A quantitative analysis of extension and distribution of lung injury in COVID-19: a prospective study based on chest computed tomography

Abstract

Background Typical features differentiate COVID-19-associated lung injury from acute respiratory distress syndrome. The clinical role of chest computed tomography (CT) in describing the progression of COVID-19-associated lung injury remains to be clarified. We investigated in COVID-19 patients the regional distribution of lung injury and the influence of clinical and laboratory features on its progression. Methods This was a prospective study. For each CT, twenty images, evenly spaced along the cranio-caudal axis, were selected. For regional analysis, each CT image was divided into three concentric subpleural regions of interest and four quadrants. Hyper-, normally, hypo- and non-inflated lung compartments were defined. Nonparametric tests were used for hypothesis testing (α = 0.05). Spearman correlation test was used to detect correlations between lung compartments and clinical features. Results Twenty-three out of 111 recruited patients were eligible for further analysis. Five hundred-sixty CT images were analyzed. Lung injury, composed by hypo- and non-inflated areas, was significantly more represented in subpleural than in core lung regions. A secondary, centripetal spread of lung injury was associated with exposure to mechanical ventilation (p < 0.04), longer spontaneous breathing (more than 14 days, p < 0.05) and non-protective tidal volume (p < 0.04). Positive fluid balance (p < 0.01), high plasma D-dimers (p < 0.01) and ferritin (p < 0.04) were associated with increased lung injury. Conclusions In a cohort of COVID-19 patients with severe respiratory failure, a predominant subpleural distribution of lung injury is observed. Prolonged spontaneous breathing and high tidal volumes, both causes of patient self-induced lung injury, are associated to an extensive involvement of more central regions. Positive fluid balance, inflammation and thrombosis are associated with lung injury. Trial registration Study registered a priori the 20th of March, 2020. Clinical Trials ID NCT04316884.

Biological effects of COVID-19 on lung cancer: Can we drive our decisions

“…Upon follow-up with microcomputed tomography images, comparing animals with spontaneous breathing through controlled and uncontrolled mechanical ventilation, moderate-to-severe lung injury was observed in the nonaerated lung compartments (37); furthermore, the study findings demonstrated significant progression of the regional volumetric strain and heterogeneity after spontaneous breathing, with subsequent damage to the lung alveoli (37). Other studies were conducted to analyze lung heterogeneity in association with the severity of ARDS and subsequent mortality, particularly in relation to alveolar wall disruption, hemorrhage, hyperemia, and inflammation (45)(46)(47)(48). Recently, researchers attempted to define a gene expression pathway related to lung homogeneity, although the gene expression is generally related to specific lung regions, in contrast with the damage, which is ubiquitous (49).…”

Mechanistic Insights Into the Immune Pathophysiology of COVID-19; An In-Depth Review

“…Fibrosis is a confirmed complication of severe SARS infection, with the severity dependent on disease duration; prior studies reporting pulmonary autopsy findings of severe COVID-19 have revealed extensive fibrotic changes ( 24 ). CT findings of such entities include extensive fibrotic changes with reticulations, traction bronchiectasis, and honeycombing ( 25 ). Predictors of post-COVID fibrosis – aptly named long COVID or PACS– include old age, male gender, comorbidities such as hypertension, diabetes mellitus, preexisting chronic pulmonary disease, and a longer duration of symptoms.…”

“…Concurrently, these conditions are also risk factors for severe COVID-19 ARDS. Indeed, lung fibrosis is a well-known sequela of ARDS irrespective of etiology ( 25 , 26 ).…”

In Vivo Visualization and Quantification of Neutrophil Elastase in Lungs of COVID-19 Patients: A First-in-Humans PET Study with11C-NES

“…Lung perfusion images were calculated using a non-negative least-squares basis function implementation of the single tissue compartment model where the pulmonary circulation blood curve was the input function, with fitted pulmonary and systemic blood volume parameters. Only voxels with density < 0.9 g/cm 3 , based on the low-dose CT images, were included in the calculations. Pulmonary perfusion was calculated as the influx rate of 15 O-water.…”

“…Recent data have shown that NETs also promote thrombosis (2). Neutrophil elastase (NE), a protease found in NGs, is part of the innate immune response, but could mediate the lung injury seen in COVID-19 (3,4,5,6,7,8). To date, in vivo studies of NE in the lungs of COVID-19 patients have not been possible.…”