Mechanical Power Correlates With Lung Inflammation Assessed by Positron-Emission Tomography in Experimental Acute Lung Injury in Pigs.

Background: Mechanical ventilation (MV) may initiate or worsen lung injury, so-called ventilator-induced lung injury (VILI). Although different mechanisms of VILI have been identified, research mainly focused on single ventilator parameters. The mechanical power (MP) summarizes the potentially damaging effects of different parameters in one single variable and has been shown to be associated with lung damage. However, to date, the association of MP with pulmonary neutrophilic inflammation, as assessed by positron-emission tomography (PET), has not been prospectively investigated in a model of clinically relevant ventilation settings yet. We hypothesized that the degree of neutrophilic inflammation correlates with MP. Methods: Eight female juvenile pigs were anesthetized and mechanically ventilated. Lung injury was induced by repetitive lung lavages followed by initial PET and computed tomography (CT) scans. Animals were then ventilated according to the acute respiratory distress syndrome (ARDS) network recommendations, using the lowest combinations of positive end-expiratory pressure and inspiratory oxygen fraction that allowed adequate oxygenation. Ventilator settings were checked and adjusted hourly. Physiological measurements were conducted every 6 h. Lung imaging was repeated 24 h after first PET/CT before animals were killed. Pulmonary neutrophilic inflammation was assessed by normalized uptake rate of 2-deoxy-2-[18F]fluoro-D-glucose (KiS), and its difference between the two PET/CT was calculated (ΔKiS). Lung aeration was assessed by lung CT scan. MP was calculated from the recorded pressure-volume curve. Statistics included the Wilcoxon tests and non-parametric Spearman correlation. Results: Normalized 18F-FDG uptake rate increased significantly from first to second PET/CT (p = 0.012). ΔKiS significantly correlated with median MP (ρ = 0.738, p = 0.037) and its elastic and resistive components, but neither with median peak, plateau, end-expiratory, driving, and transpulmonary driving pressures, nor respiratory rate (RR), elastance, or resistance. Lung mass and volume significantly decreased, whereas relative mass of hyper-aerated lung compartment increased after 24 h (p = 0.012, p = 0.036, and p = 0.025, respectively). Resistance and PaCO2 were significantly higher (p = 0.012 and p = 0.017, respectively), whereas RR, end-expiratory pressure, and MP were lower at 18 h compared to start of intervention. Conclusions: In this model of experimental acute lung injury in pigs, pulmonary neutrophilic inflammation evaluated by PET/CT increased after 24 h of MV, and correlated with MP.

Effects of Different Levels of Variability and Pressure Support Ventilation on Lung Function in Patients With Mild-Moderate Acute Respiratory Distress Syndrome.

Background: Variable pressure support ventilation (vPSV) is an assisted ventilation mode that varies the level of pressure support on a breath-by-breath basis to restore the physiological variability of breathing activity. We aimed to compare the effects of vPSV at different levels of variability and pressure support (ΔP S) in patients with acute respiratory distress syndrome (ARDS). Methods: This study was a crossover randomized clinical trial. We included patients with mild to moderate ARDS already ventilated in conventional pressure support ventilation (PSV). The study consisted of two blocks of interventions, and variability during vPSV was set as the coefficient of variation of the ΔP S level. In the first block, the effects of three levels of variability were tested at constant ΔP S: 0% (PSV0%, conventional PSV), 15% (vPSV15%), and 30% (vPSV30%). In the second block, two levels of variability (0% and variability set to achieve ±5 cmH2O variability) were tested at two ΔPS levels (baseline ΔP S and ΔP S reduced by 5 cmH2O from baseline). The following four ventilation strategies were tested in the second block: PSV with baseline ΔP S and 0% variability (PSVBL) or ±5 cmH2O variability (vPSVBL), PSV with ΔPS reduced by 5 cmH2O and 0% variability (PSV-5) or ±5 cmH2O variability (vPSV-5). Outcomes included gas exchange, respiratory mechanics, and patient-ventilator asynchronies. Results: The study enrolled 20 patients. In the first block of interventions, oxygenation and respiratory mechanics parameters did not differ between vPSV15% and vPSV30% compared with PSV0%. The variability of tidal volume (V T) was higher with vPSV15% and vPSV30% compared with PSV0%. The incidence of asynchronies and the variability of transpulmonary pressure (P L) were higher with vPSV30% compared with PSV0%. In the second block of interventions, different levels of pressure support with and without variability did not change oxygenation. The variability of V T and P L was higher with vPSV-5 compared with PSV-5, but not with vPSVBL compared with PSVBL. Conclusion: In patients with mild-moderate ARDS, the addition of variability did not improve oxygenation at different pressure support levels. Moreover, high variability levels were associated with worse patient-ventilator synchrony. Clinical Trial Registration: www.clinicaltrials.gov, identifier: NCT01683669.

Computed tomography assessment of PEEP-induced alveolar recruitment in patients with severe COVID-19 pneumonia.

BACKGROUND: There is a paucity of data concerning the optimal ventilator management in patients with COVID-19 pneumonia; particularly, the optimal levels of positive-end expiratory pressure (PEEP) are unknown. We aimed to investigate the effects of two levels of PEEP on alveolar recruitment in critically ill patients with severe COVID-19 pneumonia. METHODS: A single-center cohort study was conducted in a 39-bed intensive care unit at a university-affiliated hospital in Genoa, Italy. Chest computed tomography (CT) was performed to quantify aeration at 8 and 16 cmH2O PEEP. The primary endpoint was the amount of alveolar recruitment, defined as the change in the non-aerated compartment at the two PEEP levels on CT scan. RESULTS: Forty-two patients were included in this analysis. Alveolar recruitment was median [interquartile range] 2.7 [0.7-4.5] % of lung weight and was not associated with excess lung weight, PaO2/FiO2 ratio, respiratory system compliance, inflammatory and thrombophilia markers. Patients in the upper quartile of recruitment (recruiters), compared to non-recruiters, had comparable clinical characteristics, lung weight and gas volume. Alveolar recruitment was not different in patients with lower versus higher respiratory system compliance. In a subgroup of 20 patients with available gas exchange data, increasing PEEP decreased respiratory system compliance (median difference, MD - 9 ml/cmH2O, 95% CI from - 12 to - 6 ml/cmH2O, p < 0.001) and the ventilatory ratio (MD - 0.1, 95% CI from - 0.3 to - 0.1, p = 0.003), increased PaO2 with FiO2 = 0.5 (MD 24 mmHg, 95% CI from 12 to 51 mmHg, p < 0.001), but did not change PaO2 with FiO2 = 1.0 (MD 7 mmHg, 95% CI from - 12 to 49 mmHg, p = 0.313). Moreover, alveolar recruitment was not correlated with improvement of oxygenation or venous admixture. CONCLUSIONS: In patients with severe COVID-19 pneumonia, higher PEEP resulted in limited alveolar recruitment. These findings suggest limiting PEEP strictly to the values necessary to maintain oxygenation, thus avoiding the use of higher PEEP levels.

Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning.

BACKGROUND: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. METHODS: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2Net Pig ) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2Net Human ). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2Net Pig on the clinical data set generating u2Net Transfer . General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (S JI and S BF , respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. RESULTS: On the experimental data set, u2Net Pig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes S JI = -0.2 {95% conf. int. -0.23 : -0.16} and S BF = -0.1 {-0.5 : -0.06}. u2Net Human showed similar performance compared to u2Net Pig in JI, BF but with reduced robustness S JI = -0.29 {-0.36 : -0.22} and S BF = -0.43 {-0.54 : -0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness S JI = -0.46 {-0.52 : -0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor S BF = -0.48 {-0.59 : -0.36}. u2Net Transfer improved JI compared to u2Net Human in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2Net Transfer segmentations exhibited < 5% volume difference compared to MS. CONCLUSION: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability.