Mechanisms of oxygenation responses to proning and recruitment in COVID-19 pneumonia.

PURPOSE: This study aimed at investigating the mechanisms underlying the oxygenation response to proning and recruitment maneuvers in coronavirus disease 2019 (COVID-19) pneumonia. METHODS: Twenty-five patients with COVID-19 pneumonia, at variable times since admission (from 1 to 3 weeks), underwent computed tomography (CT) lung scans, gas-exchange and lung-mechanics measurement in supine and prone positions at 5 cmH2O and during recruiting maneuver (supine, 35 cmH2O). Within the non-aerated tissue, we differentiated the atelectatic and consolidated tissue (recruitable and non-recruitable at 35 cmH2O of airway pressure). Positive/negative response to proning/recruitment was defined as increase/decrease of PaO2/FiO2. Apparent perfusion ratio was computed as venous admixture/non aerated tissue fraction. RESULTS: The average values of venous admixture and PaO2/FiO2 ratio were similar in supine-5 and prone-5. However, the PaO2/FiO2 changes (increasing in 65% of the patients and decreasing in 35%, from supine to prone) correlated with the balance between resolution of dorsal atelectasis and formation of ventral atelectasis (p = 0.002). Dorsal consolidated tissue determined this balance, being inversely related with dorsal recruitment (p = 0.012). From supine-5 to supine-35, the apparent perfusion ratio increased from 1.38 ± 0.71 to 2.15 ± 1.15 (p = 0.004) while PaO2/FiO2 ratio increased in 52% and decreased in 48% of patients. Non-responders had consolidated tissue fraction of 0.27 ± 0.1 vs. 0.18 ± 0.1 in the responding cohort (p = 0.04). Consolidated tissue, PaCO2 and respiratory system elastance were higher in patients assessed late (all p < 0.05), suggesting, all together, "fibrotic-like" changes of the lung over time. CONCLUSION: The amount of consolidated tissue was higher in patients assessed during the third week and determined the oxygenation responses following pronation and recruitment maneuvers.

Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome.

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.

Role of total lung stress on the progression of early COVID-19 pneumonia.

PURPOSE: We investigated if the stress applied to the lung during non-invasive respiratory support may contribute to the coronavirus disease 2019 (COVID-19) progression. METHODS: Single-center, prospective, cohort study of 140 consecutive COVID-19 pneumonia patients treated in high-dependency unit with continuous positive airway pressure (n = 131) or non-invasive ventilation (n = 9). We measured quantitative lung computed tomography, esophageal pressure swings and total lung stress. RESULTS: Patients were divided in five subgroups based on their baseline PaO2/FiO2 (day 1): non-CARDS (median PaO2/FiO2 361 mmHg, IQR [323-379]), mild (224 mmHg [211-249]), mild-moderate (173 mmHg [164-185]), moderate-severe (126 mmHg [114-138]) and severe (88 mmHg [86-99], p < 0.001). Each subgroup had similar median lung weight: 1215 g [1083-1294], 1153 [888-1321], 968 [858-1253], 1060 [869-1269], and 1127 [937-1193] (p = 0.37). They also had similar non-aerated tissue fraction: 10.4% [5.9-13.7], 9.6 [7.1-15.8], 9.4 [5.8-16.7], 8.4 [6.7-12.3] and 9.4 [5.9-13.8], respectively (p = 0.85). Treatment failure of CPAP/NIV occurred in 34 patients (24.3%). Only three variables, at day one, distinguished patients with negative outcome: PaO2/FiO2 ratio (OR 0.99 [0.98-0.99], p = 0.02), esophageal pressure swing (OR 1.13 [1.01-1.27], p = 0.032) and total stress (OR 1.17 [1.06-1.31], p = 0.004). When these three variables were evaluated together in a multivariate logistic regression analysis, only the total stress was independently associated with negative outcome (OR 1.16 [1.01-1.33], p = 0.032). CONCLUSIONS: In early COVID-19 pneumonia, hypoxemia is not linked to computed tomography (CT) pathoanatomy, differently from typical ARDS. High lung stress was independently associated with the failure of non-invasive respiratory support.

The impact of ventilation-perfusion inequality in COVID-19: a computational model.

COVID-19 infection may lead to acute respiratory distress syndrome (CARDS) where severe gas exchange derangements may be associated, at least in the early stages, only with minor pulmonary infiltrates. This may suggest 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 498 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 the 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. Our 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.NEW & NOTEWORTHY Hypothesizing that the non-aerated lung fraction as evaluated by the quantitative analysis of the lung computed tomography (CT) equals shunt (VA/Q = 0), we used a computational approach to estimate the magnitude of the ventilation-perfusion inequality in severe COVID-19. The results show that a severe hyperperfusion of poorly ventilated lung region is likely the cause of the observed hypoxemia. The extensive microthrombosis or abnormal vasodilation of the pulmonary circulation may represent the pathophysiological mechanism of such VA/Q distribution.

Physiological and quantitative CT-scan characterization of COVID-19 and typical ARDS: a matched cohort study.

PURPOSE: To investigate whether COVID-19-ARDS differs from all-cause ARDS. METHODS: Thirty-two consecutive, mechanically ventilated COVID-19-ARDS patients were compared to two historical ARDS sub-populations 1:1 matched for PaO2/FiO2 or for compliance of the respiratory system. Gas exchange, hemodynamics and respiratory mechanics were recorded at 5 and 15 cmH2O PEEP. CT scan variables were measured at 5 cmH2O PEEP. RESULTS: Anthropometric characteristics were similar in COVID-19-ARDS, PaO2/FiO2-matched-ARDS and Compliance-matched-ARDS. The PaO2/FiO2-matched-ARDS and COVID-19-ARDS populations (both with PaO2/FiO2 106 ± 59 mmHg) had different respiratory system compliances (Crs) (39 ± 11 vs 49.9 ± 15.4 ml/cmH2O, p = 0.03). The Compliance-matched-ARDS and COVID-19-ARDS had similar Crs (50.1 ± 15.7 and 49.9 ± 15.4 ml/cmH2O, respectively) but significantly lower PaO2/FiO2 for the same Crs (160 ± 62 vs 106.5 ± 59.6 mmHg, p < 0.001). The three populations had similar lung weights but COVID-19-ARDS had significantly higher lung gas volume (PaO2/FiO2-matched-ARDS 930 ± 644 ml, COVID-19-ARDS 1670 ± 791 ml and Compliance-matched-ARDS 1301 ± 627 ml, p < 0.05). The venous admixture was significantly related to the non-aerated tissue in PaO2/FiO2-matched-ARDS and Compliance-matched-ARDS (p < 0.001) but unrelated in COVID-19-ARDS (p = 0.75), suggesting that hypoxemia was not only due to the extent of non-aerated tissue. Increasing PEEP from 5 to 15 cmH2O improved oxygenation in all groups. However, while lung mechanics and dead space improved in PaO2/FiO2-matched-ARDS, suggesting recruitment as primary mechanism, they remained unmodified or worsened in COVID-19-ARDS and Compliance-matched-ARDS, suggesting lower recruitment potential and/or blood flow redistribution. CONCLUSIONS: COVID-19-ARDS is a subset of ARDS characterized overall by higher compliance and lung gas volume for a given PaO2/FiO2, at least when considered within the timeframe of our study.