Prone positioning is nowadays considered as one of the most effective strategies for patients with severe acute respiratory distress syndrome (ARDS). The evolution of the pathophysiological understanding surrounding the prone position closely follows the history of ARDS. At the beginning, the focus of the prone position was the improvement in oxygenation attributed to a perfusion redistribution. However, the mechanisms behind the prone position are more complex. Indeed, the positive effects on oxygenation and CO2 clearance of the prone position are to be ascribed to a more homogeneous inflation–ventilation, to the lung/thoracic shape mismatch, and to the change of chest wall elastance. In the past 20 years, five major trials have tried, starting from different theories, hypotheses, and designs, to demonstrate the effectiveness of the prone position, which finally found its definitive place among the different ARDS supportive therapies.

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COVID-19 infection may lead to an Acute Respiratory Distress Syndrome where severe gas exchange derangements may be associated, at least in the early stages, only with minor pulmonary infiltrates. This suggests that the shunt associated to the gasless lung parenchyma is not sufficient to explain CARDS hypoxemia. We designed an algorithm (VentriQlar), based on the same conceptual grounds described by J.B West in 1969. We set 499 ventilation-perfusion (VA/Q) compartments and, after calculating their blood composition (PO2, PCO2 and pH), we randomly chose 106 combinations of five parameters controlling a bimodal distribution of blood flow. The solutions were accepted if the predicted PaO2 and PaCO2 were within 10% of the patient's values. We assumed that shunt fraction equaled the fraction of non-aerated lung tissue at the CT quantitative analysis. Five critically-ill patients later deceased were studied. The PaO2/FiO2 was 91.1±18.6 mmHg and PaCO2 69.0±16.1 mmHg. Cardiac output was 9.58±0.99 l/min. The fraction of non-aerated tissue was 0.33±0.06. The model showed that a large fraction of the blood flow was likely distributed in regions with very low VA/Q (Qmean=0.06±0.02) and a smaller fraction in regions with moderately high VA/Q. Overall LogSD, Q was 1.66 ± 0.14, suggestive of high VA/Q inequality. Data suggest that shunt alone cannot completely account for the observed hypoxemia and a significant VA/Q inequality must be present in COVID-19. The high cardiac output and the extensive microthrombosis later found in the autopsy further support the hypothesis of a pathological perfusion of non/poorly ventilated lung tissue.

BackgroundMechanical power is a summary variable including all the components which can possibly cause VILI (pressures, volume, flow, respiratory rate). Since the complexity of its mathematical computation is one of the major factors that delay its clinical use, we propose here a simple and easy to remember equation to estimate mechanical power under volume-controlled ventilation: where the mechanical power is expressed in Joules/minute, the minute ventilation (VE) in liters/minute, the inspiratory flow (F) in liters/minute, and peak pressure and positive end-expiratory pressure (PEEP) in centimeter of water. All the components of this equation are continuously displayed by any ventilator under volume-controlled ventilation without the need for clinician intervention.To test the accuracy of this new equation, we compared it with the reference formula of mechanical power that we proposed for volume-controlled ventilation in the past. The comparisons were made in a cohort of mechanically ventilated pigs (485 observations) and in a cohort of ICU patients (265 observations).ResultsBoth in pigs and in ICU patients, the correlation between our equation and the reference one was close to the identity. Indeed, the R2 ranged from 0.97 to 0.99 and the Bland-Altman showed small biases (ranging from + 0.35 to − 0.53 J/min) and proportional errors (ranging from + 0.02 to − 0.05).ConclusionsOur new equation of mechanical power for volume-controlled ventilation represents a simple and accurate alternative to the more complex ones available to date. This equation does not need any clinical intervention on the ventilator (such as an inspiratory hold) and could be easily implemented in the software of any ventilator in volume-controlled mode. This would allow the clinician to have an estimation of mechanical power at a simple glance and thus increase the clinical consciousness of this variable which is still far from being used at the bedside. Our equation carries the same limitations of all other formulas of mechanical power, the most important of which, as far as it concerns VILI prevention, are the lack of normalization and its application to the whole respiratory system (including the chest wall) and not only to the lung parenchyma.

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