Comparison of chest CT severity scoring systems for COVID-19

Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?

To measure hepatic steatosis (HS) in hospitalized COVID-19 patients using unenhanced chest computed tomography (CT) imaging and to evaluate the relationship between disease severity and prognosis in adult patients.This retrospective study included 152 consecutive hospitalized COVID-19 patients with a positive reverse transcriptase polymerase chain reaction (RT-PCR) test. The COVID-19 Reporting and Data System (CO-RADS) and the chest CT score were evaluated. HS measurements were performed based on CT images using a single region of interest placed on the right liver lobe (segments V-VII). HS was defined as a liver attenuation value <40 Hounsfield units. Data were collected and compared with the patients' prognostic parameters.Of the 152 inpatients, 137 patients (90.1%) had a CT score ≥3 and 109 patients (71.7%) had a CO-RADS score ≥4, 43 (28.2%) had HS. All patients with HS (100%) and 94/109 (86.2%) patients without HS had a CT score ≥3. There was a statistically significant difference between the two groups in terms of chest CT score (p=0.006). There was no statistically significant difference between the two groups in terms of CO-RADS score (p=0.291). The median CRP levels were significantly increased in patients with HS compared to patients without HS (p=0.023). There was no significant difference in ICU hospitalization and mortality due to the presence of HS (p>0.05).The current study revealed significantly higher chest CT scores in COVID-19 patients with HS measured on CT compared to those without HS. Opportunistic use of CT images for the detection of HS can be considered as an adjunctive tool in the risk analysis of COVID-19 patients hospitalized due to COVID-19 pneumonia.The severity of COVID-19 disease is increased in hospitalized patients with hepatosteatosis compared to patients with a normal liver. Density measurements for the evaluation of HS using opportunistic CT applications can be considered as an adjunctive tool in the prognostic evaluation of hospitalized patients with COVID-19 pneumonia. · Parlak AE, Erdem Toslak İ, Turkoglu Selcuk N. Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?. Rofo 2025; 197: 648-656.

An updated review of pulmonary radiological features of acute and chronic COVID-19

PURPOSE OF REVIEW

Significant progress has been made in our understanding of the acute and chronic clinical and radiological manifestations of coronavirus-19 (COVID-19). This article provides an updated review on pulmonary COVID-19, while highlighting the key imaging features that can identify and distinguish acute COVID-19 pneumonia and its chronic sequelae from other diseases.

RECENT FINDINGS

Acute COVID-19 pneumonia typically presents with manifestations of organizing pneumonia on computed tomography (CT). In cases of severe disease, patients clinically progress to acute respiratory distress syndrome, which manifests as diffuse alveolar damage on CT. The most common chronic imaging finding is ground-glass opacities, which commonly resolves, as well as subpleural bands and reticulation. Pulmonary fibrosis is an overall rare complication of COVID-19, with characteristic features, including architectural distortion, and traction bronchiectasis.

SUMMARY

Chest CT can be a helpful adjunct tool in both diagnosing and managing acute COVID-19 pneumonia and its chronic sequelae. It can identify high-risk cases and guide decision-making, particularly in cases of severe or complicated disease. Follow-up imaging can detect persistent lung abnormalities associated with long COVID and guide appropriate management.

The Short and Long of COVID-19: A Review of Acute and Chronic Radiologic Pulmonary Manifestations of SARS-2-CoV and Their Clinical Significance

Coronavirus disease 2019 (COVID-19) pneumonia has had catastrophic effects worldwide. Radiology, in particular computed tomography (CT) imaging, has proven to be valuable in the diagnosis, prognostication, and longitudinal assessment of those diagnosed with COVID-19 pneumonia. This article will review acute and chronic pulmonary radiologic manifestations of COVID-19 pneumonia with an emphasis on CT and also highlighting histopathology, relevant clinical details, and some notable challenges when interpreting the literature.

Corticosteroid dose escalation in non-ICU COVID-19 patients with worsening lung lesions reduces lesion severity without improving clinical outcomes

The effect of increasing corticosteroid doses on clinical outcomes and chest findings in patients with coronavirus disease (COVID-19) pneumonia and lung disease remains unknown. We aimed to investigate the effects of increasing steroid dosage on chest lesion area and clinical outcomes in patients with moderate or severe COVID-19 and progressive lung involvement on chest computed tomography (CT). A total of 105 patients with radiological progression during methylprednisolone (MP) therapy either received an increased MP dose (n = 79) or were maintained on the same MP dose (n = 26). These patients were divided into dose-increment and no-change groups according to the MP dose adjustment strategy. Clinical features, changes in CT severity scores within 7 days after steroid adjustment, and outcomes were compared between the groups. Six (7.6%) and one (3.8%) patients in the dose-increment and no-change groups, respectively, had increasing World Health Organization outcome scores 96 h after MP adjustment (P = 0.678). Length of stay [15 days (IQR: 10-24) vs. 14 days (IQR: 10-25); P = 0.994] and in-hospital death rate (7.6% vs. 3.8%; P = 0.678) showed no significant differences between the groups. Logistic regression analyses revealed that an increased MP dose was significantly associated with improvement in CT lesion area compared with no change in MP dose, but the CT lesions deteriorated subsequently (79.7% vs. 53.8%, P = 0.044). In conclusion, increasing the MP dose in patients with worsening CT findings ameliorates CT lesions but fails to prevent serious adverse outcomes.

The role of neutrophilia in hyperlactatemia, blood acidosis, impaired oxygen transport, and mortality outcome in critically ill COVID-19 patients

Introduction

COVID-19 severity and high in-hospital mortality are often associated with severe hypoxemia, hyperlactatemia, and acidosis, yet the key players driving this association remain unclear. It is generally assumed that organ damage causes toxic acidosis, but since neutrophil numbers in severe COVID-19 can exceed 80% of the total circulating leukocytes, we asked if metabolic acidosis mediated by the glycolytic neutrophils is associated with lung damage and impaired oxygen delivery in critically ill patients.

Methods

Based on prospective mortality outcome, critically ill COVID-19 patients were divided into ICU- survivors and ICU-non-survivors. Samples were analyzed to explore if correlations exist between neutrophil counts, lung damage, glycolysis, blood lactate, blood pH, hemoglobin oxygen saturation, and mortality outcome. We also interrogated isolated neutrophils, platelets, and PBMCs for glycolytic activities.

Results

Arterial blood gas analyses showed remarkable hypoxemia in non-survivors with no consistent differences in PCO2 or [HCO3 -]. The hemoglobin oxygen dissociation curve revealed a right-shift, consistent with lower blood-pH and elevated blood lactate in non-survivors. Metabolic analysis of different blood cells revealed increased glycolytic activity only when considering the total number of neutrophils.

Conclusion

This indicates the role of neutrophilia in hyperlactatemia and lung damage, subsequently contributing to mortality outcomes in severe SARS-CoV-2 infection.

Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm

Objectives: To investigate the relationship between COVID-19 pneumonia outcomes and three chest CT analysis approaches. Methods: Patients with COVID-19 pneumonia who underwent chest CT were included and divided into survivors/non-survivors and intubated/not-intubated. Chest CTs were analyzed through a (1) Total Severity Score visually quantified by an emergency (TSS1) and a thoracic radiologist (TSS2); (2) density mask technique quantifying normal parenchyma (DM_Norm 1) and ground glass opacities (DM_GGO1) repeated after the manual delineation of consolidations (DM_Norm2, DM_GGO2, DM_Consolidation); (3) texture analysis quantifying normal parenchyma (TA_Norm) and interstitial lung disease (TA_ILD). Association with outcomes was assessed through Chi-square and the Mann-Whitney test. The TSS inter-reader variability was assessed through intraclass correlation coefficient (ICC) and Bland-Altman analysis. The relationship between quantitative variables and outcomes was investigated through multivariate logistic regression analysis. Variables correlation was investigated using Spearman analysis. Results: Overall, 192 patients (mean age, 66.8 ± 15.4 years) were included. TSS was significantly higher in intubated patients but only TSS1 in survivors. TSS presented an ICC of 0.83 (0.76; 0.88) and a bias (LOA) of 1.55 (-4.69, 7.78). DM_Consolidation showed the greatest median difference between survivors/not survivors (p = 0.002). The strongest independent predictor for mortality was DM_Consolidation (AUC 0.688), while the strongest independent predictor for the intensity of care was TSS2 (0.7498). DM_Norm 2 was the singular feature independently associated with both the outcomes. DM_GGO1 strongly correlated with TA_ILD (ρ = 0.977). Conclusions: The DM technique and TA achieved consistent measurements and a better correlation with patient outcomes.

Integrated Analysis of Noncoding RNAs (PVT-1 and miR-200c) and Their Correlation with STAT4/IL-6 Axis as Reliable Biomarkers for COVID-19 Severity

Inefficient control of elevated inflammatory mediators in coronavirus disease 2019 (COVID-19) has led to health complications, prompting the exploration of efficient biomarkers for monitoring this condition. We herein sought to investigate the implications of plasmacytoma variant translocation 1 (PVT-1), microRNA-200c (miR-200c), signal transducer and activator of transcription 4 (STAT-4), and interleukin-6 (IL-6), as well as how they correlated with creatinine, C-reactive protein (CRP), and lactate dehydrogenase (LDH) activity to identify biomarkers able to the early prognosis and diagnosis of COVID-19. Our study included a total of 105 infected COVID-19 patients and 35 healthy subjects as controls. Individuals with COVID-19 showed a significant increase in CRP, creatinine, and LDH activity. In addition, COVID-19 patients exhibited significantly higher levels of IL-6. These patients also demonstrated notably elevated expressions of miR-200c and PVT-1. The expression level of STAT4 decreased in the COVID-19 patients, and this decrease was negatively correlated with creatinine and LDH activity. The levels of miR-200c and PVT-1 expressions, and their connections with IL-6 and STAT4 levels, increased significantly with the severity of COVID-19 cases. In addition, receiver operating characteristic analysis showed that PVT-1 and miR-200c could be reliable biomarkers for determining the severity of COVID-19.

Semi-quantitative CT score reflecting the degree of pulmonary infection as a risk factor of hypokalemia in COVID-19 patients: a cross-sectional study

Background

Hypokalemia is a common electrolyte disorder observed in patients afflicted with coronavirus disease 2019 (COVID-19). When COVID-19 is accompanied by pulmonary infection, chest computed tomography (CT) is the preferred diagnostic modality. This study aimed to explore the relationship between CT semi-quantitative score reflecting the degree of pulmonary infection and hypokalemia from COVID-19 patients.

Methods

A single-center, cross-sectional study was conducted to investigate patients diagnosed with COVID-19 between December 2022 and January 2023 who underwent chest CT scans upon admission revealing typical signs. These patients were categorized into two groups based on their blood potassium levels: the normokalemia group and the hypokalemia group. Medical history, symptoms, vital signs, laboratory data, and CT severity score were compared. Binary regression analysis was employed to identify risk factors associated with hypokalemia in COVID-19 patients with pulmonary infection.

Results

A total of 288 COVID-19 patients with pulmonary infection were enrolled in the study, of which 68 (23.6%) patients had hypokalemia. The CT severity score was found to be higher in the hypokalemia group compared to the normokalemia group [4.0 (3.0-5.0) vs. 3.0 (2.0-4.0), p = 0.001]. The result of binary logistic regression analysis revealed that after adjusting for sex, vomiting, sodium, and using potassium-excretion diuretics, higher CT severity score was identified as an independent risk factor for hypokalemia (OR = 1.229, 95% CI = 1.077-1.403, p = 0.002).

Conclusion

In this cohort of patients, semi-quantitative CT score reflecting the degree of pulmonary infection may serve as a risk factor of hypokalemia in COVID-19 patients.

Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19

Introduction

Patients undergoing maintenance hemodialysis are vulnerable to coronavirus disease 2019 (COVID-19), exhibiting a high risk of hospitalization and mortality. Thus, early identification and intervention are important to prevent disease progression in these patients.

Methods

This was a two-center retrospective observational study of patients on hemodialysis diagnosed with COVID-19 at the Lingang and Xuhui campuses of Shanghai Sixth People's Hospital. Patients were randomized into the training (130) and validation cohorts (54), while 59 additional patients served as an independent external validation cohort. Artificial intelligence-based parameters of chest computed tomography (CT) were quantified, and a nomogram for patient outcomes at 14 and 28 days was created by screening quantitative CT measures, clinical data, and laboratory examination items, using univariate and multivariate Cox regression models.

Results

The median dialysis duration was 48 (interquartile range, 24-96) months. Age, diabetes mellitus, serum phosphorus level, lymphocyte count, and chest CT score were identified as independent prognostic indicators and included in the nomogram. The concordance index values were 0.865, 0.914, and 0.885 in the training, internal validation, and external validation cohorts, respectively. Calibration plots showed good agreement between the expected and actual outcomes.

Conclusion

This is the first study in which a reliable nomogram was developed to predict short-term outcomes and survival probabilities in patients with COVID-19 on hemodialysis. This model may be helpful to clinicians in treating COVID-19, managing serum phosphorus, and adjusting the dialysis strategies for these vulnerable patients to prevent disease progression in the context of COVID-19 and continuous emergence of novel viruses.

Omicron variant and pulmonary involvements: a chest imaging analysis in asymptomatic and mild COVID-19

Objectives

To identify clinical characteristics and risk factors for pulmonary involvements in asymptomatic and mildly symptomatic patients infected with SARS-CoV-2 Omicron variant by chest imaging analysis.

Methods

Detailed data and chest computed tomography (CT) imaging features were retrospectively analyzed from asymptomatic and mildly symptomatic patients infected with Omicron between 24 April and 10 May 2022. We scored chest CT imaging features and categorized the patients into obvious pulmonary involvements (OPI) (score > 2) and not obvious pulmonary involvements (NOPI) (score ≤ 2) groups based on the median score. The risk factors for OPI were identified with analysis results visualized by nomogram.

Results

In total, 339 patients were included (145 were male and 194 were female), and the most frequent clinical symptoms were cough (75.5%); chest CT imaging features were mostly linear opacities (42.8%). Pulmonary involvements were more likely to be found in the left lower lung lobe, with a significant difference in the lung total severity score of the individual lung lobes (p < 0.001). Logistic regression analysis revealed age stratification [odds ratio (OR) = 1.92, 95% confidence interval (CI) (1.548-2.383); p < 0.001], prolonged nucleic acid negative conversion time (NCT) (NCT > 8d) [OR = 1.842, 95% CI (1.104-3.073); p = 0.019], and pulmonary diseases [OR = 4.698, 95% CI (1.159-19.048); p = 0.03] as independent OPI risk factors.

Conclusion

Asymptomatic and mildly symptomatic patients infected with Omicron had pulmonary involvements which were not uncommon. Potential risk factors for age stratification, prolonged NCT, and pulmonary diseases can help clinicians to identify OPI in asymptomatic and mildly symptomatic patients infected with Omicron.

Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset

BACKGROUND

Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model.

PURPOSE

This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images.

METHODS

After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences.

RESULTS

The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501.

CONCLUSION

The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic

BACKGROUND AND PURPOSE

The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features.

METHODS

A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features.

RESULTS

A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models.

CONCLUSION

Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

The Short and Long of COVID-19: A Review of Acute and Chronic Radiologic Pulmonary Manifestations of SARS-2-CoV and Their Clinical Significance

Coronavirus disease 2019 (COVID-19) pneumonia has had catastrophic effects worldwide. Radiology, in particular computed tomography (CT) imaging, has proven to be valuable in the diagnosis, prognostication, and longitudinal assessment of those diagnosed with COVID-19 pneumonia. This article will review acute and chronic pulmonary radiologic manifestations of COVID-19 pneumonia with an emphasis on CT and also highlighting histopathology, relevant clinical details, and some notable challenges when interpreting the literature.

Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs' Parenchymal Involvement Quantification in COVID-19 Patients

(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients' age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland-Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1-R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1-R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients.

CT abnormalities 3 and 12 months after hospitalization for COVID-19 and association with disease severity: A prospective cohort study

OBJECTIVES

To investigate changes in chest CT between 3 and 12 months and associations with disease severity in patients hospitalized for COVID-19 during the first wave in 2020.

MATERIALS AND METHODS

Longitudinal cohort study of patients hospitalized for COVID-19 in 2020. Chest CT was performed 3 and 12 months after admission. CT images were evaluated using a CT severity score (CSS) (0-12 scale) and recoded to an abbreviated version (0-3 scale). We analyzed determinants of the abbreviated CSS with multivariable mixed effects ordinal regression.

RESULTS

242 patients completed CT at 3 months, and 124 (mean age 62.3±13.3, 78 men) also at 12 months. Between 3 and 12 months (n = 124) CSS (0-12 scale) for ground-glass opacities (GGO) decreased from median 3 (25th-75th percentile: 0-12) at 3 months to 0.5 (0-12) at 12 months (p<0.001), but increased for parenchymal bands (p<0.001). In multivariable analysis of GGO, the odds ratio for more severe abbreviated CSS (0-3 scale) at 12 months was 0.11 (95%CI 0.11 0.05 to 0.21, p<0.001) compared to 3 months, for WHO severity category 5-7 (high-flow oxygen/non-invasive ventilation/ventilator) versus 3 (non-oxygen use) 37.16 (1.18 to 43.47, p = 0.032), and for age ≥60 compared to <60 years 4.8 (1.33 to 17.6, p = 0.016). Mosaicism was reduced at 12 compared to 3 months, OR 0.33 (95%CI 0.16 to 0.66, p = 0.002).

CONCLUSIONS

GGO and mosaicism decreased, while parenchymal bands increased from 3 to 12 months. Persistent GGO were associated with initial COVID-19 severity and age ≥60 years.

Lung volume measurement using chest CT in COVID-19 patients: a cohort study in Japan

OBJECTIVE

This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients.

METHODS

This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months.

RESULTS

The incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p<0.0001). Multivariable analysis of previously reported factors (age, sex, body mass index and comorbidities) demonstrated that CT-derived lung volume was significantly associated with critical outcomes. The low TLCct (%pred) group exhibited a higher incidence of bacterial infection, heart failure, thromboembolism, liver dysfunction and renal dysfunction than the high TLCct (%pred) group. TLCct (%pred) at 3 months was similarly divided into two groups at the median (71.8%). Among patients with follow-up CT scans, lung volumes showed a recovery trend from the time of admission to 3 months but remained lower in critical cases at 3 months.

CONCLUSION

Lower CT lung volume was associated with critical outcomes, posthospitalisation complications and slower improvement of clinical conditions in COVID-19 patients.

Severity of COVID-19 infection in patients with COVID-19 combined with diabetes

PURPOSE

This study aimed to analyse the correlation between blood glucose control and the severity of COVID-19 infection in patients with diabetes.

METHODS

Clinical and imaging data of a total of 146 patients with diabetes combined with COVID-19 who visited our hospital between December 2022 and January 2023 were retrospectively collected. The patients were divided into the 'good blood glucose control' group and the 'poor blood glucose control' group based on an assessment of their blood glucose control. The clinical data, computed tomography (CT) appearance and score and the severity of COVID-19 infection of the two groups were compared, with the severity of COVID-19 infection being the dependent variable to analyse other influencing factors.

RESULTS

The group with poor blood glucose control showed a higher lobar involvement degree and total CT severity score (CTSS) than the group with good blood glucose control (13.30 ± 5.25 vs. 10.38 ± 4.84, p < 0.05). The two groups exhibited no statistically significant differences in blood lymphocyte, leukocyte, C-reaction protein, pleural effusion, consolidation, ground glass opacity or crazy-paving signs. Logistic regression analysis showed that the total CTSS significantly influences the clinical severity of patients (odds ratio 1.585, p < 0.05), whereas fasting plasma glucose and blood glucose control are not independent factors influencing clinical severity (both p > 0.05). The area under the curve (AUC) of CTSS prediction of critical COVID-19 was 0.895 with sensitivity of 79.3% and specificity of 88.1% when the threshold value is 12.

CONCLUSION

Blood glucose control is significantly correlated with the CTSS; the higher the blood glucose is, the more severe the lung manifestation. The CTSS can also be used to evaluate and predict the clinical severity of COVID-19.

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.

Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia

The aim of our study was to establish and compare the diagnostic accuracy and clinical applicability of published chest CT severity scoring systems used for COVID-19 pneumonia assessment and to propose the most efficient CT scoring system with the highest diagnostic performance and the most accurate prediction of disease severity. This retrospective study included 218 patients with PCR-confirmed SARS-CoV-2 infection and chest CT. Two radiologists blindly evaluated CT scans and calculated nine different CT severity scores (CT SSs). The diagnostic validity of CT SSs was tested by ROC analysis. Interobserver agreement was excellent (intraclass correlation coefficient: 0.982-0.995). The predominance of either consolidations or a combination of consolidations and ground-glass opacities (GGOs) was a predictor of more severe disease (both p < 0.005), while GGO prevalence alone was not. Correlation between all CT SSs was high, ranging from 0.848 to 0.971. CT SS 30 had the highest diagnostic accuracy (AUC = 0.805) in discriminating mild from severe COVID-19 disease compared to all the other proposed scoring systems (AUC range 0.755-0.788). In conclusion, CT SS 30 achieved the highest diagnostic accuracy in predicting the severity of COVID-19 disease while maintaining simplicity, reproducibility, and applicability in complex clinical settings.

Expression patterns of lncRNA MALAT-1 in SARS-COV-2 infection and its potential effect on disease severity via miR-200c-3p and SIRT1

Downregulating Angiotensin Converting Enzyme2 (ACE2) expression may be a shared mechanism for RNA viruses.

Aim

Evaluate the expressions of ACE2 effectors: the long non-coding RNA 'MALAT-1', the micro-RNA 'miR-200c-3p' and the histone deacetylase 'SIRT1' in SARS-COV-2 patients and correlate to disease severity. Sera samples from 98 SARS-COV-2 patients and 30 healthy control participants were collected. qRT-PCR was used for MALAT-1 and miR-200c-3p expression. SIRT1 was measured using ELISA.

Results

In sera of COVID-19 patients, gene expression of miR-200c-3p is increased while MALAT-1 is decreased. SIRT1 protein level is decreased (P value < 0.001). Findings are accentuated with increased disease severity. Serum MALAT-1, miR-200c-3p and SIRT1 could be used as diagnostic markers at cut off values of 0.04 (95.9 % sensitivity), 5.59 (94.9 % sensitivity, 99 % specificity), and 7.4 (98 % sensitivity) respectively. A novel MALAT-1-miR-200c-3p-SIRT1 pathway may be involved in the regulation of SARS-COV-2 severity.

Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity

To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consisted of 124 COVID-19 patients acquired from Chulabhorn Hospital, divided into 28 cases without lung lesions and 96 cases with lung lesions categorized severity by radiologists regarding TSS. The model used a 3D-UNet along with DenseNet and ResNet models that had already been trained to separate the lobes of the lungs and figure out the percentage of lung involvement due to COVID-19 infection. It also used the Dice similarity coefficient (DSC) to measure TSS. Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with the Dice similarity coefficients of 91.52% and 76.89%, respectively. The calculated TSS values were similar to those evaluated by radiologists, with an R2 of 0.842. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.890 and 0.709, respectively.

Doubts and concerns about COVID-19 uncertainties on imaging data, clinical score, and outcomes

BACKGROUND

COVID-19 is a pandemic disease affecting predominantly the respiratory apparatus with clinical manifestations ranging from asymptomatic to respiratory failure. Chest CT is a crucial tool in diagnosing and evaluating the severity of pulmonary involvement through dedicated scoring systems. Nonetheless, many questions regarding the relationship of radiologic and clinical features of the disease have emerged in multidisciplinary meetings. The aim of this retrospective study was to explore such relationship throughout an innovative and alternative approach.

MATERIALS AND METHODS

This study included 550 patients (range 25-98 years; 354 males, mean age 66.1; 196 females, mean age 70.9) hospitalized for COVID-19 with available radiological and clinical data between 1 March 2021 and 30 April 2022. Radiological data included CO-RADS, chest CT score, dominant pattern, and typical/atypical findings detected on CT examinations. Clinical data included clinical score and outcome. The relationship between such features was investigated through the development of the main four frequently asked questions summarizing the many issues arisen in multidisciplinary meetings, as follows 1) CO-RADS, chest CT score, clinical score, and outcomes; 2) the involvement of a specific lung lobe and outcomes; 3) dominant pattern/distribution and severity score for the same chest CT score; 4) additional factors and outcomes.

RESULTS

1) If CT was suggestive for COVID, a strong correlation between CT/clinical score and prognosis was found; 2) Middle lobe CT involvement was an unfavorable prognostic criterion; 3) If CT score < 50%, the pattern was not influential, whereas if CT score > 50%, crazy paving as dominant pattern leaded to a 15% increased death rate, stacked up against other patterns, thus almost doubling it; 4) Additional factors usually did not matter, but lymph-nodes and pleural effusion worsened prognosis.

CONCLUSIONS

This study outlined those radiological features of COVID-19 most relevant towards disease severity and outcome with an innovative approach.

Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset

Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for medical treatment, whereas the prediction of disease severity suggests more suitable and specific treatment options. In this study, we publish severity scores for the 2358 COVID-19 positive images in the COVIDx8B dataset, creating one of the largest collections of publicly available COVID-19 severity data. Furthermore, we train and evaluate deep learning models on the newly created dataset to provide a first benchmark for the severity classification task. One of the main challenges of this dataset is the skewed class distribution, resulting in undesirable model performance for the most severe cases. We therefore propose and examine different augmentation strategies, specifically targeting majority and minority classes. Our augmentation strategies show significant improvements in precision and recall values for the rare and most severe cases. While the models might not yet fulfill medical requirements, they serve as an appropriate starting point for further research with the proposed dataset to optimize clinical resource allocation and treatment.

Analysis of Leukocyte Subpopulations by Flow Cytometry during Hospitalization Depending on the Severity of COVID-19 Course

The mechanisms underlying the immune response to coronavirus disease 2019 (COVID-19) and the recovery process have not been fully elucidated. The aim of the study was to analyze leukocyte subpopulations in patients at significant time points (at diagnosis, and 3 and 6 months after infection) selected according to the analysis of changes in the lungs by the CT classification system, considering the severity of the disease. The study groups consisted of severe and non-severe COVID-19 patients. There was a significant decrease in CD8+ T cells, NK and eosinophils, with an increasing percentage of neutrophils during hospitalization. We noticed lower levels of CD4 and CD8 T lymphocytes, eosinophils, basophils, and CD16+ monocytes and elevated neutrophil levels in severe patients relative to non-severe patients. Three months after infection, we observed higher levels of basophils, and after 6 months, higher CD4/CD8 ratios and T cell levels in the severe compared to non-severe group. Non-severe patients showed significant changes in the leukocyte populations studied at time of hospitalization and both within 3 months and 6 months of onset. The CT CSS classification with parameters of the flow cytometry method used for COVID-19 patients determined changes that proved useful in the initial evaluation of patients.

A Systematic Review of the Relationship between Chest CT Severity Score and Laboratory Findings and Clinical Parameters in COVID-19 Pneumonia

The COVID-19 virus has infected millions of people and became a global pandemic in 2020. The efficacy of laboratory and clinical parameters in the diagnosis and monitoring of COVID-19 has been established. The CT scan has been identified as a crucial tool in the prognostication of COVID-19 pneumonia. Moreover, it has been proposed that the CT severity score can be utilized for the diagnosis and prognostication of COVID-19 disease severity and exhibits a correlation with laboratory findings such as inflammatory markers, blood glucose levels, and clinical parameters such as endotracheal intubation, oxygen saturation, mortality, and hospital admissions. Nevertheless, the correlation between the CT severity score and clinical or laboratory parameters has not been firmly established. The objective of this study is to provide a comprehensive review of the aforementioned association. This review used a systematic approach to collate and assess the existing literature that investigates the correlation between CT severity score and laboratory and clinical parameters. The search was conducted using Embase Ovid, MEDLINE Ovid, and PubMed databases, covering the period from inception to 20 May 2023. This review identified 20 studies involving more than 8000 participants of varying designs. The findings showed that the CT severity score is positively associated with laboratory and clinical parameters in COVID-19 patients. The findings indicate that the CT severity score exhibits a satisfactory level of prognostic accuracy in predicting mortality among patients with COVID-19.

The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study

Introduction

Chest computed tomography (CT) is suitable to assess morphological changes in the lungs. Chest CT scoring systems (CCTS) have been developed and use in order to quantify the severity of pulmonary involvement in COVID-19. CCTS has also been correlated with clinical outcomes. Here we wished to use a validated, relatively simple CTSS to assess chest CT patterns and to correlate CTSS with clinical outcomes in COVID-19.

Patients and methods

Altogether 227 COVID-19 cases underwent chest CT scanning using a 128 multi-detector CT scanner (SOMATOM Go Top, Siemens Healthineers, Germany). Specific pathological features, such as ground-glass opacity (GGO), crazy-paving pattern, consolidation, fibrosis, subpleural lines, pleural effusion, lymphadenopathy and pulmonary embolism were evaluated. CTSS developed by Pan et al. (CTSS-Pan) was applied. CTSS and specific pathologies were correlated with demographic, clinical and laboratory data, A-DROP scores, as well as outcome measures. We compared CTSS-Pan to two other CT scoring systems.

Results

The mean CTSS-Pan in the 227 COVID-19 patients was 14.6 ± 6.7. The need for ICU admission (p < 0.001) and death (p < 0.001) were significantly associated with higher CTSS. With respect to chest CT patterns, crazy-paving pattern was significantly associated with ICU admission. Subpleural lines exerted significant inverse associations with ICU admission and ventilation. Lymphadenopathy was associated with all three outcome parameters. Pulmonary embolism led to ICU admission. In the ROC analysis, CTSS>18.5 significantly predicted admission to ICU (p = 0.026) and CTSS>19.5 was the cutoff for increased mortality (p < 0.001). CTSS-Pan and the two other CTSS systems exerted similar performance. With respect to clinical outcomes, CTSS-Pan might have the best performance.

Conclusion

CTSS may be suitable to assess severity and prognosis of COVID-19-associated pneumonia. CTSS and specific chest CT patterns may predict the need for ventilation, as well as mortality in COVID-19. This can help the physician to guide treatment strategies in COVID-19, as well as other pulmonary infections.

Impact of Vaccination and the Omicron Variant on COVID-19-related Chest CT Findings: A Multicenter Study

Background The SARS-CoV-2 Omicron variant has a higher infection rate than previous variants but results in less severe disease. However, the effects of Omicron and vaccination on chest CT findings are difficult to evaluate. Purpose To investigate the effect of vaccination status and predominant variant on chest CT findings, diagnostic scores, and severity scores in a multicenter sample of consecutive patients referred to emergency departments for proven COVID-19. Materials and Methods This retrospective multicenter study included adults referred to 93 emergency departments with SARS-CoV-2 infection according to a reverse-transcriptase polymerase chain reaction test and known vaccination status between July 2021 and March 2022. Clinical data and structured chest CT reports, including semiquantitative diagnostic and severity scores following the French Society of Radiology-Thoracic Imaging Society guidelines, were extracted from a teleradiology database. Observations were divided into Delta-predominant, transition, and Omicron-predominant periods. Associations between scores and variant and vaccination status were investigated with χ² tests and ordinal regressions. Multivariable analyses evaluated the influence of Omicron variant and vaccination status on the diagnostic and severity scores. Results Overall, 3876 patients were included (median age, 68 years [quartile 1 to quartile 3 range, 54-80]; 1695 women). Diagnostic and severity scores were associated with the predominant variant (Delta vs Omicron, χ² = 112.4 and 33.7, respectively; both P < .001) and vaccination status (χ² = 243.6 and 210.1; both P < .001) and their interaction (χ² = 4.3 [P = .04] and 28.7 [P < .001], respectively). In multivariable analyses, Omicron variant was associated with lower odds of typical CT findings than was Delta variant (odds ratio [OR], 0.46; P < .001). Two and three vaccine doses were associated with lower odds of demonstrating typical CT findings (OR, 0.32 and 0.20, respectively; both P < .001) and of having high severity score (OR, 0.47 and 0.33, respectively; both P < .001), compared with unvaccinated patients. Conclusion Both the Omicron variant and vaccination were associated with less typical chest CT manifestations of COVID-19 and lesser extent of disease. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Yoon and Goo in this issue.

Comparison of Computed Tomography Scoring Systems in Patients with COVID-19 and Hematological Malignancies

BACKGROUND

Numerous computed tomography (CT) scales have been proposed to assess lung involvement in COVID-19 pneumonia as well as correlate radiological findings with patient outcomes.

OBJECTIVE

Comparison of different CT scoring systems in terms of time consumption and diagnostic performance in patients with hematological malignancies and COVID-19 infection.

MATERIALS AND METHODS

Retrospective analysis included hematological patients with COVID-19 and CT performed within 10 days of diagnosis of infection. CT scans were analyzed in three different semi-quantitative scoring systems, Chest CT Severity Score (CT-SS), Chest CT Score(CT-S), amd Total Severity Score (TSS), as well as qualitative modified Total Severity Score (m-TSS). Time consumption and diagnostic performance were analyzed.

RESULTS

Fifty hematological patients were included. Based on the ICC values, excellent inter-observer reliability was found among the three semi-quantitative methods with ICC > 0.9 (p < 0.001). The inter-observer concordance was at the level of perfect agreement (kappa value = 1) for the mTSS method (p < 0.001). The three-receiver operating characteristic (ROC) curves revealed excellent and very good diagnostic accuracy for the three quantitative scoring systems. The AUC values were excellent (0.902), very good (0.899), and very good (0.881) in the CT-SS, CT-S and TSS scoring systems, respectively. Sensitivity showed high levels at 72.7%, 75%, and 65.9%, respectively, and specificity was recorded at 98.2%, 100%, 94.6% for the CT-SS, CT-S, and TSS scoring systems, respectively. Time consumption was the same for Chest CT Severity Score and TSS and was longer for Chest CT Score (p < 0.001).

CONCLUSIONS

Chest CT score and chest CT severity score have very high sensitivity and specificity in terms of diagnostic accuracy. The highest AUC values and the shortest median time of analysis in chest CT severity score indicate this method as preferred for semi-quantitative assessment of chest CT in hematological patients with COVID-19.

Impact of inactivated COVID-19 vaccines on lung injury in B.1.617.2 (Delta) variant-infected patients

BACKGROUND

Chest computerized tomography (CT) scan is an important strategy that quantifies the severity of COVID-19 pneumonia. To what extent inactivated COVID-19 vaccines could impact the COVID-19 pneumonia on chest CT is not clear.

METHODS

This study recruited 357 SARS-COV-2 B.1.617.2 (Delta) variant-infected patients admitted to the Second Hospital of Nanjing from July to August 2021. An artificial intelligence-assisted CT imaging system was used to quantify the severity of COVID-19 pneumonia. We compared the volume of infection (VOI), percentage of infection (POI) and chest CT scores among patients with different vaccination statuses.

RESULTS

Of the 357 Delta variant-infected patients included for analysis, 105 were unvaccinated, 72 were partially vaccinated and 180 were fully vaccinated. Fully vaccination had the least lung injuries when quantified by VOI (median VOI of 222.4 cm³, 126.6 cm³ and 39.9 cm³ in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001), POI (median POI of 7.60%, 3.55% and 1.20% in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001) and chest CT scores (median CT score of 8.00, 6.00 and 4.00 in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001). After adjustment for age, sex, comorbidity, time from illness onset to hospitalization and viral load, fully vaccination but not partial vaccination was significantly associated with less lung injuries quantified by VOI {adjust coefficient[95%CI] for "full vaccination": - 106.10(- 167.30,44.89); p < 0.001}, POI {adjust coefficient[95%CI] for "full vaccination": - 3.88(- 5.96, - 1.79); p = 0.001} and chest CT scores {adjust coefficient[95%CI] for "full vaccination": - 1.81(- 2.72, - 0.91); p < 0.001}. The extent of reduction of pulmonary injuries was more profound in fully vaccinated patients with older age, having underlying diseases, and being female sex, as demonstrated by relatively larger absolute values of adjusted coefficients. Finally, even within the non-severe COVID-19 population, fully vaccinated patients were found to have less lung injuries.

CONCLUSION

Fully vaccination but not partially vaccination could significantly protect lung injury manifested on chest CT. Our study provides additional evidence to encourage a full course of vaccination.

Prediction of prognosis in patients with severe COVID-19 pneumonia using CT score by emergency physicians: a single-center retrospective study

We aimed to develop a method to determine the CT score that can be easily obtained from CT images and examine its prognostic value for severe COVID pneumonia. Patients with COVID pneumonia who required ventilatory management by intubation were included. CT score was based on anatomical information in axial CT images and were divided into three sections of height from the apex to the bottom. The extent of pneumonia in each section was rated from 0 to 5 and summed. The primary outcome was the prediction of patients who died or were managed on extracorporeal membrane oxygenation (ECMO) based on the CT score at admission. Of the 71 patients included, 12 (16.9%) died or required ECMO management, and the CT score predicted death or ECMO management with ROC of 0.718 (0.561-0.875). The death or ECMO versus survival group (median [quartiles]) had a CT score of 17.75 (14.75-20) versus 13 (11-16.5), p = 0.017. In conclusion, a higher score on our generated CT score could predict the likelihood of death or ECMO management. A CT score at the time of admission allows for early preparation and transfer to a hospital that can manage patients who may need ECMO.

Impact of Vaccination and the Omicron Variant on COVID-19-related Chest CT Findings: A Multicenter Study

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant has a higher infection rate than previous variants but results in less severe disease. However, the impacts of omicron and vaccination on chest CT findings are difficult to evaluate. Purpose To investigate the impact of vaccination status and predominant variant on chest CT findings, diagnostic and severity scores in multicenter sample of consecutive patients referred to emergency departments for proven COVID-19. Materials and Methods This retrospective, multicenter study included adults referred to 93 emergency departments with SARS-CoV-2 infection according to RT-PCR and known vaccination status between July 2021 and March 2022. Clinical data and structured chest CT reports including semiquantitative diagnostic and severity scores following the French Society of Radiology-Thoracic Imaging Society guidelines were extracted from a teleradiology database. Observations were divided into 'delta-predominant', 'transition', and 'omicron-predominant' periods. Associations between scores and variant and vaccination status were investigated with Chi-square tests and ordinal regressions. Multivariable analyses evaluated the influence of omicron variant and vaccination status on the diagnostic and severity scores. Results Overall, 3876 patients were included (median age: 68 years [Q1-Q3: 54-80], 1695 females). Diagnostic and severity scores were associated with the predominant variant (delta- versus omicron-predominant, Chi-square=112.4 and 33.7, both P<.001) and vaccination (Chi-square=243.6 and 210, both P<.001) and their interaction (Chi-square=4.3, P=.04 and Chi-square=28.7, P<.001, respectively). In multivariable analyses, omicron variant was associated with lower odds of typical CT findings than delta variant (OR=0.46, P<.001). Two and three vaccine doses were associated with lower odds of demonstrating typical CT findings (OR=0.32 and OR=0.20, both P<.001), and of having high severity score (OR=0.47 and OR=0.33, both P<.001), compared with unvaccinated patients. Conclusion Both the omicron variant and vaccination were associated with less typical chest CT manifestations for COVID-19 and lesser extent of disease. See also the editorial by Yoon and Goo in this issue.

Association of subpleural ground-glass opacities with respiratory failure and RNAemia in COVID-19

OBJECTIVES

To examine the radiological patterns specifically associated with hypoxemic respiratory failure in patients with coronavirus disease (COVID-19).

METHODS

We enrolled patients with COVID-19 confirmed by qPCR in this prospective observational cohort study. We explored the association of clinical, radiological, and microbiological data with the development of hypoxemic respiratory failure after COVID-19 onset. Semi-quantitative CT scores and dominant CT patterns were retrospectively determined for each patient. The microbiological evaluation included checking the SARS-CoV-2 viral load by qPCR using nasal swab and serum specimens.

RESULTS

Of the 214 eligible patients, 75 developed hypoxemic respiratory failure and 139 did not. The CT score was significantly higher in patients who developed hypoxemic respiratory failure than in those did not (median [interquartile range]: 9 [6-14] vs 0 [0-3]; p < 0.001). The dominant CT patterns were subpleural ground-glass opacities (GGOs) extending beyond the segmental area (n = 44); defined as "extended GGOs." Multivariable analysis showed that hypoxemic respiratory failure was significantly associated with extended GGOs (odds ratio [OR] 29.6; 95% confidence interval [CI], 9.3-120; p < 0.001), and a CT score  > 4 (OR 12.7; 95% CI, 5.3-33; p < 0.001). The incidence of RNAemia was significantly higher in patients with extended GGOs (58.3%) than in those without any pulmonary lesion (14.7%; p < 0.001).

CONCLUSIONS

Extended GGOs along the subpleural area were strongly associated with hypoxemia and viremia in patients with COVID-19.

KEY POINTS

• Extended ground-glass opacities (GGOs) along the subpleural area and a CT score  > 4, in the early phase of COVID-19, were independently associated with the development of hypoxemic respiratory failure. • The absence of pulmonary lesions on CT in the early phase of COVID-19 was associated with a lower risk of developing hypoxemic respiratory failure. • Compared to patients with other CT findings, the extended GGOs and a higher CT score were also associated with a higher incidence of RNAemia.

Pneumonia Frequency and Severity in Patients With Symptomatic COVID-19: Impact of mRNA and Adenovirus Vector Vaccines

BACKGROUND. Additional evidence of the role of COVID-19 vaccination in reducing pneumonia frequency and severity in the setting of breakthrough infection could help combat ongoing vaccine hesitancy. OBJECTIVE. The purpose of this article was to compare the frequency and severity of pneumonia on chest CT in patients with confirmed COVID-19 between patients who are unvaccinated and those who are fully vaccinated by messenger RNA (mRNA) or adenovirus vector vaccines. METHODS. This retrospective single-center study included 467 patients (250 men, 217 women; mean age, 65 ± 17 [SD] years) who underwent chest CT between December 15, 2021, and February 18, 2022, during hospitalization for symptomatic COVID-19, confirmed by reverse transcriptase-polymerase chain reaction assay. A total of 216 patients were unvaccinated, and 167 and 84 patients were fully vaccinated (defined as receipt of the second dose at least 14 days before COVID-19 diagnosis) by the BNT162b2 mRNA vaccine or the ChAdOx1-S adenovirus vector vaccine, respectively. Semiquantitative CT severity scores (CT-SS; 0-25 scale) were determined; CT-SS of 0 indicated absence of pneumonia. Presence of bilateral involvement was assessed in patients with pneumonia. Associations were explored between vaccination status and CT findings. RESULTS. The frequency of the absence of pneumonia was 15% (32/216) in unvaccinated patients, 29% (24/84) in patients fully vaccinated with ChAdOx1-S vaccine, and 51% (85/167) in patients fully vaccinated with BNT162b2 vaccine (unvaccinated and ChAdOx1-S vs BNT162b2: p < .001; unvaccinated vs ChAdOx1-S: p = .08). Mean CT-SS was significantly higher in unvaccinated patients (9.7 ± 6.1) than in patients fully vaccinated with BNT162b2 (5.2 ± 6.1) or ChAdOx1-S (6.2 ± 5.9) vaccine (both p < .001). Full vaccination was significantly associated with CT-SS independent of patient age and sex (estimate = -4.46; p < .001). Frequency of bilateral lung involvement was significantly higher in unvaccinated patients (158/184, 86%) and in patients fully vaccinated with ChAdOx1-S vaccine (54/60, 90%) than in patients fully vaccinated with BNT162b2 vaccine (47/82, 57%) (both p < .001). CONCLUSION. Pneumonia frequency and severity were lower in patients with full vaccination by mRNA and adenovirus vector vaccines who experienced breakthrough infections in comparison with unvaccinated patients. CLINICAL IMPACT. The visual observation by radiologic imaging of the protective effect of vaccination on lung injury in patients with breakthrough infections provides additional evidence supporting the clinical benefit of vaccination.

Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients' Mortality

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2021 and December 2021. Results: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. Conclusions: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.

Chest CT Total Severity Score on Admission to Predict In-Hospital Mortality in COVID-19 Patients with Acute and Chronic Renal Impairment

Aim: To identify the predictors of in-hospital mortality in patients with coronavirus disease of 2019 (COVID-19) and acute renal impairment (ARI) or chronic kidney disease (CKD), and to evaluate the performance and inter-reader concordance of chest CT total severity scores (TSSs). Methods: This retrospective single-center study was conducted on symptomatic COVID-19 patients with renal impairment (either acute or chronic) and a serum creatinine of >2 mg/dL at the time of admission. The patients’ demographic characteristics, clinical data, and laboratory data were extracted from the clinical computerized medical records. All chest CT images obtained at the time of hospital admission were analyzed. Two radiologists independently assessed the pulmonary abnormalities and scored the severity using CT chest total severity score (TSS). Univariate logistic regression analysis was used to determine factors associated with in-hospital mortality. A receiver operating characteristic (ROC) curve analysis was performed for the TSS in order to identify the cut-off point that predicts mortality. Bland−Altman plots were used to evaluate agreement between the two radiologists assessing TSS. Results: A total of 100 patients were included, with a mean age of 60 years, 54 were males, 53 had ARI, and 47 had CKD. In terms of in-hospital mortality, 60 patients were classified in the non-survivor group and 40 were classified in the survivor group. The mortality rate was higher for those with ARI compared to those with CKD (p = 0.033). The univariate regression analysis showed an increasing odds of in-hospital mortality associated with higher respiratory rate (OR 1.149, 95% CI 1.057−1.248, p = 0.001), total bilirubin (OR 2.532, 95% CI 1.099−5.836, p = 0.029), lactate dehydrogenase (LDH) (OR 1.001, 95% CI 1.000−1.003, p = 0.018), CRP (OR 1.010, 95% CI 1.002−1.017, p = 0.012), invasive mechanical ventilation (MV) (OR 7.667, 95% CI 2.118−27.755, p = 0.002), a predominant pattern of pulmonary consolidation (OR 21.714, 95% CI 4.799−98.261, p < 0.001), and high TSS (OR 2.082, 95% CI 1.579−2.745, p < 0.001). The optimum cut-off value of TSS used to predict in-hospital mortality was 8.5 with a sensitivity of 86.7% and a specificity of 87.5%. There was excellent interobserver agreement (ICC > 0.9) between the two independent radiologists in their quantitative assessment of pulmonary changes using TSS. Conclusions: In-hospital mortality is high in COVID-19 patients with ARI/CKD, especially for those with ARI. High serum bilirubin, a predominant pattern of pulmonary consolidation, and TSS are the most significant predictors of mortality in these patients. Patients with a higher TSS may require more intensive hospital care. TSS is a reliable and helpful auxiliary tool for risk stratification among COVID-19 patients with ARI/CKD.

Acute Pulmonary Embolism in COVID-19: A Potential Connection between Venous Congestion and Thrombus Distribution

Background: Vascular abnormalities, including venous congestion (VC) and pulmonary embolism (PE), have been recognized as frequent COVID-19 imaging patterns and proposed as severity markers. However, the underlying pathophysiological mechanisms remain unclear. In this study, we aimed to characterize the relationship between VC, PE distribution, and alveolar opacities (AO). Methods: This multicenter observational registry (clinicaltrials.gov identifier NCT04824313) included 268 patients diagnosed with SARS-CoV-2 infection and subjected to contrast-enhanced CT between March and June 2020. Acute PE was diagnosed in 61 (22.8%) patients, including 17 females (27.9%), at a mean age of 61.7 ± 14.2 years. Demographic, laboratory, and outcome data were retrieved. We analyzed CT images at the segmental level regarding VC (qualitatively and quantitatively [diameter]), AO (semi-quantitatively as absent, <50%, or >50% involvement), clot location, and distribution related to VC and AO. Segments with vs. without PE were compared. Results: Out of 411 emboli, 82 (20%) were lobar or more proximal and 329 (80%) were segmental or subsegmental. Venous diameters were significantly higher in segments with AO (p = 0.031), unlike arteries (p = 0.138). At the segmental level, 77% of emboli were associated with VC. Overall, PE occurred in 28.2% of segments with AO vs. 21.8% without (p = 0.047). In the absence of VC, however, AO did not affect PE rates (p = 0.94). Conclusions: Vascular changes predominantly affected veins, and most PEs were located in segments with VC. In the absence of VC, AOs were not associated with the PE rate. VC might result from increased flow supported by the hypothesis of pulmonary arteriovenous anastomosis dysregulation as a relevant contributing factor.