDSCM with K2|F|. ΔENV is shown in Figure 2A for each ventilatory mode, VT, and PEEP level investigated. With the flow-dependent parameter, most cases yielded a value of ΔENV higher than the 30% level, particularly in the PCV mode (Fig. 2A). As expected, the highest difference in %E2 was observed between the VDSCM with K2 |F| and the VDSCM in the PCV mode (Fig. 2B).
A significant underestimation in %E2 was observed in PCV compared to VCV, at a given VT and PEEP level, when flow-dependencies were not considered in the regression model (−17.4% to 3.7%, smallest of the lower and highest of the upper 95% confidence intervals, all P < 0.005; Fig. 3). Additionally, in PCV mode, %E2 was always lower when flow-dependencies were not considered compared to %E2 calculated considering the flow-dependencies at identical experimental conditions (−15.4% to 2.5%, smallest of the lower and highest of the upper 95% confidence intervals, all P < 0.001; Fig. 3).
At a given VT, %E2 significantly increased with PEEP (−7.9% to −2.2%, smallest of the lower and highest of the upper 95% confidence intervals at a VT of 8 mL/kg and from −7.8% to −2.2% at a VT of 10 mL/kg, all P < 0.001). For both VTs, at a PEEP of 5 cm H2O, %E2 was almost 0, when calculated with the inclusion of a flow-dependent term, independently of the ventilatory mode (Fig. 3).
The main findings of the present work were (1) %E2 derived from the volume-dependent single compartment model, but not the Ers identified by the linear single-compartment model, was able to guide ventilator setting based on the minimization of tidal recruitment/overdistension in lung-healthy anesthetized subjects; (2) the inclusion of a flow-dependent resistance in the regression model was important for the sake of comparison among %E2 estimated with different inspiratory flow waveforms; and (3) according to the proposed criteria, the combination of a PEEP of 5 cm H2O with VT of 8 or 10 mL/kg was able to minimize cyclical recruitment/overdistension in lung-healthy patients under controlled mechanical ventilation and general anesthesia.
During general anesthesia muscle paralysis and high inspiratory oxygen fraction, currently used in oxygen administration and throughout anesthesia, tend to exacerbate alveolar and airway closure.26,8 Tidal ventilation without PEEP may result in cyclic recruitment/derecruitment; the high pressures required to reopen collapsed airways and alveoli1,27 would enhance the mechanical shear stress in the parenchyma surrounding atelectatic regions. The use of recruitment maneuvers or the adjustment of high PEEP levels, such as 10 cm H2O,5 has been proposed to avoid atelectasis and reduce intrapulmonary shunt and venous admixture.1 However, high VT or sustained pressures (used during recruitment maneuvers) and high levels of PEEP may result in tidal overdistension, thus enhancing mechanical stress in alveolar epithelium and capillary endothelium of previously aerated areas.27
In the present work we propose the use of a “distension index (%E2)” derived from the VDSCM model to guide PEEP and VT adjustment. In this model, respiratory system elastance is partitioned into volume-independent and -dependent components. Thus, we expect to be able to identify PEEP- and VT-induced tidal recruitment/overdistension and, by avoiding such phenomena, try to find the most “protective” combination of PEEP and VT for lung-healthy patients under general anesthesia.
It has been reported that %E2 presents a significant correlation with the amount of tidal hyperinflation as well as with tidal recruitment, assessed by computed tomography images in lung-healthy piglets under general anesthesia.19,28 Despite tidal recruitment and overdistension seeming to occur simultaneously even at high levels of PEEP, %E2 possibly can identify the predominant phenomenon and guide PEEP selection.19 Accordingly, in the present work high levels of PEEP (10 cm H2O) resulted in marked overdistension (with %E2 around than 30%), whereas the absence of PEEP yielded a significant tidal recruitment independently of ventilatory mode or adjusted VT (Fig. 1). Thus, a PEEP of 5 cm H2O apparently is the best compromise between the minimization of tidal recruitment and overdistension, because at this PEEP the respiratory system elastic pressure presented a quite linear shape with %E2 tending to zero.
Edibam et al.29–31 tested if ventilatory modes using different inspiratory flow patterns may alter lung strain, as measured by %E2, in acute lung injury patients. The authors used the VDSCM (but with a linear resistance) and concluded that PCV and VCV present different lung strain. In contrast, Jandre et al.31 in a numerical simulation study identified that the omission of a flow-dependent component (K2|F|) in the estimated model biases the %E2 and that this effect is dependent on the inspiratory flow pattern.
In the present study, similarly to Jandre et al.,31 the inclusion of a flow-dependent parameter (K2|F|) in the identification model considerably influenced the estimation in PCV, but not in VCV mode (Fig. 2). Likely, the higher inspiratory peak flow (Table 2), observed in all patients in the PCV mode (median difference ranging from 0.13 to 0.15 L/s with all corrected P < 0.001), may emphasize airflow dependencies influencing the estimation of %E2. Hence, the use of a flow-dependent component should be considered for the quantification of the elastic pressure concavity as well as for the comparison among different flow-waveforms. Additionally, a slight modification in the original equation for the calculation of %E2, originally proposed by Kano et al.,18 was introduced in the present work. In Equation 3, we used the absolute value of the parameter E2 in the denominator. Thus, for given values of E1, VT, and |E2|, |%E2| is the same independently of the sign of E2 (i.e., %E2 becomes an odd, symmetrical function of E2), whereas with the original formula |%E2| would be higher for a negative E2.
Different methods have been proposed to describe Paw concavity and, thus, identify tidal recruitment and overdistension, such as the “C-slice” method,32,33 or the “stress index.”34,35 %E2 seems to be equivalent to those methods but also presents the potential advantage that it can be used independently of the inspiratory flow waveform.
The quantitative analysis of the shape of the dynamic PV curve by the %E2 seems to be a relatively simple tool to guide mechanical ventilation settings to minimize lung tissue mechanical stress in healthy subjects during general anesthesia. The assumption that dynamic components, such as viscoelastic forces and/or nonhomogeneous compartments, present a small contribution within the tidal ventilation range is likely in normal lungs. It has been reported that in patients with acute respiratory distress syndrome the dynamic PV analysis presents a relative low specificity (high number of false-positive results) to detect tidal recruitment/overdistension.19 In fact, if the PV curve exhibits a sigmoid shape, the model described in this work will fail to represent the downward concavity (at the beginning of inspiration) and the upward concavity (at the end of inspiration). However, this complex shape is not expected in normal lungs during conventional mechanical ventilation with the applied ventilatory settings (PEEP ranging between 0 and 10 cm H2O and VT from 8 to 10 mL·kg−1).
Regarding the sequence of combination of VT and PEEP, one might argue that patient condition, anesthesia status, or plan, or even the amount of atelectasis must make the first combination (PEEP of 0 cm H2O and VT of 8 mL·kg−1) distinct from the last one in terms of respiratory system mechanical properties. However, it is important to note that because paired comparisons were performed, each condition refers to the same patient, same anesthetic drug, and ventilatory settings, with the exception of the mode (PCV or VCV).
Modern ventilators can easily provide estimates of %E2.22 However, additional trials in lung-healthy patients under anesthesia may be required to validate the potential protective effects of mechanical ventilation with parameters guided by %E2.
In conclusion, the distension index %E2, derived from the VDSCM considering flow-dependencies, seems to represent a feasible index to identify VT and PEEP-induced tidal recruitment/overdistension independently of the flow waveform in healthy lung anesthetized patients.
Name: Alysson Roncally Carvalho, PhD.
Contribution: This author helped design the study, analyze the data, and prepare the manuscript.
Name: Sergio A. Pacheco, MSc, MD.
Contribution: This author helped create the experimental protocol and acquire and process the data.
Name: Patricia Vieira de Souza Rocha, MSc.
Contribution: This author helped process the data.
Name: Bruno Curty Bergamini, PhD student.
Contribution: This author helped analyze the data.
Name: Luís Felipe Paula, MSc student.
Contribution: This author helped analyze the data.
Name: Frederico C. Jandre, PhD.
Contribution: This author helped design the study and process the data.
Name: Antonio Giannella Neto, DSc.
Contribution: This author helped design the study, analyze the data, and prepare the manuscript.
This manuscript was handled by: Steven L. Shafer, MD.
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