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BackgroundDespite being essential in patients with acute respiratory distress syndrome (ARDS), mechanical ventilation (MV) may cause lung injury and hemodynamic instability. Mechanical power (MP) may describe the net injurious effects of MV, but whether it reflects the hemodynamic effects of MV is currently unclear. We hypothesized that MP is also associated with cardiac output (CO) and pulmonary blood flow (PBF).Methods24 anesthetized pigs with experimental acute lung injury were ventilated for 18 h according to one of three strategies: 1) Open lung approach (OLA), 2) ARDS Network high-PEEP/FIO2 strategy (HighPEEP), or 3) low-PEEP/FIO2 strategy (LowPEEP). Total MP was assessed as the sum of energy dissipated to overcome airway resistance and energy temporarily stored in the elastic lung tissue per minute. The distribution of pulmonary perfusion was determined by positron emission tomography. Regional PBF and MP, assessed in three iso-gravitational regions of interest (ROI) with equal lung mass (ventral, middle, and dorsal ROI), were compared between groups.ResultsMP was higher in the LowPEEP than in the OLA group, while CO did not differ between groups. After 18 h, regional PBF did not differ between groups. During LowPEEP, regional MP was higher in the ventral ROI compared to OLA and HighPEEP groups (2.5 ± 0.3 vs. 1.4 ± 0.4 and 1.6 ± 0.3 J/min, respectively, P < 0.001 each), and higher in the middle ROI compared to the OLA group (2.5 ± 0.4 vs. 1.6 ± 0.5 J/min, P = 0.04). MP in the dorsal ROI did not differ between groups (1.4 ± 0.9 vs. 1.4 ± 0.5 vs. 1.3 ± 0.8 J/min, P = 0.916). Total MP was independently associated with CO [0.34 (0.09, 0.59), P = 0.020]. Regional MP was positively associated with PBF irrespective of the regions [0.52 (0.14, 0.76), P = 0.01; 0.49 (0.10, 0.74), P = 0.016; 0.64 (0.32, 0.83), P = 0.001 for ventral, middle, and dorsal ROI, respectively]. Subgroup analysis revealed a significant association of MP and CO only in the OLA group as well as a significant association between MP with regional PBF only in the HighPEEP group.ConclusionIn this model of acute lung injury in pigs ventilated with either open lung approach, high, or low PEEP tables recommended by the ARDS network, MP correlated positively with CO and regional PBF, whereby these clinically relevant lung-protective ventilation strategies influenced the associations.

Abstract 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

BackgroundIdentification 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.MethodsTwo 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 (u2NetPig) 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 (u2NetHuman). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2NetPig on the clinical data set generating u2NetTransfer. 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 (SJI and SBF, 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.ResultsOn the experimental data set, u2NetPig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes SJI = −0.2 {95% conf. int. −0.23 : −0.16} and SBF = −0.1 {−0.5 : −0.06}. u2NetHuman showed similar performance compared to u2NetPig in JI, BF but with reduced robustness SJI = −0.29 {−0.36 : −0.22} and SBF = −0.43 {−0.54 : −0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness SJI = −0.46 {−0.52 : −0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor SBF = −0.48 {−0.59 : −0.36}. u2NetTransfer improved JI compared to u2NetHuman 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 u2NetTransfer segmentations exhibited < 5% volume difference compared to MS.ConclusionCompared 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.

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.

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 (ΔPS) 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 ΔPS level. In the first block, the effects of three levels of variability were tested at constant ΔPS: 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 ΔPS and ΔPS reduced by 5 cmH2O from baseline). The following four ventilation strategies were tested in the second block: PSV with baseline ΔPS 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 (VT) was higher with vPSV15% and vPSV30% compared with PSV0%. The incidence of asynchronies and the variability of transpulmonary pressure (PL) 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 VT and PL 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.