Computed Tomographic Imaging Features of COVID-19 Pneumonia Caused by the Delta (B.1.617.2) and Omicron (B.1.1.529) Variant in a German Nested Cohort Pilot Study Group

Alisol B 23-acetate broadly inhibits coronavirus through blocking virus entry and suppresses proinflammatory T cells responses for the treatment of COVID-19

INTRODUCTION

Emerging severe acute respiratory syndrome (SARS) coronavirus (CoV)-2 causes a global health disaster and pandemic. Seeking effective anti-pan-CoVs drugs benefit critical illness patients of coronavirus disease 2019 (COVID-19) but also may play a role in emerging CoVs of the future.

OBJECTIVES

This study tested the hypothesis that alisol B 23-acetate could be a viral entry inhibitor and would have proinflammatory inhibition for COVID-19 treatment.

METHODS

SARS-CoV-2 and its variants infected several cell lines were applied to evaluate the anti-CoVs activities of alisol B 23-aceate in vitro. The effects of alisol B 23-acetate on in vivo models were assessed by using SARS-CoV-2 and its variants challenged hamster and human angiotensin-converting enzyme 2 (ACE2) transgenic mice. The target of alisol B 23-acetate to ACE2 was analyzed using hydrogen/deuterium exchange (HDX) mass spectrometry (MS).

RESULTS

Alisol B 23-acetate had inhibitory effects on different species of coronavirus. By using HDX-MS, we found that alisol B 23-acetate had inhibition potency toward ACE2. In vivo experiments showed that alisol B 23-acetate treatment remarkably decreased viral copy, reduced CD4+ T lymphocytes and CD11b+ macrophages infiltration and ameliorated lung damages in the hamster model. In Omicron variant infected human ACE2 transgenic mice, alisol B 23-acetate effectively alleviated viral load in nasal turbinate and reduced proinflammatory cytokines interleukin 17 (IL17) and interferon γ (IFNγ) in peripheral blood. The prophylactic treatment of alisol B 23-acetate by intranasal administration significantly attenuated Omicron viral load in the hamster lung tissues. Moreover, alisol B 23-acetate treatment remarkably inhibited proinflammatory responses through mitigating the secretions of IFNγ and IL17 in the cultured human and mice lymphocytes in vitro.

CONCLUSION

Alisol B 23-acetate could be a promising therapeutic agent for COVID-19 treatment and its underlying mechanisms might be attributed to viral entry inhibition and anti-inflammatory activities.

Cardioprotective effects of vaccination in hospitalized patients with COVID-19

COVID-19 vaccination has been shown to prevent and reduce the severity of COVID-19 disease. The aim of this study was to explore the cardioprotective effect of COVID-19 vaccination in hospitalized COVID-19 patients. In this retrospective, single-center cohort study, we included hospitalized COVID-19 patients with confirmed vaccination status from July 2021 to February 2022. We assessed outcomes such as acute cardiac events and cardiac biomarker levels through clinical and laboratory data. Our analysis covered 167 patients (69% male, mean age 58 years, 42% being fully vaccinated). After adjustment for confounders, vaccinated hospitalized COVID-19 patients displayed a reduced relative risk for acute cardiac events (RR: 0.33, 95% CI [0.07; 0.75]) and showed diminished troponin T levels (Cohen's d: - 0.52, 95% CI [- 1.01; - 0.14]), compared to their non-vaccinated peers. Type 2 diabetes (OR: 2.99, 95% CI [1.22; 7.35]) and existing cardiac diseases (OR: 4.31, 95% CI [1.83; 10.74]) were identified as significant risk factors for the emergence of acute cardiac events. Our findings suggest that COVID-19 vaccination may confer both direct and indirect cardioprotective effects in hospitalized COVID-19 patients.

Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant

Rationale and objectives

The computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors.

Materials and methods

This retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0-3 days), intermediate phase (4-7 days), and late phase (8-14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination.

Results

A total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88-0.94), 0.82 (95% CI 0.76-0.87), and 0.83 (95% CI 0.77-0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94-0.98), 0.90 (95% CI 0.87-0.94), and 0.89 (95% CI 0.84-0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed.

Conclusion

The time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.

Inverse association with COVID-19 vaccination status of the incidence of pneumonia after SARS-CoV-2 infection: A nationwide retrospective cohort study

BACKGROUND

Although one of the characteristics of COVID-19 is accompanied by acute pneumonia immediately after infection, large-scale cohort studies focused on this issue are lacking. In addition, there is interest in how COVID-19 vaccinations reduce the incidence of acute pneumonia for people infected with different strains of SARS-CoV-2. Thus, we assess the short-term incidence of pneumonia after COVID-19 with the vaccination and SARS-CoV-2 variants.

METHODS

As data for 2136,751 COVID-19 patients between January 01, 2020 and February 28, 2022 was collected, they were observed for one month from the day of infection. Patients in retrospective cohort study were classified according to doses of the received vaccine and the epidemic phase when SARS-CoV-2 variants prevailed. Multivariable logistic regression analysis calculated adjusted odds ratios (aOR) and 95% confidence intervals (CIs) for the pneumonia risk.

RESULTS

In B.1.1.7-B.1.351, B.1.617.2, and B.1.617.2 variants, the aORs (95% CIs; p-value) for incidence of pneumonia were 0.93 (0.89-0.98; <0.001), 0.74 (0.70-0.78; <0.001), and 0.04 (0.038-0.043; <0.001), respectively, compared to the original strain. More than 80% of patients have received the second and more doses of the vaccine (average age=44.67 years). The aORs (95% CIs; p-value) for pneumonia were 0.61 (0.58-0.64; <0.001), 0.39 (0.38-0.40; <0.001), and 0.18 (0.166-0.184; <0.001) in patients who received the first (N = 68,216), second (N = 898,838), and ≥ third doses (N = 836,173), respectively, compared to unvaccinated patients. According to the received vaccine (second dose of mRNA or viral vector), those who received BNT162b2 and mRNA-1273 (N = 787,980) had lower risk of pneumonia, compared to that in those who received h ChAdOx1 nCov-19 and AD26. COV2-S (N = 89,024).

CONCLUSIONS

Our findings suggest that the second and ≥ third doses (61% and 82% of risk aversion effect increased, respectively) of the COVID-19 vaccine can prevent the COVID-19-related pneumonia, regardless of the variants.

Vaccination protects against acute respiratory distress syndrome (ARDS) in hospitalized patients with COVID-19

This study aimed to analyze the effect of COVID-19 vaccination on the occurrence of ARDS in hospitalized COVID-19 patients. The study population of this retrospective, single-center cohort study consisted of hospitalized COVID-19 patients with known vaccination status and chest computed tomography imaging between July 2021 and February 2022. The impact of vaccination on ARDS in COVID-19 patients was assessed through logistic regression adjusting for demographic differences and confounding factors with statistical differences determined using confidence intervals and effect sizes. A total of 167 patients (69% male, average age 58 years, 95% CI [55; 60], 42% fully vaccinated) were included in the data analysis. Vaccinated COVID-19 patients had a reduced relative risk (RR) of developing ARDS (RR: 0.40, 95% CI [0.21; 0.62]). Consequently, non-vaccinated hospitalized patients had a 2.5-fold higher probability of developing ARDS. This risk reduction persisted after adjusting for several confounding variables (RR: 0.64, 95% CI [0.29; 0.94]) in multivariate analysis. The protective effect of COVID-19 vaccination increased with ARDS severity (RR: 0.61, 95% CI [0.37; 0.92]). Particularly, patients under 60 years old were at risk for ARDS onset and seemed to benefit from COVID-19 vaccination (RR: 0.51, 95% CI [0.20; 0.90]). COVID-19 vaccination showed to reduce the risk of ARDS occurrence in hospitalized COVID-19 patients, with a particularly strong effect in patients under 60 years old and those with more severe ARDS.

Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives

With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.

Survey on chest CT findings in COVID-19 patients in Okinawa, Japan: differences between the delta and omicron variants

To investigate the frequency of pneumonia and chest computed tomography (CT) findings in patients with coronavirus disease 2019 (COVID-19) during the fifth Delta variant-predominant and sixth Omicron variant-predominant waves of the COVID-19 pandemic in Okinawa, Japan. A survey on chest CT examinations for patients with COVID-19 was conducted byhospitals with board-certified radiologists who provided treatment for COVID-19 pneumonia in Okinawa Prefecture. Data from 11 facilities were investigated. Indications for chest CT; number of COVID-19 patients undergoing chest CT; number of patients with late-onset pneumonia, tracheal intubation, and number of deaths; and COVID-19 Reporting and Data System classifications of initial chest CT scans were compared by the chi-squared test between the two pandemic waves (Delta vs. Omicron variants). A total of 1944 CT scans were performed during the fifth wave, and 1178 were performed during the sixth wave. CT implementation rates, which were the number of patients with COVID-19 undergoing CT examinations divided by the total number of COVID-19 cases in Okinawa Prefecture during the waves, were 7.1% for the fifth wave and 2.1% for the sixth wave. The rates of tracheal intubation and mortality were higher in the fifth wave. Differences between the distributions of the CO-RADS classifications were statistically significant for the fifth and sixth waves (p < 0.0001). In the fifth wave, CO-RADS 5 (typical of COVID-19) was most common (65%); in the sixth wave, CO-RADS 1 (no findings of pneumonia) was most common (50%). The finding of "typical for other infection but not COVID-19" was more frequent in the sixth than in the fifth wave (13.6% vs. 1.9%, respectively). The frequencies of pneumonia and typical CT findings were higher in the fifth Delta variant-predominant wave, and nontypical CT findings were more frequent in the sixth Omicron variant-predominant wave of the COVID-19 pandemic in Okinawa, Japan.

Comparison between original SARS-CoV-2 strain and omicron variant on thin-section chest CT imaging of COVID-19 pneumonia

OBJECTIVES

We investigated different computed tomography (CT) features between Omicron-variant and original-strain SARS-CoV‑2 pneumonia to facilitate the clinical management.

MATERIALS AND METHODS

Medical records were retrospectively reviewed to select patients with original-strain SARS-CoV‑2 pneumonia from February 22 to April 22, 2020, or Omicron-variant SARS-CoV‑2 pneumonia from March 26 to May 31, 2022. Data on the demographics, comorbidities, symptoms, clinical types, and CT features were compared between the two groups.

RESULTS

There were 62 and 78 patients with original-strain or Omicron-variant SARS-CoV‑2 pneumonia, respectively. There were no differences between the two groups in terms of age, sex, clinical types, symptoms, and comorbidities. The main CT features differed between the two groups (p = 0.003). There were 37 (59.7%) and 20 (25.6%) patients with ground-glass opacities (GGO) in the original-strain and Omicron-variant pneumonia, respectively. A consolidation pattern was more frequently observed in the Omicron-variant than original-strain pneumonia (62.8% vs. 24.2%). There was no difference in crazy-paving pattern between the original-strain and Omicron-variant pneumonia (16.1% vs. 11.6%). Pleural effusion was observed more often in Omicron-variant pneumonia, while subpleural lesions were more common in the original-strain pneumonia. The CT score in the Omicron-variant group was higher than that in the original-strain group for critical-type (17.00, 16.00-18.00 vs. 16.00, 14.00-17.00, p = 0.031) and for severe-type (13.00, 12.00-14.00 vs 12.00, 10.75-13.00, p = 0.027) pneumonia.

CONCLUSION

The main CT finding of the Omicron-variant SARS-CoV‑2 pneumonia included consolidations and pleural effusion. By contrast, CT findings of original-strain SARS-CoV‑2 pneumonia showed frequent GGO and subpleural lesions, but without pleural effusion. The CT scores were also higher in the critical and severe types of Omicron-variant than original-strain pneumonia.

A Pictorial Essay Describing the CT Imaging Features of COVID-19 Cases throughout the Pandemic with a Special Focus on Lung Manifestations and Extrapulmonary Vascular Abdominal Complications

With the Omicron wave, SARS-CoV-2 infections improved, with less lung involvement and few cases of severe manifestations. In this pictorial review, there is a summary of the pathogenesis with particular focus on the interaction of the immune system and gut and lung axis in both pulmonary and extrapulmonary manifestations of COVID-19 and the computed tomography (CT) imaging features of COVID-19 pneumonia from the beginning of the pandemic, describing the typical features of COVID-19 pneumonia following the Delta variant and the atypical features appearing during the Omicron wave. There is also an outline of the typical features of COVID-19 pneumonia in cases of breakthrough infection, including secondary lung complications such as acute respiratory distress disease (ARDS), pneumomediastinum, pneumothorax, and lung pulmonary thromboembolism, which were more frequent during the first waves of the pandemic. Finally, there is a description of vascular extrapulmonary complications, including both ischemic and hemorrhagic abdominal complications.

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Background

In December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.

Methods

Our study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.

Results

The receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.

Conclusion

Our study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

Effect of vitamin D status on adult COVID-19 pneumonia induced by Delta variant: A longitudinal, real-world cohort study

Objective

The effect of vitamin D status on adult COVID-19 pneumonia induced by Delta variant remains to be further explored.

Methods

A longitudinal, real-world cohort study was performed. Artificial intelligence (AI) was used to identify and measure pneumonia lesions. All cases with pneumonia were divided into the vitamin D deficiency (VDD) and control groups according to serum 25-hydroxyvitamin D concentration. Lesion dynamics were observed within six time periods after the onset of pneumonia.

Results

A total of 161 cases were included, of which 101 (63%) were male and 46 (29%) presented with pneumonia. The median age and baseline 25-hydroxyvitamin D concentrations were 37 years and 21 ng/ml, respectively. Age, fibrinogen, and SARS-CoV-2 IgG titer on admission were independent predictors for the onset of pneumonia. After the onset of pneumonia, patients in the VDD group (n = 18) had higher percentage of fever (33 vs. 7.1%; p = 0.04) than those in the control group (n = 28); the interval of pneumonia resolution was longer (28 vs. 21 days; p = 0.02); lesions progressed more rapidly (p = 0.01) within 3 to 7 days and improved more slowly (p = 0.007) within more than 28 days; notably, simultaneous interleukin-6 (18.7 vs. 14.6 pg/ml; p = 0.04) levels were higher, and cycle thresholds for N gene (22.8 vs. 31.3; p = 0.04) and ORF1ab gene (20.9 vs. 28.7; p = 0.03) were lower within 3 to 7 days.

Conclusion

Vitamin D status may have effects on the progression and resolution, but not the onset of Delta variant-induced pneumonia in adults. Computed tomography image diagnosis system based on AI may have promising applications in the surveillance and diagnosis of novel SARS-CoV-2 variant-induced pneumonia.