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Original Article

Regional filling characteristics of the lungs in mechanically ventilated patients with acute lung injury

Hinz, J.*; Gehoff, A.*; Moerer, O.*; Frerichs, I.; Hahn, G.*; Hellige, G.*; Quintel, M.*

Author Information
European Journal of Anaesthesiology: May 2007 - Volume 24 - Issue 5 - p 414-424
doi: 10.1017/S0265021506001517

Abstract

Introduction

Mechanical ventilation induces secondary lung injury by inappropriate ventilator settings [1]. It is postulated that lung damage results from cyclic closing and reopening of collapsed alveoli and/or pulmonary overdistension and leads to an increase in inflammatory cytokines [2], so that the lung itself is an active trigger in the multiple-organ failure sequence [35]. It was shown that cytokine release can be reduced by measuring lung mechanics with pressure–volume curves [2,6]. Amato and colleagues showed an improved survival of mechanically ventilated patients by the use of a protective ventilatory strategy guided by pressure–volume curves avoiding cyclic closing and reopening of collapsed alveoli and pulmonary overdistension [7]. Nevertheless, the measurement of pressure–volume (PV) curves is limited in a clinical setting [8,9]. A PV curve obtained from the whole lung is not representative of the respiratory mechanics in all lung regions. Differences in regional compliances were determined in a large number of patients with acute respiratory distress syndrome (ARDS) [10] and it was shown that regional PV curves differ from conventional PV curves [1114]. Therefore, it has been recommended to take regional inhomogeneities of the diseased lung into consideration to optimize ventilatory settings in patients with ARDS [15].

The reconstruction of regional PV curves has methodological limitations. It is based on the measurement of regional lung aeration and regional intrapulmonary airway pressure within different lung regions. Regional aeration can be assessed by a computer tomogram scan [1618] or by electrical impedance tomography (EIT) [11,14]. Until now the measurement of regional intrapulmonary pressures within the lung is impossible. Therefore, pressures are estimated from airway pressure at the airway opening during specific manoeuvres (i.e. low flow manoeuvre) in order to minimize the influence of airway resistance on the measurement of regional lung mechanics [19].

A technique for regional lung mechanics measurement is desirable, which may detect cyclic closing and reopening of alveoli and pulmonary overdistension independent of regional intrapulmonary pressure. EIT has already been used for the measurement of global and regional lung volume change [20] as well as global and regional ventilation [2123]. Regional lung mechanics can be possibly estimated from plots of regional lung volume vs. global lung volume in order to characterize the regional filling characteristics of the lungs and its diversity during mechanical ventilation in patients with acute lung injury.

Thus, the objective of our study was the description and validation of a technique measuring regional filling characteristics of the lungs in mechanically ventilated patients with acute lung injury.

Methods

After approval by the local Ethics Committee, the investigation was performed in the Intensive Care Unit of the Department of Anaesthesiology, Emergency and Intensive Care Medicine at the University Hospital in Goettingen, Germany. Informed consent was obtained from the next of kin of 20 mechanically ventilated patients. We measured the regional filling characteristics of the lungs by EIT. During these measurements, continuous sedation with intravenous propofol was provided to achieve a Ramsay sedation score of 4–5 [24]. Lung injury score (LIS) was calculated as proposed by Murray and colleagues [25]. For patients' characteristics see Table 1.

The following inclusion criteria were used:

  1. mechanically ventilated patient with acute respiratory failure (PaO2/FiO2 < 300 mmHg);
  2. mechanical ventilation >24 h before onset of the study;
  3. age ≥ 18 yr;
  4. informed written consent of the next kin;
  5. clinically indicated arterial blood pressure measurement.

Ventilation

Mechanical ventilation was provided (Evita 4; Dräger AG, Lübeck, Germany). For all patient measurements, we used identical breathing circuits (Intersurgical Complete Respiratory Systems, Wokingham Berkshire, UK). The attending physicians set the ventilator in order to obtain normocapnia (PaCO2 35–45 mmHg). Positive end-expiratory pressure and fraction of oxygen in inspired gas were set to achieve an oxyhaemoglobin saturation >95%.

Haemodynamics and gas exchange

Arterial blood sampling was performed via a 20-G catheter in the radial or femoral artery. Arterial blood gases were analysed by an ABL 300 and OSM 3 Haemoximeter (Radiometer, Copenhagen, Denmark). Electrocardiogram and systemic arterial and central venous pressures were displayed on a bedside monitor together with oxyhaemoglobin saturation (Datex AS/3, Datex Divison Instrumentarium Corp., Helsinki, Finland) and recorded with reference to atmospheric pressure at the mid thoracic level at end-expiration.

Respiratory system compliance

Respiratory static compliance was measured during controlled mechanical ventilation as proposed by Gottfried and colleagues [26]. Airway pressure (Huba Control, Würenlos, Switzerland) was measured at the y-piece of the respirator. Tidal volume was calculated from gas flow measured by a pneumotachograph (flow head: Fleisch No. 2, Fleisch, Lausanne, Switzerland, differential pressure transducer: Huba Control, Würenlos, Switzerland). Data were sampled at 200 Hz and stored for offline evaluation on a personal computer. Compliance was calculated according to VT/(PePee) at end-expiratory and end-inspiratory zero-flow conditions, where VT is the tidal volume and Pei and Pee are the end-inspiratory and end-expiratory pressures, respectively.

Electrical impedance tomography

The basic principle of EIT has already been presented in detail elsewhere [21]. Briefly, 16 surface electrodes (Blue sensor BR-50-K; Medicotest A/S, Olstyke, Denmark) were placed around the thorax in one transverse plane corresponding to the sixth intercostal parasternal space and one reference electrode on the abdomen and connected to an EIT device (Goe-MF; EIT Group Goettingen, Goettingen, Germany). For data collection, an alternating current (5 mA p–p, 50 kHz) was injected using one pair of adjacent electrodes. The resulting surface potentials depended on the regional air content of the lungs and were measured between the remaining adjacent electrode pairs. All 16 adjacent electrode pairs were used, one pair after the other, as injecting electrodes with the surface potential measured with the remaining electrodes. One data collection cycle was completed when all pairs of adjacent electrodes had been used once as injecting electrodes. Therefore, one measurement cycle consisted of 208 surface potential differences (16 current injections × 13 voltage measurements) (Fig. 1). These 208 surface potential differences were normalized to the mean surface potential during the measurement period. The normalized surface potential differences were used for the reconstruction of regional impedance changes by a back-projection algorithm. This back-projection algorithm calculated the changes in impedance within the observed plane of the thorax with time. It reconstructed tomographic EIT images of 912 pixels showing these local impedance changes in a circular area within a 32 × 32 matrix. From each measurement cycle one EIT image was reconstructed. The sampling rate of the EIT system was 13 images s1.

Figure 1.
Figure 1.:
Principle of EIT. For data collection, an alternating current (5 mA p–p, 50 kHz) was injected between one pair of adjacent electrodes. The resulting potential differences were measured between the remaining adjacent electrode pairs. All 16 adjacent electrode pairs were used, one pair after the other, as injecting electrodes with the potential differences measured with the remaining electrodes. One EIT image was completed when all pairs of adjacent electrodes had been used once as injecting electrodes.

Because an EIT image summarizes regions from the lungs, chest wall and mediastinum, the boundaries of the lungs were determined from the functional EIT ventilation images (f-EIT) [27]. The impedance variance of the lungs is higher than in other thoracic structures (i.e. chest wall, mediastinum) owing to the great impedance changes associated with air content changes [20,28]. The chest wall contributes solely to the outer boundary of the f-EIT image. The mean impedance variation of this boundary was set as the limit to define the ‘EIT lung regions'. Owing to this approach, the number of lung regions observed by EIT varies from patient to patient. Data were stored for offline evaluation on a personal computer (Pentium III 800 MHz; Microsoft Windows XP).

Regional filling characteristics of the lungs

Regional filling characteristics of the lungs were measured during one mechanical inspiration in the observed transverse plane for all selected pixels of the EIT image representing lung regions. Regional tidal volumes were calculated from regional impedance in each selected pixel of the EIT image. Global tidal volume was calculated from the summarized impedance of all selected pixels. In the present study, regional tidal volumes and global tidal volumes were sampled with 13 EIT images s1 by an EIT system (Goe-MF; EIT Group Goettingen, Goettingen, Germany) in all selected pixels of the EIT image representing lung regions. The acquired data were filtered with a digital Butterworth filter (cut-off frequency 50 min1, filter order 10).

Regional filling characteristics of the selected regions of the lungs were calculated from tracings of regional tidal volume vs. global tidal volume beginning at inspiration and ending at end-inspiration, according to a procedure described in [29]. Regional tidal volume and global tidal volume were calculated as fractions of 1.0. According to Milic-Emili and colleagues [30], these plots of regional tidal volume vs. global tidal volume were fitted to a polynomial function of the second degree (y = ax2 + bx + c, with ‘a' the polynomial coefficient of the second degree). The quality of fitting was checked by simultaneously plotting the measured and fitted data points. Fittings were accepted if the square of the correlation coefficient (R2) was above 0.90. In case of R2 < 0.90, regional filling characteristics of the selected regions were excluded from further calculations. The polynomial coefficient of second-degree ‘a' characterizes the curve linearity of the plot. It describes the lung filling characteristics of the selected region. Figure 2 shows three examples of different regional filling characteristics. A polynomial coefficient of nearly zero indicates regional tidal volume change, which occurs during the whole inspiration homogeneously. Positive values of the polynomial coefficient indicate initial low regional tidal volume change compared with the average filling characteristics. This might occur during recruitment of the region. Negative values of the polynomial coefficient indicate late low regional tidal volume change compared with the average filling characteristics, which might occur during hyperinflation of the region.

Figure 2.
Figure 2.:
Three examples out of 274 different regional polynomial coefficients in one mechanically ventilated patient suffering from pneumonia. Regional polynomial coefficients were calculated from plots of regional tidal volume vs. global tidal volume obtained by EIT in the sixth intercostal thoracic plane and fitted to a polynomial of second degree (y = ax2 + bx + c, with ‘a' the polynomial coefficient of second degree). For further details, see text.

Statistics

Calculations were performed using the STATISTICA software package (Statistica 5.1; StatSoft Inc., Tulsa, OK, USA) on a personal computer (Pentium III 800 MHz, Microsoft Windows XP). All data are presented as min–max (median) unless stated otherwise. Normal distribution was tested by the Kolmogorov–Smirnov Test. Linear regression analysis using the least-squares method was applied for correlation analysis. For all statistical tests, P < 0.05 was considered to be significant.

Results

In 20 mechanically ventilated patients, regional filling characteristics were studied with EIT. The patients were 22–80 (median 49) yr old. Nine patients suffered from pneumonia, six patients had atelectasis near the diaphragm, four patients had an ARDS and one patient had a lung contusion. Static respiratory system compliance varied from 15 to 100 (median 40) mL mbar1. For patients' characteristics and respirator settings, see Tables 1 2.

Table 1
Table 1:
Patient characteristics.
Table 2
Table 2:
Ventilator settings, airway plateau pressure (PAWPlateau) and positive end-expiratory pressure (PEEP) of 20 patients ventilated either in a pressure-controlled mode (PCV) or in a volume-controlled mode (VCV).

We checked the stability of the fitting algorithm to the polynomial coefficient in order to calculate regional filling characteristics by calculating a linear correlation of two fitting results from the same inspiration. We found an excellent linear correlation, according to the equation y = 1.0x + 0 (R2 = 1.0). Additionally, we calculated the linear correlation of the fitting results from two different inspirations of the same patient, according to the equation y = 0.99x + 12 (R2 = 0.92). The quality of curve fittings to the polynomial of second degree from the selected regions was excellent with r2 = 0.91–0.99 (median 0.96). However, owing to our quality of fitting criteria 2–15 (median 10), regions were excluded from further calculations of regional filling characteristics.

We studied regional filling characteristics with a polynomial coefficient in 112–443 (median 239) regions. The minimal regional polynomial coefficient varied from −2.80 to −0.56 (median −1.16), while the maximal regional polynomial coefficient varied from 0.58 to 3.65 (median 1.41). Thirtyone percent of the observed lung regions showed negative polynomial coefficients (−3 to −0.2), 41% showed polynomial coefficients nearly zero (−0.2 to +0.2) and 28% showed positive polynomial coefficients (0.2 to +3). All results are summarized in Table 3 and Figure 3.

Table 3
Table 3:
Compliance, counts of observed lung regions (Regions) and range of regional polynomial coefficient in 20 mechanically ventilated patients.
Figure 3.
Figure 3.:
Histographic presentation of the distribution of regional polynomial coefficients in a transversal thoracic slice in 20 mechanically ventilated patients. Distribution of regional polynomial coefficients from the summarized patients as minimum, maximum and median. Regional polynomial coefficients were calculated from plots of regional tidal volume vs. global tidal volume obtained by EIT in the sixth intercostal thoracic plane and fitted to a polynomial of second degree (y = ax2 + bx + c, with ‘a' the polynomial coefficient of second degree). For further details, see text. Note that the distribution of regional polynomial coefficients may offer the possibility of adjusting the ventilatory setting, so that end-expiratory lung collapse and end-inspiratory overdistension can be avoided in the majority of lung regions. However, it also demonstrates all the flaws in using global lung mechanic measurements.

We found a gravity-dependent distribution of the polynomial coefficient within the lungs going from the sternum to the spine. We calculated mostly negative polynomial coefficients, which are suspicious for hyperinflation, in the ventral part of the lungs. In contrast, in the dorsal part of the lungs mostly positive polynomial coefficients, suspicious for cycling collapse and recruitment, were found in the dorsal part of the lung (Fig. 4).

Figure 4.
Figure 4.:
Polynomial coefficient of the left and right lungs of nine mechanically ventilated patients with acute lung injury in dorsal to ventral direction in an about 3 cm slice of the thorax measured by EIT (means and standard deviation). For further details, see text.

Discussion

In our study, we have used a technique measuring regional lung mechanics in mechanically ventilated patients with acute lung injury by EIT characterizing the non-uniformity of regional filling characteristics of the lungs. This technique is based on a modified concept introduced by Milic-Emili and colleagues, who plotted regional lung volumes vs. global lung volume in order to characterize the regional filling characteristics of the lungs [30]. Regional and global tidal volumes were measured by EIT [21], which has already been used for the measurement of global and regional tidal volumes as well as global and regional ventilations. The modification based on the increased number of up to 912 lung regions was observed by EIT, while Milic-Emili and colleagues observed 12 lung regions. We found a broad heterogeneity of regional filling characteristics of the lungs during tidal mechanical ventilation. Additionally, we detected a gravity-dependent distribution of the polynomial coefficient within the lungs going from the sternum to the spine.

Electrical impedance tomography

The technique used to measure regional filling characteristics is based on EIT [21]. It was developed in the early eighties by Barber and Brown [21]. EIT is a non-invasive technique with the potential to monitor regional lung mechanics [31], which has already been used for the measurement of global and regional tidal volumes [20] as well as global and regional ventilations [22,23]. In former studies, regional ventilation was calculated on the basis of f-EIT images, introduced by Hahn and colleagues [27]. This technique generates one quasi static functional image from a series of EIT images over time. However, this technique delivers no dynamic information on regional lung filling characteristics. Therefore, in our study, regional filling characteristics were calculated from tracings of regional lung impedance and global lung impedance [30], in order to characterize the regional filling characteristics of the lungs and its diversity during mechanical ventilation in patients with acute lung injury. It was shown that regional and global lung impedance correlates with regional and global lung volume [20,32,33]. Hahn and colleagues observed the thickness of the EIT plane in an electrolytic model. The form of the plane is a toroid, and within ±3 cm from the centre of the plane the sensitivity of impedance change decreased to 50% [34]. Therefore, the estimated thickness of the EIT plane was assumed to be about 3 cm. According to Hahn and colleagues, the minimal detectable lung volume by EIT is in the range of 9–29 mL [35]. Brown and Barber found a spatial resolution of approximately 8% of the thorax diameter, so that a resolution of 8 mL is achieved [36]. This limited resolution may influence our results by summarizing regional filling characteristics of different alveoli. The time resolution of up to 44 EIT images s1 is sufficient to follow an inspiration and the calculation of regional filling characteristics. We performed an offline analysis of regional filling characteristics by choosing one inspiration. In general, calculation of regional filling characteristics should be possible online after the inspiration is finished.

An EIT image summarizes impedance variation from the thorax, including the lung, chest wall and mediastinum, induced by varying air content as well as the pulsatile blood flow within tissues [28,37]. In earlier studies, we used a fixed limit of 20% of the maximal impedance variation to define lung regions, which may lead to an underestimation [27]. In this study, we introduced a new concept for the detection of lung regions. We assumed that impedance change in the outer boundary of the EIT image is generated by the chest wall. Therefore, the limit of impedance change was defined for each patient individually and the results show a lower limit than the fixed one used in former studies. This new concept was possible owing to the improved signal-to-noise ratio of the used EIT hardware [38].

The dimension of the thorax changes with movement of the chest, which occurs during mechanical ventilation [39]. This is a consequence of the known low sensitivity of EIT systems measuring relative impedance to configuration changes. Therefore, one important methodological aspect is that it is limited to a single transverse thoracic plane. It has been demonstrated that aeration gradients are found not only in the anterior–posterior but also in the cephalo-caudal direction [10,40]. During mechanical ventilation, the lungs may displace in the caudal direction, so that the EIT measures different lung planes. This effect may have played a significant role in the present study. However, this is the same criticism against computed tomography studies, which have been shown to be misleading in the presence of cranial to caudal heterogeneity. Therefore, analysing regional lung filling characteristics in a single transverse plane may underestimate heterogeneity of regional ventilation. In the future, the introduction of additional EIT planes or an optimized current injection pattern may solve this problem [28]. In this investigation, commercially available electrodes were used for EIT measurements. For better contact of the electrodes, hairy patients were shaved at the sites of fixation. The EIT system monitored continuously the quality of contact between the skin and the electrodes. Thereby inadequate electrode contact is easily detected.

Regional lung filling characteristics

Reproducibility of impedance measurements was examined in a former study. The long-term stability of the impedance measurement during the first minute, and after 10, 20, 30 and 40 min, showed a variation of 1.5–6.1% (median 3.1%) [33]. We checked the stability of the fitting algorithm to the polynomial coefficient in order to calculate regional lung filling characteristics by calculating a linear correlation of two fitting results from the same inspiration. We found an excellent linear correlation. Additionally, we calculated the linear correlation of the fitting results from two different inspirations. The better linear correlation between two different inspirations may depend on physiological differences of regional lung mechanics between the two different inspirations.

Regional lung mechanics and its diversity were examined in former studies. Differences in regional compliances were measured in a large series of ARDS patients [10]. Furthermore, in animal studies with lavage-induced lung injury, it was shown that regional PV curves differed from conventional PV curves [1113]. However, one methodological problem during PV measurements was the impossibility of measuring regional airway pressure, which is needed for the reconstruction of regional PV curves. Regional airway pressures are estimated from airway pressure at the airway opening during specific manoeuvres, which minimize the influence of airway resistance [19].

The technique measuring dynamic regional lung mechanics used in this study is independent of regional pressure measurement. It is based on a modified concept introduced by Milic-Emili and colleagues, who plotted regional lung volume vs. global lung volume in order to characterize the regional distribution of gas volume within the lungs [30]. They used 12 scintillation counters fitted at the thorax to calculate regional lung volumes and a gas dilution technique to calculate global lung volume. They instructed healthy subjects to hold an inspiration for about 5 s at different lung volumes until the subject reached total lung capacity. From plots of regional vs. global lung volume, they calculated the regional distribution of inspired gas. They found an uneven regional distribution of inspired gas in the lungs. In contrast to the concept of Milic-Emili and colleagues, we were interested in the effects of regional filling characteristics of the lungs during tidal breathing in mechanically ventilated patients. Because of the improved technique of EIT, which offers a high time and spatial resolution (see Electrical impedance tomography section), we were able to follow regional and global tidal volumes of the lungs during undisturbed mechanical ventilation. This concept might provide further insights into regional lung mechanics.

The quantification of regional lung filling characteristics is based on the concept of the calculation of a polynomial coefficient from fittings of regional vs. global tidal volume to a polynomial of the second degree (Figs 2 5). Until now, regional filling characteristics have not been validated or compared with other imaging techniques. However, so far no techniques exist with a comparable time resolution of 45 images s1, which offers the possibility of detecting fast regional dynamic changes, like cyclic reopening of lung regions or its hyperinflation. In the future, functional magnetic resonance imaging of the lung using hyperpolarized 3-helium gas may deliver a comparable time resolution [41]. We found a gravity-dependent distribution of the polynomial coefficient within the lungs going from the sternum to the spine. We calculated mostly negative polynomial coefficients, which are suspicious for hyperinflation in the ventral part of the lungs. In contrast, mostly positive polynomial coefficients suspicious for cycling collapse and recruitment were found in the dorsal part of the lung (Fig. 4).

Figure 5.
Figure 5.:
Regional filling characteristics of 112 pulmonary regions in one mechanically ventilated patient suffering from pneumonia. Regional filling characteristics were calculated from plots of regional tidal volume vs. global tidal volume obtained by EIT and fitted to a polynomial of second degree (y = ax2 + bx + c, with ‘a' the polynomial coefficient of second degree). The polynomial coefficient of second degree ‘a' characterizes the curve linearity of the plot. Three characteristic curves (A–D) are emphasized. (A) Negative values of the polynomial coefficient indicate a late low regional tidal volume change compared with the average filling characteristics, which might occur during hyperinflation of the region. (B) Positive values of the polynomial coefficient indicate initial low regional tidal volume change compared with the average filling characteristics. This might occur during recruitment of the region. (C) Values of nearly zero of the polynomial coefficient indicate regional tidal volume change, which occurs during the whole inspiration homogeneously. (D) During inspiration, we found a paradox behaviour (derecruitment) in one lung region.

Implications for respiratory therapy

Figure 5 shows the regional filling characteristics of 111 pulmonary regions in one mechanically ventilated patient suffering from pneumonia. Figure 3 shows the graphic representation of the distribution of regional polynomial coefficients in a transverse thoracic slice in mechanically ventilated patients. We found a broad heterogeneity of regional filling characteristics of the lungs during normal tidal mechanical ventilation. We found regions with a regional polynomial coefficient >0, which possibly shows a collapse at end-expiration during normal tidal mechanical ventilation. This might depend on an inappropriate low positive end-expiratory pressure, which is necessary to keep the lung open. Furthermore, we found regions with a regional polynomial coefficient <0, which possibly shows hyperinflation of the observed region, which might depend on too high a plateau pressure during mechanical ventilation. Additionally, some regions show a linear relationship between regional and global lung volume. Furthermore, this technique offers topographic visualization of regional filling characteristics (Fig. 4). The advantage of measurement of regional lung mechanics by EIT is the possibility of bedside use. The measurement of regional filling characteristics, the graphic representation of regional polynomial coefficients and its topographic visualization may offer the possibility of adjusting the ventilatory setting, so that end-expiratory lung collapse and end-inspiratory overdistension can be avoided in the majority of lung regions.

In conclusion, regional filling characteristics of the lungs show regional broad heterogeneity. Therefore, bedside measurements of regional filling characteristics of the lungs by EIT may be a helpful tool in adjusting respiratory settings during mechanical ventilation to optimize lung recruitment and to avoid overdistension. It applies a non-pressure-related assessment to the regional mechanics of lung inflation and gives a view of the real problems underlying ventilatory strategies dependent on global characteristics showing the flaw in the use of global airway pressure to indicate safety in lung ventilation.

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Keywords:

ELECTRICAL IMPEDANCE TOMOGRAPHY, electrical impedance; RESPIRATORY PHYSIOLOGY; RESPIRATION ARTIFICIAL; VENTILATION PERFUSION RATIO; ACUTE LUNG INJURY

© 2007 European Society of Anaesthesiology