Positional Therapy and Regional Pulmonary Ventilation

Abstract

Background: Prone ventilation redistributes lung inflation along the gravitational axis; however, localized, nongravitational effects of body position are less well characterized. The authors hypothesize that positional inflation improvements follow both gravitational and nongravitational distributions. This study is a nonoverlapping reanalysis of previously published large animal data.

Methods: Five intubated, mechanically ventilated pigs were imaged before and after lung injury by tracheal injection of hydrochloric acid (2 ml/kg). Computed tomography scans were performed at 5 and 10 cm H2O positive end-expiratory pressure (PEEP) in both prone and supine positions. All paired prone-supine images were digitally aligned to each other. Each unit of lung tissue was assigned to three clusters (K-means) according to positional changes of its density and dimensions. The regional cluster distribution was analyzed. Units of tissue displaying lung recruitment were mapped.

Results: We characterized three tissue clusters on computed tomography: deflation (increased tissue density and contraction), limited response (stable density and volume), and reinflation (decreased density and expansion). The respective clusters occupied (mean ± SD including all studied conditions) 29.3 ± 12.9%, 47.6 ± 11.4%, and 23.1 ± 8.3% of total lung mass, with similar distributions before and after lung injury. Reinflation was slightly greater at higher PEEP after injury. Larger proportions of the reinflation cluster were contained in the dorsal versus ventral (86.4 ± 8.5% vs. 13.6 ± 8.5%, P < 0.001) and in the caudal versus cranial (63.4 ± 11.2% vs. 36.6 ± 11.2%, P < 0.001) regions of the lung. After injury, prone positioning recruited 64.5 ± 36.7 g of tissue (11.4 ± 6.7% of total lung mass) at lower PEEP, and 49.9 ± 12.9 g (8.9 ± 2.8% of total mass) at higher PEEP; more than 59.0% of this recruitment was caudal.

Conclusions: During mechanical ventilation, lung reinflation and recruitment by the prone positioning were primarily localized in the dorso-caudal lung. The local effects of positioning in this lung region may determine its clinical efficacy.

Figure 1.

Outline of image analysis procedures. Step 1: a mask outlining the lung borders was obtained for each set of paired supine and prone images. Step 2: each segmented prone mask was registered to the corresponding supine mask using, in sequence: rigid, affine, and deformable registration algorithms, and finally generating a transform (Φ_M). Step 3: registration of the prone to supine image was performed, building on the previously obtained transform Φ_M. A map of the Jacobian was also created during this step to illustrate voxel-by-voxel lung volume changes due to position change. Step 4: The masks of the paired images were applied, and further image analysis was performed on the segmented target supine and warped-prone images (shown with inverted orientation to highlight similarities with the target supine image). Finally, subtraction maps and frequency distributions of intensity differences (ΔHU) were created for each pair of supine and prone images. Hounsfield Unit (HU) distributions were used to map lung recruitment and derecruitment with voxel resolution and, together with the Jacobian distributions, to perform cluster analysis of the inflation and deflation characteristics of each voxel.

Figure 2.

Representative images obtained at baseline healthy conditions are shown in A) at PEEP 5 cmH₂O and in B) at PEEP10 cmH₂O. Left panels: axial, coronal, and sagittal views of the target supine images, warped-prone image (shown inverted), and original (non-warped) prone image. Middle panel: subtraction maps plotting the density difference (ΔHU) between warped-prone and supine images are shown side-by-side with the Jacobian maps for each view. An expanding Jacobian value was associated with a decrease in density when changing position from supine to prone. Right panels: cumulative frequency distributions of Hounsfield Unit (HU) and Jacobian values were plotted for the whole lung.

Figure 3.

Images were repeated in all tested conditions after lung injury induction by acid aspiration and are shown in A) at PEEP 5 cmH₂O and in B) at PEEP 10 cmH₂O. The left panels show the target supine images, warped-prone image (shown inverted), and original (non-warped) prone image. The middle panel shows the ΔHU and Jacobian maps side-by-side, with the corresponding cumulative frequency distribution in the right panels.

Figure 4.

Three-cluster K-mean analysis was performed on images obtained at healthy baseline and after injury at both PEEP 5 and 10 cmH₂O at end-inspiration (EI). On the left, frequency distributions of normalized ΔHU and Jacobian values are shown, with the identified three clusters highlighted in blue (deflation), black (limited response) and red (reinflation). On the right, the topographic distribution of voxels included in each cluster is shown in binary maps, using the same color scheme as in the frequency distributions. The maps show a distribution of the clusters along the vertical (gravitational) axis, although non-gravitational heterogeneity was visible. In addition, the most dependent cluster was the smallest one and was located in the dorso-caudal regions of the lung.

Figure 5.

Segment analysis among all end expiratory (EI) conditions partitioned the lung into 10 segments of equal mass along the A. Dorsal-Ventral axis and B. the Caudal-Cephalad axis. The cluster frequencies of reinflation (red), limited response (black), and deflation (blue) are displayed at the level of each segment. The far-right column shows the spatial orientation of the 10 equal mass segments which were used in the compartment analysis of cluster distribution. In A. the top 5 segments correspond to the ventral compartment and the bottom 5 segments are the dorsal compartment. In B. segments that contain a pixel adjacent to the diaphragm are considered to be in the diaphragmatic compartment. The dorsal and caudal bins were shaded with red color.

Figure 6.

Paired computed tomography scans obtained in the supine and prone positions are shown with the corresponding recruitment maps. Registration was performed between supine and prone images, yielding maps of position-related (from supine to prone) recruitment (R) and derecruitment (D) at PEEP 5 cmH₂O (top row) and PEEP 10 cmH₂O (second row). Recruited voxels are shown in blue; derecruitment was small and shown in red. Numeric values are shown for each map. In addition, images at PEEP 5 and 10 cmH₂O were registered to each other, showing the PEEP-related recruitment in the supine and prone positions (bottom row).