Increased Lung Immune Metabolic Activity in COVID-19 Survivors.

PURPOSE: We quantified lung glycolytic metabolic activity, clinical symptoms and inflammation, coagulation, and endothelial activation biomarkers in 2019 coronavirus disease (COVID-19) pneumonia survivors. METHODS: Adults previously hospitalized with moderate to severe COVID-19 pneumonia were prospectively included. Subjects filled out a questionnaire on clinical consequences, underwent chest CT and 18 F-FDG PET/CT, and provided blood samples on the same day. Forty-five volunteers served as control subjects. Analysis of CT images and quantitative voxel-based analysis of PET/CT images were performed for both groups. 18 F-FDG uptake in the whole-lung volume and in high- and low-attenuation areas was calculated and normalized to liver values. Quantification of plasma markers of inflammation (interleukin 6), d -dimer, and endothelial cell activation (angiopoietins 1 and 2, vascular cell adhesion molecule 1, and intercellular adhesion molecule 1) was also performed. RESULTS: We enrolled 53 COVID-19 survivors (62.3% were male; median age, 50 years). All survivors reported at least 1 persistent symptom, and 41.5% reported more than 6 symptoms. The mean lung density was greater in survivors than in control subjects, and more metabolic activity was observed in normal and dense lung areas, even months after symptom onset. Plasma proinflammatory, coagulation, and endothelial activation biomarker concentrations were also significantly higher in survivors. CONCLUSION: We observed more metabolic activity in areas of high and normal lung attenuation several months after moderate to severe COVID-19 pneumonia. In addition, plasma markers of thromboinflammation and endothelial activation persisted. These findings may have implications for our understanding of the in vivo pathogenesis and long-lasting effects of COVID-19 pneumonia.

Estimating COVID-19 Pneumonia Extent and Severity From Chest Computed Tomography.

BACKGROUND: COVID-19 pneumonia extension is assessed by computed tomography (CT) with the ratio between the volume of abnormal pulmonary opacities (PO) and CT-estimated lung volume (CTLV). CT-estimated lung weight (CTLW) also correlates with pneumonia severity. However, both CTLV and CTLW depend on demographic and anthropometric variables. PURPOSES: To estimate the extent and severity of COVID-19 pneumonia adjusting the volume and weight of abnormal PO to the predicted CTLV (pCTLV) and CTLW (pCTLW), respectively, and to evaluate their possible association with clinical and radiological outcomes. METHODS: Chest CT from 103 COVID-19 and 86 healthy subjects were examined retrospectively. In controls, predictive equations for estimating pCTLV and pCTLW were assessed. COVID-19 pneumonia extent and severity were then defined as the ratio between the volume and the weight of abnormal PO expressed as a percentage of the pCTLV and pCTLW, respectively. A ROC analysis was used to test differential diagnosis ability of the proposed method in COVID-19 and controls. The degree of pneumonia extent and severity was assessed with Z-scores relative to the average volume and weight of PO in controls. Accordingly, COVID-19 patients were classified as with limited, moderate and diffuse pneumonia extent and as with mild, moderate and severe pneumonia severity. RESULTS: In controls, CTLV could be predicted by sex and height (adjusted R 2 = 0.57; P < 0.001) while CTLW by age, sex, and height (adjusted R 2 = 0.6; P < 0.001). The cutoff of 20% (AUC = 0.91, 95%CI 0.88-0.93) for pneumonia extent and of 50% (AUC = 0.91, 95%CI 0.89-0.92) for pneumonia severity were obtained. Pneumonia extent were better correlated when expressed as a percentage of the pCTLV and pCTLW (r = 0.85, P < 0.001), respectively. COVID-19 patients with diffuse and severe pneumonia at admission presented significantly higher CRP concentration, intra-hospital mortality, ICU stay and ventilatory support necessity, than those with moderate and limited/mild pneumonia. Moreover, pneumonia severity, but not extent, was positively and moderately correlated with age (r = 0.46) and CRP concentration (r = 0.44). CONCLUSION: The proposed estimation of COVID-19 pneumonia extent and severity might be useful for clinical and radiological patient stratification.

COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis.

Purpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT). Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (