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Introduction: The use of the pulmonary artery catheter has decreased overtime; central venous blood gases are generally used in place of mixed venous samples. We want to evaluate the accuracy of oxygen and carbon dioxide related parameters from a central versus a mixed venous sample, and whether this difference is influenced by mechanical ventilation. Materials and Methods: We analyzed 78 healthy female piglets ventilated with different mechanical power. Results: There was a significant difference in oxygen‐derived parameters between samples taken from the central venous and mixed venous blood (Sv¯$$ \overline{v} $$O2 = 74.6%, ScvO2 = 83%, p < 0.0001). Conversely, CO2‐related parameters were similar, with strong correlation. Ventilation with higher mechanical power and PEEP increased the difference between oxygen saturations, (Δ[ScvO2−Sv¯$$ \overline{v} $$O2 ] = 7.22% vs. 10.0% respectively in the low and high MP groups, p = 0.020); carbon dioxide‐related parameters remained unchanged (p = 0.344). Conclusions: The venous oxygen saturation (central or mixed) may be influenced by the effects of mechanical ventilation. Therefore, central venous data should be interpreted with more caution when using higher mechanical power. On the contrary, carbon dioxide‐derived parameters are more stable and similar between the two sampling sites, independently of mechanical power or positive end expiratory pressures. [ABSTRACT FROM AUTHOR]

The amount of energy delivered to the respiratory system is recognized as a cause of ventilator-induced lung injury (VILI). How energy dissipation within the lung parenchyma causes damage is still a matter of debate. Expiratory flow control has been proposed as a strategy to reduce the energy dissipated into the respiratory system during expiration and, possibly, VILI. We studied 22 healthy pigs (29 ± 2 kg), which were randomized into a control (n = 11) and a valve group (n = 11), where the expiratory flow was controlled through a variable resistor. Both groups were ventilated with the same tidal volume, positive end-expiratory pressure (PEEP), and inspiratory flow. Electric impedance tomography was continuously acquired. At completion, lung weight, wet-todry ratios, and histology were evaluated. The total mechanical power was similar in the control and valve groups (8.54 ± 0.83 J•min-1 and 8.42 ± 0.54 J•min-1, respectively, P = 0.552). The total energy dissipated within the whole system (circuit þ respiratory system) was remarkably different (4.34 ± 0.66 vs. 2.62 ± 0.31 J/min, P < 0.001). However, most of this energy was dissipated across the endotracheal tube (2.87 ± 0.3 vs. 1.88 ± 0.2 J/min, P < 0.001). The amount dissipated into the respiratory system averaged 1.45 ± 0.5 in controls versus 0.73 ± 0.16 J•min-1 in the valve group, P < 0.001. Although respiratory mechanics, gas exchange, hemodynamics, wet-to-dry ratios, and histology were similar in the two groups, the decrease of end-expiratory lung impedance was significantly greater in the control group (P = 0.02). We conclude that with our experimental conditions, the reduction of energy dissipated in the respiratory system did not lead to appreciable differences in VILI. NEW & NOTEWORTHY Energy dissipation within the respiratory system is a factor promoting ventilator-induced lung injury (VILI). In this animal study, we modulated the expiratory flow, reducing the energy dissipated in the system. However, this reduction happened mostly across the endotracheal tube, and only partly in the respiratory system. Therefore, in healthy lungs, the advantage in energy dissipation does not reduce VILI, but the advantages might be more relevant in diseased lungs under injurious ventilation. [ABSTRACT FROM AUTHOR]

Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R 2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically. [ABSTRACT FROM AUTHOR]

Background: Ventilatory ratio (VR) has been proposed as an alternative approach to estimate physiological dead space. However, the absolute value of VR, at constant dead space, might be affected by venous admixture and CO2 volume expired per minute (VCO2).Methods: This was a retrospective, observational study of mechanically ventilated patients with acute respiratory distress syndrome (ARDS) in the UK and Italy. Venous admixture was either directly measured or estimated using the surrogate measure PaO2/FiO2 ratio. VCO2 was estimated through the resting energy expenditure derived from the Harris-Benedict formula.Results: A total of 641 mechanically ventilated patients with mild (n=65), moderate (n=363), or severe (n=213) ARDS were studied. Venous admixture was measured (n=153 patients) or estimated using the PaO2/FiO2 ratio (n=448). The VR increased exponentially as a function of the dead space, and the absolute values of this relationship were a function of VCO2. At a physiological dead space of 0.6, VR was 1.1, 1.4, and 1.7 in patients with VCO2 equal to 200, 250, and 300, respectively. VR was independently associated with mortality (odds ratio [OR]=2.5; 95% confidence interval [CI], 1.8-3.5), but was not associated when adjusted for VD/VTphys, VCO2, PaO2/FiO2 (ORadj=1.2; 95% CI, 0.7-2.1). These three variables remained independent predictors of ICU mortality (VD/VTphys [ORadj=17.9; 95% CI, 1.8-185; P<0.05]; VCO2 [ORadj=0.99; 95% CI, 0.99-1.00; P<0.001]; and PaO2/FiO2 (ORadj=0.99; 95% CI, 0.99-1.00; P<0.001]).Conclusions: VR is a useful aggregate variable associated with outcome, but variables not associated with ventilation (VCO2 and venous admixture) strongly contribute to the high values of VR seen in patients with severe illness. [ABSTRACT FROM AUTHOR]