The pleural gradient does not reflect the superimposed pressure in patients with class III obesity.

BACKGROUND: The superimposed pressure is the primary determinant of the pleural pressure gradient. Obesity is associated with elevated end-expiratory esophageal pressure, regardless of lung disease severity, and the superimposed pressure might not be the only determinant of the pleural pressure gradient. The study aims to measure partitioned respiratory mechanics and superimposed pressure in a cohort of patients admitted to the ICU with and without class III obesity (BMI ≥ 40 kg/m2), and to quantify the amount of thoracic adipose tissue and muscle through advanced imaging techniques. METHODS: This is a single-center observational study including ICU-admitted patients with acute respiratory failure who underwent a chest computed tomography scan within three days before/after esophageal manometry. The superimposed pressure was calculated from lung density and height of the largest axial lung slice. Automated deep-learning pipelines segmented lung parenchyma and quantified thoracic adipose tissue and skeletal muscle. RESULTS: N = 18 participants (50% female, age 60 [30-66] years), with 9 having BMI < 30 and 9  ≥ 40 kg/m2. Groups showed no significant differences in age, sex, clinical severity scores, or mortality. Patients with BMI ≥ 40 exhibited higher esophageal pressure (15.8 ± 2.6 vs. 8.3 ± 4.9 cmH2O, p = 0.001), higher pleural pressure gradient (11.1 ± 4.5 vs. 6.3 ± 4.9 cmH2O, p = 0.04), while superimposed pressure did not differ (6.8 ± 1.1 vs. 6.5 ± 1.5 cmH2O, p = 0.59). Subcutaneous and intrathoracic adipose tissue were significantly higher in subjects with BMI ≥ 40 and correlated positively with esophageal pressure and pleural pressure gradient (p < 0.05). Muscle areas did not differ between groups. CONCLUSIONS: In patients with class III obesity, the superimposed pressure does not approximate the pleural pressure gradient, which is higher than in patients with lower BMI. The quantity and distribution of subcutaneous and intrathoracic adiposity also contribute to increased pleural pressure gradients in individuals with BMI ≥ 40. This study introduces a novel physiological concept that provides a solid rationale for tailoring mechanical ventilation in patients with high BMI, where specific guidelines recommendations are lacking.

Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.

BACKGROUND: Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes. METHODS: This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories. RESULTS: Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables. CONCLUSIONS: Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

Derivation and external validation of predictive models for invasive mechanical ventilation in intensive care unit patients with COVID-19.

BACKGROUND: This study aimed to develop prognostic models for predicting the need for invasive mechanical ventilation (IMV) in intensive care unit (ICU) patients with COVID-19 and compare their performance with the Respiratory rate-OXygenation (ROX) index. METHODS: A retrospective cohort study was conducted using data collected between March 2020 and August 2021 at three hospitals in Rio de Janeiro, Brazil. ICU patients aged 18 years and older with a diagnosis of COVID-19 were screened. The exclusion criteria were patients who received IMV within the first 24 h of ICU admission, pregnancy, clinical decision for minimal end-of-life care and missing primary outcome data. Clinical and laboratory variables were collected. Multiple logistic regression analysis was performed to select predictor variables. Models were based on the lowest Akaike Information Criteria (AIC) and lowest AIC with significant p values. Assessment of predictive performance was done for discrimination and calibration. Areas under the curves (AUC)s were compared using DeLong's algorithm. Models were validated externally using an international database. RESULTS: Of 656 patients screened, 346 patients were included; 155 required IMV (44.8%), 191 did not (55.2%), and 207 patients were male (59.8%). According to the lowest AIC, arterial hypertension, diabetes mellitus, obesity, Sequential Organ Failure Assessment (SOFA) score, heart rate, respiratory rate, peripheral oxygen saturation (SpO2), temperature, respiratory effort signals, and leukocytes were identified as predictors of IMV at hospital admission. According to AIC with significant p values, SOFA score, SpO2, and respiratory effort signals were the best predictors of IMV; odds ratios (95% confidence interval): 1.46 (1.07-2.05), 0.81 (0.72-0.90), 9.13 (3.29-28.67), respectively. The ROX index at admission was lower in the IMV group than in the non-IMV group (7.3 [5.2-9.8] versus 9.6 [6.8-12.9], p < 0.001, respectively). In the external validation population, the area under the curve (AUC) of the ROX index was 0.683 (accuracy 63%), the AIC model showed an AUC of 0.703 (accuracy 69%), and the lowest AIC model with significant p values had an AUC of 0.725 (accuracy 79%). CONCLUSIONS: In the development population of ICU patients with COVID-19, SOFA score, SpO2, and respiratory effort signals predicted the need for IMV better than the ROX index. In the external validation population, although the AUCs did not differ significantly, the accuracy was higher when using SOFA score, SpO2, and respiratory effort signals compared to the ROX index. This suggests that these variables may be more useful in predicting the need for IMV in ICU patients with COVID-19. GOV IDENTIFIER: NCT05663528.

Imaging the pulmonary vasculature in acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) is characterized by a redistribution of regional lung perfusion that impairs gas exchange. While speculative, experimental evidence suggests that perfusion redistribution may contribute to regional inflammation and modify disease progression. Unfortunately, tools to visualize and quantify lung perfusion in patients with ARDS are lacking. This review explores recent advances in perfusion imaging techniques that aim to understand the pulmonary circulation in ARDS. Dynamic contrast-enhanced computed tomography captures first-pass kinetics of intravenously injected dye during continuous scan acquisitions. Different contrast characteristics and kinetic modeling have improved its topographic measurement of pulmonary perfusion with high spatial and temporal resolution. Dual-energy computed tomography can map the pulmonary blood volume of the whole lung with limited radiation exposure, enabling its application in clinical research. Electrical impedance tomography can obtain serial topographic assessments of perfusion at the bedside in response to treatments such as inhaled nitric oxide and prone position. Ongoing technological improvements and emerging techniques will enhance lung perfusion imaging and aid its incorporation into the care of patients with ARDS.

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.

Regional lung perfusion using different indicators in electrical impedance tomography.

Management of acute respiratory distress syndrome (ARDS) is classically guided by protecting the injured lung and mitigating damage from mechanical ventilation. Yet the natural history of ARDS is also dictated by disruption in lung perfusion. Unfortunately, diagnosis and treatment are hampered by the lack of bedside perfusion monitoring. Electrical impedance tomography is a portable imaging technique that can estimate regional lung perfusion in experimental settings from the kinetic analysis of a bolus of an indicator with high conductivity. Hypertonic sodium chloride has been the standard indicator. However, hypertonic sodium chloride is often inaccessible in the hospital, limiting practical adoption. We investigated whether regional lung perfusion measured using electrical impedance tomography is comparable between indicators. Using a swine lung injury model, we determined regional lung perfusion (% of total perfusion) in five pigs, comparing 12% sodium chloride to 8.4% sodium bicarbonate across stages of lung injury and experimental conditions (body position, positive end-expiratory pressure). Regional lung perfusion for four lung regions was determined from maximum slope analysis of the indicator-based impedance signal. Estimates of regional lung perfusion between indicators were compared in the lung overall and within four lung regions. Regional lung perfusion estimated with a sodium bicarbonate indicator agreed with a hypertonic sodium chloride indicator overall (mean bias 0%, limits of agreement -8.43%, 8.43%) and within lung quadrants. The difference in regional lung perfusion between indicators did not change across experimental conditions. Sodium bicarbonate may be a comparable indicator to estimate regional lung perfusion using electrical impedance tomography.NEW & NOTEWORTHY Electrical impedance tomography is an emerging tool to measure regional lung perfusion using kinetic analysis of a conductive indicator. Hypertonic sodium chloride is the standard agent used. We measured regional lung perfusion using another indicator, comparing hypertonic sodium chloride to sodium bicarbonate in an experimental swine lung injury model. We found strong agreement between the two indicators. Sodium bicarbonate may be a comparable indicator to measure regional lung perfusion with electrical impedance tomography.

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.

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.

Modulation of pulmonary blood flow in patients with acute respiratory failure.

BACKGROUND: Impairment of ventilation and perfusion (V/Q) matching is a common mechanism leading to hypoxemia in patients with acute respiratory failure requiring intensive care unit (ICU) admission. While ventilation has been thoroughly investigated, little progress has been made to monitor pulmonary perfusion at the bedside and treat impaired blood distribution. The study aimed to assess real-time changes in regional pulmonary perfusion in response to a therapeutic intervention. METHODS: Single-center prospective study that enrolled adult patients with ARDS caused by SARS-Cov-2 who were sedated, paralyzed, and mechanically ventilated. The distribution of pulmonary perfusion was assessed through electrical impedance tomography (EIT) after the injection of a 10-ml bolus of hypertonic saline. The therapeutic intervention consisted in the administration of inhaled nitric oxide (iNO), as rescue therapy for refractory hypoxemia. Each patient underwent two 15-min steps at 0 and 20 ppm iNO, respectively. At each step, respiratory, gas exchange, and hemodynamic parameters were recorded, and V/Q distribution was measured, with unchanged ventilatory settings. RESULTS: Ten 65 [56-75] years old patients with moderate (40%) and severe (60%) ARDS were studied 10 [4-20] days after intubation. Gas exchange improved at 20 ppm iNO (PaO2/FiO2 from 86 ± 16 to 110 ± 30 mmHg, p = 0.001; venous admixture from 51 ± 8 to 45 ± 7%, p = 0.0045; dead space from 29 ± 8 to 25 ± 6%, p = 0.008). The respiratory system's elastic properties and ventilation distribution were unaltered by iNO. Hemodynamics did not change after gas initiation (cardiac output 7.6 ± 1.9 vs. 7.7 ± 1.9 L/min, p = 0.66). The EIT pixel perfusion maps showed a variety of patterns of changes in pulmonary blood flow, whose increase positively correlated with PaO2/FiO2 increase (R2 = 0.50, p = 0.049). CONCLUSIONS: The assessment of lung perfusion is feasible at the bedside and blood distribution can be modulated with effects that are visualized in vivo. These findings might lay the foundations for testing new therapies aimed at optimizing the regional perfusion in the lungs.

A Prospective Observational Study on Short and Long-Term Outcomes of COVID-19 Patients with Acute Hypoxic Respiratory Failure Treated with High-Flow Nasal Cannula.

(1) The use of high-flow nasal cannula (HFNC) combined with frequent respiratory monitoring in patients with acute hypoxic respiratory failure due to COVID-19 has been shown to reduce intubation and mechanical ventilation. (2) This prospective, single-center, observational study included consecutive adult patients with COVID-19 pneumonia treated with a high-flow nasal cannula. Hemodynamic parameters, respiratory rate, inspiratory fraction of oxygen (FiO2), saturation of oxygen (SpO2), and the ratio of oxygen saturation to respiratory rate (ROX) were recorded prior to treatment initiation and every 2 h for 24 h. A 6-month follow-up questionnaire was also conducted. (3) Over the study period, 153 of 187 patients were eligible for HFNC. Of these patients, 80% required intubation and 37% of the intubated patients died in hospital. Male sex (OR = 4.65; 95% CI [1.28; 20.6], p = 0.03) and higher BMI (OR = 2.63; 95% CI [1.14; 6.76], p = 0.03) were associated with an increased risk for new limitations at 6-months after hospital discharge. (4) 20% of patients who received HFNC did not require intubation and were discharged alive from the hospital. Male sex and higher BMI were associated with poor long-term functional outcomes.

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.

Computational lung modelling in respiratory medicine.

Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure-function relationship in the 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.