Physiologically variable ventilation reduces regional lung inflammation in a pediatric model of acute respiratory distress syndrome

Changes in lung mechanics and ventilation-perfusion match: comparison of pulmonary air- and thromboembolism in rats

BACKGROUND

Pulmonary air embolism (AE) and thromboembolism lead to severe ventilation-perfusion defects. The spatial distribution of pulmonary perfusion dysfunctions differs substantially in the two pulmonary embolism pathologies, and the effects on respiratory mechanics, gas exchange, and ventilation-perfusion match have not been compared within a study. Therefore, we compared changes in indices reflecting airway and respiratory tissue mechanics, gas exchange, and capnography when pulmonary embolism was induced by venous injection of air as a model of gas embolism or by clamping the main pulmonary artery to mimic severe thromboembolism.

METHODS

Anesthetized and mechanically ventilated rats (n = 9) were measured under baseline conditions after inducing pulmonary AE by injecting 0.1 mL air into the femoral vein and after occluding the left pulmonary artery (LPAO). Changes in mechanical parameters were assessed by forced oscillations to measure airway resistance, lung tissue damping, and elastance. The arterial partial pressures of oxygen (PaO2) and carbon dioxide (PaCO2) were determined by blood gas analyses. Gas exchange indices were also assessed by measuring end-tidal CO2 concentration (ETCO2), shape factors, and dead space parameters by volumetric capnography.

RESULTS

In the presence of a uniform decrease in ETCO2 in the two embolism models, marked elevations in the bronchial tone and compromised lung tissue mechanics were noted after LPAO, whereas AE did not affect lung mechanics. Conversely, only AE deteriorated PaO2, and PaCO2, while LPAO did not affect these outcomes. Neither AE nor LPAO caused changes in the anatomical or physiological dead space, while both embolism models resulted in elevated alveolar dead space indices incorporating intrapulmonary shunting.

CONCLUSIONS

Our findings indicate that severe focal hypocapnia following LPAO triggers bronchoconstriction redirecting airflow to well-perfused lung areas, thereby maintaining normal oxygenation, and the CO2 elimination ability of the lungs. However, hypocapnia in diffuse pulmonary perfusion after AE may not reach the threshold level to induce lung mechanical changes; thus, the compensatory mechanisms to match ventilation to perfusion are activated less effectively.

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.

Ultrasound Lung Image under Artificial Intelligence Algorithm in Diagnosis of Neonatal Respiratory Distress Syndrome

In order to analyze the application of ultrasonic lung imaging diagnosis model based on artificial intelligence algorithm in neonatal respiratory distress syndrome (NRDS), an ultrasonic lung imaging diagnosis model based on a deep residual network (DRN) was proposed. In this study, 90 premature infants in the hospital were selected as the research object and divided into the experimental group (45 cases) and control group (45 cases) according to whether or not they have NRDS. DRN was compared with the deep residual network (DRWSR) based on wavelet domain, deep residual network detection with normalization framework (Fisher-DRN), and distorted image edge detection preprocessor (DIEDP). Then, it was applied to the diagnosis of NRDS. The clinical data and ultrasound imaging results of infants with NRDS and ordinary premature infants were compared. The results showed that the gestational age, birth weight, and Apgar scores of the NRDS group were remarkably lower than those of ordinary children (P < 0.05). In addition, the segmentation accuracy, image feature extraction accuracy, algorithm convergence, and time loss of the DRN algorithm were better than the other three algorithms, and the differences were considerable (P < 0.05). In children with NRDS, the positive rate of abnormal pleural line, disappearance of A line, appearance of B line, and alveolar interstitial syndrome (AIS) test in the results of lung ultrasound examination in children with NRDS were all 100%. The lung consolidation became 70.8%, and the white lung-like change was 50.1%, both of which were higher than those of ordinary preterm infants, and the differences were considerable (P < 0.05). The diagnostic model of this study predicted that the AUC area of grade 1-2, grade 2-3, and grade 3-4 NRDS were 0.962, 0.881, and 0.902, respectively. To sum up, the ultrasound lung imaging diagnosis model based on the DRN algorithm had good diagnostic performance in children with NRDS and can provide useful information for clinical NRDS diagnosis and treatment.

Physiologically variable ventilation prevents lung function deterioration in a model of pulmonary fibrosis

Positive pressure ventilation exerts an increased stress and strain in the presence of pulmonary fibrosis. Thus, ventilation strategies that avoid high pressures while maintaining lung aeration are of paramount importance. While physiologically variable ventilation (PVV) has proven beneficial in various models of pulmonary disease, its potential advantages in pulmonary fibrosis have not been investigated. Therefore, we assessed the benefit of PVV over conventional pressure-controlled ventilation (PCV) in a model of pulmonary fibrosis. Lung fibrosis was induced with intratracheal bleomycin in rabbits. Fifty days later, the animals were randomized to receive 6 hours of either PCV (n=10) or PVV (n=11). The PVV pattern was prerecorded in spontaneously breathing, healthy rabbits. Respiratory mechanics and gas exchange were assessed hourly, end-expiratory lung volume and intrapulmonary shunt fraction were measured at hours 0 and 6. Histological and cellular analyses were performed. Fifty days after bleomycin treatment, the rabbits presented elevated specific airway resistance (69±26% [mean±95%confidence interval]), specific tissue damping (38±15%) and specific elastance (47±16%) along with histological evidence of fibrosis. Six hours of PCV led to increased respiratory airway resistance (Raw, 111±30%), tissue damping (G, 36±13%) and elastance (H, 58±14%), and decreased end-expiratory lung volume (EELV, -26±7%) and oxygenation (PaO₂/FiO₂, -14±5%). The time-matched changes in the PVV group were significantly lower for G (22±9%), H (41±6%), EELV (-13±6%) and PaO₂/FiO₂ ratio (-3±5%, p<0.05 for all). There was no difference in histopathology between the ventilation modes. Thus, prolonged application of PVV prevented the deterioration of gas exchange by reducing atelectasis development in bleomycin-induced lung fibrosis.

Constant Tidal Volume Ventilation and Surfactant Dysfunction: An Overlooked Cause of Ventilator-Induced Lung Injury

Ventilator-induced lung injury (VILI) is currently ascribed to volutrauma and/or atelectrauma but the effect of constant tidal volume ventilation (CVTV) has received little attention. This Perspective summarizes the literature documenting that CVTV causes VILI and reviews the mechanisms by which it occurs. Surfactant is continuously inactivated, depleted, displaced or desorbed as a function of the duration of ventilation, the tidal volume, the level of PEEP and possibly the respiratory rate. Accordingly, surfactant must be continuously replenished and secretion primarily depends on intermittent delivery of large ventilatory excursions. The surfactant abnormalities resulting from CVTV result in atelectasis and VILI. While surfactant secretion is reduced by the absence of intermittent deep breaths continuous administration of large tidal volumes depletes surfactant and impairs subsequent secretion. Low or normal lung volumes result in desorption of surfactant. PEEP can be protective by reducing surface film collapse and subsequent film rupture on re-expansion, and/or by reducing surfactant displacement into the airways, but PEEP can also down-regulate surfactant release. Conclusions: The effect of CVTV on surfactant is complex. If attention is not paid to facilitating surfactant secretion and limiting its inactivation, depletion, desorption or displacement surface tension will increase and atelectasis and VILI will occur.