Since the outbreak declared by the World Health Organization (WHO) in January 2020 caused by a new coronavirus, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), responsible for the coronavirus disease named COVID-19, understanding its pathophysiological mechanism is challenging in order to explain its high mortality rate (1-3) .
The severity of COVID-19 is partly explained by the involvement of the pulmonary parenchyma, responsible for a pneumonia sometimes leading to an acute respiratory distress that can cause death. However, other mechanisms associated with the alveolar viral damage appear to play a key role in the progression and severity of the disease. One of the main assumptions is the association of COVID-19 with a severe systemic inflammatory reaction and vasculopathy which is responsible for endothelial lesions, coagulopathy and significantly increased thromboembolic events (4-9). This recently described COVID-19 chest-predominant vasculopathy (10), mainly affecting small vessels, was suggested to be named “novel pulmonary specific vasculopathy” by Fogarty et al. (4) or “microvascular COVID-19 lung vessels obstructive thromboinflammatory syndrome” by Ciceri et al. (8) as distinct to disseminated intravascular coagulopathy frequently found in systemic infections. Several studies reported major coagulation disorders, mostly found in mild and severe COVID-19 patients. Elevated plasma levels of fibrinogen degradation products, as an inflammatory marker, and D-dimers constitute independent risk biomarker of disease severity with poor prognosis (3,11-14).
Chest imaging plays a central role in the diagnosis and evaluation of the severity of lung parenchyma involvement in patients with severe grade infection (15-17). Common chest computed tomography (CT) pattern of COVID-19 pneumonia as well as its outcome are now well known (18-23). While unenhanced chest CT has earned a key role in the assessment of COVID-19 pneumonia severity (3,24-30), very few studies have searched for these vascular abnormalities and pulmonary embolic events. First statements on pulmonary vascular changes have been reported on unenhanced chest CT scan, seemingly thickening and vascular enlargement (30) mostly in subsegmental vessels (31-33). Increased prevalence of acute pulmonary embolism in COVID-19 has been reported in few recent studies, estimated between 20.6% and 40% (12,34,35). Moreover, pulmonary embolism appears to be a severity marker (12,34,36,37). Interestingly, in all studies these pulmonary embolisms are mostly subsegmental (34,37). In parallel, hematological and post-mortem studies have highlighted pathological changes on lung microvasculature with pulmonary microvascular thrombosis associated with the common diffuse alveolar damage observed in acute respiratory distress (4,8,38-41). These data suggest that this local pulmonary intravascular coagulopathy may explain the pulmonary artery thrombosis found on pulmonary CT angiography. Yet, those vascular changes on small subsegmental vessels and microvascular thrombosis may be underestimated by pulmonary CT angiography.
We hypothesize that pulmonary dual-energy CT (DECT) angiography could provide valuable information on these vascular abnormalities in COVID-19 patients by evaluating lung perfusion disorders in addition to chest CT pattern. The objective of our study was thus to describe lung perfusion disorders assessed by pulmonary DECT angiography in severe COVID-19 patients.
Study type and inclusion criteria
The inclusion criteria were consecutive adult patients (≥18 years old) with a reverse transcriptase-polymerase chain reaction (RT-PCR) diagnosis (NucleoSpin® RNA Virus kit, Macherey-Nagel Inc., Bethlehem, PA, USA) of SARS-CoV-2 who underwent a pulmonary DECT angiography scan between March 16th 2020 and April 22th 2020. Pulmonary DECT angiography was performed when clinical signs of severe grade infection were present (oxygen saturation below 92%, polypnea over 25 cycles per minute, fever over 40 °C, increasing oxygen needs), need for invasive mechanical ventilation, or when the patient suffered from comorbidities of active neoplasia, immunosuppression, history of organ or bone-marrow transplantation.
Exclusion criteria were: (I) patients with unenhanced chest CT scans; (II) patients with monoenergetic pulmonary CT angiography; (III) patients with artifacts on pulmonary DECT angiography hindering image quality.
A flowchart of the study population is shown in Figure 1. Eighty-five patients were included and underwent further analysis. Demographics and clinico-biological data were gathered from medical charts. The following data were reported: (I) information concerning COVID-19 (onset of symptoms, delay between first symptoms and pulmonary DECT angiography realization, RT-PCR results), (II) care status of the patient at pulmonary DECT realization (ambulatory care, conventional care unit, critical care unit), (III) need for invasive mechanical ventilation at the time of the DECT or in the following 12 hours after the pulmonary DECT angiography.
Pulmonary DECT angiography protocol
CT scans were acquired in helical dual-energy mode with a single-source rapid kilovolt switching CT scanner (Revolution, GE Healthcare, Milwaukee, WI, USA) after intravenous injection of 70 mL iodinated contrast agent (Iomeprol 400 mg I/mL, Bracco Imaging, Milan, IT, USA) at a flow rate of 4 mL/s, triggered on the main pulmonary artery. CT scan settings were set with tube voltage between 80 and 140 kV every 0.5 milliseconds, 128×0.625 mm (total collimation width 80 mm), rotation time 0.8 s, dose modulation (reference level of 280 mA), pitch 0.992 and CTDIvol 13.6 mGy.
Imaging results were first reviewed by two chest radiologists (J Behr and A Busse-Coté with respectively 11 and 2 years of experience in chest imaging) on a PACS workstation (Carestream Health, Rochester, NY, USA). Readers were blinded to the patient’s status, clinical and biological features. They were asked to assess the presence or absence of acute pulmonary artery thrombosis, defined as a filling defect within pulmonary vessels. When present, readers were asked to report the extent of emboli: unilateral or bilateral, number of lung lobes involved, and its topography: proximal, lobar and segmental or subsegmental. In case of discordance between readers, a simultaneous reading to reach consensus was achieved.
Pulmonary DECT angiography semi quantitative analysis was done on a dedicated spectral imaging post-processing software (Thoracic VCAR with GSI pulmonary perfusion, Advantage Workstation platform, Version 3.2 Ext 3.3, GE Healthcare, Milwaukee, WI, USA). Initial post-treatment was lung segmentation, excluding small pulmonary vessels and airways. A semi-quantitative analysis focusing on the presence of parenchymal ischemia was performed using iodine lung color maps. Readers were blinded to the presence or not of acute pulmonary artery thrombosis. They were asked to assess the presence of parenchymal ischemia, defined as subpleural wedge shaped or systematized hypoperfusion on iodine lung color maps (Figure 2). In case of discordance between readers, a simultaneous reading to reach consensus was achieved. When observed, the volume of parenchymal ischemia was segmented and measured (in cm3 and relative to total lungs volume in percent).
Quantitative analysis of segmented lung parenchyma led to total lungs volume (cm3), average and standard deviation of iodine lungs concentration (100 µg/cm3) measurements. Total iodine lungs quantity (100 µg) was defined as the product of total lungs volume and average of iodine lungs concentration. The lung parenchyma was divided into three volumes (absolutes and relatives to total lung volume) thanks to maps based on HU values as follows: normal = (−1,024 HU; −730 HU), ground glass = (−729 HU; −647 HU) (42), and consolidation = (−646 HU; +1,024 HU). In the absence of definition in the literature, another threshold was defined for dense consolidation ≥0 HU. The volume of relative perfusion deficit was measured, defined by an iodine concentration <200 µg/cm3 (43,44). This volume may be impacted by contrast agent iodine volume, flow rate and timing of imaging. In order to describe the distribution of perfusion deficit area within the lungs, the relatively less perfused quartile lungs volume was segmented by adjusting the iodine concentration threshold so that the relative perfusion deficit volume was 25% of total lungs volume. This allowed to calculate the intersections between this hypoperfused quartile volume and the three sub-parts (normal, ground glass, consolidation) of the lung parenchyma, and in the end to assess the repartition (%) of hypoperfused quartile volume between normal lung, ground glass and consolidation.
Quantitative data was expressed as mean ± standard deviation (normally distributed data) or median and 25% and 75% interquartile ranges (IQR) (non-normal distribution parameter). Categorical variables were expressed as number (percentage). Continuous variables were compared between the two groups by using the Wilcoxon test (non-normal distribution verified by Shapiro-Wilk tests) a student test when distribution was normal. Qualitative variables were compared with Pearson χ2 or Fisher exact tests, as appropriate. Inter-reader agreement was analyzed using weighted kappa statistics for the presence of parenchymal ischemia at semi-quantitative analysis. A P value <0.05 was considered statistically significant. All analyses were performed with R version 3.4.4 (R Core Team 2017).
The mean age of the patients was 65±13 years old with a sex ratio of 1.83 (55 men/30 women). Among the 85 patients, 8 (9%) received ambulatory care, 47 (55%) were hospitalized in a conventional care unit and 30 (35%) were admitted to critical care unit. A total of 29 (34%) patients required invasive mechanical ventilation. The characteristics of the study population are detailed in Table 1.
Twenty-nine patients (34%) were diagnosed with acute pulmonary artery thrombosis. Among these, 16 (55%) were bilateral; 24 (83%) were segmental or subsegmental, 1 was lobar (3%) and 4 (14%) were mixed. None was proximal. The mean age was not statistically different in the patients with or without pulmonary artery thrombosis (65±14 and 66±13 respectively, P=0.698).
Semi-quantitative DECT analysis
As portrayed in Table 1, parenchymal ischemia was described in 58 patients (68%), in both groups, with no significant difference between patients with and without pulmonary artery thrombosis (Figures 2,3) (35 vs. 23, P=0.144). Inter-reader agreement of parenchymal ischemia between readers 1 and 2 was substantial (0.74; IQR: 0.59–0.89). Volume of ischemia was however significantly higher in patients with acute pulmonary artery thrombosis [29 (IQR, 8–100) vs. 8 (IQR, 0–45) cm3, P=0.041]. Other semi quantitative DECT analysis’ results are detailed in Table 1.
Quantitative lung analysis
Quantitative lung analysis’ characteristics are detailed in Table 1. Patients with acute pulmonary artery thrombosis had significantly less normal parenchyma (55%; IQR, 32–65%) than patients with no acute pulmonary artery thrombosis (62%; IQR, 47–77%), P=0.027). Patients with acute pulmonary artery thrombosis also had significantly more consolidations [36% (IQR, 24–57%) vs. 26% (IQR, 17–39%), P=0.029] and dense consolidations [8% (IQR, 3–14%) vs. 4% (IQR, 1–6%), P=0.025, respectively].
Quantitative DECT analysis
Total iodine lungs quantity, standard deviation of iodine lungs concentration, relative perfusion deficit and the repartition (%) of hypoperfused quartile volume between normal lung, ground glass and consolidation are presented in Table 1. The hypoperfused areas were predominantly located within normal lung (Figure 4), and ground glass opacities and consolidations appeared mostly hyperperfused (Figure 5).
To our knowledge, lung perfusion disorders have not yet been analysed by pulmonary DECT angiography in COVID-19 patient. Hypoperfused areas consistent with ischemia were searched for and quantified, with readers blinded for the diagnosis of acute pulmonary artery thrombosis. Although pulmonary ischemic areas were more frequent and larger in the pulmonary artery thrombosis subgroup, interestingly we found a significant number of them in the absence of pulmonary artery thrombosis subgroup, mainly distal and of smaller volume. Recently, publications (4,8,35,40,41) have reported microvascular lesions especially endothelial lesions and pulmonary vessels microthrombosis associated with COVID-19 and seem to be related to fatal outcome and death (41). We hypothesize that the small pulmonary ischemic areas that we detected on pulmonary DECT angiography with no visible pulmonary artery thrombosis could be the imaging consequence of these microvascular lesions and “in-situ” microthrombosis in opposition to real pulmonary artery thrombosis coming from the periphery. As suggested by Thachil et al. (10) it would be appropriated to refer to “pulmonary in-situ thrombosis” in COVID-19 rather than the denomination “pulmonary embolism”. This distinction is not only semantical, as thrombosis treatment would rely not only on anticoagulant therapy but on antiplatelet agents in addition with anticoagulant (10). Beyond the illustration of a physiopathological mechanism of COVID-19, further studies will be necessary to look for an association of perfusion features with patients’ outcome under treatment.
Lung perfusion analysis in these patients is challenging. Indeed, iodine maps in COVID-19 patients show extreme heterogeneity: areas of highly perfused consolidations, areas of ground glass opacities with normal or high perfusion and hypoperfused normally ventilated lung parenchyma. We hypothesize that high iodine levels in consolidations could be linked with the inflammatory nature of these consolidations. A higher density threshold helps to reduce the amount of non-consolidated lung mistakenly included in a continuous lung segmentation based on density, especially when considering dense, incompletely ventilated, lung parenchyma.
We acknowledge some limitations of this study. First its retrospective monocentric design could lead to a selection bias. We limited this bias by including consecutive patients. Secondly, we excluded patients (21%) when DECT quality was too low due to technics inherent artifacts (overweight, beam hardening artifacts). This relatively high rate of exclusion is explained by the accumulation of these artifacts in COVID-19 critical care patients. A reading bias related to any semi-quantitative analysis also exists, which has been minimized by double-blind reading with secondary consensus. Finally, iodine concentration is strongly correlated to lung parenchyma blood perfusion and can be impacted by contrast media variables as well as patient’s heart flow and blood volume.
Pulmonary perfusion evaluated by iodine concentration maps shows extreme heterogeneity in COVID-19 patients and lower iodine levels in normal parenchyma. Pulmonary ischemic areas were more frequent and larger in patients with pulmonary artery thrombosis. Pulmonary DECT angiography revealed a significant number of pulmonary ischemic areas even in the absence of visible pulmonary arterial thrombosis. This may reflect microthrombosis associated with COVID-19 pneumonia.
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-708). The authors have no conflicts of interest to declare.
Ethical Statement: In the context of the COVID-19 pandemic, our national commission for the protection of personal data considered that, for single-centre observational research associated with COVID-19, the need for information of patients and families was waived. Our study protocol followed the ethical guidelines of the Helsinki declaration (as revised in 2013) and was approved by our institutional review board.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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