Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis

Chest CT features of COVID-19 in vaccinated versus unvaccinated patients: use of CT severity score and outcome analysis

OBJECTIVES

To evaluate the impact of vaccination on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and moreover on coronavirus disease 2019 (COVID-19) pneumonia, by assessing the extent of lung disease using the CT severity score (CTSS).

METHODS

Between September 2021 and February 2022, SARS-CoV-2 positive patients who underwent chest CT were retrospectively enrolled. Anamnestic and clinical data, including vaccination status, were obtained. All CT scans were evaluated by two readers using the CTSS, based on a 25-point scale. Univariate and multivariate logistic regression analyses were performed to evaluate the associations between CTSS and clinical or demographic variables. An outcome analysis was used to differentiate clinical outcome between vaccinated and unvaccinated patients.

RESULTS

Of the 1040 patients (537 males, 503 females; median age 58 years), 678 (65.2%) were vaccinated and 362 (34.8%) unvaccinated. Vaccinated patients showed significantly lower CTSS compared to unvaccinated patients (p < 0.001), also when patients without lung involvement (CTSS = 0) were excluded (p < 0.001). Older age, male gender and lower number of doses administered were associated with higher CTSS, however, in the multivariate analysis, vaccination status resulted to be the variable with the strongest association with CTSS. Clinical outcomes were significantly worse in unvaccinated patients, including higher number of ICU admissions and higher mortality rates.

CONCLUSIONS

Lung involvement during COVID-19 was significantly less severe in vaccinated patients compared with unvaccinated patients, who also showed worse clinical outcomes. Vaccination status was the strongest variable associated to the severity of COVID-related, more than age, gender, and number of doses administered.

6-month consequences of COVID-19 in patients discharged from hospital: a cohort study

BACKGROUND

The long-term health consequences of COVID-19 remain largely unclear. The aim of this study was to describe the long-term health consequences of patients with COVID-19 who have been discharged from hospital and investigate the associated risk factors, in particular disease severity.

METHODS

We did an ambidirectional cohort study of patients with confirmed COVID-19 who had been discharged from Jin Yin-tan Hospital (Wuhan, China) between Jan 7 and May 29, 2020. Patients who died before follow-up; patients for whom follow-up would be difficult because of psychotic disorders, dementia, or readmission to hospital; those who were unable to move freely due to concomitant osteoarthropathy or immobile before or after discharge due to diseases such as stroke or pulmonary embolism; those who declined to participate; those who could not be contacted; and those living outside of Wuhan or in nursing or welfare homes were all excluded. All patients were interviewed with a series of questionnaires for evaluation of symptoms and health-related quality of life, underwent physical examinations and a 6-min walking test, and received blood tests. A stratified sampling procedure was used to sample patients according to their highest seven-category scale during their hospital stay as 3, 4, and 5-6, to receive pulmonary function test, high resolution CT of the chest, and ultrasonography. Enrolled patients who had participated in the Lopinavir Trial for Suppression of SARS-CoV-2 in China received SARS-CoV-2 antibody tests. Multivariable adjusted linear or logistic regression models were used to evaluate the association between disease severity and long-term health consequences.

FINDINGS

In total, 1733 of 2469 discharged patients with COVID-19 were enrolled after 736 were excluded. Patients had a median age of 57·0 years (IQR 47·0-65·0) and 897 (52%) were male and 836 (48%) were female. The follow-up study was done from June 16 to Sept 3, 2020, and the median follow-up time after symptom onset was 186·0 days (175·0-199·0). Fatigue or muscle weakness (52%, 855 of 1654) and sleep difficulties (26%, 437 of 1655) were the most common symptoms. Anxiety or depression was reported among 23% (367 of 1616) of patients. The proportions of 6-min walking distance less than the lower limit of the normal range were 17% for those at severity scale 3, 13% for severity scale 4, and 28% for severity scale 5-6. The corresponding proportions of patients with diffusion impairment were 22% for severity scale 3, 29% for scale 4, and 56% for scale 5-6, and median CT scores were 3·0 (IQR 2·0-5·0) for severity scale 3, 4·0 (3·0-5·0) for scale 4, and 5·0 (4·0-6·0) for scale 5-6. After multivariable adjustment, patients showed an odds ratio (OR) of 1·61 (95% CI 0·80-3·25) for scale 4 versus scale 3 and 4·60 (1·85-11·48) for scale 5-6 versus scale 3 for diffusion impairment; OR 0·88 (0·66-1·17) for scale 4 versus scale 3 and OR 1·76 (1·05-2·96) for scale 5-6 versus scale 3 for anxiety or depression, and OR 0·87 (0·68-1·11) for scale 4 versus scale 3 and 2·75 (1·61-4·69) for scale 5-6 versus scale 3 for fatigue or muscle weakness. Of 94 patients with blood antibodies tested at follow-up, the seropositivity (96·2% vs 58·5%) and median titres (19·0 vs 10·0) of the neutralising antibodies were significantly lower compared with at the acute phase. 107 of 822 participants without acute kidney injury and with an estimated glomerular filtration rate (eGFR) of 90 mL/min per 1·73 m2 or more at acute phase had eGFR less than 90 mL/min per 1·73 m2 at follow-up.

INTERPRETATION

At 6 months after acute infection, COVID-19 survivors were mainly troubled with fatigue or muscle weakness, sleep difficulties, and anxiety or depression. Patients who were more severely ill during their hospital stay had more severe impaired pulmonary diffusion capacities and abnormal chest imaging manifestations, and are the main target population for intervention of long-term recovery.

FUNDING

National Natural Science Foundation of China, Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, National Key Research and Development Program of China, Major Projects of National Science and Technology on New Drug Creation and Development of Pulmonary Tuberculosis, and Peking Union Medical College Foundation.

Clinicopathological Outlines of Post-COVID-19 Pulmonary Fibrosis Compared with Idiopathic Pulmonary Fibrosis

This review brings together the current knowledge regarding the risk factors and the clinical, radiologic, and histological features of both post-COVID-19 pulmonary fibrosis (PCPF) and idiopathic pulmonary fibrosis (IPF), describing the similarities and the disparities between these two diseases, using numerous databases to identify relevant articles published in English through October 2022. This review would help clinicians, pathologists, and researchers make an accurate diagnosis, which can help identify the group of patients selected for anti-fibrotic therapies and future therapeutic perspectives.

Identification of Histopathological Biomarkers in Fatal Cases of Coronavirus Disease: A Study on Lung Tissue

We aimed to evaluate the primary lung postmortem macro- and microscopic biomarkers and factors associated with diffuse alveolar damage in patients with fatal coronavirus (COVID-19). We retrospectively analyzed lung tissue collected from autopsies performed in Cluj-Napoca, Romania, between April 2020 and April 2021 on patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We examined 79 patients with confirmed SARS-CoV-2 infection, ages 34 to 96 years, split into two groups using the cut-off value of 70 years. Arterial hypertension (38%) and type 2 diabetes mellitus (19%) were the most common comorbidities with similar distribution between groups (p-values > 0.14). Macroscopically, bloody exudate was more frequently observed among patients < 70 years (33/36 vs. 29/43, p-value = 0.0091). Diffuse alveolar damage (53.1%) was similarly observed among the evaluated groups (p-value = 0.1354). Histopathological biomarkers of alveolar edema in 83.5% of patients, interstitial pneumonia in 74.7%, and microthrombi in 39.2% of cases were most frequently observed. Half of the evaluated lungs had an Ashcroft score of up to 2 and an alveolar air capacity of up to 12.5%. Bronchopneumonia (11/43 vs. 3/36, p-value = 0.0456) and interstitial edema (9/43 vs. 2/36, p-value = 0.0493) were significantly more frequent in older patients. Age (median: 67.5 vs. 77 years, p-value = 0.023) and infection with the beta variant of the virus (p-value = 0.0071) proved to be significant factors associated with diffuse alveolar damage.

Dominant CT Patterns and Immune Responses during the Early Infection Phases of Different SARS-CoV-2 Variants

Ground-glass opacity (GGO) and organizing pneumonia (OP) are dominant pulmonary CT lesions associated with COVID-19. However, the role of different immune responses in these CT patterns remains unclear, particularly following the emergence of the Omicron variant. In this prospective observational study, we recruited patients hospitalized with COVID-19, before and after the emergence of Omicron variants. Semi-quantitative CT scores and dominant CT patterns were retrospectively determined for all patients within five days of symptom onset. Serum levels of IFN-α, IL-6, CXCL10, and VEGF were assessed using ELISA. Serum-neutralizing activity was measured using a pseudovirus assay. We enrolled 48 patients with Omicron variants and 137 with precedent variants. While the frequency of GGO patterns was similar between the two groups, the OP pattern was significantly more frequent in patients with precedent variants. In patients with precedent variants, IFN-α and CXCL10 levels were strongly correlated with GGO, whereas neutralizing activity and VEGF were correlated with OP. The correlation between IFN-α levels and CT scores was lower in patients with Omicron than in those with precedent variants. Compared to preceding variants, infection with the Omicron variant is characterized by a less frequent OP pattern and a weaker correlation between serum IFN-α and CT scores.

Qualitative and semi-quantitative ultrasound assessment in delta and Omicron Covid-19 patients: data from high volume reference center

OBJECTIVE

to evaluate the efficacy of US, both qualitatively and semi-quantitatively, in the selection of treatment for the Covid-19 patient, using patient triage as the gold standard.

METHODS

Patients admitted to the Covid-19 clinic to be treated with monoclonal antibodies (mAb) or retroviral treatment and undergoing lung ultrasound (US) were selected from the radiological data set between December 2021 and May 2022 according to the following inclusion criteria: patients with proven Omicron variant and Delta Covid-19 infection; patients with known Covid-19 vaccination with at least two doses. Lung US (LUS) was performed by experienced radiologists. The presence, location, and distribution of abnormalities, such as B-lines, thickening or ruptures of the pleural line, consolidations, and air bronchograms, were evaluated. The anomalous findings in each scan were classified according to the LUS scoring system. Nonparametric statistical tests were performed.

RESULTS

The LUS score median value in the patients with Omicron variant was 1.5 (1-20) while the LUS score median value in the patients with Delta variant was 7 (3-24). A difference statistically significant was observed for LUS score values among the patients with Delta variant between the two US examinations (p value = 0.045 at Kruskal Wallis test). There was a difference in median LUS score values between hospitalized and non-hospitalized patients for both the Omicron and Delta groups (p value = 0.02 on the Kruskal Wallis test). For Delta patients groups the sensitivity, specificity, positive and negative predictive values, considering a value of 14 for LUS score for the hospitalization, were of 85.29%, 44.44%, 85.29% and 76.74% respectively.

CONCLUSIONS

LUS is an interesting diagnostic tool in the context of Covid-19, it could allow to identify the typical pattern of diffuse interstitial pulmonary syndrome and could guide the correct management of patients.

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.

Extension of Lung Damage at Chest Computed Tomography in Severely Ill COVID-19 Patients Treated with Interleukin-6 Receptor Blockers Correlates with Inflammatory Cytokines Production and Prognosis

Elevated inflammatory markers are associated with severe coronavirus disease 2019 (COVID-19), and some patients benefit from Interleukin (IL)-6 pathway inhibitors. Different chest computed tomography (CT) scoring systems have shown a prognostic value in COVID-19, but not specifically in anti-IL-6-treated patients at high risk of respiratory failure. We aimed to explore the relationship between baseline CT findings and inflammatory conditions and to evaluate the prognostic value of chest CT scores and laboratory findings in COVID-19 patients specifically treated with anti-IL-6. Baseline CT lung involvement was assessed in 51 hospitalized COVID-19 patients naive to glucocorticoids and other immunosuppressants using four CT scoring systems. CT data were correlated with systemic inflammation and 30-day prognosis after anti-IL-6 treatment. All the considered CT scores showed a negative correlation with pulmonary function and a positive one with C-reactive protein (CRP), IL-6, IL-8, and Tumor Necrosis Factor α (TNF-α) serum levels. All the performed scores were prognostic factors, but the disease extension assessed by the six-lung-zone CT score (S24) was the only independently associated with intensive care unit (ICU) admission (p = 0.04). In conclusion, CT involvement correlates with laboratory inflammation markers and is an independent prognostic factor in COVID-19 patients representing a further tool to implement prognostic stratification in hospitalized patients.

Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters

BACKGROUND

The purpose of the study was to compare the results of AI (artificial intelligence) analysis of the extent of pulmonary lesions on HRCT (high resolution computed tomography) images in COVID-19 pneumonia, with clinical data including laboratory markers of inflammation, to verify whether AI HRCT assessment can predict the clinical severity of COVID-19 pneumonia.

METHODS

The analyzed group consisted of 388 patients with COVID-19 pneumonia, with automatically analyzed HRCT parameters of volume: AIV (absolute inflammation), AGV (absolute ground glass), ACV (absolute consolidation), PIV (percentage inflammation), PGV (percentage ground glass), PCV (percentage consolidation). Clinical data included: age, sex, admission parameters: respiratory rate, oxygen saturation, CRP (C-reactive protein), IL6 (interleukin 6), IG - immature granulocytes, WBC (white blood count), neutrophil count, lymphocyte count, serum ferritin, LDH (lactate dehydrogenase), NIH (National Institute of Health) severity score; parameters of clinical course: in-hospital death, transfer to the ICU (intensive care unit), length of hospital stay.

RESULTS

The highest correlation coefficients were found for PGV, PIV, with LDH (respectively 0.65, 0.64); PIV, PGV, with oxygen saturation (respectively - 0.53, -0.52); AIV, AGV, with CRP (respectively 0.48, 0.46); AGV, AIV, with ferritin (respectively 0.46, 0.45). Patients with critical pneumonia had significantly lower oxygen saturation, and higher levels of immune-inflammatory biomarkers on admission. The radiological parameters of lung involvement proved to be strong predictors of transfer to the ICU (in particular, PGV ≥ cut-off point 29% with Odds Ratio (OR): 7.53) and in-hospital death (in particular: AIV ≥ cut-off point 831 cm³ with OR: 4.31).

CONCLUSIONS

Automatic analysis of HRCT images by AI may be a valuable method for predicting the severity of COVID-19 pneumonia. The radiological parameters of lung involvement correlate with laboratory markers of inflammation, and are strong predictors of transfer to the ICU and in-hospital death from COVID-19.

TRIAL REGISTRATION

National Center for Research and Development CRACoV-HHS project, contract number SZPITALE-JEDNOIMIENNE/18/2020.

Human mesenchymal stem cell therapy in severe COVID-19 patients: 2-year follow-up results of a randomized, double-blind, placebo-controlled trial

BACKGROUND

Long-term effects of human mesenchymal stem cell (MSC) treatment on COVID-19 patients have not been fully characterized. The aim of this study was to evaluate the safety and efficacy of a MSC treatment administered to severe COVID-19 patients enrolled in our previous randomized, double-blind, placebo-controlled clinical trial (NCT04288102).

METHODS

A total of 100 patients experiencing severe COVID-19 received either MSC treatment (n = 65, 4 × 10⁷ cells per infusion) or a placebo (n = 35) combined with standard of care on days 0, 3, and 6. Patients were subsequently evaluated 18 and 24 months after treatment to evaluate the long-term safety and efficacy of the MSC treatment. Outcomes measured included: 6-min walking distance (6-MWD), lung imaging, quality of life according to the Short Form 36 questionnaire (SF-36), COVID-19-related symptoms, titers of SARS-CoV-2 neutralizing antibodies, tumor markers, and MSC-related adverse events (AEs).

FINDINGS

Two years after treatment, a marginally smaller proportion of patients had a 6-MWD below the lower limit of the normal range in the MSC group than in the placebo group (OR = 0.19, 95% CI: 0.04-0.80, Fisher's exact test, p = 0.015). At month 18, the general health score from the SF-36 was higher in the MSC group than in the placebo group (50.00 vs. 35.00, 95% CI: 0.00-20.00, Wilcoxon rank sum test, p = 0.018). Total severity score of lung imaging and the titer of neutralizing antibodies were similar between the two groups at months 18 and 24. There was no difference in AEs or tumor markers at the 2-year follow-up between the two groups.

INTERPRETATION

Long-term safety was observed for the COVID-19 patients who received MSC treatment. However, efficacy of MSC treatment was not significantly sustained through the end of the 2-year follow-up period.

FUNDING

The National Key Research and Development Program of China (2022YFA1105604, 2020YFC0860900, 2022YFC2304401), the specific research fund of The Innovation Platform for Academicians of Hainan Province (YSPTZX202216) and the Fund of National Clinical Center for Infectious Diseases, PLA General Hospital (NCRC-ID202105,413FZT6).

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.

3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients

Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external validation groups included 608, 153, and 290 patients, respectively. Images, clinical, and laboratory data were fed into different customizations of a dense neural network to choose the best performing architecture for the prediction of mortality, intubation, and intensive care unit (ICU) admission. The AI model tested on CT and clinical features displayed accuracy, sensitivity, specificity, and ROC-AUC, respectively, of 91.7%, 90.5%, 92.4%, and 95% for the prediction of patient's mortality; 91.3%, 91.5%, 89.8%, and 95% for intubation; and 89.6%, 90.2%, 86.5%, and 94% for ICU admission (internal validation) in the testing cohort. The performance was lower in the validation cohort for mortality (71.7%, 55.6%, 74.8%, 72%), intubation (72.6%, 74.7%, 45.7%, 64%), and ICU admission (74.7%, 77%, 46%, 70%) prediction. The addition of the available laboratory data led to an increase in sensitivity for patient's mortality (66%) and specificity for intubation and ICU admission (50%, 52%, respectively), while the other metrics maintained similar performance results. We present a deep-learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients. KEY POINTS: • 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (> 90%) mortality, ICU admittance, and intubation in COVID-19 patients. • The model slightly increased prediction results when laboratory data were added to the analysis, despite data imbalance. However, the model accuracy dropped when CT images were not considered in the analysis, implying an important role of CT in predicting outcomes.

Correlation between CT Score and KL-6: A Severity Assessing in Juvenile Dermatomyositis Associated Interstitial Lung Disease

Background. There is no radiological measurement to estimate the severity of pediatrics juvenile dermatomyositis (JDM) with interstitial lung disease (ILD). We validated the effectiveness of CT scoring assessment in JDM patients with ILD. Aim. To establish a CT scoring system and calculate CT scores in JDM patients with ILD and to determine its reliability and the correlation with Krebs von den Lungen-6 (KL-6). Methods. The study totally enrolled 46 JDM-ILD patients and 16 JDM without ILD (non-ILD, NILD) patients. The chest CT images (7.0 ± 3.6 years; 32 male and 30 female) were all analyzed. CT scores of six lung zones were retrospectively calculated, included image pattern score and distribution range score. Image pattern score was defined as follows: increased broncho-vascular bundle (1 point); ground glass opacity (GGO) (2 points); consolidation (3 points); GGO with bronchiectasis (4 points); consolidation with bronchiectasis (5 points); and honeycomb lung (6 points). Distribution range score was defined as no infiltrate (0 point); <30% (1 point); 30%–60% (2 points); and ≥60% (3 points). Two pediatric radiologists reviewed all CT images independently. The ROC curve was established, and the optimal cutoff score for severity discrimination was set. Results. The agreement between two observers was excellent, and the ICC was 0.930 (95% CI 0.882–0.959, $p<0.01$ ). CT score and KL-6 level had a positive linear correlation (r = 0.784, $p<0.01$ ). However, the correlation between CT scores of different lung zone and KL-6 level was different. The KL-6 cut off level suggested for JDM with ILD was 209.0 U/ml, with 73.9% sensitivity and 87.5% specificity, and the area under curve was (AUC) 0.864 ( $p<0.01$ ). Conclusion. The CT scoring system we established, as a semiquantitative method, can effectively evaluate ILD in JDM-PM patients and provide reliable evidence for treatment.

Correlations of renal parenchymal attenuations and CT severity scores on three consecutive CTs in COVID-19 patients

Aim: We aimed to investigate the correlation between the temporal changes of computed tomography severity score (CT-SS) ​​and mean renal parenchymal attenuation (MRPA) values in consecutive chest computed topographies (CT). Material and Method: This retrospective, single-center study included 65 (≥18 years) COVID-19 patients with positive RT-PCR tests. A radiologist calculated three consecutive chest CT-SSs and measured the MPRAs on CTs from the upper half of each kidney included in the cross-section. Paired samples test and Wilcoxon signed-rank test were used to evaluate the temporal changes of mean renal parenchymal attenuation (RPA) and median CT-SS values, in three consecutive CTs. Spearman's test was used to evaluate the correlation of each RPA and CT-SS value on three consecutive CTs. Results: The study population included 65 patients with a mean age of 61.49±13.91 years. A total of 36/65 (55.4%) were male. We found a significant increase between the first and second CT-SS (p

Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients

Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors (N = 224) versus non-survivors (N = 28) in the general floor group, and (ii) survivors (N = 92) versus non-survivors (N = 56) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors (p < 0.05), and non-survivor CXR scores deteriorated at outcome (p < 0.05) whereas survivor CXR scores did not (p > 0.05). For IMV patients, survivor and non-survivor CXR scores were similar at intubation (p > 0.05), and both improved at outcome (p < 0.05), with survivor scores showing greater improvement (p < 0.05). Hospitalization and IMV duration were not different between groups (p > 0.05). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count (p < 0.05). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression.

Long-term effects of COVID-19 on lungs and the clinical relevance: a 6-month prospective cohort study

Background: We aimed to explore the prevalence of prolonged symptoms, pulmonary impairments and residual disease on chest tomography (CT) in COVID-19 patients at 6 months after acute illness. Methods: In this prospective, single-center study, hospitalized patients with radiologically and laboratory-confirmed COVID-19 were included. Results: A high proportion of the 116 patients reported persistent symptoms (n = 54; 46.6%). On follow-up CT, 33 patients (28.4%) demonstrated residual disease. Multivariate analyses revealed that only neutrophil-to-lymphocyte ratio was an independent predictor for residual disease. Conclusion: Hospitalized patients with mild/moderate COVID-19 still had persistent symptoms and were prone to develop long-term pulmonary sequelae on chest CT. However, it did not have a significant effect on long-term pulmonary functions.

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.

Three-month follow-up after severe COVID-19 infection: are chest CT results associated with respiratory outcomes and respiratory recovery in COVID-19 patients?

BACKGROUND

CT Severity Score (CT-SS) can be used to assess the extent of severe coronavirus disease 19 (COVID-19) pneumonia. Follow-up CT-SS in patients surviving COVID-19-associated hyperinflammation and its correlation with respiratory parameters remains unknown. This study aims to assess the association between CT-SS and respiratory outcomes, both in hospital and at three months after hospitalization.

METHODS

Patients from the COVID-19 High-intensity Immunosuppression in Cytokine storm Syndrome (CHIC) study surviving hospitalization due to COVID-19 associated hyperinflammation were invited for follow-up assessment at three months after hospitalization. Results of CT-SS three months after hospitalization were compared with CT-SS at hospital admission. CT-SS at admission and at 3-months were correlated with respiratory status during hospitalization and with patient reported outcomes as well as pulmonary- and exercise function tests at 3-months after hospitalization.

RESULTS

A total of 113 patients were included. Mean CT-SS decreased by 40.4% (SD 27.6) in three months (P < 0.001). CT-SS during hospitalization was higher in patients requiring more oxygen (P < 0.001). CT-SS at 3-months was higher in patients with more dyspnoea (CT-SS 8.31 (3.98) in patients with modified Medical Council Dyspnoea scale (mMRC) 0-2 vs. 11.03 (4.47) in those with mMRC 3-4). CT-SS at 3-months was also higher in patients with a more impaired pulmonary function (7.4 (3.6) in patients with diffusing capacity for carbon monoxide (DLCO) > 80%pred vs. 14.3 (3.2) in those with DLCO < 40%pred, P = 0.002).

CONCLUSION

Patients surviving hospitalization for COVID-19-associated hyperinflammation with higher CT-SS have worse respiratory outcome, both in-hospital and at 3-months after hospitalization. Strict monitoring of patients with high CT-SS is therefore warranted.

Novel COVID-19 Pneumonia: CT Manifestations and Pattern of Evolution

Background This study aims to provide a better knowledge of COVID-19 that will aid in the formulation of future health policy by detailing the pathophysiology, case detection, and treatment, as well as management and prevention activities. Methodology A cross-sectional, prospective study was conducted at the Department of Radio-Diagnosis and Imaging, Shri B.M. Patil Medical College, Vijayapura. A total of 90 patients who presented with clinical features of COVID-19 and patients above the age of 18 years suspected of COVID-19 who were referred to the Department of Radio-Diagnosis and Imaging were included in the study. Results The classical findings which are observed on CT imaging in patients with COVID-19 include the presence of ground-glass opacities which are bilateral in distribution predominantly affecting the lower lobes with a posterior predilection. Overall, more than 33% of the patients who recovered from severe COVID-19 had lung abnormalities resembling fibrosis on follow-up imaging performed within two weeks of the commencement of the disease. These individuals were older and had more severe sicknesses during the acute period. Conclusions Chest CT can detect COVID-19 progression or secondary cardiopulmonary problems such as acute respiratory distress syndrome, pulmonary embolism, superimposed pneumonia, or heart failure. Future research into the prognostic value of chest CT in COVID-19 is required.

The Risk Factors and Outcomes for Radiological Abnormalities in Early Convalescence of COVID-19 Patients Caused by the SARS-CoV-2 Omicron Variant: A Retrospective, Multicenter Follow-up Study

BACKGROUND

The emergence of the severe acute respiratory syndrome coronavirus 2 omicron variant has been triggering the new wave of coronavirus disease 2019 (COVID-19) globally. However, the risk factors and outcomes for radiological abnormalities in the early convalescent stage (1 month after diagnosis) of omicron infected patients are still unknown.

METHODS

Patients were retrospectively enrolled if they were admitted to the hospital due to COVID-19. The chest computed tomography (CT) images and clinical data obtained at baseline (at the time of the first CT image that showed abnormalities after diagnosis) and 1 month after diagnosis were longitudinally analyzed. Uni-/multi-variable logistic regression tests were performed to explore independent risk factors for radiological abnormalities at baseline and residual pulmonary abnormalities after 1 month.

RESULTS

We assessed 316 COVID-19 patients, including 47% with radiological abnormalities at baseline and 23% with residual pulmonary abnormalities at 1-month follow-up. In a multivariate regression analysis, age ≥ 50 years, body mass index ≥ 23.87, days after vaccination ≥ 81 days, lymphocyte count ≤ 1.21 × 10^-9/L, interleukin-6 (IL-6) ≥ 10.05 pg/mL and IgG ≤ 14.140 S/CO were independent risk factors for CT abnormalities at baseline. The age ≥ 47 years, presence of interlobular septal thickening and IL-6 ≥ 5.85 pg/mL were the independent risk factors for residual pulmonary abnormalities at 1-month follow-up. For residual abnormalities group, the patients with less consolidations and more parenchymal bands at baseline could progress on CT score after 1 month. There were no significant changes in the number of involved lung lobes and total CT score during the early convalescent stage.

CONCLUSION

The higher IL-6 level was a common independent risk factor for CT abnormalities at baseline and residual pulmonary abnormalities at 1-month follow-up. There were no obvious radiographic changes during the early convalescent stage in patients with residual pulmonary abnormalities.

Lung tropism in hospitalized patients following infection with SARS-CoV-2 variants from D614G to Omicron BA.2

BACKGROUND

The genetic and pathogenic characteristics of SARS-CoV-2 have evolved from the original isolated strains; however, the changes in viral virulence have not been fully defined. In this study, we analyzed the association between the severity of the pathogenesis of pneumonia in humans and SARS-CoV-2 variants that have been prevalent to date.

METHODS

We examined changes in the variants and tropism of SARS-CoV-2. A total of 514 patients admitted between February 2020 and August 2022 were included and evaluated for pneumonia by computed tomography (CT) as a surrogate of viral tropism.

RESULTS

The prevalence of pneumonia for each variant was as follows: D614G (57%, 65/114), Alpha (67%, 41/61), Delta (49%, 41/84), Omicron BA.1.1 (26%, 43/163), and Omicron BA.2 (11%, 10/92). The pneumonia prevalence in unvaccinated patients progressively declined from 70% to 11% as the variants changed: D614G (56%, 61/108), Alpha (70%, 26/37), Delta (60%, 38/63), BA.1.1 (52%, 15/29), and BA.2 (11%, 2/19). The presence of pneumonia in vaccinated patients was as follows: Delta (16%, 3/19), BA.1.1 (21%, 27/129), and BA.2 (11%, 8/73). Compared with D614G, the areas of lung involvement were also significantly reduced in BA.1.1 and BA.2 variants.

CONCLUSIONS

Compared with previous variants, there was a marked decrease in pneumonia prevalence and lung involvement in patients infected with Omicron owing to decreased tropism in the lungs that hindered viral proliferation in the alveolar epithelial tissue. Nevertheless, older, high-risk patients with comorbidities who are infected with an Omicron variant can still develop pneumonia and require early treatment.

The Role of Pulmonary Function Testing and Lung Imaging in the Long-Term Follow-Up of Patients with COVID-19 Pneumonia

BACKGROUND

Post-COVID-19 Interstitial Lung Disease (PC-ILD) is characterized by fibrotic-like signs at high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs) abnormalities after SARS-CoV-2 infection. It is still not clear how frequent these tests should be performed to rule out long-term consequences of COVID-19 pneumonia.

OBJECTIVES

The aims of our study were to evaluate the incidence and risk factors of PC-ILD and possibly to propose a long-term follow-up program.

METHOD

One-hundred patients, hospitalized in our ward for moderate to critical COVID-19, underwent two follow-up visits at three and 15 months in which PFTs and HRCT were performed.

RESULTS

At the 15-month follow-up, 8% of patients showed residual radiological and functional signs consistent with PC-ILD. All but one of these patients had already demonstrated PFTs and HRCT alterations at first follow-up visit, and the last 1 patient showed worsening of lung function during follow-up. These findings highlight the negative predictive value of PFTs at 3-month follow-up for the development of PC-ILD. Aging, severity of COVID-19, and degree of pulmonary involvement during acute infection proved to be significant risk factors for developing PC-ILD.

CONCLUSIONS

Our study highlights the importance of PFTs in the long-term follow-up of patients affected by moderate to critical COVID-19 pneumonia. Further studies are needed to confirm our hypothesis that HRCT should be performed only in patients with PFTs abnormalities.

Serial Changes in Blood-Cell-Count-Derived and CRP-Derived Inflammatory Indices of COVID-19 Patients

The aim of the study was to investigate the serial changes in inflammatory indices derived from blood cell counts and C-reactive protein (CRP) levels in COVID-19 patients with good and poor outcomes. We retrospectively analyzed the serial changes in the inflammatory indices in 169 COVID-19 patients. Comparative analyses were performed on the first and last days of a hospital stay or death and serially from day 1 to day 30 from the symptom onset. On admission, non-survivors had higher CRP to lymphocytes ratio (CLR) and multi-inflammatory index (MII) values than survivors, while at the time of discharge/death, the largest differences were found for the neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and MII. A significant decrease in NLR, CLR, and MII by the time of discharge was documented in the survivors, and a significant increase in NLR was documented in the non-survivors. The NLR was the only one that remained significant from days 7-30 of disease in intergroup comparisons. The correlation between the indices and the outcome was observed starting from days 13-15. The changes in the index values over time proved to be more helpful in predicting COVID-19 outcomes than those measured on admission. The values of the inflammatory indices could reliably predict the outcome no earlier than days 13-15 of the disease.

Using machine learning to improve our understanding of COVID-19 infection in children

PURPOSE

Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not.

METHODS

A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection.

RESULTS

Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection.

CONCLUSIONS

In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score.

Post-COVID-19 Syndrome Comprehensive Assessment: From Clinical Diagnosis to Imaging and Biochemical-Guided Diagnosis and Management

The COVID-19 outbreak was first reported in 2019, causing massive morbidity and mortality. The majority of the COVID-19 patients survived and developed Post-COVID-19 Syndrome (PC19S) of varying severity. Currently, the diagnosis of PC19S is achieved through history and symptomatology that cannot be explained by an alternative diagnosis. However, the heavy reliance on subjective reporting is prone to reporting errors. Besides, there is no unified diagnostic assessment tool to classify the clinical severity of patients. This leads to significant difficulties when managing patients in terms of public resource utilization, clinical progression monitorization and rehabilitation plan formulation. This narrative review aims to review current evidence of diagnosis based on triple assessment: clinical symptomatology, biochemical analysis and imaging evidence. Further assessment tools can be developed based on triple assessment to monitor patient's clinical progression, prognosis and intervals of monitoring. It also highlights the high-risk features of patients for closer and earlier monitoring. Rehabilitation programs and related clinical trials are evaluated; however, most of them focus on cardiorespiratory fitness and psychiatric presentations such as anxiety and depression. Further research is required to establish an objective and comprehensive assessment tool to facilitate clinical management and rehabilitation plans.

Obesity-Associated Hepatic Steatosis, Somatotropic Axis Impairment, and Ferritin Levels Are Strong Predictors of COVID-19 Severity

The full spectrum of SARS-CoV-2-infected patients has not yet been defined. This study aimed to evaluate which parameters derived from CT, inflammatory, and hormonal markers could explain the clinical variability of COVID-19. We performed a retrospective study including SARS-CoV-2-infected patients hospitalized from March 2020 to May 2021 at the Umberto I Polyclinic of Rome. Patients were divided into four groups according to the degree of respiratory failure. Routine laboratory examinations, BMI, liver steatosis indices, liver CT attenuation, ferritin, and IGF-1 serum levels were assessed and correlated with severity. Analysis of variance between groups showed that patients with worse prognoses had higher BMI and ferritin levels, but lower liver density, albumin, GH, and IGF-1. ROC analysis confirmed the prognostic accuracy of IGF-1 in discriminating between patients who experienced death/severe respiratory failure and those who did not (AUC 0.688, CI: 0.587 to 0.789, p < 0.001). A multivariate analysis considering the degrees of severity of the disease as the dependent variable and ferritin, liver density, and the standard deviation score of IGF-1 as regressors showed that all three parameters were significant predictors. Ferritin, IGF-1, and liver steatosis account for the increased risk of poor prognosis in COVID-19 patients with obesity.

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.

Stratification of COVID-19 Severity Using SeptiCyte RAPID, a Novel Host Immune Response Test

SeptiCyte® RAPID is a gene expression assay measuring the relative expression levels of host response genes PLA2G7 and PLAC8, indicative of a dysregulated immune response during sepsis. As severe forms of COVID-19 may be considered viral sepsis, we evaluated SeptiCyte RAPID in a series of 94 patients admitted to Foch Hospital (Suresnes, France) with proven SARS-CoV-2 infection. EDTA blood was collected in the emergency department (ED) in 67 cases, in the intensive care unit (ICU) in 23 cases and in conventional units in 4 cases. SeptiScore (0-15 scale) increased with COVID-19 severity. Patients in ICU had the highest SeptiScores, producing values comparable to 8 patients with culture-confirmed bacterial sepsis. Receiver operating characteristic (ROC) curve analysis had an area under the curve (AUC) of 0.81 for discriminating patients requiring ICU admission from patients who were immediately discharged or from patients requiring hospitalization in conventional units. SeptiScores increased with the extent of the lung injury. For 68 patients, a chest computed tomography (CT) scan was performed within 24 h of COVID-19 diagnosis. SeptiScore >7 suggested lung injury ≥50% (AUC = 0.86). SeptiCyte RAPID was compared to other biomarkers for discriminating Critical + Severe COVID-19 in ICU, versus Moderate + Mild COVID-19 not in ICU. The mean AUC for SeptiCyte RAPID was superior to that of any individual biomarker or combination thereof. In contrast to C-reactive protein (CRP), correlation of SeptiScore with lung injury was not impacted by treatment with anti-inflammatory agents. SeptiCyte RAPID can be a useful tool to identify patients with severe forms of COVID-19 in ED, as well as during follow-up.

Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier

Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the p-value criterion from the Wilcoxon rank-sum test below 0.005 were used as selected features for further analysis. Then five feature reduction methods, Forward Feature selection, minimum Redundancy Maximum Relevance, Relieff, Linear Discriminant Analysis, and Neighborhood Component Analysis were utilized to select the best combination of features. Finally, seven classifiers frameworks, random forest (RF), support vector machine, logistic regression (LR), K nearest neighbors, Artifical neural network, bagging, and boosting were used to predict the mortality outcome of COVID-19 patients. The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively. Furthermore, the RF classifier with hierarchical feature selection algorithms via Forward Feature selection had the highest classification power with an accuracy of 92.08 ± 2.56. Therefore, our proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.

Evaluation of Characteristics and Outcomes of COVID-19 Between First Wave and Second Wave: Study From a Tertiary Cancer Care Centre, Delhi, India

Background and objectives The second wave of coronavirus disease-19 (COVID-19) had several severe consequences in the form of rising cases, deaths, and overwhelming health infrastructure in India. However, the similarities and differences between the characteristics of the first and second waves have yet to be explained. The objectives of the study were to compare the incidence, clinical management, and mortality rates between two waves. Methods The COVID-19 data collated from Rajiv Gandhi Cancer Institute and Research Centre, Delhi between the first wave (1 April 2020 to 27 February 2021) and second wave (1 March 2021 to 30 June 2021) were evaluated in terms of incidence, the clinical course of the disease, and mortality rates. Results The number of subjects hospitalized in the first and second waves was 289 and 564, respectively. Compared to the first wave, the proportion of patients with severe disease was higher (9.7% vs. 37.8%). Several parameters such as age group, grade of disease, the reason for hospitalization, values of peripheral oxygen saturation, type of respiratory support, response to therapy, vital status, and others show statistically significant differences between the two waves (P<0.001). The mortality rate in the second wave was significantly higher (20.2% vs. 2.4%, P<0.001) than in the first wave. Interpretation and conclusions The clinical course and outcomes of COVID-19 significantly differ between the first and second waves. There is a higher incidence of hospitalized patients (66.1% vs. 33.9%) with drastically increased case fatality rate in the second wave. Disease severity in the first wave is four times lower than in the second wave. The second wave was quite devastating, which led to the shortage of critical care facilities and the loss of a significant number of lives.

COVID associated pulmonary mucormycosis: Outcomes of surgical therapy

OBJECTIVES

This study aims at reporting the surgical outcomes of COVID associated pulmonary mucormycosis with special emphasis on surgical mortality.

METHODS

This prospective observational study was conducted in a dedicated thoracic surgical unit in Gurugram, India over 18 months. An analysis of demography, peri-operative variables were carried out. Various parameters were analysed to assess the factors affecting mortality.

RESULTS

Total of 44 patients with diagnosis of CAPM were managed during the study period. All were started on anti-fungal therapy. However, 33 patients (75%) were operated whereas rest 11 (25%) were not considered suitable for surgery. In the surgical cohort (n  =  33), there were 20 males (60.6%) and 13 females (39.4%), with a mean age of 54.8 years (range, 33-72 years). The mean duration of the symptoms was 1.1 weeks. Non-anatomical wedge resection of lobe(s) was performed in 5 patients (15.1%), lobectomy/bi-lobectomy was required in 26 patients (78.9%) and left pneumonectomy in 2 patients (6%). There were 5 peri-operative deaths (15.1%), all due to fungal sepsis. ECOG scale > 2 (P  ≤  0.001), higher Charlson Comorbidity Index score > 2 (P  =  0.04) and pneumonectomy (P  =  0.02) were the predictors of mortality. On comparison with NCPM, there was no difference in the incidence of post-operative complications (P  =  0.50) and the post-operative mortality (P  =  0.69).

CONCLUSION

Aggressive surgical resection with clear margins should be offered in CAPM, whenever feasible. Surgery for CAPM was not associated with higher post-operative complications including mortality compared to Non-COVID Pulmonary Mucormycosis.

Pulmonary Recovery 12 Months after Non-Severe and Severe COVID-19: The Prospective Swiss COVID-19 Lung Study

BACKGROUND

Lung function impairment persists in some patients for months after acute coronavirus disease 2019 (COVID-19). Long-term lung function, radiological features, and their association remain to be clarified.

OBJECTIVES

We aimed to prospectively investigate lung function and radiological abnormalities over 12 months after severe and non-severe COVID-19.

METHODS

584 patients were included in the Swiss COVID-19 lung study. We assessed lung function at 3, 6, and 12 months after acute COVID-19 and compared chest computed tomography (CT) imaging to lung functional abnormalities.

RESULTS

At 12 months, diffusion capacity for carbon monoxide (DLCOcorr) was lower after severe COVID-19 compared to non-severe COVID-19 (74.9% vs. 85.2% predicted, p < 0.001). Similarly, minimal oxygen saturation on 6-min walk test and total lung capacity were lower after severe COVID-19 (89.6% vs. 92.2%, p = 0.004, respectively, 88.2% vs. 95.1% predicted, p = 0.011). The difference for forced vital capacity (91.6% vs. 96.3% predicted, p = 0.082) was not statistically significant. Between 3 and 12 months, lung function improved in both groups and differences in DLCO between non-severe and severe COVID-19 patients decreased. In patients with chest CT scans at 12 months, we observed a correlation between radiological abnormalities and reduced lung function. While the overall extent of radiological abnormalities diminished over time, the frequency of mosaic attenuation and curvilinear patterns increased.

CONCLUSIONS

In this prospective cohort study, patients who had severe COVID-19 had diminished lung function over the first year compared to those after non-severe COVID-19, albeit with a greater extent of recovery in the severe disease group.

Can a chest HRCT-based crash course on COVID-19 cases make inexperienced thoracic radiologists readily available to face the next pandemic?

OBJECTIVE

To test the inter-reader agreement in assessing lung disease extent, HRCT signs, and Radiological Society of North America (RSNA) categorization between a chest-devoted radiologist (CR) and two HRCT-naïve radiology residents (RR1 and RR2) after the latter attended a COVID-19-based chest high-resolution computed tomography (HRCT) "crash course".

METHODS

The course was built by retrospective inclusion of 150 patients who underwent HRCT for COVID-19 pneumonia between November 2020 and January 2021. During a first 10-days-long "training phase", RR1 and RR2 read a pool of 100/150 HRCTs, receiving day-by-day access to CR reports as feedback. In the subsequent 2-days-long "test phase", they were asked to report 50/150 HRCTs with no feedback. Test phase reports of RR1/RR2 were then compared with CR using unweighted or linearly-weighted Cohen's kappa (k) statistic and intraclass correlation coefficient (ICC).

RESULTS

We observed almost perfect agreement in assessing disease extent between RR1-CR (k = 0.83, p < 0.001) and RR2-CR (k = 0.88, p < 0.001). The agreement between RR1-CR and RR2-CR on consolidation, crazy paving pattern, organizing pneumonia (OP) pattern, and pulmonary artery (PA) diameter was substantial (k = 0.65 and k = 0.68), moderate (k = 0.42 and k = 0.51), slight (k = 0.10 and k = 0.20), and good-to-excellent (ICC = 0.87 and ICC = 0.91), respectively. The agreement in providing RSNA categorization was moderate for R1 versus CR (k = 0.56) and substantial for R2 versus CR (k = 0.67).

CONCLUSION

HRCT-naïve readers showed an acceptable overall agreement with CR, supporting the hypothesis that a crash course can be a tool to readily make non-subspecialty radiologists available to cooperate in reading high burden of HRCT examinations during a pandemic/epidemic.

Twelve-month clinical, functional, and radiological outcomes in patients hospitalized for SARS-CoV-2 pneumonia

To assess long-term clinical, radiological, and functional follow-up of patients hospitalized for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pneumonia of different grades of severity. Two-hundred-thirty and three patients (Group 1, patients needed invasive mechanical ventilation, n = 69; Group 2, patients needed noninvasive mechanical ventilation, n = 78; Group 3, patients needed <12 L/min of O₂ supply, n = 96) with a postdischarge follow-up >12 months were studied. Follow-up visits, chest computed tomography (CT) scan and pulmonary function tests (diffusing capacity of the lung for carbon monoxide [DLCO], 6-min walking tests [6MWT], spirometry) were done at 3, 6, and 12 months after discharge. Male sex was more frequent in Group 1 (n = 50, 72.5%) compared with Group 2 (n = 49, 62.5%) and Group 3 (n = 44, 51.2%), p = 0.024. Group 2 patients had more comorbidities and higher BMI compared with others. At Month 12, the main reported symptoms were fatigue (mainly in Group 3) and dyspnea; most symptoms resolved during follow-up, except brain fog, memory loss, and anosmia/dysgeusia that, when present at Month 3, usually persisted at Month 12. DLCO and 6MWT normalized at Month 12 in almost all patients. Only nine patients (13%) in Group 1 had a normal chest CT at Month 12, while 20 (29%) had >3 abnormalities, compared with 14 (17.9%) in Group 2 and 11 (11.4%) in Group 3, respectively (p = 0.04). Different clinical symptoms persist up to 12 months in patients hospitalized for SARS-CoV-2 pneumonia. Despite the persistence of abnormalities at chest CT scan after 12 months, an impairment of pulmonary function persists only in a minority of subjects. A longer follow-up is needed to assess the evolution of radiological abnormalities in COVID-19 population.

Long-term lung perfusion changes related to COVID-19: a dual energy computed tomography study

PURPOSE

Although the findings of acute new coronavirus disease (COVID-19) infection on dual-energy computed tomography (DECT) have recently been defined, the long-term changes in lung perfusion associated with COVID-19 pneumonia have not yet been clarified. We aimed to examine the longterm course of lung perfusion in COVID-19 pneumonia cases using DECT and to compare changes in lung perfusion to clinical and laboratory findings.

METHODS

On initial and follow-up DECT scans, the presence and extent of perfusion deficit (PD) and parenchymal changes were assessed. The associations between PD presence and laboratory parameters, initial DECT severity score, and symptoms were evaluated.

RESULTS

The study population included 18 females and 26 males with an average age of 61.32 ± 11.3 years. Follow-up DECT examinations were performed after the mean of 83.12 ± 7.1 (80-94 days) days. PDs were detected on the follow-up DECT scans of 16 (36.3%) patients. These 16 patients also had ground-glass parenchymal lesions on the follow-up DECT scans. Patients with persistent lung PDs had significantly higher mean initial D-dimer, fibrinogen, and C-reactive protein values than patients without PDs. Patients with persistent PDs also had significantly higher rates of persistent symptoms.

CONCLUSION

Ground-glass opacities and lung PDs associated with COVID-19 pneumonia can persist for up to 80-90 days. Dual-energy computed tomography can be used to reveal long-term parenchymal and perfusion changes. Persistent PDs are commonly seen together with persistent COVID-19 symptoms.

COVID-19 diagnostic approaches with an extensive focus on computed tomography in accurate diagnosis, prognosis, staging, and follow-up

Although a long time has passed since its outbreak, there is currently no specific treatment for COVID-19, and it seems that the most appropriate strategy to combat this pandemic is to identify and isolate infected individuals. Various clinical diagnosis methods such as molecular techniques, serologic assays, and imaging techniques have been developed to identify suspected patients. Although reverse transcription-quantitative PCR (RT-qPCR) has emerged as a reference standard method for diagnosis of SARS-CoV-2, the high rate of false-negative results and limited supplies to meet current demand are the main shortcoming of this technique. Based on a comprehensive literature review, imaging techniques, particularly computed tomography (CT), show an acceptable level of sensitivity in the diagnosis and follow-up of COVID-19. Indeed, because lung infection or pneumonia is a common complication of COVID-19, the chest CT scan can be an alternative testing method in the early diagnosis and treatment assessment of the disease. In this review, we summarize all the currently available frontline diagnostic tools for the detection of SARS-CoV-2-infected individuals and highlight the value of chest CT scan in the diagnosis, prognosis, staging, management, and follow-up of infected patients.

Post-traumatic stress among COVID-19 survivors: A descriptive study of hospitalized first-wave survivors

Introduction

The coronavirus Severe Acute Respiratory Syndrome Coronavirus Type 1 induces a severe respiratory disease, coronavirus disease 2019 (COVID-19). After Severe Acute Respiratory Syndrome Coronavirus Type 1 and Middle East Respiratory Syndrome infection, increased post-traumatic stress disorder (PTSD) rates were described.

Methods

This single-centred, prospective study aimed to evaluate the rates of PTSD in patients who were hospitalized for COVID-19. Inclusion criteria were COVID-19 patients hospitalized in the intensive care unit (ICU) or in a standard unit with at least 2 L/min oxygen. Six months post-hospitalization, subjects were assessed for PTSD using a validated screening tool, the Post-Traumatic Stress Checklist-5 (PCL-5).

Results

A total of 40 patients were included. No demographic differences between the ICU and non-ICU groups were found. The mean PCL-5 score for the population was 8.85±10. The mean PCL-5 score was 6.7±8 in the ICU group and 10.5±11 in the non-ICU group (P=0.27). We screened one patient with a positive PCL-5 score and one with a possible PCL-5 cluster score. Nine patients had a PCL-5 score of up to 15. Seven patients reported no symptoms. Seven patients accepted a psychological follow-up: one for PTSD, three for possible PTSD and three for other psychological problems.

Discussion

The PCL-5 tool can be used by lung physicians during consultations to identify patients for whom follow-up mental health assessment and treatment for PTSD are warranted.

Conclusion

Lung physicians should be aware of the risk of PTSD in patients hospitalized for COVID-19 and ensure appropriate screening and follow-up care.

Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI)

OBJECTIVES

Only few published artificial intelligence (AI) studies for COVID-19 imaging have been externally validated. Assessing the generalizability of developed models is essential, especially when considering clinical implementation. We report the development of the International Consortium for COVID-19 Imaging AI (ICOVAI) model and perform independent external validation.

METHODS

The ICOVAI model was developed using multicenter data (n = 1286 CT scans) to quantify disease extent and assess COVID-19 likelihood using the COVID-19 Reporting and Data System (CO-RADS). A ResUNet model was modified to automatically delineate lung contours and infectious lung opacities on CT scans, after which a random forest predicted the CO-RADS score. After internal testing, the model was externally validated on a multicenter dataset (n = 400) by independent researchers. CO-RADS classification performance was calculated using linearly weighted Cohen's kappa and segmentation performance using Dice Similarity Coefficient (DSC).

RESULTS

Regarding internal versus external testing, segmentation performance of lung contours was equally excellent (DSC = 0.97 vs. DSC = 0.97, p = 0.97). Lung opacities segmentation performance was adequate internally (DSC = 0.76), but significantly worse on external validation (DSC = 0.59, p < 0.0001). For CO-RADS classification, agreement with radiologists on the internal set was substantial (kappa = 0.78), but significantly lower on the external set (kappa = 0.62, p < 0.0001).

CONCLUSION

In this multicenter study, a model developed for CO-RADS score prediction and quantification of COVID-19 disease extent was found to have a significant reduction in performance on independent external validation versus internal testing. The limited reproducibility of the model restricted its potential for clinical use. The study demonstrates the importance of independent external validation of AI models.

KEY POINTS

• The ICOVAI model for prediction of CO-RADS and quantification of disease extent on chest CT of COVID-19 patients was developed using a large sample of multicenter data. • There was substantial performance on internal testing; however, performance was significantly reduced on external validation, performed by independent researchers. The limited generalizability of the model restricts its potential for clinical use. • Results of AI models for COVID-19 imaging on internal tests may not generalize well to external data, demonstrating the importance of independent external validation.

The value of chest X-ray and CT severity scoring systems in the diagnosis of COVID-19: A review

Coronavirus disease 2019 (COVID-19) is caused by a coronavirus family member known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The main laboratory test to confirm the quick diagnosis of COVID-19 infection is reverse transcription-polymerase chain reaction (RT-PCR) based on nasal or throat swab sampling. A small percentage of false-negative RT-PCR results have been reported. The RT-PCR test has a sensitivity of 50-72%, which could be attributed to a low viral load in test specimens or laboratory errors. In contrast, chest CT has shown 56-98% of sensitivity in diagnosing COVID-19 at initial presentation and has been suggested to be useful in correcting false negatives from RT-PCR. Chest X-rays and CT scans have been proposed to predict COVID-19 disease severity by displaying the score of lung involvement and thus providing information about the diagnosis and prognosis of COVID-19 infection. As a result, the current study provides a comprehensive overview of the utility of the severity score index using X-rays and CT scans in diagnosing patients with COVID-19 when compared to RT-PCR.

Urokinase System in Pathogenesis of Pulmonary Fibrosis: A Hidden Threat of COVID-19

Pulmonary fibrosis is a common and threatening post-COVID-19 complication with poorly resolved molecular mechanisms and no established treatment. The plasminogen activator system, including urokinase (uPA) and urokinase receptor (uPAR), is involved in the pathogenesis of COVID-19 and contributes to the development of lung injury and post-COVID-19 pulmonary fibrosis, although their cellular and molecular underpinnings still remain obscure. The aim of the current study was to assess the role of uPA and uPAR in the pathogenesis of pulmonary fibrosis. We analyzed uPA and uPAR expression in human lung tissues from COVID-19 patients with pulmonary fibrosis using single-cell RNA-seq and immunohistochemistry. We modeled lung fibrosis in Plau-/- and Plaur-/- mice upon bleomycin instillation and explored the effect of uPAR downregulation in A549 and BEAS-2B lung epithelial cells. We found that uPAR expression drastically decreased in the epithelial airway basal cells and monocyte/macrophage cells, whereas uPA accumulation significantly increased in tissue samples of COVID-19 patients. Lung injury and fibrosis in Plaur-/- vs. WT mice upon bleomycin instillation revealed that uPAR deficiency resulted in pro-fibrogenic uPA accumulation, IL-6 and ACE2 upregulation in lung tissues and was associated with severe fibrosis, weight loss and poor survival. uPAR downregulation in A549 and BEAS-2B was linked to an increased N-cadherin expression, indicating the onset of epithelial-mesenchymal transition and potentially contributing to pulmonary fibrosis. Here for the first time, we demonstrate that plasminogen treatment reversed lung fibrosis in Plaur-/- mice: the intravenous injection of 1 mg of plasminogen on the 21st day of bleomycin-induced fibrosis resulted in a more than a two-fold decrease in the area of lung fibrosis as compared to non-treated mice as evaluated by the 42nd day. The expression and function of the plasminogen activator system are dysregulated upon COVID-19 infection, leading to excessive pulmonary fibrosis and worsening the prognosis. The potential of plasminogen as a life-saving treatment for non-resolving post-COVID-19 pulmonary fibrosis warrants further investigation.

Are the thorax Computed Tomography findings of ICU patients diagnosed with COVID-19 pneumonia related to the duration of hospital stay and mortality?

INTRODUCTION

Quantitative thorax Computed Tomography (CT) is used to determine the severity of COVID-19 pneumonia. With a new approach, quantitative thoracic CT is to contribute to the triage of patients with severe COVID-19 pneumonia in the ICU and to evaluate its relation with mortality by taking into account the vaccination status.

METHODS

Fifty-six patients who had a diagnosis of COVID-19 pneumonia confirmed in the adult ICU were evaluated retrospectively. To evaluate the degree of parenchymal involvement, the quantitative CT "craniocaudal diameter of the thorax/craniocaudal largest lesion diameter (CCDT/CCDL)" ratio and semi-quantitative total CT severity scores (TCTSS) (0:0%; 1:1-25%; 2:26-50%; 3:51-75% and 4:76-100%) were calculated. Both methods were analyzed with comparative ROC curves for predicting mortality. The effects of vaccines on thorax CT findings and laboratory parameters were also investigated.

RESULTS

The sensitivities and specificities were found to be 72.5%, 75.61%, and 80%, 73.33% when CCDT/CCDL and TCTSS cutoff value was taken <1.4, and >9, respectively, to predict mortality in COVID-19 pneumonia (Area Under the Curve = AUC = 0.797 and 0.752). Both methods predicted mortality well and no statistical differences were detected between them (p = 0.3618). In vaccinated patients, CRP was higher (p = 0.045), and LDH and ferritin were lower (p = 0.049, p = 0.004). The number of lobes involved was lower in the vaccinated group (p = 0.001).

CONCLUSIONS

The quantitative CT score (CCDT/CCDL) may play as important a role as TCTSS in diagnosing COVID-19 pneumonia, determining the severity of the disease, and predicting the related mortality. COVID-19 vaccines may affect laboratory parameters and cause less pneumonia on thoracic CT than in unvaccinated individuals.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

BACKGROUND

Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).

OBJECTIVES

To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.

METHODS

The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.

RESULTS

A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.

CONCLUSION

We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

Chest computed tomography of suspected COVID-19 pneumonia in the Emergency Department: comparative analysis between patients with different vaccination status

Purpose

To identify differences in chest computed tomography (CT) of the symptomatic coronavirus disease 2019 (COVID-19) population according to the patients' severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination status (non-vaccinated, vaccinated with incomplete or complete vaccination cycle).

Material and methods

CT examinations performed in the Emergency Department (ED) in May-November 2021 for suspected COVID-19 pneumonia with a positive SARS-CoV-2 test were retrospectively included. Personal data were compared for vaccination status. One 13-year experienced radiologist and two 4th-year radiology residents independently evaluated chest CT scans according to CO-RADS and ACR COVID classifications. In possible COVID-19 pneumonia cases, defined as CO-RADS 3 to 5 (ACR indeterminate and typical) by each reader, high involvement CT score (≥ 25%) and CT patterns (presence of ground glass opacities, consolidations, crazy paving areas) were compared for vaccination status.

Results

184 patients with known vaccination status were included in the analysis: 111 non-vaccinated (60%) for SARS-CoV-2 infection, 21 (11%) with an incomplete vaccination cycle, and 52 (28%) with a complete vaccination cycle (6 different vaccine types). Multivariate logistic regression showed that the only factor predicting the absence of pneumonia (CO-RADS 1 and ACR negative cases) for the 3 readers was a complete vaccination cycle (OR = 12.8-13.1compared to non-vaccinated patients, p ≤ 0.032). Neither CT score nor CT patterns of possible COVID-19 pneumonia showed any statistically significant correlation with vaccination status for the 3 readers.

Conclusions

Symptomatic SARS-CoV-2-infected patients with a complete vaccination cycle had much higher odds of showing a negative CT chest examination in ED compared to non-vaccinated patients. Neither CT involvement nor CT patterns of interstitial pneumonia showed differences across different vaccination status.

Assessment of determining factors for severity of NeoCOVIDiabetes in India: A pan India multicentric retrospective study

BACKGROUND AND AIMS

There is a bidirectional relationship between COVID-19 and diabetes. The primary objective of this study was to estimate the prevalence of patients newly detected to have diabetes (NDD) who recovered from COVID-19 in India whilst comparing NDD with patients without diabetes (ND) and those who have known to have diabetes (KD) in terms of glycemic status pre- and post-COVID with disease severity.

MATERIALS & METHODOLOGY

There were 2212 participants enrolled from 15 sites, with 1630 active participants after the respective execution of selection criteria. Data collection was done using a specialized Case Record Form (CRF). Planned statistical analysis and descriptive statistics were concluded for significance between patient groups on various parameters.

RESULT

The differences in age between the study groups were statistically significant. The average blood glucose at COVID-19 onset was significantly higher in KD than in NDD. Significantly more proportion of NDD (83%) had been hospitalized for COVID management when compared to KD (45%) and ND (55%). The NDD group received higher doses of steroids than the other two groups. On average, patients in the NDD group who received at least one vaccination (one dose or two doses) had a higher High-Resolution Computed Tomography (HRCT) score. Patients who had not been vaccinated in ND and KD groups experienced a higher HRCT score.

CONCLUSION

Prospective metabolism studies in post-acute COVID-19 will be required to understand the etiology, prognosis, and treatment opportunities.

Allergic rhinitis associated with the degree of pulmonary involvement due to COVID-19 in patients from a peruvian general hospital

OBJECTIVES.

To evaluate the association between allergic rhinitis and the degree of pulmonary involvement in patients with COVID-19 and to determine the frequencies of the main variables.

MATERIALS AND METHODS.

An observational, cross-sectional and analytical study was carried out by reviewing the medical records of patients diagnosed with COVID-19 from the Cayetano Heredia National Hospital between 2020 and 2021. We obtained information regarding the history of allergic rhinitis; pulmonary involvement was assessed by non-contrast tomography results using the chest computed tomography (CT) score. Data regarding sociodemographic and clinical variables was also obtained. Both crude (PR) and adjusted (aPR) prevalence ratios with their respective 95% confidence intervals (CI) were estimated. We also used a generalized linear Poisson family model with log link function and robust variances.

RESULTS.

We evaluated 434 patients, who were mostly male, older than 60 years and had no relevant medical history. Of these, 56.2% had a history of allergic rhinitis and 43.1% had moderate to severe pulmonary involvement. The adjusted regression model showed that the history of allergic rhinitis reduced the severity of COVID-19 according to the pulmonary involvement assessed by the CT score (aPR: 0.70; 95%CI: 0.56-0.88; p=0.002).

CONCLUSIONS.

The history of allergic rhinitis resulted in a 30.0% decrease in COVID-19 severity according to the CT score in hospitalized patients.

Association between lung opacities and visceral fat in COVID-19 patients

Purpose

To assess the relationship between the amount of the epigastric visceral fat area and the severity of pneumonia in the course of COVID-19 using chest computed tomography (CT) examinations.

Material and methods

177 patients (54 female), with COVID-19 infection were included. A routine chest CT was performed to assess the severity of pneumonia. The affected lung tissue as well as semi-quantitative scales such as the Chest CT Score and Total Opacity Score were calculated using SyngoVia VB30A CT Pneumonia Analysis software. The epigastric region area of visceral fat (L1) was also determined.

Results

The mean value of the visceral adipose tissue area was 196.23 ± 101.36 cm². The area of adipose tissue significantly correlated with the percentage of the affected lung tissue (r = 0.1476; p = 0.050), the Chest CT Score (r = 0.2086; p = 0.005), and the Total Opacity Score (r = 0.1744; p = 0.200). The mean area of adipose tissue in the age group ≥ 65 years was 216.13 ± 105.19 cm², while in the group < 65 years, it was 169.18 ± 89.69 cm². This difference was statistically significant (p = 0.002).

Conclusions

The study showed a relationship between the area of visceral adipose tissue and the degree of lung inflammation in COVID-19 disease in patients under 65 years of age.

Lactate dehydrogenase and PaO2/FiO2 ratio at admission helps to predict CT score in patients with COVID-19: An observational study

INTRODUCTION

Since the beginning of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic an important tool for patients with Coronavirus Disease 2019 (COVID-19) has been the computed tomography (CT) scan, but not always available in some settings The aim was to find a cut-off that can predict worsening in patients with COVID-19 assessed with a computed tomography (CT) scan and to find laboratory, clinical or demographic parameters that may correlate with a higher CT score.

METHODS

We performed a multi-center, observational, retrospective study involving seventeen COVID-19 Units in southern Italy, including all 321 adult patients hospitalized with a diagnosis of COVID-19 who underwent at admission a CT evaluated using Pan score.

RESULTS

Considering the clinical outcome and Pan score, the best cut-off point to discriminate a severe outcome was 12.5. High lactate dehydrogenase (LDH) serum value and low PaO2/FiO2 ratio (P/F) resulted independently associated with a high CT score. The Area Under Curve (AUC) analysis showed that the best cut-off point for LDH was 367.5 U/L and for P/F 164.5. Moreover, the patients with LDH> 367.5 U/L and P/F < 164.5 showed more frequently a severe CT score than those with LDH< 367.5 U/L and P/F> 164.5, 83.4%, vs 20%, respectively.

CONCLUSIONS

A direct correlation was observed between CT score value and outcome of COVID-19, such as CT score and high LDH levels and low P/F ratio at admission. Clinical or laboratory tools that predict the outcome at admission to hospital are useful to avoiding the overload of hospital facilities.

The role of Lung Ultrasound in the diagnosis of SARS-COV-2 disease in pregnant women

AIM

To evaluate the role of lung ultrasound (LUS) in recognizing lung abnormalities in pregnant women affected by COVID-19 pneumonia.

MATERIALS AND METHODS

An observational study analyzing LUS patterns in 60 consecutively enrolled pregnant women affected by COVID-19 infection was performed. LUS was performed by using a standardized protocol by Soldati et al. The scoring system of LUS findings ranged from 0 to 3 in increasing alteration severity. The highest score obtained from each landmark was reported and the sum of the 12 zones examined was calculated.

RESULTS

Patients were divided into two groups: 26 (43.3%) patients with respiratory symptoms and 32 (53.3%) patients without respiratory symptoms; 2 patients were asymptomatic (3.3%). Among the patients with respiratory symptoms 3 (12.5%) had dyspnea that required a mild Oxygen therapy. A significant correlation was found between respiratory symptoms and LUS score (p < 0.001) and between gestational weeks and respiratory symptoms (p = 0.023). Regression analysis showed that age and respiratory symptoms were risk factors for highest LUS score (p < 0.005).

DISCUSSION

LUS can affect the clinical decision course and can help in stratifying patients according to its findings. The lack of ionizing radiation and its repeatability makes it a reliable diagnostic tool in the management of pregnant women.

Clinical Characteristics and Predictors of In-Hospital Mortality of Patients Hospitalized with COVID-19 Infection

Background: The identification of parameters that would serve as predictors of prognosis in COVID-19 patients is very important. In this study, we assessed independent factors of in-hospital mortality of COVID-19 patients during the second wave of the pandemic. Material and methods: The study group consisted of patients admitted to two hospitals and diagnosed with COVID-19 between October 2020 and May 2021. Clinical and demographic features, the presence of comorbidities, laboratory parameters, and radiological findings at admission were recorded. The relationship of these parameters with in-hospital mortality was evaluated. Results: A total of 1040 COVID-19 patients (553 men and 487 women) qualified for the study. The in-hospital mortality rate was 26% across all patients. In multiple logistic regression analysis, age ≥ 70 years with OR = 7.8 (95% CI 3.17−19.32), p < 0.001, saturation at admission without oxygen ≤ 87% with OR = 3.6 (95% CI 1.49−8.64), p = 0.004, the presence of typical COVID-19-related lung abnormalities visualized in chest computed tomography ≥40% with OR = 2.5 (95% CI 1.05−6.23), p = 0.037, and a concomitant diagnosis of coronary artery disease with OR = 3.5 (95% CI 1.38−9.10), p = 0.009 were evaluated as independent risk factors for in-hospital mortality. Conclusion: The relationship between clinical and laboratory markers, as well as the advancement of lung involvement by typical COVID-19-related abnormalities in computed tomography of the chest, and mortality is very important for the prognosis of these patients and the determination of treatment strategies during the COVID-19 pandemic.