Airway and tissue mechanics in ventilated patients with pneumonia

Respiratory Physiology & Neurobiology 171 (2010) 101–109

Contents lists available at ScienceDirect

Respiratory Physiology & Neurobiology journal homepage: www.elsevier.com/locate/resphysiol

Airway and tissue mechanics in ventilated patients with pneumonia András Lorx a , Béla Suki b , Magdolna Hercsuth a , Barna Szabó a , István Pénzes a , Krisztina Boda c , Zoltán Hantos c,∗ a

Department of Anesthesiology and Intensive Therapy, Semmelweis University, Budapest, Hungary Department of Biomedical Engineering, Boston University, Boston, MA, USA c Department of Medical Informatics and Engineering, University of Szeged, Szeged, Hungary b

a r t i c l e

i n f o

Article history: Accepted 2 March 2010 Keywords: Respiratory mechanics Airway resistance Elastance Heterogeneity

a b s t r a c t We applied the low-frequency forced oscillation technique (LFOT) to measure respiratory impedance (Zrs) at various positive end-expiratory pressures (PEEPs) in 14 sedated and intubated patients with pneumonia classified into a mild (Group 1) and a severe group (Group 2) based on lung injury scores. The Zrs spectra were fit with the constant-phase (CP) model including Newtonian resistance (RN ) and tissue damping (G) and elastance (H), a distributed airway resistance (DR) and a distributed tissue elastance (DH) model. Using the CP model, all parameters revealed a negative PEEP dependence (p < 0.001) in Group 2 and H was higher in Group 2 (p = 0.014). The variability of H from the DH model was nearly significantly larger in Group 1 (p = 0.061). Following bronchodilator inhalation, G significantly decreased (p = 0.009). Thus, the CP model provides a robust partitioning of Zrs into tissue properties and RN , a surrogate for airway resistance, while the distributed models suggest that lung heterogeneity decreases with increasing PEEP. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Despite significant effort to improve the outcome of acute respiratory failure (ARF) due to pneumonia, this disease remains a serious clinical problem with relatively high mortality rate. Patients with severe ARF and acute respiratory distress syndrome (ARDS) invariably require intubation and mechanical ventilation (Confalonieri et al., 1999). The outcome of patient care critically depends on how mechanical ventilation is managed and the ventilation strategy is often based on simple measurements of the mechanical properties of the respiratory system. At frequencies including the rates of natural breathing and mechanical ventilation, the mechanical properties of the normal lung and the respiratory system can be described accurately by the so-called constant-phase (CP) model (Hantos et al., 1992) which is in fact a single compartment system composed of an airway unit connected in series with a tissue unit. The former consists of a Newtonian resistance and an inertance while the latter is modelled as the complex CP impedance comprised of tissue resistance and reactance with identical frequency dependence and hence a constant phase angle. In diseases such as ARDS, pathological changes in the

∗ Corresponding author at: Department of Medical Informatics, University of Szeged, Koranyi fasor 9, P.O. Box 427, H-6701 Szeged, Hungary. Tel.: +36 62 545077; fax: +36 62 544566. E-mail address: [email protected] (Z. Hantos). 1569-9048/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.resp.2010.03.004

lungs lead to significant differences in regional lung compliance and airway resistance. Depending on the type of pneumonia (parenchymal, bronchial or lobar), alterations in mechanical properties may occur at different length scales from the level of the bronchi down to the alveoli or to the interstitium. Such heterogeneities will in turn determine the regional distribution of ventilation during mechanical ventilation. For example, if a region undergoes significant surfactant deficiency, the corresponding compliance decreases and the region receives reduced tidal ventilation while other regions with normal compliance can easily become over-ventilated. Such uneven ventilation and stretching of the epithelium is known to have a significant impact on the outcome of long-term mechanical ventilation (Brower et al., 2000). Hence it is important to estimate the contribution of airways and tissues to total lung mechanical properties as well as to gain insight into the heterogeneity of the mechanical properties around the breathing frequencies. Furthermore, since positive end-expiratory pressure (PEEP) is perhaps the most important tool in maintaining an open lung, these properties should be obtained as a function PEEP or lung volume. In pneumonia-related ARF and ARDS, only a few studies reported results and they were somewhat contradictory regarding the lung volume dependence of the mechanical parameters (Mols et al., 2001; Pesenti et al., 1991; Wright and Bernard, 1989; Wright et al., 1994). Furthermore, the mechanical properties are usually obtained using the rapid occlusion technique which is influenced by the dynamic conditions and therefore partitioning of total impedance to tissues and airways invariably depends on lung

102

A. Lorx et al. / Respiratory Physiology & Neurobiology 171 (2010) 101–109

volume history. The low-frequency forced oscillation technique (LFOT) offers a simple and rapid assessment of respiratory system impedance (Zrs) under near steady-state conditions which can also be carried out at various PEEP levels to map the lung volume dependence of the mechanical parameters. Since the LFOT has been used in mechanically ventilated normal subjects (Navajas et al., 1990), in patients with normal lung function and undergoing cardiac surgery (Babik et al., 2003; Farre et al., 1998) as well as in those with chronic obstructive pulmonary disease (Farre et al., 1998; Lorx et al., 2009). The first goal of this study was to extend the LFOT to ARF and ARDS patients with pneumonia as the original etiology. A second goal was to investigate whether Zrs data in this patient population carry sufficient information about the mechanical heterogeneity of the lungs. To this end, we collected low-frequency Zrs spectra in a population of patients with pneumonia and fit the data with two extensions of the CP model: the first model incorporated the effects of airway heterogeneities whereas the second model included nonuniform alveolar tissue elastance. Finally, the mechanical parameters were also estimated before and after the application of a bronchodilator agent in order to investigate whether any of the parameters were reversible in this patient population.

2. Methods 2.1. Subjects The study was approved by the Semmelweis University local ethical committee in agreement with the Hungarian Medical Research Council, Scientific and Human Research Ethical Committee. Informed consent was obtained from all patients or their relatives. We investigated 14 patients admitted to the ICU. The patients were intubated and ventilated at admission because of acute respiratory insufficiency due to community-acquired pneumonia (CAP). The criteria for CAP at admission were fever, cough, sputum production, dyspnoea, crackles, new infiltrate on chest-X-ray, and a characteristic change in white blood cell count and plasma inflammatory parameters (Bartlett and Mundy, 1995). Additionally, they were admitted to the ICU if two or more of the ATS criteria for severe CAP were fulfilled including respiratory rate above 30/min, PaO2 /FiO2 below 250, bilateral or multiple lobe involvement on chest-X-ray, systolic blood pressure below 90 mmHg, or diastolic blood pressure below 60 mmHg and/or need for vasopressor. The demographic, anthropometric and clinical characteristics of the patients are summarized in Table 1.

Patients were classified into two groups according to the severity of their condition. We used the lung injury score (LIS) (Murray et al., 1988) calculated from three parameters (the number of involved quadrants on chest-X-ray, PaO2 /FiO2 , and applied PEEP) to evaluate the illness severity. A LIS of higher than 2.5 was assumed to indicate severe lung injury or ARDS and these patients were assigned to Group 2, while patients with a LIS of 2.5 or lower (Group 1) had a milder form of CAP. There were no statistically significant differences between the groups in age, weight, height or BMI. Patients were sedated with propofol (1.5–4 mg kg−1 h−1 ) to establish a sedation level around a Ramsay score (Ramsay et al., 1974) of 2–3; this was followed by intubation with a cuffed endotracheal tube (ETT) in the 7–9-mm ID range (Portex, Hythe, UK). Mechanical ventilation was then applied using the pressure control mode with 50% inspired oxygen fraction, a PEEP level between 5 and 12 cmH2 O, a tidal volume of 6–8 ml kg−1 and an inspiratory time between 0.8 and 1.2 s. The respiratory rate was adjusted between 14 and 25 breaths per min to achieve a pH of ∼7.37. Patients were in supine position with a 30◦ upper body elevation, and did not receive any bronchoactive therapy for 3 h before the assessment of the mechanical properties of the respiratory system using LFOT. Arterial blood gases (ABL 800 Flex Radiometer, Copenhagen, Denmark), end-tidal carbon dioxide, arterial saturation, ECG and blood pressure were continuously monitored. For the LFOT measurements, deep sedation was introduced with propofol (a 0.5–1.5 ml kg−1 bolus followed by a 6–12 mg kg−1 h−1 infusion as required). In patients for whom anaesthesia alone was not sufficient to provide a total apnea of 12 s, muscle relaxation with vecuronium was also administered (a 4–8 mg bolus, with supplementary fractional boluses for maintenance as needed). Ventilation was then stopped for the LFOT measurement period and restored immediately afterwards with the preset ventilatory parameters. 2.2. Measurement of Zrs The Zrs spectra were obtained at the PEEP levels 3, 5, 7, 10 and 13 cmH2 O, adjusted in a random sequence, according to the LFOT setup and protocol described previously (Lorx et al., 2009). Further details of the application of LFOT in anesthetized and mechanically ventilated subjects during apneic intervals have been described previously (Babik et al., 2003; Lorx et al., 2009). Briefly, a loudspeaker-in-box system delivered pseudorandom pressure oscillations (peak-to-peak size < 2.5 cmH2 O) with frequencies between 0.4 and 4.8 Hz into the ETT for 12 s of endexpiratory pause. Three-to-five measurements were collected at each PEEP level, with >1 min intervals of ventilation resumed between the recordings, and the Zrs spectra were ensemble-

Table 1 Demographic, anthropometric and clinical characteristics of the patients. Patient #

Gender

Age (years)

1 2 3 4 5 6 7 8 9 10 11 12 13 14

M F F M M M F F F M M M M M

67 46 55 56 78 59 87 42 56 75 72 69 30 52 60.2 ± 12.3

PaO2 /FiO2 316.7 314.3 418.9 330.0 353.3 291.4 311.4 199.7 325.0 194.5 172.8 238.3 180.2 161.4 272 ± 69.3

LIS

Group

CX-ray

Quadrants

Weight

Height

0.33 0.33 0.33 0.66 0.66 0.66 1.00 1.33 1.66 2.66 2.66 2.66 3.00 3.33

1 1 1 1 1 1 1 1 1 2 2 2 2 2

Lobar Bronchial Bronchial Lobar Bronchial Bronchial Lobar Lobar Lobar Diffuse-parenchymal Diffuse-parenchymal Diffuse-parenchymal Diffuse-parenchymal Diffuse-parenchymal

1 1 1 1 1 1 2 1 2 4 3 3 4 4

60 79 45 90 100 70 70 65 75 74 60 57 50 104

156 175 157 174 164 166 172 183 165 155 167 159 162 160

1.5 ± 1

2.1 + 1.1

71.4 ± 13.4

165.4 ± 6.4

F, female; M, male; LIS, lung injury score; CX-ray, chest-X-ray; Quadrants, number of infiltrated quadrants on the CX-ray; BMI, body-mass index.

BMI 24.7 25.8 18.3 29.7 37.2 25.4 23.7 17.5 27.5 30.8 21.5 22.5 19.1 40.6 26 ± 5.1

Berodual Yes No Yes Yes Yes Yes Yes No Yes Yes Yes No Yes No

A. Lorx et al. / Respiratory Physiology & Neurobiology 171 (2010) 101–109

averaged for each condition. In 10 patients, the measurements of Zrs were repeated at PEEP levels of 3, 7 and 10 cmH2 O, applied in this order, following administration of 2 ml of nebulized fenoterol hydrobromide plus ipratropium bromide (Berodual) solution. The ETT was considered at the LFOT frequencies an impedance in series with the respiratory system, and this impedance was measured separately as follows. Each ETT used was connected at its distal end to a 3-l plastic container and the oscillation signal amplitude was adjusted to provide a flow amplitude similar to those in the patients. LFOT was then applied in an identical manner as during measurements in patients, the impedance of the ETT was determined and subtracted from each patient’s total input impedance before model fitting. Cardiogenic components of the pressure and flow signals often corrupted the estimates of Zrs at oscillation frequencies coinciding with or close to the heart rate and its harmonics, which were characterized by large coefficients of variation in the repeated measurements and were excluded from further analysis. 2.3. Modelling and parameter estimation The mean Zrs spectra were first evaluated by fitting the single compartment model of Zrs at low frequencies (Hantos et al., 1992) that yields estimates for a frequency-independent (Newtonian) resistance (RN ), inertance (I), and the constant-phase coefficients of tissue damping (G) and elastance (H). In the CP model (Hantos et al., 1992), the tissue impedance (Zti ) is calculated as Zti (ω) =

G − jH , ω˛

with ˛ =

2 arctan 

H  G

(1)

where ω is the circular frequency and j is the imaginary unit. The exponent ˛ describes the frequency dependence of tissue resistance (Rti = G/(ω)˛ ) and tissue elastance (Eti = H(ω)1−˛ ). The respiratory system impedance can then be obtained by adding the impedance of the airway tree (Zaw ) in series to the tissue impedance where Zaw is given by Zaw = RN + jωI

(2)

Hysteresivity (Fredberg and Stamenovic, 1989) () was calculated as  = G/H. In this model, there are four parameters to estimate from the impedance spectra: RN , I, G and H. To test whether airway heterogeneities influenced Zrs, the airway tree was modelled as a set of parallel pathways each composed of a Newtonian resistance R, and an inertance, I, connected in series with a tissue compartment (Suki et al., 1997). The values of G and H were the same in each pathway while R was varied according to a hyperbolic distribution function defined as n(R) = B/R where B is a normalizing constant and R was allowed to vary between a minimum (Rmin ) and a maximum (Rmax ) value. In this model, five parameters (G, H, Rmin , Rmax and I) were determined. Total resistance of the model, RN , can be calculated from n(R). Because R is not normally distributed, we used the median value of n(R) defined as the value Rm of n(R) which separates n(R) into two regions each having the same area, 0.5. This calculation yields the following: Rm =



Rmin Rmax

(3)

In order to characterize the heterogeneity of the model, we introduced an index, the coefficient of variability, defined as COV =

Rmax − Rmin Rm

(4)

This model is referred to as the distributed-resistance (DR) model. To test for the presence of tissue elastance heterogeneities, the airway tree was represented by a set of parallel pathways each composed of an in series connection of RN , I and a tissue compartment (Ito et al., 2004). In each pathway, RN and I were the same while the

103

values of the elastance H were different. Since H was distributed, ˛ in Eq. (1) would also be distributed which would not allow for a simple solution of the network. To avoid this difficulty, Eq. (1) is written as Zti (ωn ) =

( − j)H  , ωn˛

with ˛ =

2 arctan 

1 

(5)

By assuming that each tissue compartment had the same , ˛ would not depend on H in Eq. (5). Similar to the DR model, the distribution function n(H ) was also hyperbolic, n(H ) = B/H , between a minimum (Hmin ) and maximum (Hmax ) value. In this model, five parameters (RN , I, , Hmin , and Hmax ) were determined. The median (Hm ) and the COV of the elastance of the network are analogous to the expressions in Eqs. (3) and (4), respectively. The parameter G was calculated as G = Hm . This model is referred to as the distributed-elastance model (DH). In both the DR and DH models, the total input impedance of the networks can be analytically obtained as described previously (Ito et al., 2004; Suki et al., 1997) which allows a formal fitting of these models to measured impedance spectra. The parameters in all three models were then estimated by using a global optimization procedure (Csendes, 1989). The model performance was characterized by the root-mean-square differences between measured and model data, normalized by impedance magnitude at the corresponding frequency and expressed as percentage (F%). Since the DR and DH models are associated with multiple parameter sets that provide similarly good fits to the measured data, the parameter estimation algorithm was run ≥50 times on every Zrs spectrum, and the parameter sets with the lowest F% were selected. 2.4. Statistical analysis Two-way repeated measures ANOVA was employed to evaluate the dependences of the CP model parameters and the COV values of the distributed models on PEEP in the two subject groups. Three-way repeated measures ANOVA was used initially to study the effects of bronchodilator inhalation and PEEP on the CP model parameters in the two groups. Since the effect of the patient group and all interactions with groups were not significant, the final analysis was performed with a two-way repeated measures ANOVA with two within-subject factors (PEEP and inhalation). The Holm–Sidak test was used for pairwise comparisons. Three-way repeated measures ANOVA was employed for the comparison of the lumped model parameters obtained from different models and at different levels of PEEP. 3. Results Zrs spectra in two representative subjects from each group are presented in Fig. 1. Elevation of PEEP from the lowest to the highest level resulted in a marked decrease in the real part of Zrs (Rrs) at all frequencies and in both patients, which was accompanied with a significantly milder increase in the imaginary part (Xrs). Both Rrs and Xrs have significantly larger magnitudes in the Group 2 subject at all frequencies. 3.1. CP model Fig. 1 also demonstrates the quality of fit of the CP model which generally captured the main features of the data. Overall, the CP model was consistent with the majority of the Zrs spectra in both groups; however, in some patients small systematic errors could be observed with a slight underestimation of Rrs and overestimation of Xrs above 3 Hz as well as a slight overestimation of the magnitude of both Rrs and Xrs below 1 Hz. Model fitting in these cases can result in a moderate increase in the estimate of G and an inertance that typically had a value close to zero.

104

A. Lorx et al. / Respiratory Physiology & Neurobiology 171 (2010) 101–109

Fig. 1. Mean ± SD values of respiratory resistance (Rrs) and reactance spectra (Xrs) measured in representative subjects from Group 1 (left) and Group 2 (right) at PEEP levels of 3 cmH2 O (closed symbols) and 13 cmH2 O (open symbols). Solid lines indicate the best fit impedance curves of the constant-phase model.

Generally, the parameters in the CP model tended to show an inverse relation with PEEP with higher values in Group 2, although the differences did not always reach a statistically significant level (Fig. 2). The CP model fits the data similarly in both groups at all PEEP levels since there were no differences in F% between groups or PEEP levels, nor was there any interaction between group and PEEP (data not shown).

With regard to RN , two-way repeated measure ANOVA revealed a significant overall PEEP dependence (p < 0.001). Individually, we found significant differences only in Group 1 with RN being significantly lower at PEEPs of 10 and 13 than at 3 cmH2 O. While the median RN was higher in Group 2 at every PEEP, due to the large inter-subject variability in both groups, this difference did not reach the level of statistical significance.

Fig. 2. Comparison of the median and interquartiles (25–75%) of the Newtonian resistance (RN ), tissue damping (G), elastance (H) and hysteresivity () at transrespiratory pressures of 3, 5, 7, 10 and 13 cmH2 O, estimated from the constant-phase model. Significant differences between the two groups at any given PEEP are indicated with p values vertically oriented. Significant differences within a group at a given PEEP compared to its value at PEEP cmH2 O are shown by horizontally oriented p values.

A. Lorx et al. / Respiratory Physiology & Neurobiology 171 (2010) 101–109

105

A strong inverse relation was also seen between G and PEEP (p < 0.001), which was primarily due to the significant decrease in G with PEEP in Group 2 as demonstrated by the pairwise comparisons. Although the values of G tended to be higher in Group 2 which was statistically significant at the PEEP of 3 cmH2 O, overall significance between the two groups was not reached. H was markedly higher in Group 2 (p = 0.014) and also displayed a strong overall PEEP dependence (p < 0.001). However, there was no interaction between group and PEEP despite the fact that the PEEP dependence reached statistical significance only in Group 2.  showed a significant PEEP dependence (p = 0.003), but there was no difference between the groups and there was no interaction either. I (data not shown) exhibited a PEEP dependent behaviour (p = 0.007) with significant interaction between group and PEEP (p = 0.016); however, the I in the two groups did not differ from each other.

3.2. DH and DR models Comparison of the three different models using three-way repeated measure ANOVA showed a statistically significant dependence of F% on model type (p = 0.001) but not on PEEP (Fig. 3). Pairwise comparisons revealed a slight but significant improvement in F% at nearly all PEEP levels when the DH or DR model was fitted to the data instead of the CP model (p = 0.003 for both). There was no statistically significant difference between the performances of the two distributed models. Also note that even though the reductions in F% by the distributed models were statistically significant, the actual improvements were small, always less than 0.5%. The non-distributed parameters in the distributed models (RN in the DH model, H in the DR model) were similar to those estimated by the CP model (Fig. 4). The regression between RN from the CP and the DH models had a slope of 0.95 with r2 = 0.956 with only a few data points falling significantly below the line of identity. Simi-

Fig. 3. Mean ± SEM values of the fitting error (F%) of the constant-phase (CP), distributed-elastance (DH) and distributed-resistance (DR) models. Data are pooled for the Group 1 and 2 subjects. Values of p are the results of pairwise comparisons (DH vs CP and DR vs CP).

larly, the regression between H from the CP and the DR models was very strong with a slope of 1.02 and r2 = 0.995 and with no outliers. With regard to , the relations between the distributed and the CP models were considerably weaker (r2 = 0.848 and r2 = 0.564 for the DH and DR models, respectively). The deterioration of these relations was a result of significant scatter in the data due primarily to the fact that the distributed models sometimes produced unrealistically low estimates of . The distributed models also provide information on modelbased heterogeneity of airway resistance (DR model) or tissue elastance (DH model). The values of the COV of RN and H in the two groups are shown as a function of PEEP in Fig. 5. Generally,

Fig. 4. Correlation of parameters between the distributed and the CP models. (A) RN from the DH model vs RN from CP model; (C): H from DR model vs H from CP model; (B and D) hysteresivity () from the DH and the DR model (respectively) compared to the  from the CP model.

106

A. Lorx et al. / Respiratory Physiology & Neurobiology 171 (2010) 101–109

patient population in these reports is often heterogeneous (Mols et al., 2001; Pesenti et al., 1991; Wright and Bernard, 1989). Hence, we specifically studied the PEEP dependence of the mechanical properties of the respiratory system in a relatively homogeneous population of patients with pneumonia. The main findings of this study are that (1) LFOT provides useful information about the mechanical condition of patients with pneumonia in the ICU; (2) simple modelling of the impedance data allows the partitioning of respiratory mechanics to total resistance and tissue properties characterizing the periphery of the lung; and (3) in this population of patients, most of the abnormalities are reflected in tissue elastance. Numerous studies exist that used LFOT in animal models of ARDS (Allen et al., 2002, 2007; Bellardine Black et al., 2007; Kaczka et al., 2005, 2009), and the quality of the measured Zrs spectra obtained in animals deeply anesthetized, paralyzed and tracheotomized are usually better than that in human studies. Consequently, extracting parameters from these data using complex models such as the distributed models can also be more reliable than in human studies. On the other hand, animal models of ARDS such acid aspiration injury (Allen et al., 2007), treatment with oleic acid (Kaczka et al., 2005) or LPS (Ingenito et al., 2001) can be significantly different from the pathophysiology of human pneumonia. Since no such data exist in the literature, we aimed at measuring respiratory impedance using LFOT in patients with pneumonia and analyzing the results using both a single compartment model and two separate distributed models of the lung. Before interpreting our results, we discuss several factors that can influence the quality of impedance data. 4.1. Impedance spectra Fig. 5. Coefficient of variability (COV) of airway resistance (R, top) and tissue elastance (H, bottom) estimated from the DR and DH models, respectively, as a function of PEEP in Groups 1 and 2.

COV appears to be larger in Group 1 with both models and at most PEEP levels. However, due to large inter-individual variations, there was no difference between the two groups and the PEEP dependence was not significant. Nevertheless, it is interesting to note that with the DH model, COV was nearly significantly larger in Group 1 (p = 0.061). 3.3. Bronchodilator therapy In a subgroup of the patients (see Table 1), the effects of bronchodilator administration were examined (Fig. 6). As mentioned above, no separate analyses were carried out for Groups 1 and 2. Also, because the improvement by the DH and DR models was minor, the results are reported only in terms of the CP model. The statistical analysis showed that bronchodilator inhalation significantly decreased G (p = 0.009), but no change occurred in RN , H,  (Fig. 6) and F% (not shown). The PEEP dependence before and after of inhalation was significant for G (p = 0.001), H (p = 0.004), RN (p = 0.003) and for  (p = 0.05). Additionally, we found a significant interaction between PEEP and bronchoactive therapy for the parameter G (p = 0.048), whereas for H, the interaction was close to significant (p = 0.055). These results indicate a more dominant effect of the bronchodilator Berodual at low PEEP compared to practically no effect at 10 cmH2 O PEEP. 4. Discussion The primary goal of the current study was twofold. First, we aimed at extending the LFOT to patients with ARF and ARDS. Second, while some limited results have been published, the ARDS

During mechanical ventilation of patients with ARDS, absolute lung volume plays a key role in the outcome of a specific ventilation strategy, since a decrease in lung volume due to atelectasis increases the regional tidal volumes in the open regions and hence possibly the development of volutrauma. It is therefore important to map the lung volume dependence of the mechanical properties of the respiratory system. While absolute lung volume in this study was not assessed, we established specific airway pressure levels set by the PEEP for the measurement of Zrs at various time points. However, the same PEEP might not always have resulted in similar absolute lung volumes. Indeed, lung volume in patients with ARDS at a specific airway pressure can depend considerably on the previous volume history through the processes of recruitment and derecruitment, in addition to tissue viscoelasticity. This is important when the Zrs spectra obtained at the same PEEP but collected at different time points were averaged. More crucial is perhaps the potential effects of lung volumes when the mechanical parameters corresponding to pre- and postinhalation are compared at identical airway pressure levels. While changes in lung volume can induce real alterations in the mechanical parameters, there are additional factors that can directly influence the quality of the data. For example, if the heart frequency and its harmonics are close to any of the input frequencies, it can contaminate the Zrs spectra. In this study, all Zrs spectra were examined individually and frequency points were excluded from the analysis if the variability of the impedance was high. Unfortunately, decreasing the number of frequencies can influence the estimation of the model parameters especially in the distributed models. Additionally, in a few cases, the frequencydependent decrease of Rrs below 2 Hz was considerably diminished compared to that seen in Fig. 1. In such cases, fitting the heterogeneous models that incorporate two different mechanisms for the frequency dependence of Rrs becomes difficult and can result in non-physiological as well as non-unique parameter estimates

A. Lorx et al. / Respiratory Physiology & Neurobiology 171 (2010) 101–109

107

Fig. 6. Effects of bronchodilator (Berodual) administration on the median and interquartiles (25–75%) values of Newtonian resistance (RN ), tissue damping (G), elastance (H) and hysteresivity () before and after bronchodilatation at transrespiratory pressures of 3, 7 and 10 cmH2 O. Values of p in the horizontal direction indicate the significance level of differences between the parameter at a given PEEP compared to its value at PEEP of 3 cmH2 O. When there was no effect of inhalation, the data before and after inhalation were combined and the p values above the horizontal brackets at a given PEEP indicate the significance level compared to that at PEEP of 3 cmH2 O. Significant changes to therapy at any given PEEP are indicated with vertical p values.

with reduced reliability. Longer measurement time could solve the problem; however, that was not feasible in these patients. Another possibility that might contribute to the quality of the Zrs data is the time variation of the mechanical properties of the lung during the apneic period of the measurement. The lung volume history immediately before the data acquisition can introduce stress adaptation in the tissues. It is also possible that derecruitment occurs during the apneic period especially at low PEEP levels. Both of these mechanisms would result in changes in the mechanical properties which in turn would slightly reduce the smoothness of Zrs with a subsequent decrease in the reliability of the mechanical parameters extracted from the Zrs spectra especially in the case of the DR and DH models.

ature (Ramsay et al., 1974). We found that we could characterize all of our patients using LIS, even those who required mechanical ventilation, but had a FiO2 /PaO2 ratio above 300. The LIS scores were calculated by involving only three parameters without including respiratory compliance since the PEEP dependence of this compliance was one of the target measurement points, and also because the compliance was in fact lung volume dependent. Patients with a LIS score >2.5 were considered to have sever lung injury, and hence they were appropriate to be compared to ARDS patients. Of the 5 patients with LIS > 2.5, 4 also had ARDS while the remaining one qualified only as a patient with ALI since the PaO2 /FiO2 was 238.

4.2. Subject population

The experimental data were first analyzed in terms of the single compartment CP model. There was a strong overall PEEP dependence of RN (Fig. 2A) suggesting that airways dilated with increasing airway pressure. While RN in Group 2 tended to be higher than in Group 1, the large inter-individual variation of RN with PEEP precluded a statistically significant difference. Possible explanations include airway closure/opening and parenchymal tethering differently contributing to RN in the two groups. The decreases in G and H with elevated PEEP are in accordance with at least one previous finding in ventilated subjects (Dechman et al., 1995). According to the constant-phase description of tissue impedance (Hantos et al., 1992), the parameter G represents tissue damping or resistance (Fig. 2B). However, it has been shown that airway heterogeneities can significantly contribute to the apparent value of G (Lutchen et al., 1996). The strong negative PEEP dependence of G in our case suggests recruitment as well as airway dilation related attenuation of heterogeneities with increasing PEEP. The most straightforward parameter to interpret is H (Fig. 2C) which showed a strong negative PEEP dependence as well as significant difference between the two groups. From tissue strip studies it is known that tissue stiff-

Currently, there are no data in the literature regarding the mechanical properties of the respiratory system in patients with ARF and ARDS obtained using LFOT. Previous measurements which include the occlusion technique, or other simple measurement techniques (Mols et al., 2001; Pesenti et al., 1991; Wright and Bernard, 1989), are somewhat difficult to directly relate to Zrs acquired during apnea in this study. On the other hand, there is no control population in which similar measurements would be reported in normal subjects. We are aware of only two studies in which LFOT and constant-phase modelling were applied in subjects with normal lungs (Babik et al., 2003; Kaczka et al., 1997). The values for RN , G, H and  measured in closed chest subjects at a PEEP of 2 cmH2 O before and after cardiopulmonary bypass are similar in magnitude to those in Group 1 at a PEEP of 3 cmH2 O; however, H in Group 2 is higher than that reported in (Babik et al., 2003). Due to the lack of controls, i.e. healthy sedated subjects measured at various PEEP levels, we attempted to group our patients based on the LIS which is a widely used scoring system in the liter-

4.3. CP model-based analysis

108

A. Lorx et al. / Respiratory Physiology & Neurobiology 171 (2010) 101–109

ness increases with increasing mean strain level (Maksym et al., 1998; Yuan et al., 1997). Thus, since H is less sensitive to heterogeneities (Lutchen et al., 1996), any decrease in H with increasing PEEP must be due to recruitment of alveoli. In Group 2, H was significantly higher than in Group 1 indicating a more severe parenchymal involvement that may result from increased surface tension at the air–liquid interface or the presence of edema. The fact that  defined as G/H exhibited a statistically significant negative dependence on PEEP implies that the rate of decrease of G with PEEP is stronger than that of H. Indeed, as mentioned above both heterogeneity and recruitment contribute to G but only recruitment contributes to H. Studies that report PEEP or lung volume dependence of resistance are rare. Using the rapid occlusion technique, Pesenti et al. found a slight increase in resistance with the elevation of PEEP which also contributed to an increase in the viscoelasic properties (Rrs) while the Newtonian resistance remained constant. The authors hypothesized that the elevated Rrs was due to an increase in heterogeneity, a pendelluft phenomenon. These results contradict to the PEEP dependence of RN and G (Fig. 2) found in this study. One should realize, however, that the rapid interrupter technique employs much more dynamic conditions than the LFOT. Indeed, data are collected while inspiration is interrupted whereas during LFOT, mean lung volume is constant and the measurement is carried out under quasi-static conditions. Additionally, the results from the interrupter technique may correspond to frequencies higher than those applied in the LFOT (Bates et al., 1992). Nevertheless, extracting RN , a surrogate of airway resistance, from Zrs is more reliable than estimating it from the interrupter technique because Rrs is primarily contributed to by RN above 4 Hz whereas tissue resistance and heterogeneities contribute to Rrs below 2 Hz, providing a convenient partitioning. We should also point out that the patient population reported by Pesenti et al. (1991) was different from ours in that it included both primary and secondary ARDS patients. In another study conducted on a mixed ARDS population using the slice method, Mols et al. (2001) found that pulmonary resistance can change during tidal breathing in a complex manner. Although our population was more homogeneous in that the ARDS condition developed on the basis of pneumonia alone, the values of RN and G and their PEEP dependence varied highly from patient to patient representing different etiologies and/or lung conditions. Wright and Bernard (1989) reported resistance values in a mixed ARDS population that were five times higher than those in normal subjects. These authors also observed a very diverse patient-topatient variation in resistance in their ARDS group. We can thus conclude that there is a wide inter-subject variability of resistance independent of the applied measurement technique. Furthermore, RN plays an important role in the elevated total lung resistance of ARDS patients since it is approximately 50% of Rrs around the breathing frequencies (Fig. 1). Nevertheless, the difference between airway resistance in normal subjects and ARDS patients is moderate. 4.4. Distributed model-based analysis We also explored the utility of two models that are more complex than the CP model in that they incorporate a set of parallel pathways and hence they allow the estimation of heterogeneity of airway related pathway resistance in the DR model, or the heterogeneity of tissue elastance in the DH model. These models have the potential to partition Rrs into Newtonian resistance, tissue resistance and resistance associated with heterogeneities due to parallel time constant inequalities. Indeed, in an elastase-induced mouse model of emphysema, the functional heterogeneity characterized by the coefficient of variation of H was in quantitative agreement with structural heterogeneity measured by the coefficient of variation of airspace sizes (Ito et al., 2004). Similarly,

there was a significant correlation between heterogeneity assessed with the DR model and the standard deviation of airway diameters obtained from computed tomography (Kaczka et al., 2009). While these models contain one extra parameter compared to the CP model, it is surprising that they offer only a modest albeit statistically significant improvement in model fitting (<10% drop in F%) using group comparisons. In order to verify that this improvement also occurred at the level of individual impedances, we used the Akaike criterion in a similar, but not identical manner as described by Kaczka et al. (2007). First, we calculated the variance of the impedance magnitudes and averaged it over the measured frequencies. Next, we estimated the chi-square statistic from the relative errors assuming a simple proportionality between impedance and its variance and applied the corrected Akaike criterion for small sample sizes on an individual basis (Burnham and Anderson, 2002). The results showed that both the DR and DH models brought statistically significant improvements over the CP model only in 10% of the data sets. The cost of this minor improvement was, however, a significantly decreased reliability of the estimated parameter values. Despite the fact that both models invariably produce different errors and parameters when fitted multiple times to the same Zrs spectrum, the values of H estimated from the DR model and RN from the DH model correlated well with those obtained from the CP model (Fig. 4). The estimation of G was less reliable which resulted in the spurious values of . The advantage of the distributed models is that they allow the characterization of functional heterogeneities in terms of the COV of H or RN . As shown in Fig. 5, the heterogeneity tends to decrease with increasing PEEP. Even though this drop in COV with PEEP was not statistically significant, it is likely a true signal because as PEEP is increased, alveolar airspaces are recruited and hence the lung should become more homogeneous. Interestingly, COV in Group 1 was nearly statistically significantly higher than in Group 2 (p = 0.061). Together with the significantly higher H in Group 2, this may suggest that in Group 2, a large portion of the lung was uniformly collapsed resulting in a smaller but more homogeneously injured lung. In general, heterogeneity does not always need to correspond with the degree of lung injury or impairment. Nevertheless, we should stress here that, as mentioned above, our data in sedated human patients with ALI/ARDS may not allow a fully reliable partitioning of Rrs into airway resistance, tissue resistance and heterogeneities. Hence, the potential insight gained from these more complex models has to be taken cautiously. 4.5. Clinical implications While the results of the bronchodilator inhalation were evaluated in terms of all three models, the use of the DR and DH models did not lead to any new insight compared to the results obtained from the CP model. Hence, the discussion is limited to the CP modelbased results. The data showed that the administration of Berodual did not result in a significant decrease in RN (Fig. 6). This finding is in contrast to previous studies in which the authors suggested that abnormalities in airflow resistance were at least partially due to bronchospasm which is reversed by the bronchodilator (Wright et al., 1994). In the present study, the large patient-to-patient variation in RN may have contributed to the lack of a decrease in RN . Indeed, some patients displayed a large decrease in RN while others showed no drop or even a slight increase. There was a mild decrease in RN especially at 3 cmH2 O of PEEP but without reaching the level of statistical significance. The only parameter that did change significantly after inhalation was G. The decrease in G suggests some improvement in the peripheral heterogeneity, while the fact that H did not change implies no real recruitment at low PEEP levels facilitated by the bronchodilator therapy. At PEEP of 10 cmH2 O, however, the inhalation had practically no effect on the mechanical parameters. A possible explanation is that the higher airway

A. Lorx et al. / Respiratory Physiology & Neurobiology 171 (2010) 101–109

pressure already eliminated part of the airway heterogeneity, and the administration of bronchodilator had no further effect on any of the parameters. Additionally, the anesthetic propofol itself can act as a bronchodilator (Brown et al., 2001) and hence may have mitigated the effect of Berodual. However, the bronchodilatory effect of propofol does not appear to have clinical significance (Brown and Wagner, 1999). Overall, we conclude that bronchospasm was perhaps not the uniform cause of altered resistance in this population of patients. Despite the fact that the population was homogeneous in terms of underlying disease, namely pneumonia, we found that the patients still showed a very heterogeneous picture in terms of how the mechanical parameters depended on PEEP and changed with inhalation. This may have some important clinical implications. Detailed mapping of the PEEP dependence of the parameters including COV may help identifying the optimal PEEP on an individual basis since the lungs of certain patients can be recruited and the heterogeneities can be reduced by increasing PEEP while in others this is not the case. In order to avoid overinflation, those patients that do not respond to an increase in PEEP should not be ventilated at a high PEEP, whereas those patients who do respond to PEEP, a higher setting is advantageous since G and H can in fact be lower at a higher PEEP. Furthermore, the magnitude of H is a measure of the available lung volume for ventilation. Thus, given a PEEP, tidal volume should be selected based on H. 5. Conclusions In this study, we have extended the application of LFOT to patients with ARF due to pneumonia. The LFOT provides impedance spectra rich in information on the mechanical condition of the patient. Application of the single compartment CP model to the data obtained by LFOT provides a first and robust partitioning of the impedance to elastic components and Newtonian resistance, a surrogate for airway resistance. Although the heterogeneous models did not improve drastically, they helped further interpreting the results. We thus conclude that the combination of LFOT and inverse modelling presented in this study has the potential for a better selection of PEEP and mechanical ventilation settings on an individual basis. Acknowledgments This study was partially funded by Hungarian Scientific Research Fund 67700 and NIH HL59215. References Allen, G., Lundblad, L.K., Parsons, P., Bates, J.H., 2002. Transient mechanical benefits of a deep inflation in the injured mouse lung. J. Appl. Physiol. 93, 1709–1715. Allen, G.B., Leclair, T., Cloutier, M., Thompson-Figueroa, J., Bates, J.H., 2007. The response to recruitment worsens with progression of lung injury and fibrin accumulation in a mouse model of acid aspiration. Am. J. Physiol. Lung Cell. Mol. Physiol. 292, L1580–1589. Babik, B., Asztalos, T., Peták, F., Deák, Z.I., Hantos, Z., 2003. Changes in respiratory mechanics during cardiac surgery. Anesth. Anal. 96, 1280–1287. Bartlett, J.G., Mundy, L.M., 1995. Community-acquired pneumonia. N. Engl. J. Med. 333, 1618–1624. Bates, J.H., Daróczy, B., Hantos, Z., 1992. A comparison of interrupter and forced oscillation measurements of respiratory resistance in the dog. J. Appl. Physiol. 72, 46–52. Bellardine Black, C.L., Hoffman, A.M., Tsai, L.W., Ingenito, E.P., Suki, B., Kaczka, D.W., Simon, B.A., Lutchen, K.R., 2007. Relationship between dynamic respiratory mechanics and disease heterogeneity in sheep lavage injury. Crit. Care Med. 35, 870–878. Brower, R.G., Matthay, M.A., Morris, A., Schoenfeld, D., Thompson, B.T., Wheeler, A., Wiedemann, H.P., Arroliga, A.C., Fisher, C.J., Komara, J.J., Perez-Trepichio, P., Parsons, P.E., Wolkin, R., Welsh, C., Fulkerson, W.J., MacIntyre, N., Mallatratt, L., Sebastian, M., McConnell, R., Wilcox, C., Govert, J., Thompson, D., Clemmer, T., Davis, R., Orme, J., Weaver, L., Grissom, C., Eskelson, M., Young, M., Gooder, V., McBride, K., Lawton, C., d’Hulst, J., Peerless, J.R., Smith, C., Brownlee, J., Pluss, W.,

109

Kallet, R., Luce, J.M., Gottlieb, J., Elmer, M., Girod, A., Park, P., Daniel, B., Gropper, M., Abraham, E., Piedalue, F., Glodowski, J., Lockrem, J., McIntyre, R., Reid, K., Stevens, C., Kalous, D., Silverman, H.J., Shanholtz, C., Corral, W., Toews, G.B., Arnoldi, D., Bartlett, R.H., Dechert, R., Watts, C., Lanken, P.N., Anderson, H., Finkel, B., Hanson, C.W., Barton, R., Mone, M., Hudson, L.D., Lee, C., Carter, G., Maier, R.V., Steinberg, K.P., Bernard, G., Stroud, M., Swindell, B., Stone, L., Collins, L., Mogan, S., Ancukiewicz, M., Hayden, D., Molay, F., Ringwood, N., Wenzlow, G., Kazeroonian, A.S., Gail, D.B., Bosken, C.H., Randall, P., Waclawiw, M., Spragg, R.G., Boyett, J., Kelley, J., Leeper, K., Secundy, M.G., Slutsky, A., Hyers, T.M., Emerson, S.S., Garcia, J.G.N., Marini, J.J., Pingleton, S.K., Shasby, M.D., Sibbald, W.J., 2000. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. The acute respiratory distress syndrome network. N. Engl. J. Med. 342, 1301–1308. Brown, R.H., Greenberg, R.S., Wagner, E.M., 2001. Efficacy of propofol to prevent bronchoconstriction: effects of preservative. Anesthesiology 94, 851–855 (discussion 856A). Brown, R.H., Wagner, E.M., 1999. Mechanisms of bronchoprotection by anesthetic induction agents: propofol versus ketamine. Anesthesiology 90, 822–828. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach, 2nd ed. Springer. Confalonieri, M., Potena, A., Carbone, G., Porta, R.D., Tolley, E.A., Umberto Meduri, G., 1999. Acute respiratory failure in patients with severe community-acquired pneumonia. A prospective randomized evaluation of noninvasive ventilation. Am. J. Respir. Crit. Care Med. 160, 1585–1591. Csendes, T., 1989. Nonlinear parameter estimation by global optimization—efficiency and reliability. Acta Cybernetica 8, 361–370. Dechman, G.S., Chartrand, D.A., Ruiz-Neto, P.P., Bates, J.H., 1995. The effect of changing end-expiratory pressure on respiratory system mechanics in open- and closed-chest anesthetized, paralyzed patients. Anesth. Anal. 81, 279–286. Farre, R., Ferrer, M., Rotger, M., Torres, A., Navajas, D., 1998. Respiratory mechanics in ventilated copd patients: forced oscillation versus occlusion techniques. Eur. Respir. J. 12, 170–176. Fredberg, J.J., Stamenovic, D., 1989. On the imperfect elasticity of lung tissue. J. Appl. Physiol. 67, 2408–2419. Hantos, Z., Daróczy, B., Suki, B., Nagy, S., Fredberg, J.J., 1992. Input impedance and peripheral inhomogeneity of dog lungs. J. Appl. Physiol. 72, 168–178. Ingenito, E.P., Mora, R., Cullivan, M., Marzan, Y., Haley, K., Mark, L., Sonna, L.A., 2001. Decreased surfactant protein-b expression and surfactant dysfunction in a murine model of acute lung injury. Am. J. Respir. Cell. Mol. Biol. 25, 35–44. Ito, S., Ingenito, E.P., Arold, S.P., Parameswaran, H., Tgavalekos, N.T., Lutchen, K.R., Suki, B., 2004. Tissue heterogeneity in the mouse lung: effects of elastase treatment. J. Appl. Physiol. 97, 204–212. Kaczka, D.W., Brown, R.H., Mitzner, W., 2009. Assessment of heterogeneous airway constriction in dogs: a structure–function analysis. J. Appl. Physiol. 106, 520–530. Kaczka, D.W., Hager, D.N., Hawley, M.L., Simon, B.A., 2005. Quantifying mechanical heterogeneity in canine acute lung injury: impact of mean airway pressure. Anesthesiology 103, 306–317. Kaczka, D.W., Ingenito, E.P., Suki, B., Lutchen, K.R., 1997. Partitioning airway and lung tissue resistances in humans: effects of bronchoconstriction. J. Appl. Physiol. 82, 1531–1541. Kaczka, D.W., Massa, C.B., Simon, B.A., 2007. Reliability of estimating stochastic lung tissue heterogeneity from pulmonary impedance spectra: a forward-inverse modeling study. Ann. Biomed. Eng. 35, 1722–1738. Lorx, A., Szabó, B., Hercsuth, M., Pénzes, I., Hantos, Z., 2009. Low-frequency assessment of airway and tissue mechanics in ventilated copd patients. J. Appl. Physiol. 107, 1884–1892. Lutchen, K.R., Hantos, Z., Petak, F., Adamicza, A., Suki, B., 1996. Airway inhomogeneities contribute to apparent lung tissue mechanics during constriction. J. Appl. Physiol. 80, 1841–1849. Maksym, G.N., Kearney, R.E., Bates, J.H., 1998. Nonparametric block-structured modeling of lung tissue strip mechanics. Ann. Biomed. Eng. 26, 242–252. Mols, G., Kessler, V., Benzing, A., Lichtwarck-Aschoff, M., Geiger, K., Guttmann, J., 2001. Is pulmonary resistance constant, within the range of tidal volume ventilation, in patients with ards? Br. J. Anaesth. 86, 176–182. Murray, J.F., Matthay, M.A., Luce, J.M., Flick, M.R., 1988. An expanded definition of the adult respiratory distress syndrome. Am. Rev. Respir. Dis. 138, 720–723. Navajas, D., Farre, R., Canet, J., Rotger, M., Sanchis, J., 1990. Respiratory input impedance in anesthetized paralyzed patients. J. Appl. Physiol. 69, 1372–1379. Pesenti, A., Pelosi, P., Rossi, N., Virtuani, A., Brazzi, L., Rossi, A., 1991. The effects of positive end-expiratory pressure on respiratory resistance in patients with the adult respiratory distress syndrome and in normal anesthetized subjects. Am. Rev. Respir. Dis. 144, 101–107. Ramsay, M.A.E., Savege, T.M., Simpson, B.R.J., Goodwin, R., 1974. Controlled sedation with alphaxalone–alphadolone. Br. Med. J. 2, 656–659. Suki, B., Yuan, H., Zhang, Q., Lutchen, K.R., 1997. Partitioning of lung tissue response and inhomogeneous airway constriction at the airway opening. J. Appl. Physiol. 82, 1349–1359. Wright, P.E., Bernard, G.R., 1989. The role of airflow resistance in patients with the adult respiratory distress syndrome. Am. Rev. Respir. Dis. 139, 1169–1174. Wright, P.E., Carmichael, L.C., Bernard, G.R., 1994. Effect of bronchodilators on lung mechanics in the acute respiratory distress syndrome (ards). Chest 106, 1517–1523. Yuan, H., Ingenito, E.P., Suki, B., 1997. Dynamic properties of lung parenchyma: mechanical contributions of fiber network and interstitial cells. J. Appl. Physiol. 83, 1420–1431 (discussion 1418–1429).