Effects of Lung Injury and Abdominal Insufflation on Respiratory Mechanics and Lung Volume During Time-Controlled Adaptive Ventilation.

BACKGROUND: Lung volume measurements are important for monitoring functional aeration and recruitment, and may help guide adjustments in ventilator settings. The expiratory phase of APRV may provide physiologic information about lung volume based on the expiratory flow-time slope, angle, and time to approach a no-flow state (TExp). We hypothesized that expiratory flow rate would correlate with estimated lung volume (ELV), as measured using a modified nitrogen washout/washin technique in a large animal lung injury model. METHODS: Eight pigs (35.2±1.0kg) were mechanically ventilated using an Engström Carescape R860 on the APRV mode. All settings were held constant except the expiratory duration (TLow), which was adjusted based on the expiratory flow curve. Abdominal pressure was increased to 15mmHg in normal and Tween-injured lungs to replicate a combination of pulmonary and extrapulmonary lung injury. ELV was estimated using the Carescape FRC InView Tool. The expiratory flow-time slope and TExp were measured from the expiratory flow profile. RESULTS: Lung elastance increased with Tween-induced lung injury from 29.3±7.3cmH2O/L to 39.9±15.1cmH2O/L and chest wall elastance increased with increasing intra-abdominal pressures from 15.3±4.1cmH2O/L to 25.7±10.0cmH2O/L in the normal lung and 15.8±6.0cmH2O/L to 33.0±6.2cmH2O/L in the Tween-injured lung (p=0.39). ELV decreased from 1.90±0.83L in the Tween-Injured lung to 0.67±0.1L by increasing intra-abdominal pressures to 15mmHg. This had a significant correlation with a TExp decrease from 2.3±0.8s to 1.0±0.1s in the Tween-injured group with increasing insufflation pressures (ρ = 0.95) and with the expiratory flow-time slope, which increased from 0.29±0.06L/s2 to 0.63±0.05L/s2 (ρ = 0.78). CONCLUSIONS: Changes in ELV over time, and the TExp and flow-time slope, can be used to demonstrate evolving lung injury during APRV. Using the slope to infer changes in functional lung volume represents a unique, reproducible, real-time, bedside technique that does not interrupt ventilation and may be used for clinical interpretation.

Mechanical Models of Collagen Networks for Understanding Changes in the Failure Properties of Aging Skin.

Skin undergoes mechanical alterations due to changes in the composition and structure of the collagenous dermis with aging. Previous studies have conflicting findings, with both increased and decreased stiffness reported for aging skin. The underlying structure-function relationships that drive age-related changes are complex and difficult to study individually. One potential contributor to these variations is the accumulation of nonenzymatic crosslinks within collagen fibers, which affect dermal collagen remodeling and mechanical properties. Specifically, these crosslinks make individual fibers stiffer in their plastic loading region and lead to increased fragmentation of the collagenous network. To better understand the influence of these changes, we investigated the impact of nonenzymatic crosslink changes on the dermal microstructure using discrete fiber networks representative of the dermal microstructure. Our findings suggest that stiffening the plastic region of collagen's mechanical response has minimal effects on network-level stiffness and failure stresses. Conversely, simulating fragmentation through a loss of connectivity substantially reduces network stiffness and failure stress, while increasing stretch ratios at failure.

Time-Controlled Adaptive Ventilation (TCAV): a personalized strategy for lung protection.

Acute respiratory distress syndrome (ARDS) alters the dynamics of lung inflation during mechanical ventilation. Repetitive alveolar collapse and expansion (RACE) predisposes the lung to ventilator-induced lung injury (VILI). Two broad approaches are currently used to minimize VILI: (1) low tidal volume (LVT) with low-moderate positive end-expiratory pressure (PEEP); and (2) open lung approach (OLA). The LVT approach attempts to protect already open lung tissue from overdistension, while simultaneously resting collapsed tissue by excluding it from the cycle of mechanical ventilation. By contrast, the OLA attempts to reinflate potentially recruitable lung, usually over a period of seconds to minutes using higher PEEP used to prevent progressive loss of end-expiratory lung volume (EELV) and RACE. However, even with these protective strategies, clinical studies have shown that ARDS-related mortality remains unacceptably high with a scarcity of effective interventions over the last two decades. One of the main limitations these varied interventions demonstrate to benefit is the observed clinical and pathologic heterogeneity in ARDS. We have developed an alternative ventilation strategy known as the Time Controlled Adaptive Ventilation (TCAV) method of applying the Airway Pressure Release Ventilation (APRV) mode, which takes advantage of the heterogeneous time- and pressure-dependent collapse and reopening of lung units. The TCAV method is a closed-loop system where the expiratory duration personalizes VT and EELV. Personalization of TCAV is informed and tuned with changes in respiratory system compliance (CRS) measured by the slope of the expiratory flow curve during passive exhalation. Two potentially beneficial features of TCAV are: (i) the expiratory duration is personalized to a given patient's lung physiology, which promotes alveolar stabilization by halting the progressive collapse of alveoli, thereby minimizing the time for the reopened lung to collapse again in the next expiration, and (ii) an extended inspiratory phase at a fixed inflation pressure after alveolar stabilization gradually reopens a small amount of tissue with each breath. Subsequently, densely collapsed regions are slowly ratcheted open over a period of hours, or even days. Thus, TCAV has the potential to minimize VILI, reducing ARDS-related morbidity and mortality.

Local fractal dimension of collagen detects increased spatial complexity in fibrosis.

Increase of collagen content and reorganization characterizes fibrosis but quantifying the latter remains challenging. Spatially complex structures are often analyzed via the fractal dimension; however, established methods for calculating this quantity either provide a single dimension for an entire object or a spatially distributed dimension that only considers binary images. These neglect valuable information related to collagen density in images of fibrotic tissue. We sought to develop a fractal analysis that can be applied to 3-dimensional (3D) images of fibrotic tissue. A fractal dimension map for each image was calculated by determining a single fractal dimension for a small area surrounding each image pixel, using fiber thickness as the third dimension. We found that this local fractal dimension increased with age and with progression of fibrosis regardless of collagen content. Our new method of distributed 3D fractal analysis can thus distinguish between changes in collagen content and organization induced by fibrosis.

Sustained vs. Intratidal Recruitment in the Injured Lung During Airway Pressure Release Ventilation: A Computational Modeling Perspective.

INTRODUCTION: During mechanical ventilation, cyclic recruitment and derecruitment (R/D) of alveoli result in focal points of heterogeneous stress throughout the lung. In the acutely injured lung, the rates at which alveoli can be recruited or derecruited may also be altered, requiring longer times at higher pressure levels to be recruited during inspiration, but shorter times at lower pressure levels to minimize collapse during exhalation. In this study, we used a computational model to simulate the effects of airway pressure release ventilation (APRV) on acinar recruitment, with varying inspiratory pressure levels and durations of exhalation. MATERIALS AND METHODS: The computational model consisted of a ventilator pressure source, a distensible breathing circuit, an endotracheal tube, and a porcine lung consisting of recruited and derecruited zones, as well as a transitional zone capable of intratidal R/D. Lung injury was simulated by modifying each acinus with an inflation-dependent surface tension. APRV was simulated for an inhalation duration (Thigh) of 4.0 seconds, inspiratory pressures (Phigh) of 28 and 40 cmH2O, and exhalation durations (Tlow) ranging from 0.2 to 1.5 seconds. RESULTS: Both sustained acinar recruitment and intratidal R/D within the subtree were consistently higher for Phigh of 40 cmH2O vs. 28 cmH2O, regardless of Tlow. Increasing Tlow was associated with decreasing sustained acinar recruitment, but increasing intratidal R/D, within the subtree. Increasing Tlow was associated with decreasing elastance of both the total respiratory system and transitional subtree of the model. CONCLUSIONS: Our computational model demonstrates the confounding effects of cyclic R/D, sustained recruitment, and parenchymal strain stiffening on estimates of total lung elastance during APRV. Increasing inspiratory pressures leads to not only more sustained recruitment of unstable acini but also more intratidal R/D. Our model indicates that higher inspiratory pressures should be used in conjunction with shorter exhalation times, to avoid increasing intratidal R/D.

Ratchet recruitment in the acute respiratory distress syndrome: lessons from the newborn cry.

Patients with acute respiratory distress syndrome (ARDS) have few treatment options other than supportive mechanical ventilation. The mortality associated with ARDS remains unacceptably high, and mechanical ventilation itself has the potential to increase mortality further by unintended ventilator-induced lung injury (VILI). Thus, there is motivation to improve management of ventilation in patients with ARDS. The immediate goal of mechanical ventilation in ARDS should be to prevent atelectrauma resulting from repetitive alveolar collapse and reopening. However, a long-term goal should be to re-open collapsed and edematous regions of the lung and reduce regions of high mechanical stress that lead to regional volutrauma. In this paper, we consider the proposed strategy used by the full-term newborn to open the fluid-filled lung during the initial breaths of life, by ratcheting tissues opened over a series of initial breaths with brief expirations. The newborn's cry after birth shares key similarities with the Airway Pressure Release Ventilation (APRV) modality, in which the expiratory duration is sufficiently short to minimize end-expiratory derecruitment. Using a simple computational model of the injured lung, we demonstrate that APRV can slowly open even the most recalcitrant alveoli with extended periods of high inspiratory pressure, while reducing alveolar re-collapse with brief expirations. These processes together comprise a ratchet mechanism by which the lung is progressively recruited, similar to the manner in which the newborn lung is aerated during a series of cries, albeit over longer time scales.

Electrical Impedance Tomography Identifies Evolution of Regional Perfusion in a Porcine Model of Acute Respiratory Distress Syndrome.

BACKGROUND: Bedside electrical impedance tomography could be useful to visualize evolving pulmonary perfusion distributions when acute respiratory distress syndrome worsens or in response to ventilatory and positional therapies. In experimental acute respiratory distress syndrome, this study evaluated the agreement of electrical impedance tomography and dynamic contrast-enhanced computed tomography perfusion distributions at two injury time points and in response to increased positive end-expiratory pressure (PEEP) and prone position. METHODS: Eleven mechanically ventilated (VT 8 ml · kg-1) Yorkshire pigs (five male, six female) received bronchial hydrochloric acid (3.5 ml · kg-1) to invoke lung injury. Electrical impedance tomography and computed tomography perfusion images were obtained at 2 h (early injury) and 24 h (late injury) after injury in supine position with PEEP 5 and 10 cm H2O. In eight animals, electrical impedance tomography and computed tomography perfusion imaging were also conducted in the prone position. Electrical impedance tomography perfusion (QEIT) and computed tomography perfusion (QCT) values (as percentages of image total) were compared in eight vertical regions across injury stages, levels of PEEP, and body positions using mixed-effects linear regression. The primary outcome was agreement between QEIT and QCT, defined using limits of agreement and Pearson correlation coefficient. RESULTS: Pao2/Fio2 decreased over the course of the experiment (healthy to early injury, -253 [95% CI, -317 to -189]; early to late injury, -88 [95% CI, -151 to -24]). The limits of agreement between QEIT and QCT were -4.66% and 4.73% for the middle 50% quantile of average regional perfusion, and the correlation coefficient was 0.88 (95% CI, 0.86 to 0.90]; P < 0.001). Electrical impedance tomography and computed tomography showed similar perfusion redistributions over injury stages and in response to increased PEEP. QEIT redistributions after positional therapy underestimated QCT in ventral regions and overestimated QCT in dorsal regions. CONCLUSIONS: Electrical impedance tomography closely approximated computed tomography perfusion measures in experimental acute respiratory distress syndrome, in the supine position, over injury progression and with increased PEEP. Further validation is needed to determine the accuracy of electrical impedance tomography in measuring perfusion redistributions after positional changes.

First Stabilize and then Gradually Recruit: A Paradigm Shift in Protective Mechanical Ventilation for Acute Lung Injury.

Acute respiratory distress syndrome (ARDS) is associated with a heterogeneous pattern of injury throughout the lung parenchyma that alters regional alveolar opening and collapse time constants. Such heterogeneity leads to atelectasis and repetitive alveolar collapse and expansion (RACE). The net effect is a progressive loss of lung volume with secondary ventilator-induced lung injury (VILI). Previous concepts of ARDS pathophysiology envisioned a two-compartment system: a small amount of normally aerated lung tissue in the non-dependent regions (termed "baby lung"); and a collapsed and edematous tissue in dependent regions. Based on such compartmentalization, two protective ventilation strategies have been developed: (1) a "protective lung approach" (PLA), designed to reduce overdistension in the remaining aerated compartment using a low tidal volume; and (2) an "open lung approach" (OLA), which first attempts to open the collapsed lung tissue over a short time frame (seconds or minutes) with an initial recruitment maneuver, and then stabilize newly recruited tissue using titrated positive end-expiratory pressure (PEEP). A more recent understanding of ARDS pathophysiology identifies regional alveolar instability and collapse (i.e., hidden micro-atelectasis) in both lung compartments as a primary VILI mechanism. Based on this understanding, we propose an alternative strategy to ventilating the injured lung, which we term a "stabilize lung approach" (SLA). The SLA is designed to immediately stabilize the lung and reduce RACE while gradually reopening collapsed tissue over hours or days. At the core of SLA is time-controlled adaptive ventilation (TCAV), a method to adjust the parameters of the airway pressure release ventilation (APRV) modality. Since the acutely injured lung at any given airway pressure requires more time for alveolar recruitment and less time for alveolar collapse, SLA adjusts inspiratory and expiratory durations and inflation pressure levels. The TCAV method SLA reverses the open first and stabilize second OLA method by: (i) immediately stabilizing lung tissue using a very brief exhalation time (≤0.5 s), so that alveoli simply do not have sufficient time to collapse. The exhalation duration is personalized and adaptive to individual respiratory mechanical properties (i.e., elastic recoil); and (ii) gradually recruiting collapsed lung tissue using an inflate and brake ratchet combined with an extended inspiratory duration (4-6 s) method. Translational animal studies, clinical statistical analysis, and case reports support the use of TCAV as an efficacious lung protective strategy.

A specific combination of laboratory data is associated with overweight lungs in patients with COVID-19 pneumonia at hospital admission: secondary cross-sectional analysis of a randomized clinical trial.

Background: Lung weight may be measured with quantitative chest computed tomography (CT) in patients with COVID-19 to characterize the severity of pulmonary edema and assess prognosis. However, this quantitative analysis is often not accessible, which led to the hypothesis that specific laboratory data may help identify overweight lungs. Methods: This cross-sectional study was a secondary analysis of data from SARITA2, a randomized clinical trial comparing nitazoxanide and placebo in patients with COVID-19 pneumonia. Adult patients (≥18 years) requiring supplemental oxygen due to COVID-19 pneumonia were enrolled between April 20 and October 15, 2020, in 19 hospitals in Brazil. The weight of the lungs as well as laboratory data [hemoglobin, leukocytes, neutrophils, lymphocytes, C-reactive protein, D-dimer, lactate dehydrogenase (LDH), and ferritin] and 47 additional specific blood biomarkers were assessed. Results: Ninety-three patients were included in the study: 46 patients presented with underweight lungs (defined by ≤0% of excess lung weight) and 47 patients presented with overweight lungs (>0% of excess lung weight). Leukocytes, neutrophils, D-dimer, and LDH were higher in patients with overweight lungs. Among the 47 blood biomarkers investigated, interferon alpha 2 protein was higher and leukocyte inhibitory factor was lower in patients with overweight lungs. According to CombiROC analysis, the combinations of D-dimer/LDH/leukocytes, D-dimer/LDH/neutrophils, and D-dimer/LDH/leukocytes/neutrophils achieved the highest area under the curve with the best accuracy to detect overweight lungs. Conclusion: The combinations of these specific laboratory data: D-dimer/LDH/leukocytes or D-dimer/LDH/neutrophils or D-dimer/LDH/leukocytes/neutrophils were the best predictors of overweight lungs in patients with COVID-19 pneumonia at hospital admission. Clinical trial registration: Brazilian Registry of Clinical Trials (REBEC) number RBR-88bs9x and ClinicalTrials.gov number NCT04561219.

Native adiponectin plays a role in the adipocyte-mediated conversion of fibroblasts to myofibroblasts.

Adipocytes regulate tissues through production of adipokines that can act both locally and systemically. Adipocytes also have been found to play a critical role in regulating the healing process. To better understand this role, we developed a three-dimensional human adipocyte spheroid system that has an adipokine profile similar to in vivo adipose tissues. Previously, we found that conditioned medium from these spheroids induces human dermal fibroblast conversion into highly contractile, collagen-producing myofibroblasts through a transforming growth factor beta-1 (TGF-ß1) independent pathway. Here, we sought to identify how mature adipocytes signal to dermal fibroblasts through adipokines to induce myofibroblast conversion. By using molecular weight fractionation, heat inactivation and lipid depletion, we determined mature adipocytes secrete a factor that is 30-100 kDa, heat labile and lipid associated that induces myofibroblast conversion. We also show that the depletion of the adipokine adiponectin, which fits those physico-chemical parameters, eliminates the ability of adipocyte-conditioned media to induce fibroblast to myofibroblast conversion. Interestingly, native adiponectin secreted by cultured adipocytes consistently elicited a stronger level of α-smooth muscle actin expression than exogenously added adiponectin. Thus, adiponectin secreted by mature adipocytes induces fibroblast to myofibroblast conversion and may lead to a phenotype of myofibroblasts distinct from TGF-ß1-induced myofibroblasts.

Improving pulmonary perfusion assessment by dynamic contrast-enhanced computed tomography in an experimental lung injury model.

Pulmonary perfusion has been poorly characterized in acute respiratory distress syndrome (ARDS). Optimizing protocols to measure pulmonary blood flow (PBF) via dynamic contrast-enhanced (DCE) computed tomography (CT) could improve understanding of how ARDS alters pulmonary perfusion. In this study, comparative evaluations of injection protocols and tracer-kinetic analysis models were performed based on DCE-CT data measured in ventilated pigs with and without lung injury. Ten Yorkshire pigs (five with lung injury, five healthy) were anesthetized, intubated, and mechanically ventilated; lung injury was induced by bronchial hydrochloric acid instillation. Each DCE-CT scan was obtained during a 30-s end-expiratory breath-hold. Reproducibility of PBF measurements was evaluated in three pigs. In eight pigs, undiluted and diluted Isovue-370 were separately injected to evaluate the effect of contrast viscosity on estimated PBF values. PBF was estimated with the peak-enhancement and the steepest-slope approach. Total-lung PBF was estimated in two healthy pigs to compare with cardiac output measured invasively by thermodilution in the pulmonary artery. Repeated measurements in the same animals yielded a good reproducibility of computed PBF maps. Injecting diluted isovue-370 resulted in smaller contrast-time curves in the pulmonary artery (P < 0.01) and vein (P < 0.01) without substantially diminishing peak signal intensity (P = 0.46 in the pulmonary artery) compared with the pure contrast agent since its viscosity is closer to that of blood. As compared with the peak-enhancement model, PBF values estimated by the steepest-slope model with diluted contrast were much closer to the cardiac output (R2 = 0.82) as compared with the peak-enhancement model. DCE-CT using the steepest-slope model and diluted contrast agent provided reliable quantitative estimates of PBF.NEW & NOTEWORTHY Dynamic contrast-enhanced CT using a lower-viscosity contrast agent in combination with tracer-kinetic analysis by the steepest-slope model improves pulmonary blood flow measurements and assessment of regional distributions of lung perfusion.

Multiscale stiffness of human emphysematous precision cut lung slices.

Emphysema is a debilitating disease that remodels the lung leading to reduced tissue stiffness. Thus, understanding emphysema progression requires assessing lung stiffness at both the tissue and alveolar scales. Here, we introduce an approach to determine multiscale tissue stiffness and apply it to precision-cut lung slices (PCLS). First, we established a framework for measuring stiffness of thin, disk-like samples. We then designed a device to verify this concept and validated its measuring capabilities using known samples. Next, we compared healthy and emphysematous human PCLS and found that the latter was 50% softer. Through computational network modeling, we discovered that this reduced macroscopic tissue stiffness was due to both microscopic septal wall remodeling and structural deterioration. Lastly, through protein expression profiling, we identified a wide spectrum of enzymes that can drive septal wall remodeling, which, together with mechanical forces, lead to rupture and structural deterioration of the emphysematous lung parenchyma.

Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network.

BACKGROUND: Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction. PURPOSE: We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available. METHODS: A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high-resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass- and structure-preserving registration algorithm. The Jacobian image was calculated from the registration-derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration-derived Jacobian images using local, regional, and global evaluation metrics. RESULTS: Statistical analysis revealed that both factors - network input and output space - were significant determinants for change in evaluation metrics. Paired-input models performed better than single-input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired-input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single-input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration-derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2 ) for paired-input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single-input models was 0.598 and 0.346. After correcting for effort, registration-derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired-input models r2 was 0.899 for both FRC and TLC output spaces, and for single-input models r2 was 0.803 and 0.862, respectively. CONCLUSIONS: Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single-input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image.

An agent-based model of tissue maintenance and self-repair.

We hypothesized that a system that possesses the capacity for ongoing maintenance of its tissues will necessarily also have the capacity to self-heal following a perturbation. We used an agent-based model of tissue maintenance to investigate this idea, and in particular to determine the extent to which the current state of the tissue must influence cell behavior in order for tissue maintenance and self-healing to be stable. We show that a mean level of tissue density is robustly maintained when catabolic agents digest tissue at a rate proportional to local tissue density, but that the spatial heterogeneity of the tissue at homeostasis increases with the rate at which tissue is digested. The rate of self-healing is also increased by increasing either the amount of tissue removed or deposited at each time step by catabolic or anabolic agents, respectively, and by increasing the density of both agent types on the tissue. We also found that tissue maintenance and self-healing are stable with an alternate rule in which cells move preferentially to tissue regions of low density. The most basic form of self-healing can thus be achieved with cells that follow very simple rules of behavior, provided these rules are based in some way on the current state of the local tissue. Straightforward mechanisms can accelerate the rate of self-healing, as might be beneficial to the organism.

Modeling the influence of gravity and the mechanical properties of elastin and collagen fibers on alveolar and lung pressure-volume curves.

The relationship between pressure (P) and volume (V) in the human lung has been extensively studied. However, the combined effects of gravity and the mechanical properties of elastin and collagen on alveolar and lung P-V curves during breathing are not well understood. Here, we extended a previously established thick-walled spherical model of a single alveolus with wavy collagen fibers during positive pressure inflation. First, we updated the model for negative pressure-driven inflation that allowed incorporation of a gravity-induced pleural pressure gradient to predict how the static alveolar P-V relations vary spatially throughout an upright human lung. Second, by introducing dynamic surface tension and collagen viscoelasticity, we computed the hysteresis loop of the lung P-V curve. The model was tested by comparing its predicted regional ventilation to literature data, which offered insight into the effects of microgravity on ventilation. The model has also produced novel testable predictions for future experiments about the variation of mechanical stresses in the septal walls and the contribution of collagen and elastin fibers to the P-V curve and throughout the lung. The model may help us better understand how mechanical stresses arising from breathing and pleural pressure variations affect regional cellular mechanotransduction in the lung.

Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN.

BACKGROUND: Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data. METHODS: We trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists. RESULTS: The COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations. CONCLUSION: Our CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.

Assessment of Heterogeneity in Lung Structure and Function During Mechanical Ventilation: A Review of Methodologies.

The mammalian lung is characterized by heterogeneity in both its structure and function, by incorporating an asymmetric branching airway tree optimized for maintenance of efficient ventilation, perfusion, and gas exchange. Despite potential benefits of naturally occurring heterogeneity in the lungs, there may also be detrimental effects arising from pathologic processes, which may result in deficiencies in gas transport and exchange. Regardless of etiology, pathologic heterogeneity results in the maldistribution of regional ventilation and perfusion, impairments in gas exchange, and increased work of breathing. In extreme situations, heterogeneity may result in respiratory failure, necessitating support with a mechanical ventilator. This review will present a summary of measurement techniques for assessing and quantifying heterogeneity in respiratory system structure and function during mechanical ventilation. These methods have been grouped according to four broad categories: (1) inverse modeling of heterogeneous mechanical function; (2) capnography and washout techniques to measure heterogeneity of gas transport; (3) measurements of heterogeneous deformation on the surface of the lung; and finally (4) imaging techniques used to observe spatially-distributed ventilation or regional deformation. Each technique varies with regard to spatial and temporal resolution, degrees of invasiveness, risks posed to patients, as well as suitability for clinical implementation. Nonetheless, each technique provides a unique perspective on the manifestations and consequences of mechanical heterogeneity in the diseased lung.

Lung aeration, ventilation, and perfusion imaging.

PURPOSE OF REVIEW: Lung imaging is a cornerstone of the management of patients admitted to the intensive care unit (ICU), providing anatomical and functional information on the respiratory system function. The aim of this review is to provide an overview of mechanisms and applications of conventional and emerging lung imaging techniques in critically ill patients. RECENT FINDINGS: Chest radiographs provide information on lung structure and have several limitations in the ICU setting; however, scoring systems can be used to stratify patient severity and predict clinical outcomes. Computed tomography (CT) is the gold standard for assessment of lung aeration but requires moving the patients to the CT facility. Dual-energy CT has been recently applied to simultaneous study of lung aeration and perfusion in patients with respiratory failure. Lung ultrasound has an established role in the routine bedside assessment of ICU patients, but has poor spatial resolution and largely relies on the analysis of artifacts. Electrical impedance tomography is an emerging technique capable of depicting ventilation and perfusion at the bedside and at the regional level. SUMMARY: Clinicians should be confident with the technical aspects, indications, and limitations of each lung imaging technique to improve patient care.

Early versus late intubation in COVID-19 patients failing helmet CPAP: A quantitative computed tomography study.

PURPOSE: To describe the effects of timing of intubation in COVID-19 patients that fail helmet continuous positive airway pressure (h-CPAP) on progression and severity of disease. METHODS: COVID-19 patients that failed h-CPAP, required intubation, and underwent chest computed tomography (CT) at two levels of positive end-expiratory pressure (PEEP, 8 and 16 cmH2O) were included in this retrospective study. Patients were divided in two groups (early versus late) based on the duration of h-CPAP before intubation. Endpoints included percentage of non-aerated lung tissue at PEEP of 8 cmH2O, respiratory system compliance and oxygenation. RESULTS: Fifty-two patients were included and classified in early (h-CPAP for ≤2 days, N = 26) and late groups (h-CPAP for >2 days, N = 26). Patients in the late compared to early intubation group presented: 1) lower respiratory system compliance (median difference, MD -7 mL/cmH2O, p = 0.044) and PaO2/FiO2 (MD -29 mmHg, p = 0.047), 2) higher percentage of non-aerated lung tissue (MD 7.2%, p = 0.023) and 3) similar lung recruitment increasing PEEP from 8 to 16 cmH2O (MD 0.1%, p = 0.964). CONCLUSIONS: In COVID-19 patients receiving h-CPAP, late intubation was associated with worse clinical presentation at ICU admission and more advanced disease. The possible detrimental effects of delaying intubation should be carefully considered in these patients.