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.

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.

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 severity of COVID-19 infections varies among individuals, prompting research into factors that may influence outcomes. Numerous studies have investigated the conditions that influence the intensity of illness caused by COVID-19. These factors include the Interferon-Induced Transmembrane Protein 3 (IFITM3) rs12252 polymorphism. We investigate whether the polymorphism rs12252 plays a role within our population and observe the differences in other parameters between the mild and severe forms of the disease.

The observational study examining the IFTIM3 rs12252 polymorphism based on the level of COVID-19 severity and differences between inflammatory markers. The study included 51 participants, with 31 severe and 20 mild cases.

The average age of participants was 54 years, and 16.1% of patients with severe symptoms were diagnosed with the AG genotype. Patients showing serious symptoms had significantly higher ESR, CRP, Fibrinogen, LDH, and D-dimer levels than those with mild symptoms.

This study discovered a notable correlation between the G allele of IFITM3 rs12252, inflammatory markers, CT scan score, and COVID-19 severity.

The use of lung ultrasound (LUS) has recently become vital in the diagnosis and prognosis of various respiratory diseases. Its role in COVID-19 requires further investigation.

Twenty-five consecutive, non-ICU hospitalized COVID-19 patients were included. LUS was performed on admission and sequentially every 3 days at 8 points in the chest. Based on the LUS findings a score was designed. Logarithmic regression models and ROC curve analysis were applied.

A statistically significant positive correlation was found between LUS score at admission and the severity of SARS-COV-2 infection. Higher LUS score was significantly associated with lower PaO2/FiO2 ratio, use of HFNC, longer hospitalization and greater extent of chest CT infiltrates. A significant association between LUS score and risk of death or intubation or HFNC was found. For one point of increase in the score, risk of death or intubation or HFNC increased 1.93-fold (95% CI 1.02 to 3.65). The predictive role of the score was very satisfactory (area under the ROC curve = 0.87).

Lung ultrasound findings were significantly positively associated with clinical and radiological markers of severity of SARS-CoV-2 pneumonia. It therefore constitutes a promising and reliable technique for assessing pneumonia, comparable to chest CT.

The variable origins of persistent breathlessness after coronavirus disease 2019 (COVID-19) have hindered efforts to decipher the immunopathology of lung sequelae. Here we analyzed hundreds of cellular and molecular features in the context of discrete pulmonary phenotypes to define the systemic immune landscape of post-COVID lung disease. Cluster analysis of lung physiology measures highlighted two phenotypes of restrictive lung disease that differed according to their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5+CD95+CD8+ T cell perturbations in milder lung disease but attenuated T cell responses hallmarked by elevated CXCL13 in more severe disease. Distinct sets of cells, mediators and autoantibodies distinguished each restrictive phenotype and differed from those of patients without substantial lung involvement. These differences were reflected in divergent T cell-based type 1 networks according to the severity of lung disease. Our findings, which provide an immunological basis for active lung injury versus advanced disease after COVID-19, might offer new targets for treatment.

The clinical spectrum of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is broad; it can range from asymptomatic cases to mild upper respiratory tract illness, respiratory failure, and severe multiorgan failure resulting in death. Therefore, it is important to identify the clinical course of the disease and the factors associated with mortality.

The aim of this study is to identify the risk factors associated with the severity of the disease, intensive care unit admission, and mortality in COVID-19 patients.

A total of 1075 patients with clinical and radiological findings compatible with COVID-19 pneumonia and positive SARS-CoV-2 PCR were selected and retrospectively screened. All included patients were classified according to the 7th edition of the 2019 Coronavirus Disease Guidelines published by the National Health Commission of China.

It was observed that elevated white blood count (WBC) increased the severity of COVID-19 by 3.26 times and the risk of intensive care unit (ICU) admission by 3.47 times. Patients with high D-dimer levels had a 91% increased risk, and those with high fibrinogen levels had a 2.08 times higher risk of severe disease. High C-reactive protein (CRP) values were found to increase disease severity by 6.89 times, mortality by 12.84 times, and ICU admission by 3.37 times.

Identifying the factors associated with disease severity, ICU admission, and mortality in COVID-19 patients could help reduce disability and mortality rates in pandemics.

Background and Objectives: Long COVID as a condition typically manifests itself three months after the initial onset of SARS-CoV-2 infection, with symptoms persisting for a minimum of two months. The aim of the present research was to identify potential predictors of post-COVID-19 syndrome (long COVID-19) and to evaluate factors associated with the presence of post-COVID-19 interstitial lung disease and functional decline. Materials and Methods: 210 patients hospitalized for confirmed SARS-CoV-2 infections mild, moderate, severe, and critical form have been evaluated at 3, 6 and twelve months. Results: Among them only one has been with a suspicion of interstitial lung disease after one year, the rest had no change in the lung. No risk factor from the baseline/3-month or 6-month evaluations significantly influenced patients' status at 12 months. The longer the duration of hospitalization, the lower the FVC and DLCO were at 3 months, but the longer the duration of hospitalization, the higher the number of symptoms at 3 months and 6 months. In a multivariate linear regression analysis, the number of hospitalization days remained statistically significant only for the number of symptoms at 3 months and 6 months. Conclusions: Long COVID seems to be related to the severity of the initial disease, and among the most persistent. Post-COVID-19 interstitial lung disease does not seem to be a frequent entity, as in our study only 0.5% out of 210 patients had it.

COVID-19-related persistent olfactory dysfunction (OD) presents remarkable interindividual differences, and little is known about the host genetic factors that are involved in its etiopathogenesis. The goal of this study was to explore the genetic factors underpinning COVID-19-related OD through the analysis of Whole Genome Sequencing data of 153 affected subjects, focusing on genes involved in antiviral response regulation. An innovative approach was developed, namely the assessment of the association between a "gene score", defined as the ratio of the number of homozygous alternative variants within the gene to its length, and participants' olfactory function. The analysis highlighted how an increased gene score in the ACE2 gene is associated with a worse olfactory performance, while an increased gene score in the IFI44 and NDUFAF4 genes is associated with a better olfactory function. Considering the physiological role of the proteins encoded by these genes, it can be hypothesized that a reduced expression of ACE2 may be associated with a protracted and severe inflammatory response in the olfactory epithelium, thus worsening patients' smell abilities. Conversely, an increased gene score in IFI44 and NDUFAF4 might be associated with a decreased inflammatory response, thus correlating with a better olfactory performance. Overall, this study identified new host genetic factors that may play a pivotal role in determining COVID-19-related OD heterogeneity, possibly enabling more personalized and effective clinical management for affected individuals.

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.

To investigate the serum surfactant protein D (SP-D) level required to determine the diagnosis and prognosis of coronavirus disease 2019 (COVID-19), and create a new scale for disease prognosis prediction.

This study was conducted among 64 patients with COVID-19 symptoms and 16 healthy volunteers. The participants were assessed by comparing "Controls/Patients", "PCR-negative/PCR-positive", "Simple COVID-19/Acute respiratory distress syndrome (ARDS)-accompanied COVID-19", "Mild ARDS/Moderate-severe ARDS", and "Survived/Dead" subgroups. Serum SP-D levels and pulmonary infiltration area (volume and percentage) measurements on CT were compared between the groups. A new scale, the "CHARISMA Score", was created by logistic regression method for a complete prognosis assessment. This includes confusion, heart rate, age, respiratory rate, percentage of infiltration on CT, serum SP-D level, mean arterial pressure and SaO2.

Serum SP-D levels differed significantly across the groups. There was a strong correlation between SP-D levels and infiltration volumes. CHARISMA scores were higher in the severe than in the mild ARDS group and in patients who died than in survivors. In the receiver operating characteristic curve analysis of the CHARISMA scores, a cutoff value of 4 indicated mortality.

Serum SP-D levels can be used to determine COVID-19 diagnosis and prognosis, and the CHARISMA score can be used to predict prognosis and mortality risk in patients with COVID-19 pneumonia.

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.

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.

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).

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.

Objectives Long-term consequences of COVID-19 in the post-pandemic era are still being investigated. Despite the growing data on COVID-19, there remains a lack of information regarding predictors of poor outcomes among chronic obstructive pulmonary disease (COPD) patients with COVID-19.  Methods A single-center prospective cohort study was conducted with a total of 172 adult COPD patients with COVID-19 pneumonia included. Univariable and multivariable analyses were conducted to define independent factors associated with ICU admission, need for mechanical ventilation, or all-cause mortality in 30 days following COVID-19 pneumonia. receiver operating characteristic (ROC) analyses evaluated the diagnostic performance of the independent predictors. Results Out of all the patients, 73 (42.4%) experienced poor outcomes. Lower forced expiratory volume in the first second (FEV1) (OR= 0.949, p= 0.004), higher radiological severity score (OR= 1.15, p= 0.004), and lower respiratory rate oxygenation (ROX) index (OR= 0.867, p<0.001) were independently associated with poor outcomes. ROX index was found a better predictor of poor outcome than oxygen saturation (SaO2)/fraction of inspired oxygen (FiO2) and partial pressure of oxygen (PaO2)/FiO2 ratio (area under the curve (AUC)=0.80 vs. 0.73, p=0.01; AUC= 0. 80 vs. 0.63 p=0.001). A significant decline in FEV1 values compared to baseline values was observed (55.9 ±12.9 vs. 62.1±10.0; p<0.001). Conclusion Lower baseline FEV1, higher COVID-19 radiological severity score, and lower ROX index are strongly associated with poor outcomes in COPD patients with COVID-19.

The aim was to compare the computed tomography (CT) semi-quantitative severity scoring (CT-SS) system assessments of COVID-19 pneumonia on initial and follow-up examinations according to the presence of comorbidities. Of the 278 real-time reverse transcription-polymerase chain reaction positive patients, 108 with a follow-up CT scan were evaluated. Then, all CT images were independently reviewed for CT-SS analysis by two reviewers. Reviewers were unaware of the patient laboratory and clinical findings. A quarter of patients had negative findings on their initial CTs. Sixty-one (56.4%) patients showed progression. Disease progression was more frequently observed in patients with type 2 diabetes mellitus (DM) and malignancies (p=0.044 and p=0.019, respectively). Follow-up CTs of patients with comorbidities, especially those with cardiovascular disease (56.4%) and type 2 DM (70.0%), demonstrated an increased frequency of diffuse involvement. The white lung sign was more frequently observed in patients with malignancies (60.0%). In this study, COVID-19 patients with comorbidity showed a higher rate of disease progression than those without comorbidity. Patients with comorbidities more frequently had severe CT findings with high CT-SS. These findings may serve as a guide in the COVID-19 pneumonia follow-up and treatment.

The World Health Organization has declared "with great hope" an end to COVID-19 as a public health emergency. The vaccination drive that started in December 2020 played a crucial role in controlling the pandemic. However, the pace at which COVID-19 vaccines were developed and deployed for general population use led to vaccine hesitancy, largely owing to concerns regarding the safety and efficacy of the vaccines. Radiology has been instrumental in demonstrating the extent of pulmonary involvement and identification of the complications of COVID-19, and the same holds true for vaccine-related complications. This review summarizes the existing body of radiological literature regarding the efficacy, adverse events, and imaging pitfalls that accompany the global rollout of various COVID-19 vaccines.

Coronavirus disease 2019 (COVID-19) still poses a threat to people's physical and mental health. We proposed a new semi-quantitative visual classification method for COVID-19, and this study aimed to evaluate the clinical usefulness and feasibility of lung field-based severity score (LFSS).

This retrospective study included 794 COVID-19 patients from two hospitals in China between December 2022 and January 2023. Six lung fields on the axial computed tomography (CT) were defined. LFSS and eighteen clinical characteristics were evaluated. LFSS was based on summing up the parenchymal opacification involving each lung field, which was scored as 0 (0%), 1 (1-24%), 2 (25-49%), 3 (50-74%), or 4 (75-100%), respectively (range of LFSS from 0 to 24). Total pneumonia burden (TPB) was calculated using the U-net model. The correlation between LFSS and TPB was analyzed. After performing logistic regression analysis, an LFSS-based model, clinical-based model and combined model were developed. Receiver operating characteristic curves were used to evaluate and compare the performance of three models.

LFSS, age, chronic liver disease, chronic kidney disease, white blood cell, neutrophils, lymphocytes and C-reactive protein differed significantly between the non-critical and critical group (all P<0.05). There was a strong positive correlation of LFSS and TPB (Pearson correlation coefficient =0.767, P<0.001). The area under curves of LFSS-based model, clinical-based model and combined model were 0.799 [95% confidence interval (CI): 0.770-0.827], 0.758 (95% CI: 0.727-0.788), and 0.848 (95% CI: 0.821-0.872), respectively.

The LFSS derived from chest CT may be a potential new tool to help identify COVID-19 patients at high risk of progressing to critical disease.

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, existing detection methods for COVID-19 severity assessment often lack consideration of organs and tissues other than the lungs, which limits the accuracy and reliability of these predictive models.

The retrospective study included data from 515 COVID-19 patients (Cohort 1, n=415; Cohort 2, n=100) from two centers (Shanghai Public Health Center and Brazil Niteroi Hospital) between January 2020 and July 2020. Firstly, a three-stage EAT segmentation method was proposed by combining object detection and segmentation networks. Lung and EAT radiomics features were then extracted, and feature selection was performed. Finally, a hybrid model, based on seven machine learning models, was built for detecting COVID-19 severity. The hybrid model's performance and uncertainty were evaluated in both internal and external validation cohorts.

For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (±0.011) and 0.968 (±0.005), respectively. For severity detection, the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) of the hybrid model increased by 0.09 (p<0.001), 19.3 % (p<0.05), and 18.0 % (p<0.05) in the internal validation cohort, and by 0.06 (p<0.001), 18.0 % (p<0.05) and 18.0 % (p<0.05) in the external validation cohort, respectively. Uncertainty and radiomics features analysis confirmed the interpretability of increased certainty in case prediction after inclusion of EAT features.

This study proposed a novel three-stage EAT extraction method. We demonstrated that adding EAT radiomics features to a COVID-19 severity detection model results in increased accuracy and reduced uncertainty. The value of these features was also confirmed through feature importance ranking and visualization.

Among immunocompromised hosts, leukemia patients, and hematopoietic cell transplant recipients are particularly vulnerable, facing challenges in balancing coronavirus disease 2019 (COVID-19) management with their underlying conditions. In this How I Treat article, we discuss how we approach severe acute respiratory syndrome coronavirus 2 infections in daily clinical practice, considering the existing body of literature and for topics where the available data are not sufficient to provide adequate guidance, we provide our opinion based on our clinical expertise and experience. Diagnostic approaches include nasopharyngeal swabs for polymerase chain reaction testing and chest computed tomography scans for symptomatic patients at risk of disease progression. Preventive measures involve strict infection control protocols and prioritizing vaccination for both patients and their families. Decisions regarding chemotherapy or hematopoietic cell transplantation in leukemia patients with COVID-19 require careful consideration of factors such as COVID-19 severity and treatment urgency. Treatment protocols include early initiation of antiviral therapy, with nirmatrelvir/ritonavir or remdesivir. For cases of prolonged viral shedding, distinguishing between viable and non-viable viruses remains challenging but is crucial for determining contagiousness and guiding management decisions. Overall, individualized approaches considering immune status, clinical presentation, and viral kinetics are essential for effectively managing COVID-19 in leukemia patients.

Broncho-alveolar lavage (BAL) is indicated in cases of uncertain diagnosis but high suspicion of Sars-Cov-2 infection allowing to collect material for microbiological culture to define the presence of coinfection or super-infection. This prospective study investigated the correlation between chest computed tomography (CT) findings, Covid-19 Reporting and Data System score, and clinical outcomes in Coronavirus disease 2019 (Covid-19) patients who underwent BAL with the aim of predicting outcomes such as lung coinfection, respiratory failure, and hospitalization length based on chest CT abnormalities. Study population included 34 patients (range 38-90 years old; 20 males, 14 females) with a positive nucleic acid amplification test for Covid-19 infection, suitable BAL examination, and good quality chest CT scan in the absence of lung cancer history. Pulmonary coinfections were found in 20.6% of patients, predominantly caused by bacteria. Specific correlations were found between right middle lobe involvement and pulmonary co-infections. Severe lung injury (PaO2/FiO2 ratio of 100-200) was associated with substantial involvement of right middle, right upper, and left lower lobes. No significant correlation was found between chest CT findings and inflammatory markers (C-reactive protein, procalcitonin) or hospitalization length of stay. Specific chest CT patterns, especially in right middle lobe, could serve as indicators for the presence of co-infections and disease severity in noncritically ill Covid-19 patients, aiding clinicians in timely interventions and personalized treatment strategies.

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.

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.

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.

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.

In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading.

The fact that some SARS-CoV-2 pneumonia patients benefit from changing body position, and some from continuous positive airways pressure (CPAP), indicates the functional character of hypoxia. We hypothesize that such effects could be explained by the closure of small airways. To prove the hypothesis, we evaluated the patency of small airways in 30 oxygen-dependent, spontaneously breathing patients with SARS-CoV-2 pneumonia during their hospital stay using the FOT method and then compared the results with data obtained three months later. During the acute period, total resistance (R5) and peripheral resistance (R5-20) rose above the upper limit of normal (ULN) in 28% and 50% of all patients, respectively. Reactance indices X5, AX and Fres exceeded ULN in 55%, 68% and 66% of cases. Significant correlations were observed between PaO2/FiO2, the time spent in the hospital and R5, X5, AX and Fres. After 3 months, 18 patients were re-examined. During the hospital stay, 11 of them had risen above the upper limit of normal (ULN), for both resistance (R5-20) and reactance (X5, AX) values. Three months later, ULN for R5-20 was exceeded in only four individuals, but ULN for X5 and AX was exceeded in five individuals. Lung function examination revealed a combined restrictive/obstructive ventilatory failure and reduced CO transfer factor. We interpret these changes as lung tissue remodeling due to the process of fibrosis. We conclude that during acute period of SARS-CoV-2 pneumonia, dilated pulmonary blood vessels and parenchymal oedema induce functional closure of small airways, which in turn induce atelectasis with pulmonary right-to-left shunting, followed by the resulting hypoxemia.

(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.

To evaluate the association of a validated chest computed tomography (Chest-CT) severity score in COVID-19 patients with their respiratory outcome in the Intensive Care Unit.

A single-center, prospective study evaluated patients with positive RT-PCR for COVID-19, who underwent Chest-CT and had a final COVID-19 clinical diagnosis needing invasive mechanical ventilation in the ICU. The admission chest-CT was evaluated according to a validated Chest-CT Severity Score in COVID-19 (Chest-CTSS) divided into low ≤50% (<14 points) and >50% high (≥14 points) lung parenchyma involvement. The association between the initial score and their pulmonary clinical outcomes was evaluated.

121 patients were clustered into the > 50% lung involvement group and 105 patients into the ≤ 50% lung involvement group. Patients ≤ 50% lung involvement (<14 points) group presented lower PEEP levels and FiO2 values, respectively GEE P = 0.09 and P = 0.04. The adjusted COX model found higher hazard to stay longer on invasive mechanical ventilation HR: 1.69, 95% CI, 1.02-2.80, P = 0.042 and the adjusted logistic regression model showed increased risk ventilator-associated pneumonia OR = 1.85 95% CI 1.01-3.39 for COVID-19 patients with > 50% lung involvement (≥14 points) on Chest-CT at ICU admission.

COVID-19 patients with >50% lung involvement on Chest-CT admission presented higher chances to stay longer on invasive mechanical ventilation and more chances to developed ventilator-associated pneumonia.

In treating acute hypoxemic respiratory failure (AHRF) caused by coronavirus disease 2019 (COVID-19), clinicians choose respiratory therapies such as low-flow nasal cannula oxygenation, high-flow nasal cannula oxygenation, or mechanical ventilation after assessment of the patient's condition. Chest computed tomography (CT) imaging contributes significantly to diagnosing COVID-19 pneumonia. However, the costs and potential harm to patients from radiation exposure need to be considered. This study was performed to predict the quantitative extent of COVID-19 acute lung injury using clinical indicators such as an oxygenation index and blood test results.

We analyzed data from 192 patients with COVID-19 AHRF. Multiple logistic regression was used to determine correlations between the lung infiltration volume (LIV) and other pathophysiological or biochemical laboratory parameters.

Among 13 clinical parameters, we identified the oxygen saturation/fraction of inspired oxygen ratio (SF ratio) and serum lactate dehydrogenase (LD) concentration as factors associated with the LIV. In the binary classification of an LIV of ≥20 % or not and with the borderline LD = 2.2 × [SF ratio]-182.4, the accuracy, precision, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.828, 0.818, 23.400, and 0.870, respectively.

These data suggest that acute lung injury due to COVID-19 pneumonia can be estimated using the SF ratio and LD concentration without a CT scan. These findings may provide significant clinical benefit by allowing clinicians to predict acute lung injury levels using simple, minimally invasive assessment of oxygenation capacity and biochemical blood tests.

The Omicron strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally. However, it remains uncertain whether variable airflow limitation (VAL) occurs during the recovery phase after contracting the Omicron variant. To address this question, we conducted a study to examine the occurrence of VAL in patients infected with the Omicron variant (BA.1) of SARS-CoV-2, and we also investigated the potential risk factors associated with this phenomenon.

We summarized and analyzed data taken from the electronic health records of recovering patients who had contracted the Omicron variant. The information was obtained from the Shuixi Branch of our Hospital during the period from January 22 to February 24, 2022. We focused on examining the occurrence of VAL and identifying the associated risk factors among these patients.

In this case-control study, a total of 176 patients were enrolled. The occurrence of VAL was observed in 9.66% (17 individuals). Patients with VAL showed significantly elevated levels of the modified Borg dyspnea score, daytime cough score, night-time cough score, chest computed tomography severity score, and Treg ratio compared to those without VAL. Additionally, patients with VAL had a lower 6MWD value compared to those without it. Logistic regression analysis demonstrated that the modified Borg dyspnea score independently increased the risk of Omicron infection with VAL, with an odds ratio of 3.375, and a 95% confidence interval ranging from 1.537 to 7.408, with a P-value of .002.

There is a possibility of experiencing VAL in certain patients recovering from the SARS-CoV-2 Omicron variant infection. The modified Borg dyspnea score has been identified as a standalone risk factor for the occurrence of VAL in SARS-CoV-2 Omicron infection.

Evaluations have shown that the severity of pulmonary involvement is very important in the mortality rate of patients with coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis.

This study was a cross-sectional study with a sample size of 197 patients, including all patients admitted to Rasoul Akram Hospital, with positive polymerase chain reaction, to investigate the relationship between computed tomography (CT) severity score and mortality. The demographic data and CT scan findings (including the pattern, side, and distribution of involvement), co-morbidities, and lab data were collected. Finally, gathered data were analyzed by SPSS-26.

119 (60.4%) patients were male, and 78 (39.6%) were female. The mean age was 58.58 ± 17.3 years. Totally, 61 patients died; of those, 41 (67.2%) were admitted to the intensive care unit (ICU), so there was a significant relation between death and ICU admission (P value = 0.000). Diabetes was the most common co-morbidity, followed by hypertension and IHD. There was no significant relation between co-morbidities and death (P value = 0.13). The most common patterns of CTs were interlobular septal thickening and ground glass opacities, and a higher CT severity score was in the second week from the onset of symptoms, which was associated with more mortality (P value < 0.05).

Our study showed that a patient with a higher CT severity score of the second week had a higher risk of mortality. Also, association of the CT severity score, laboratory data, and symptoms could be applicable in predicting the patient's condition.

The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.

The variable etiology of persistent breathlessness after COVID-19 have confounded efforts to decipher the immunopathology of lung sequelae. Here, we analyzed hundreds of cellular and molecular features in the context of discrete pulmonary phenotypes to define the systemic immune landscape of post-COVID lung disease. Cluster analysis of lung physiology measures highlighted two phenotypes of restrictive lung disease that differed by their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5+CD95+ CD8+ T-cell perturbations in mild-to-moderate lung disease, but attenuated T-cell responses hallmarked by elevated CXCL13 in more severe disease. Distinct sets of cells, mediators, and autoantibodies distinguished each restrictive phenotype, and differed from those of patients without significant lung involvement. These differences were reflected in divergent T-cell-based type 1 networks according to severity of lung disease. Our findings, which provide an immunological basis for active lung injury versus advanced disease after COVID-19, might offer new targets for treatment.

We address the lack of research regarding aerodynamic events behind respiratory distress at COVID-19. The use of chest CT enables quantification of pneumonia extent; however, there is a paucity of data regarding the impact of airflow changes. We reviewed 31 COVID-19 patients who were admitted in March 2020 with varying severity of pulmonary disease. Lung volumes were segmented and measured on CT images and patient-specific models of the lungs were created. Incompressible, laminar, and three-dimensional Navier-Stokes equations were used for the fluid dynamics (CFD) analyses of ten patients (five mild, five pneumonia). Of 31 patients, 17 were female, 18 had pneumonia, and 2 were deceased. Effective lung volume decreased in the general group, but the involvement of the right lung was prominent in dyspnea patients. CFD analyses revealed that the mass flow distribution was significantly distorted in pneumonia cases with diminished flow rate towards the right lung. In addition, the distribution of flow parameters showed mild group had less airway resistance with higher velocity (1.228 m/s vs 1.572 m/s) and higher static pressure values at airway branches (1.5112 Pa vs 1.3024 Pa). Therefore, we conclude that airway resistance and mass flow rate distribution are as important as the radiological involvement degree in defining the disease severity.

The substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows.

This multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation.

Of 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%.

In addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis.

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.

To investigate the performance of a novel radiological-metabolic scoring (RM-S) system to predict mortality and intensive care unit (ICU) requirements among COVID-19 patients and to compare performance with the chest computed-tomography severity-scoring (C-CT-SS). The RMS was created from scoring systems such as visual coronary-artery-calcification scoring (V-CAC-S), hepatic-steatosis scoring (HS-S) and pancreatic-steatosis scoring (PS-S).

Between May 2021 and January 2022, 397 patients with COVID-19 were included in this retrospective cohort study. All demographic, clinical and laboratory data and chest CT images of patients were retrospectively reviewed. RM-S, V-CAC-S, HS-S, PS-S and C-CT-SS scores were calculated, and their performance in predicting mortality and ICU requirement were evaluated by univariate and multivariable analyses.

A total of 32 (8.1%) patients died, and 77 (19.4%) patients required ICU admission. Mortality and ICU admission were both associated with older age (p < 0.001). Sex distribution was similar in the deceased vs. survivor and ICU vs. non-ICU comparisons (p = 0.974 and p = 0.626, respectively). Multiple logistic regression revealed that mortality was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe RM-S category (p = 0.010), while ICU requirement was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe V-CAC-S category (p = 0.010).

RM-S, C-CT-SS, and V-CAC-S are useful tools that can be used to predict patients with poor prognoses for COVID-19. Long-term prospective follow-up of patients with high RM-S scores can be useful for predicting long COVID.

Patients with auto-antibodies neutralizing type I interferons (anti-IFN auto-Abs) are at risk of severe forms of coronavirus disease 19 (COVID-19). The chest computed tomography (CT) scan characteristics of critically ill COVID-19 patients harboring these auto-Abs have never been reported.

Bicentric ancillary study of the ANTICOV study (observational prospective cohort of severe COVID-19 patients admitted to the intensive care unit (ICU) for hypoxemic acute respiratory failure between March 2020 and May 2021) on chest CT scan characteristics (severity score, parenchymal, pleural, vascular patterns). Anti-IFN auto-Abs were detected using a luciferase neutralization reporting assay. Imaging data were collected through independent blinded reading of two thoracic radiologists of chest CT studies performed at ICU admission (± 72 h). The primary outcome measure was the evaluation of severity by the total severity score (TSS) and the CT severity score (CTSS) according to the presence or absence of anti-IFN auto-Abs.

Two hundred thirty-one critically ill COVID-19 patients were included in the study (mean age 59.5 ± 12.7 years; males 74.6%). Day 90 mortality was 29.5% (n = 72/244). There was a trend towards more severe radiological lesions in patients with anti-IFN auto-Abs than in others, not reaching statistical significance (median CTSS 27.5 (21.0-34.8) versus 24.0 (19.0-30.0), p = 0.052; median TSS 14.5 (10.2-17.0) versus 12.0 (9.0-15.0), p = 0.070). The extra-parenchymal evaluation found no difference in the proportion of patients with pleural effusion, mediastinal lymphadenopathy, or thymal abnormalities in the two populations. The prevalence of pulmonary embolism was not significantly different between groups (8.7% versus 5.3%, p = 0.623, n = 175).

There was no significant difference in disease severity as evaluated by chest CT in severe COVID-19 patients admitted to the ICU for hypoxemic acute respiratory failure with or without anti-IFN auto-Abs.

Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.

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.

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.

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.

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.

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

Lung lesions can increase the CT number and affect the water-equivalent diameter (Dw), Dw-based conversion factor (CFw), and Dw-based size-specific dose estimate (SSDEw). We evaluated the effect of COVID-19 lesions and total severity score (TSS) on radiation dose considering the effect of automatic tube current modulation (ATCM) and fixed tube current (FTC). A total of 186 chest CT scans were categorised into five TSS groups, including healthy, minimal, mild, moderate and severe. The effective diameter (Deff), Dw, CFw, Deff-based conversion factor (CFeff), volume computed tomography dose index (CTDIVol), pathological dose impact factor (PDIF) 1 and SSDEw were calculated. TSS was correlated with Dw (r = 0.29, p-value = 0.001), CTDIVol (ATCM) (r = 0.23, p = 0.001) and PDIF (r = - 0.51, p-value = 0.001). $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (FTC) was significantly different among all groups. $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (ATCM) was greater for moderate (13%) and mild (14%) groups. Increasing TSS increase the Dw and causes a decrease in CFw and $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (FTC), and can increase $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (ATCM) in some Dw ranges.