CT quantitative analysis in patients with severe coronavirus disease 2019 and its correlation with laboratory examination results

OBJECTIVE: To quantitatively analyze the longitudinal changes of ground-glass opacity (GGO), consolidation and total lesion in patients infected with severe coronavirus disease 2019 (COVID-19), and its correlation with laboratory examination results. MATERIALS AND METHODS: All 76 computed tomography (CT) images and laboratory examination results from the admission to discharge of 15 patients confirmed with severe COVID-19 were reviewed, whereas the GGO volume ratio, consolidation volume ratio, and total lesion volume ratio in different stages were analyzed. The correlations of lesions volume ratio and laboratory examination results were investigated. RESULTS: Four stages were identified based on the degree of lung involvement from day 1 to day 28 after disease onset. GGO was the most common CT manifestation in the four stages. The peak of lung involvement was at around stage 2, and corresponding total lesion volume ratio, GGO volume ratio, and consolidation volume ratio were 17.48 (13.44−24.33), 12.11 (7.34−17.08), and 5.51 (2.58−8.58), respectively. Total lesion volume ratio was positively correlated with neutrophil percentage, C-reactive protein (CRP), high-sensitivity CRP (Hs-CRP), procalcitonin, lactate dehydrogenase (LD), and creatine kinase isoenzyme MB (CK-MB), but negatively correlated with lymphocyte count, lymphocyte percentage, arterial oxygen saturation, and arterial oxygen tension. Consolidation volume ratio was correlated with most above laboratory examination results except Hs-CRP, LD, and CK-MB. GGO, however, was only correlated with lymphocyte count. CONCLUSION: CT quantitative parameters could show longitudinal changes well. Total lesion volume ratio and consolidation volume ratio are well correlated with laboratory examination results, suggesting that CT quantitative parameters may be an effective tool to reflect the changes in the condition.

Unlocking the promise of mRNA therapeutics.

The extraordinary success of mRNA vaccines against coronavirus disease 2019 (COVID-19) has renewed interest in mRNA as a means of delivering therapeutic proteins. Early clinical trials of mRNA therapeutics include studies of paracrine vascular endothelial growth factor (VEGF) mRNA for heart failure and of CRISPR-Cas9 mRNA for a congenital liver-specific storage disease. However, a series of challenges remains to be addressed before mRNA can be established as a general therapeutic modality with broad relevance to both rare and common diseases. An array of new technologies is being developed to surmount these challenges, including approaches to optimize mRNA cargos, lipid carriers with inherent tissue tropism and in vivo percutaneous delivery systems. The judicious integration of these advances may unlock the promise of biologically targeted mRNA therapeutics, beyond vaccines and other immunostimulatory agents, for the treatment of diverse clinical indications.

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT.

Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression,i.e.,from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, andF1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.

Cell Entry of Animal Coronaviruses.

Coronaviruses (CoVs) are a group of enveloped positive-sense RNA viruses and can cause deadly diseases in animals and humans. Cell entry is the first and essential step of successful virus infection and can be divided into two ongoing steps: cell binding and membrane fusion. Over the past two decades, stimulated by the global outbreak of SARS-CoV and pandemic of SARS-CoV-2, numerous efforts have been made in the CoV research. As a result, significant progress has been achieved in our understanding of the cell entry process. Here, we review the current knowledge of this essential process, including the viral and host components involved in cell binding and membrane fusion, molecular mechanisms of their interactions, and the sites of virus entry. We highlight the recent findings of host restriction factors that inhibit CoVs entry. This knowledge not only enhances our understanding of the cell entry process, pathogenesis, tissue tropism, host range, and interspecies-transmission of CoVs but also provides a theoretical basis to design effective preventive and therapeutic strategies to control CoVs infection.

Coronary artery calcification and risk of mortality and adverse outcomes in patients with COVID-19: a Chinese multicenter retrospective cohort study.

Background: Coronary artery calcification (CAC) is an independent risk factor of major adverse cardiovascular events; however, the impact of CAC on in-hospital death and adverse clinical outcomes in patients with coronavirus disease 2019 (COVID-19) remains unclear. Objective: To explore the association between CAC and in-hospital mortality and adverse events in patients with COVID-19. Methods: This multicenter retrospective cohort study enrolled 2067 laboratory-confirmed COVID-19 patients with definitive clinical outcomes (death or discharge) admitted from 22 tertiary hospitals in China between January 3, 2020 and April 2, 2020. Demographic, clinical, laboratory results, chest CT findings, and CAC on admission were collected. The primary outcome was in-hospital death and the secondary outcome was composed of in-hospital death, admission to intensive care unit (ICU), and requiring mechanical ventilation. Multivariable Cox regression analysis and Kaplan-Meier plots were used to explore the association between CAC and in-hospital death and adverse clinical outcomes. Results: The mean age was 50 years (SD,16) and 1097 (53.1%) were male. A total of 177 patients showed high CAC level, and compared with patients with low CAC, these patients were older (mean age: 49 vs. 69 years, P < 0.001) and more likely to be male (52.0% vs. 65.0%, P = 0.001). Comorbidities, including cardiovascular disease (CVD) ([33.3%, 59/177] vs. [4.7%, 89/1890], P < 0.001), presented more often among patients with high CAC, compared with patients with low CAC. As for laboratory results, patients with high CAC had higher rates of increased D-dimer, LDH, as well as CK-MB (all P < 0.05). The mean CT severity score in high CAC group was also higher than low CAC group (12.6 vs. 11.1, P = 0.005). In multivariable Cox regression model, patients with high CAC were at a higher risk of in-hospital death (hazard ratio [HR], 1.731; 95% CI 1.010-2.971, P = 0.046) and adverse clinical outcomes (HR, 1.611; 95% CL 1.087-2.387, P = 0.018). Conclusion: High CAC is a risk factor associated with in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC. Supplementary Information: The online version contains supplementary material available at 10.1007/s42058-021-00072-4.

Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19.

PURPOSE: To evaluate the value of chest CT severity score (CT-SS) in differentiating clinical forms of coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A total of 102 patients with COVID-19 confirmed by a positive result from real-time reverse transcription polymerase chain reaction on throat swabs who underwent chest CT (53 men and 49 women, 15-79 years old, 84 cases with mild and 18 cases with severe disease) were included in the study. The CT-SS was defined by summing up individual scores from 20 lung regions; scores of 0, 1, and 2 were respectively assigned for each region if parenchymal opacification involved 0%, less than 50%, or equal to or more than 50% of each region (theoretic range of CT-SS from 0 to 40). The clinical and laboratory data were collected, and patients were clinically subdivided according to disease severity according to the Chinese National Health Commission guidelines. RESULTS: The posterior segment of upper lobe (left, 68 of 102; right, 68 of 102), superior segment of lower lobe (left, 79 of 102; right, 79 of 102), lateral basal segment (left, 79 of 102; right, 70 of 102), and posterior basal segment of lower lobe (left, 81 of 102; right, 83 of 102) were the most frequently involved sites in COVID-19. Lung opacification mainly involved the lower lobes, in comparison with middle-upper lobes. No significant differences in distribution of the disease were seen between right and left lungs. The individual scores in each lung and the total CT-SS were higher in severe COVID-19 when compared with mild cases (P < .05). The optimal CT-SS threshold for identifying severe COVID-19 was 19.5 (area under curve = 0.892), with 83.3% sensitivity and 94% specificity. CONCLUSION: The CT-SS could be used to evaluate the severity of pulmonary involvement quickly and objectively in patients with COVID-19.© RSNA, 2020.

CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19.

OBJECTIVE: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. RESULTS: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. CONCLUSION: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

Coronavirus disease 2019: initial chest CT findings.

OBJECTIVES: To systematically analyze CT findings during the early and progressive stages of natural course of coronavirus disease 2019 and also to explore possible changes in pulmonary parenchymal abnormalities during these two stages. METHODS: We retrospectively reviewed the initial chest CT data of 62 confirmed coronavirus disease 2019 patients (34 men, 28 women; age range 20-91 years old) who did not receive any antiviral treatment between January 21 and February 4, 2020, in Chongqing, China. Patients were assigned to the early-stage group (onset of symptoms within 4 days) or progressive-stage group (onset of symptoms within 4-7 days) for analysis. CT characteristics and the distribution, size, and CT score of pulmonary parenchymal abnormalities were assessed. RESULTS: In our study, the major characteristic of coronavirus disease 2019 was ground-glass opacity (61.3%), followed by ground-glass opacity with consolidation (35.5%), rounded opacities (25.8%), a crazy-paving pattern (25.8%), and an air bronchogram (22.6%). No patient presented cavitation, a reticular pattern, or bronchial wall thickening. The CT scores of the progressive-stage group were significantly greater than those of the early-stage group (p = 0.004). CONCLUSIONS: Multiple ground-glass opacities with consolidations in the periphery of the lungs were the primary CT characteristic of coronavirus disease 2019. CT score can be used to evaluate the severity of the disease. If these typical alterations are found, then the differential diagnosis of coronavirus disease 2019 must be considered. KEY POINTS: • Multiple GGOs with consolidations in the periphery of the lungs were the primary CT characteristic of COVID-19. • The halo sign may be a special CT feature in the early-stage COVID-19 patients. • Significantly increased CT score may indicate the aggravation of COVID-19 in the progressive stage.