Prevalence and Determinants of COVID-19 Vaccine Hesitancy Among the Ethiopian Population: A Systematic Review

Introduction

Although vaccination is the most effective way to end the COVID-19 pandemic, there are growing concerns that vaccine hesitancy may undermine its effectiveness. In Ethiopia, vaccine hesitancy forms a major challenge to the uptake of COVID-19 vaccines. This systematic review examined the prevalence and determinants of the COVID-19 vaccine hesitancy among the Ethiopian population.

Methods

A systematic search of articles was conducted in PubMed, EBSCO, Google Scholar, and Semantic Scholar. Studies that evaluated the prevalence and determinants of COVID-19 vaccine hesitancy in Ethiopia were included. The identified determinants of COVID-19 vaccine hesitancy were analyzed based on the frequency of occurrence in the included studies.

Results

The overall COVID-19 vaccine hesitancy level in the Ethiopian population ranged from 14.1% to 68.7%. The high COVID-19 vaccine hesitancy rate observed in this study was due to contextual factors, individual factors, and vaccine-specific factors. Young age, female sex, living in rural areas, lack of adequate information, and lower education are the most common contextual determinants of the COVID-19 vaccine hesitancy. Lack of awareness about the COVID-19 vaccine, low risk of COVID-19, poor adherence to COVID-19 prevention strategies, and negative attitude towards the COVID-19 vaccine are the most frequently reported personal factors. Common vaccine-specific determinants of COVID-19 vaccine hesitation are the side effects of the vaccine and doubts about its effectiveness of the vaccine.

Conclusion

Our review showed that COVID-19 vaccine hesitancy remains highly prevalent and varied across regions of Ethiopia, with a wide variety of factors associated with it. Potential interventions on the benefits of vaccination and the adverse effects of vaccine rejection are crucial to enhance COVID-19 vaccine uptake among the Ethiopian population.

Delayed COVID‐19‐induced cytokine storm after root canal therapy with favorable response to plasmapheresis, tocilizumab, and methylprednisolone pulses therapy: A case report

COVID‐19 showed different characteristics, and many cases showed clinical manifestations that could not be attributed to other conditions. We present a 22‐year‐old woman who had an uneventful recovery from COVID‐19, and after that, she developed a cytokine storm and a worsening clinical condition 2 days after dental root canal therapy.

Twelve Months with COVID-19: What Gastroenterologists Need to Know

Corona virus disease-19 (COVID-19) is the latest global pandemic. COVID-19 is mainly transmitted through respiratory droplets and, apart from respiratory symptoms, patients often present with gastrointestinal symptoms and liver involvement. Given the high percentage of COVID-19 patients that present with gastrointestinal symptoms (GIS), in this review, we report a practical up-to-date reference for the physician in their clinical practice with patients affected by chronic gastrointestinal (GI) diseases (inflammatory bowel disease, coeliac disease, chronic liver disease) at the time of COVID-19. First, we summarised data on the origin and pathogenetic mechanism of SARS-CoV-2. Then, we performed a literature search up to December 2020 examining clinical manifestations of GI involvement. Next, we illustrated and summarised the most recent guidelines on how to adhere to GI procedures (endoscopy, liver biopsy, faecal transplantation), maintaining social distance and how to deal with immunosuppressive treatment. Finally, we focussed on some special conditions such as faecal-oral transmission and gut microbiota. The rapid accumulation of information relating to this condition makes it particularly essential to revise the literature to take account of the most recent publications for medical consultation and patient care.

Tetraspanin CD53 controls T cell immunity through regulation of CD45RO stability, mobility, and function

T cells depend on the phosphatase CD45 to initiate T cell receptor signaling. Although the critical role of CD45 in T cells is established, the mechanisms controlling function and localization in the membrane are not well understood. Moreover, the regulation of specific CD45 isoforms in T cell signaling remains unresolved. By using unbiased mass spectrometry, we identify the tetraspanin CD53 as a partner of CD45 and show that CD53 controls CD45 function and T cell activation. CD53-negative T cells (Cd53^-/-) exhibit substantial proliferation defects, and Cd53^-/- mice show impaired tumor rejection and reduced IFNγ-producing T cells compared with wild-type mice. Investigation into the mechanism reveals that CD53 is required for CD45RO expression and mobility. In addition, CD53 is shown to stabilize CD45 on the membrane and is required for optimal phosphatase activity and subsequent Lck activation. Together, our findings reveal CD53 as a regulator of CD45 activity required for T cell immunity.

The prognostic value of biomarker levels and chest imaging in patients with COVID-19 presenting to the emergency department

Introduction

We aimed to compare the prognostic value of a quantitative CT severity score with several laboratory parameters, particularly C-reactive protein, Procalcitonin, Neutrophil to lymphocyte ratio, D-dimer, ferritin, lactate dehydrogenase, lactate, troponin and B-type Natriuretic Peptide in predicting in-hospital mortality.

Methods

This was a retrospective chart review study of COVID-19 patients who presented to the Emergency Department of a tertiary care center between February and December 2020. All patients ≥18 years old who tested positive for the COVID-19 real-time reverse transcriptase polymerase chain reaction and underwent CT imaging at presentation were included. The primary outcome was the prognostic ability of CT severity score versus biomarkers in predicting in-hospital mortality.

Results

The AUCs were: D-dimer (AUC: 0.67 95% CI = 0.57–0.77), CT severity score (0.66, 95% CI = 0.55–0.77), LDH (0.66, 95% CI = 0.55–0.77), Pro-BNP (0.65, 95% CI = 0.55–0.76), NLR (0.64, 95% CI = 0.53–0.75) and troponin (0.64, 95% CI = 0.52–0.75). In the stepwise logistic regression, age (OR = 1.07 95% CI = 1.05–1.09), obesity (OR = 2.02 95% CI = 1.25–3.26), neutrophil/lymphocyte ratio (OR = 1.02 95% CI = 1.01–1.04), CRP (OR = 1.01 95% CI = 1.004–1.01), lactate dehydrogenase (OR = 1.003 95% CI = 1.001–1.004) and CT severity score (OR = 1.17 95% CI = 1.12–1.23) were significantly associated with in-hospital mortality.

Conclusion

In summary, CT severity score outperformed several biomarkers as a prognostic tool for covid related mortality. In COVID-19 patients requiring lung imaging, such as patients requiring ICU admission, patients with abnormal vital signs and those requiring mechanical ventilation, the results suggest obtaining and calculating the CT severity score to use it as a prognostic tool. If a CT was not performed, the results suggest using LDH, CRP or NLR if already done as prognostic tools in COVID-19 as these biomarkers were also found to be prognostic in COVID-19 patients.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer “LungCTAnalyzer” and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

Radiological Finding of Crazy-Paving Pattern in COVID-19 Pneumonia

The recent global pandemic of coronavirus disease 2019 (COVID-19) has brought many radiographic findings in other respiratory disease processes. One of these radiological findings is crazy paving. This paper discusses crazy paving in a 75-year-old female with dyspnea, nonproductive cough, pleuritic chest pain, and a polymerase chain reaction (PCR) positive test for COVID-19 infection. Chest CT showed ground-glass opacities and interlobular septal thickening consistent with a crazy-paving appearance. As part of the common CT findings of patients with active COVID-19 infection, crazy paving should prompt the interpreting radiologist to consider COVID-19 pneumonia as part of the differential.

Efficacy of a rehabilitation protocol on pulmonary and respiratory muscle function and ultrasound evaluation of diaphragm and quadriceps femoris in patients with post-COVID-19 syndrome: a series of cases

This study assessed the efficacy of a pulmonary rehabilitation (PR) on pulmonary and respiratory muscle function and thickness of quadriceps femoris and diaphragm of patients with post-COVID-19 syndrome. This series of cases followed nine patients who performed PR twice a week for six weeks. Patients received the following PR program: lung expansion therapy; respiratory muscle training; upper limb strength exercises; aerobic exercises on a treadmill. After the program, we observed increased peak expiratory flow (26.03%), forced vital capacity (FVC) (7.14%), forced expiratory volume in the first second (FEV1) (9.55%), and ratio between FEV1/FVC (6.19%). All parameters of respiratory muscle strength and endurance were improved. Diaphragmatic thickening fraction and thickness of quadriceps femoris also improved, whereas echo intensity of quadriceps reduced. Thus, RP protocol improved pulmonary function, respiratory muscle strength and endurance, and thickness of diaphragm and quadriceps femoris, and reduced echo intensity of quadriceps in patients with post-COVID-19 syndrome.

Citizen Compliance to Prevents COVID 19 after Vaccination

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) has been happening for the past 2 years and makes people bored to follow health protocol. On the other hand, the most effective way to prevent COVID is the health protocol. Vaccines catch people off guard, and there is potential for ignoring health protocols. AIM: The aim of this study is to describe citizen compliance to prevent COVID-19. Consist of wearing a mask, keep distance, and washing hands. METHODS: A descriptive studies with 76 respondents that choosing by simple random sampling. Data obtained by means of direct interviews with respondents. Instrument asks to respondent recorded by Google form. Data were analyzed using frequency and percentage. RESULTS: the most participant agree and strongly agree to prevent COVID-19 consists of wearing a mask, keep distance, and washing hands. Only 1 person declare that not agree with her habits to keep distance to other people. CONCLUSION: Indonesian people have good habits in preventing COVID-19, even though the COVID transmission rate is increasing.

A Vicious Cycle: In Severe and Critically Ill COVID-19 Patients

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is one of the fastest-evolving viral diseases that has instigated a worldwide pandemic. Severe inflammatory syndrome and venous thrombosis are commonly noted in COVID-19 patients with severe and critical illness, contributing to the poor prognosis. Interleukin (IL)-6, a major complex inflammatory cytokine, is an independent factor in predicting the severity of COVID-19 disease in patients. IL-6 and tumor necrosis factor (TNF)-α participate in COVID-19-induced cytokine storm, causing endothelial cell damage and upregulation of plasminogen activator inhibitor-1 (PAI-1) levels. In addition, IL-6 and PAI-1 form a vicious cycle of inflammation and thrombosis, which may contribute to the poor prognosis of patients with severe COVID-19. Targeted inhibition of IL-6 and PAI-1 signal transduction appears to improve treatment outcomes in severely and critically ill COVID-19 patients suffering from cytokine storms and venous thrombosis. Motivated by studies highlighting the relationship between inflammatory cytokines and thrombosis in viral immunology, we provide an overview of the immunothrombosis and immunoinflammation vicious loop between IL-6 and PAI-1. Our goal is that understanding this ferocious circle will benefit critically ill patients with COVID-19 worldwide.

Pulmonary Nodule Clinical Trial Data Collection and Intelligent Differential Diagnosis for Medical Internet of Things

In this paper, the medical Internet of things (IoT) is used to pool data from clinical trials of pulmonary nodules, and on this basis, intelligent differential diagnosis techniques are investigated. A filtered orthogonal frequency division multiplexing model based on polarisation coding is proposed, where the input data are fed to a modulator after polarisation cascade coding, and the system performance is analysed under a medical Internet of things modulated additive Gaussian white noise channel. The above polarisation-coded filtered orthogonal frequency division multiplexing system components are applied to electroencephalogram (EEG) signal transmission, to which a threshold compression module and a vector reconstruction module are added to address the system power burden associated with the acquisition and transmission of large amounts of real-time EEG data in the medical IoT. In the threshold compression module, the inherent characteristics of EEG signals are analysed, and the generated EEG data are decomposed into multiple symbolic streams and compressed by applying different thresholds to improve the compression ratio while ensuring the quality of service of the application. A deep neural network-based approach is proposed for the detection and diagnosis of lung nodules. Automatic identification and measurement of simulated lung nodules and the corresponding volumes of nodules in images under different conditions are applied. The sensitivity of each AIADS in identifying lung nodules under different convolution kernel conditions, false positives (FP), false negatives (FN), relative volume errors (RVE), the miss detection rate (MDR) for different types of lung nodules, and the performance of each system in predicting the four types of nodules are calculated. In this paper, an interpretable multibranch feature convolutional neural network model is proposed for the diagnosis of benign and malignant lung nodules. It is demonstrated that the proposed model not only yields interpretable lung nodule classification results but also achieves better lung nodule classification performance with an accuracy rate of 97.8%.

Nitrogen dioxide pollution increases vulnerability to COVID-19 through altered immune function

Previous ecological studies suggest the existence of possible interplays between the exposure to air pollutants and SARS-CoV-2 infection. Confirmations at individual level, however, are lacking. To explore the relationships between previous exposure to particulate matter < 10 μm (PM₁₀) and nitrogen dioxide (NO₂), the clinical outcome following hospital admittance, and lymphocyte subsets in COVID-19 patients with pneumonia. In 147 geocoded patients, we assessed the individual exposure to PM₁₀ and NO₂ in the 2 weeks before hospital admittance. We divided subjects according to the clinical outcome (i.e., discharge at home vs in-hospital death), and explored the lymphocyte-related immune function as an index possibly affecting individual vulnerability to the infection. As compared with discharged subjects, patients who underwent in-hospital death presented neutrophilia, lymphopenia, lower number of T CD45, CD3, CD4, CD16/56 + CD3 + , and B CD19 + cells, and higher previous exposure to NO₂, but not PM₁₀. Age and previous NO₂ exposure were independent predictors for mortality. NO₂ concentrations were also negatively related with the number of CD45, CD3, and CD4 cells. Previous NO₂ exposure is a co-factor independently affecting the mortality risk in infected individuals, through negative immune effects. Lymphopenia and altered lymphocyte subsets might precede viral infection due to nonmodifiable (i.e., age) and external (i.e., air pollution) factors. Thus, decreasing the burden of air pollutants should be a valuable primary prevention measure to reduce individual susceptibility to SARS-CoV-2 infection and mortality.

Chest CT Characteristics are Strongly Predictive of Mortality in Patients with COVID-19 Pneumonia: A Multicentric Cohort Study

Rationale and Objectives

The novel coronavirus (COVID-19) has presented a significant and urgent threat to global health and there has been a need to identify prognostic factors in COVID-19 patients. The aim of this study was to determine whether chest computed tomography (CT) characteristics had any prognostic value in patients with COVID-19.

Materials and Methods

A retrospective analysis of COVID-19 patients who underwent a chest CT-scan was performed in four medical centers. The prognostic value of chest CT results was assessed using a multivariable survival analysis with the Cox model. The characteristics included in the model were the degree of lung involvement, ground glass opacities, nodular consolidations, linear consolidations, a peripheral topography, a predominantly inferior lung involvement, pleural effusion, and crazy paving. The model was also adjusted on age, sex, and the center in which the patient was hospitalized. The primary endpoint was 30-day in-hospital mortality. A second model used a composite endpoint of admission to an intensive care unit or 30-day in-hospital mortality.

Results

A total of 515 patients with available follow-up information were included. Advanced age, a degree of pulmonary involvement ≥50% (Hazard Ratio 2.25 [95% CI: 1.378-3.671], p = 0.001), nodular consolidations and pleural effusions were associated with lower 30-day in-hospital survival rates. An exploratory subgroup analysis showed a 60.6% mortality rate in patients over 75 with ≥50% lung involvement on a CT-scan.

Conclusion

Chest CT findings such as the percentage of pulmonary involvement ≥50%, pleural effusion and nodular consolidation were strongly associated with 30-day mortality in COVID-19 patients. CT examinations are essential for the assessment of severe COVID-19 patients and their results must be considered when making care management decisions.

Outcomes of Simplified Lung Ultrasound Exam in COVID-19: Implications for Self-Imaging

OBJECTIVES

Lung ultrasound B-lines represent interstitial thickening or edema and relate to mortality in COVID-19. As B-lines can be detected with minimal training using point-of-care ultrasound (POCUS), we examined the frequency, clinical associations, and outcomes of B-lines when found using a simplified POCUS method in acutely ill patients with COVID-19.

METHODS

In this retrospective cohort study, hospital data from COVID-19 patients who had undergone lung imaging during standard echocardiography or POCUS were reviewed for an ultrasound lung comet (ULC) sign, defined as the presence of ≥3 B-lines from images of only the antero-apex of either lung (ULC+). Clinical risk factors, oximetry and radiographic results, and disease severity were analyzed for associations with ULC+. Clinical risk factors and ULC+ were analyzed for associations with hospital mortality or the need for intensive care in multivariable models.

RESULTS

Of N = 160 patients, age (mean ± standard deviation) was 64.8 ± 15.5 years, and 46 (29%) died. ULC+ was present in 100/160 (62%) of patients overall, in 81/103 (79%) of severe-or-greater disease versus 19/57 (33%) of moderate-or-less disease (P < .0001) and was associated with mortality (odds ratio [OR] = 2.4 [95% confidence interval [CI]: 1.1-5.4], P = .02) and the need for intensive care (OR = 5.23 [95% CI: 2.42-12.40], P < .0001). In the multivariable models, symptom duration and severe-or-greater disease were associated with ULC+, and ULC+, diabetes, and symptom duration were associated with the need for intensive care.

CONCLUSIONS

B-lines in the upper chest were common and related to disease severity, intensive care, and hospital mortality in COVID-19. Validation of a simplified lung POCUS exam could provide the evidence basis for a self-imaging application during the pandemic.

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).

Computational study and design of effective siRNAs to silence structural proteins associated genes of Indian SARS-CoV-2 strains

SARS-CoV-2 is a highly transmissible and pathogenic coronavirus that first emerged in late 2019 and has since triggered a pandemic of acute respiratory disease named COVID-19 which poses a significant threat to all public health institutions in the absence of specific antiviral treatment. Since the outbreak began in March 2020, India has reported 4.77 lakh Coronavirus deaths, according to the World Health Organization (WHO). The innate RNA interference (RNAi) pathway, on the other hand, allows for the development of nucleic acid-based antiviral drugs in which complementary small interfering RNAs (siRNAs) mediate the post-transcriptional gene silencing (PTGS) of target mRNA. Therefore, in this current study, the potential of RNAi was harnessed to construct siRNA molecules that target the consensus regions of specific structural proteins associated genes of SARS-CoV-2, such as the envelope protein gene (E), membrane protein gene (M), nucleocapsid phosphoprotein gene (N), and surface glycoprotein gene (S) which are important for the viral pathogenesis. Conserved sequences of 811 SARS-CoV-2 strains from around India were collected to design 21 nucleotides long siRNA duplex based on various computational algorithms and parameters targeting E, M, N and S genes. The proposed siRNA molecules possessed sufficient nucleotide-based and other features for effective gene silencing and BLAST results revealed that siRNAs' targets have no significant matches across the whole human genome. Hence, siRNAs were found to have no off-target effects on the genome, ruling out the possibility of off-target silencing. Finally, out of 157 computationally identified siRNAs, only 4 effective siRNA molecules were selected for each target gene which is proposed to exert the best action based on GC content, free energy of folding, free energy of binding, melting temperature, heat capacity and molecular docking analysis with Human AGO2 protein. Our engineered siRNA candidates could be used as a genome-level therapeutic treatment against various sequenced SARS-CoV-2 strains in India. However, future applications will necessitate additional validations in vitro and in vivo animal models.

COVID-WideNet-A capsule network for COVID-19 detection

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.

Clinicodemographic profile and predictors of poor outcome in hospitalised COVID-19 patients: a single-centre, retrospective cohort study from India

OBJECTIVES

Primary objective was to study the clinicodemographic profile of hospitalised COVID-19 patients at a tertiary-care centre in India. Secondary objective was to identify predictors of poor outcome.

SETTING

Single centre tertiary-care level.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Consecutively hospitalised adults patients with COVID-19.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome variable was in-hospital mortality. Covariables were known comorbidities, clinical features, vital signs at the time of admission and on days 3-5 of admission, and initial laboratory investigations.

RESULTS

Intergroup differences were tested using χ² or Fischer's exact tests, Student's t-test or Mann-Whitney U test. Predictors of mortality were evaluated using multivariate logistic regression model. Out of 4102 SARS-CoV-2 positive patients admitted during 1-year period, 3268 (79.66%) survived to discharge and 834 (20.33%) died in the hospital. Mortality rates increased with age. Death was more common among males (OR 1.51, 95% CI 1.25 to 1.81). Out of 261 cases analysed in detail, 55.1% were in mild, 32.5% in moderate and 12.2% in severe triage category. Most common clinical presentations in the subgroup were fever (73.2%), cough/coryza (65.5%) and breathlessness (54%). Hypertension (45.2%), diabetes mellitus (41.8%) and chronic kidney disease (CKD; 6.1%) were common comorbidities. Disease severity on admission (adjusted OR 12.53, 95% CI 4.92 to 31.91, p<0.01), coagulation defect (33.21, 3.85-302.1, p<0.01), CKD (5.67, 1.08-29.64, p=0.04), high urea (11.05, 3.9-31.02, p<0.01), high prothrombin time (3.91, 1.59-9.65, p<0.01) and elevated ferritin (1.02, 1.00-1.03, p=0.02) were associated with poor outcome on multivariate regression. A strong predictor of mortality was disease progression on days 3-5 of admission (adjusted OR 13.66 95% CI 3.47 to 53.68).

CONCLUSION

COVID-19 related mortality in hospitalised adult patients at our center was similar to the developed countries. Progression in disease severity on days 3-5 of admission or days 6-13 of illness onset acts as 'turning point' for timely referral or treatment intensification for optimum use of resources.

Current strategies in diagnostics and therapeutics against novel coronavirus disease (COVID-19)

The epidemic of COVID-19 spread quickly through China and engulfed all of the countries across the globe. Several advances have been made in understanding the novel coronavirus's pathophysiology and in the development of newer diagnostics with pinpoint accuracy. Several newer therapeutic methods have either been accepted or are awaiting acceptance. In many countries, vaccination programs have been rolled out. Despite all these efforts, coronavirus still exists, though with lesser propensity. Multiple new forms of the novel coronavirus unexpectedly appeared in various areas of the world, undermining previously existing diagnosis and care protocols. This article highlights our understanding of the novel coronavirus's symptoms in brief, pathogenesis, diagnostics, and therapeutic strategies to contain COVID-19. The clinical findings, including serological, radiological, and other advanced diagnostic strategies, contributed much to control the disease. To date, supportive interventions have been used in tandem with potent antiviral therapies such as remdesivir, lopinavir/ritonavir, or corticosteroids with a level of trust in the care of COVID-19 patients. However, in several areas of the world, vaccination initiatives took place; the vaccines' safety and efficacy to control the outbreak is yet to be identified. This review concludes that improvement in therapies and diagnostics for COVID-19 must continually be explored as new variants constantly emerge.

Comparison between Suspected and Confirmed COVID-19 Respiratory Patients: What Is beyond the PCR Test

COVID-19 modified the healthcare system. Nasal-pharyngeal swab (NPS), with real-time reverse transcriptase-polymerase (PCR), is the gold standard for the diagnosis; however, there are difficulties related to the procedure that may postpone it. The study aims to evaluate whether other elements than the PCR-NPS are reliable and confirm the diagnosis of COVID-19. This is a cross-sectional study on data from the Lung Unit of Pavia (confirmed) and at the Emergency Unit of Palermo (suspected). COVID-19 was confirmed by positive NPS, suspected tested negative. We compared clinical, laboratory and radiological variables and performed Logistic regression to estimate which variables increased the risk of COVID-19. The derived ROC-AUCcurve, assessed the accuracy of the model to distinguish between COVID-19 suspected and confirmed. We selected 50 confirmed and 103 suspected cases. High Reactive C-Protein (OR: 1.02; CI95%: 0.11–1.02), suggestive CT-images (OR: 11.43; CI95%: 3.01–43.3), dyspnea (OR: 10.48; CI95%: 2.08–52.7) and respiratory failure (OR: 5.84; CI95%: 1.73–19.75) increased the risk of COVID-19, whereas pleural effusion decreased the risk (OR: 0.15; CI95%: 0.04–0.63). ROC confirmed the discriminative role of these variables between suspected and confirmed COVID-19 (AUC 0.91). Clinical, laboratory and imaging features predict the diagnosis of COVID-19, independently from the NPS result.

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree

Background

Chest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters.

Methods

Patients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia without hypoxia, (3) hypoxia without respiratory failure, and (4) respiratory failure. High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between −600 and −250 Hounsfield units (HU). Decision tree models were built with clinical variables and initial laboratory values (model 1) and including quantitative CT parameters in addition to them (model 2).

Results

A total of 387 patients were analyzed. The mean age was 57.8 years, and 50.3% were women. HAA increased as the severity of respiratory outcome increased. HAA showed a moderate correlation with lactate dehydrogenases (LDH) and C-reactive protein (CRP). In the decision tree of model 1, the CRP, fibrinogen, LDH, and gene Ct value were chosen as classifiers whereas LDH, HAA, fibrinogen, vaccination status, and neutrophil (%) were chosen in model 2. For predicting respiratory failure, the decision tree built with quantitative CT parameters showed a greater accuracy than the model without CT parameters.

Conclusions

The decision tree could provide higher accuracy for predicting respiratory failure when quantitative CT parameters were considered in addition to clinical characteristics, PCR Ct value, and blood biomarkers.

The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system

The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6^th to 8^th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow.

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COVID-19 patients with 341 longitudinal CT scans and paired clinical data included

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A new AI model for the prediction of COVID-19 progression was developed

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CT scans show significant add-on value over clinical data for the prediction

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Day 6–8 after the onset of COVID-19 symptoms is an ideal time window for a CT scan

Thoracic imaging tests for the diagnosis of COVID-19

BACKGROUND

Our March 2021 edition of this review showed thoracic imaging computed tomography (CT) to be sensitive and moderately specific in diagnosing COVID-19 pneumonia. This new edition is an update of the review.

OBJECTIVES

Our objectives were to evaluate the diagnostic accuracy of thoracic imaging in people with suspected COVID-19; assess the rate of positive imaging in people who had an initial reverse transcriptase polymerase chain reaction (RT-PCR) negative result and a positive RT-PCR result on follow-up; and evaluate the accuracy of thoracic imaging for screening COVID-19 in asymptomatic individuals. The secondary objective was to assess threshold effects of index test positivity on accuracy.

SEARCH METHODS

We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 17 February 2021. We did not apply any language restrictions.

SELECTION CRITERIA

We included diagnostic accuracy studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19. Studies had to assess chest CT, chest X-ray, or ultrasound of the lungs for the diagnosis of COVID-19, use a reference standard that included RT-PCR, and report estimates of test accuracy or provide data from which we could compute estimates. We excluded studies that used imaging as part of the reference standard and studies that excluded participants with normal index test results.

DATA COLLECTION AND ANALYSIS

The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using QUADAS-2. We presented sensitivity and specificity per study on paired forest plots, and summarized pooled estimates in tables. We used a bivariate meta-analysis model where appropriate.

MAIN RESULTS

We included 98 studies in this review. Of these, 94 were included for evaluating the diagnostic accuracy of thoracic imaging in the evaluation of people with suspected COVID-19. Eight studies were included for assessing the rate of positive imaging in individuals with initial RT-PCR negative results and positive RT-PCR results on follow-up, and 10 studies were included for evaluating the accuracy of thoracic imaging for imagining asymptomatic individuals. For all 98 included studies, risk of bias was high or unclear in 52 (53%) studies with respect to participant selection, in 64 (65%) studies with respect to reference standard, in 46 (47%) studies with respect to index test, and in 48 (49%) studies with respect to flow and timing. Concerns about the applicability of the evidence to: participants were high or unclear in eight (8%) studies; index test were high or unclear in seven (7%) studies; and reference standard were high or unclear in seven (7%) studies. Imaging in people with suspected COVID-19 We included 94 studies. Eighty-seven studies evaluated one imaging modality, and seven studies evaluated two imaging modalities. All studies used RT-PCR alone or in combination with other criteria (for example, clinical signs and symptoms, positive contacts) as the reference standard for the diagnosis of COVID-19. For chest CT (69 studies, 28285 participants, 14,342 (51%) cases), sensitivities ranged from 45% to 100%, and specificities from 10% to 99%. The pooled sensitivity of chest CT was 86.9% (95% confidence interval (CI) 83.6 to 89.6), and pooled specificity was 78.3% (95% CI 73.7 to 82.3). Definition for index test positivity was a source of heterogeneity for sensitivity, but not specificity. Reference standard was not a source of heterogeneity. For chest X-ray (17 studies, 8529 participants, 5303 (62%) cases), the sensitivity ranged from 44% to 94% and specificity from 24 to 93%. The pooled sensitivity of chest X-ray was 73.1% (95% CI 64. to -80.5), and pooled specificity was 73.3% (95% CI 61.9 to 82.2). Definition for index test positivity was not found to be a source of heterogeneity. Definition for index test positivity and reference standard were not found to be sources of heterogeneity. For ultrasound of the lungs (15 studies, 2410 participants, 1158 (48%) cases), the sensitivity ranged from 73% to 94% and the specificity ranged from 21% to 98%. The pooled sensitivity of ultrasound was 88.9% (95% CI 84.9 to 92.0), and the pooled specificity was 72.2% (95% CI 58.8 to 82.5). Definition for index test positivity and reference standard were not found to be sources of heterogeneity. Indirect comparisons of modalities evaluated across all 94 studies indicated that chest CT and ultrasound gave higher sensitivity estimates than X-ray (P = 0.0003 and P = 0.001, respectively). Chest CT and ultrasound gave similar sensitivities (P=0.42). All modalities had similar specificities (CT versus X-ray P = 0.36; CT versus ultrasound P = 0.32; X-ray versus ultrasound P = 0.89). Imaging in PCR-negative people who subsequently became positive For rate of positive imaging in individuals with initial RT-PCR negative results, we included 8 studies (7 CT, 1 ultrasound) with a total of 198 participants suspected of having COVID-19, all of whom had a final diagnosis of COVID-19. Most studies (7/8) evaluated CT. Of 177 participants with initially negative RT-PCR who had positive RT-PCR results on follow-up testing, 75.8% (95% CI 45.3 to 92.2) had positive CT findings. Imaging in asymptomatic PCR-positive people For imaging asymptomatic individuals, we included 10 studies (7 CT, 1 X-ray, 2 ultrasound) with a total of 3548 asymptomatic participants, of whom 364 (10%) had a final diagnosis of COVID-19. For chest CT (7 studies, 3134 participants, 315 (10%) cases), the pooled sensitivity was 55.7% (95% CI 35.4 to 74.3) and the pooled specificity was 91.1% (95% CI 82.6 to 95.7).

AUTHORS' CONCLUSIONS

Chest CT and ultrasound of the lungs are sensitive and moderately specific in diagnosing COVID-19. Chest X-ray is moderately sensitive and moderately specific in diagnosing COVID-19. Thus, chest CT and ultrasound may have more utility for ruling out COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. The uncertainty resulting from high or unclear risk of bias and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results.

Complications Risk Assessment and Imaging Findings of Thermal Ablation Treatment in Liver Cancers: What the Radiologist Should Expect

One of the major fields of application of ablation treatment is liver tumors. With respect to HCC, ablation treatments are considered as upfront treatments in patients with early-stage disease, while in colorectal liver metastases (CLM), they can be employed as an upfront treatment or in association with surgical resection. The main prognostic feature of ablation is the tumor size, since the goal of the treatment is the necrosis of all viable tumor tissue with an adequate tumor-free margin. Radiofrequency ablation (RFA) and microwave ablation (MWA) are the most employed ablation techniques. Ablation therapies in HCC and liver metastases have presented a challenge to radiologists, who need to assess response to determine complication-related treatment. Complications, defined as any unexpected variation from a procedural course, and adverse events, defined as any actual or potential injury related to the treatment, could occur either during the procedure or afterwards. To date, RFA and MWA have shown no statistically significant differences in mortality rates or major or minor complications. To reduce the rate of major complications, patient selection and risk assessment are essential. To determine the right cost-benefit ratio for the ablation method to be used, it is necessary to identify patients at high risk of infections, coagulation disorders and previous abdominal surgery interventions. Based on risk assessment, during the procedure as part of surveillance, the radiologists should pay attention to several complications, such as vascular, biliary, mechanical and infectious. Multiphase CT is an imaging tool chosen in emergency settings. The radiologist should report technical success, treatment efficacy, and complications. The complications should be assessed according to well-defined classification systems, and these complications should be categorized consistently according to severity and time of occurrence.

The association of obesity with the progression and outcome of COVID-19: The insight from an artificial-intelligence-based imaging quantitative analysis on computed tomography

AIMS

To explore the association of obesity with the progression and outcome of coronavirus disease 2019 (COVID-19) at the acute period and 5-month follow-up from the perspectives of computed tomography (CT) imaging with artificial intelligence (AI)-based quantitative evaluation, which may help to predict the risk of obese COVID-19 patients progressing to severe and critical disease.

MATERIALS AND METHODS

This retrospective cohort enrolled 213 hospitalized COVID-19 patients. Patients were classified into three groups according to their body mass index (BMI): normal weight (from 18.5 to <24 kg/m² ), overweight (from 24 to <28 kg/m² ) and obesity (≥28 kg/m² ).

RESULTS

Compared with normal-weight patients, patients with higher BMI were associated with more lung involvements in lung CT examination (lung lesions volume [cm³ ], normal weight vs. overweight vs. obesity; 175.5[34.0-414.9] vs. 261.7[73.3-576.2] vs. 395.8[101.6-1135.6]; p = 0.002), and were more inclined to deterioration at the acute period. At the 5-month follow-up, the lung residual lesion was more serious (residual total lung lesions volume [cm³ ], normal weight vs. overweight vs. obesity; 4.8[0.0-27.4] vs. 10.7[0.0-55.5] vs. 30.1[9.5-91.1]; p = 0.015), and the absorption rates were lower for higher BMI patients (absorption rates of total lung lesions volume [%], normal weight vs. overweight vs. obesity; 99.6[94.0-100.0] vs. 98.9[85.2-100.0] vs. 88.5[66.5-95.2]; p = 0.013). The clinical-plus-AI parameter model was superior to the clinical-only parameter model in the prediction of disease deterioration (areas under the ROC curve, 0.884 vs. 0.794, p < 0.05).

CONCLUSIONS

Obesity was associated with severe pneumonia lesions on CT and adverse clinical outcomes. The AI-based model with combinational use of clinical and CT parameters had incremental prognostic value over the clinical parameters alone.

Incidence, clinical associations and outcomes of intrathoracic complications with and without ARDS in COVID-19 pneumonia

PURPOSE

To determine the incidence and clinical predictors of intrathoracic complications in COVID-19 patients, and the association with outcomes.

METHODS

In this retrospective cross-sectional study, we included 976 patients (age 61 ± 17 years, 62% male) who tested positive for SARS-CoV-2 between March 3-April 4, 2020 and underwent chest imaging. 3836 radiographs from 976 patients and 105 CTs from 88 patients were reviewed for intrathoracic complications, including pneumothorax, pneumomediastinum, pneumopericardium, lobar collapse, pleural effusion, and pneumatocele formation.

RESULTS

There was a high rate of intrathoracic complications (197/976, 20%). Pleural effusion was the most common complication (168/976, 17%). Pneumothorax (30/976, 3%) and pneumatoceles (9/88, 10%) were also frequent. History of hypertension and high initial CXR severity score were independent risk factors for complications. Patients with any intrathoracic complication during admission had an over 11-fold risk of ICU admission (adjusted odds ratio [aOR] 11.2, p < 0.0001) and intubation (aOR 12.4, p < 0.0001), over 50% reduction in successful extubation (aOR 0.49, p = 0.02) and longer length of stay (median 13 versus 5 days, p < 0.0001). There was no difference in overall survival between patients with and without any complication (log-rank p = 0.94).

CONCLUSION

In COVID-19 patients who underwent chest imaging, 1 in 5 patients have an intrathoracic complication, which are associated with higher level of care and prolonged hospital stay. Hypertension history and high CXR severity score confer an increased risk of complication.

SUMMARY

Intrathoracic complications in COVID-19 are common and are predictive of ICU admission, need for intubation, less successful extubation, and longer length of stay but are not predictive of mortality.

COVID-19 image classification using deep learning: Advances, challenges and opportunities

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.

Diagnosing and differentiating viral pneumonia and COVID-19 using X-ray images

Coronavirus-caused diseases are common worldwide and might worsen both human health and the world economy. Most people may instantly encounter coronavirus in their life and may result in pneumonia. Nowadays, the world is fighting against the new coronavirus: COVID-19. The rate of increase is high, and the world got caught the disease unprepared. In most regions of the world, COVID-19 test is not possible due to the absence of the diagnostic kit, even if the kit exists, its false-negative (giving a negative result for a person infected with COVID-19) rate is high. Also, early detection of COVID-19 is crucial to keep its morbidity and mortality rates low. The symptoms of pneumonia are alike, and COVID-19 is no exception. The chest X-ray is the main reference in diagnosing pneumonia. Thus, the need for radiologists has been increased considerably not only to detect COVID-19 but also to identify other abnormalities it caused. Herein, a transfer learning-based multi-class convolutional neural network model was proposed for the automatic detection of pneumonia and also for differentiating non-COVID-19 pneumonia and COVID-19. The model that inputs chest X-ray images is capable of extracting radiographic patterns on chest X-ray images to turn into valuable information and monitor structural differences in the lungs caused by the diseases. The model was developed by two public datasets: Cohen dataset and Kermany dataset. The model achieves an average training accuracy of 0.9886, an average training recall of 0.9829, and an average training precision of 0.9837. Moreover, the average training false-positive and false-negative rates are 0.0085 and 0.0171, respectively. Conversely, the model's test set metrics such as average accuracy, average recall, and average precision are 97.78%, 96.67%, and 96.67%, respectively. According to the simulation results, the proposed model is promising, can quickly and accurately classify chest images, and helps doctors as the second reader in their final decision.

ERS International Congress 2021: highlights from the Clinical Techniques, Imaging and Endoscopy Assembly

This article summarises the highlights from the European Respiratory Society's “Clinical techniques, imaging and endoscopy” Assembly 14 presented at the virtual 2021 European Respiratory Society International Congress. Cutting-edge innovative developments in both diagnostic approaches and therapeutic strategies in patients with lung cancer, interstitial lung disease, obstructive airway disorders and infectious diseases were presented on this year's interactive congress platform. In this article, the Assembly 14 subgroups summarise the key take home messages given new research outcomes and place them in the context of the current knowledge.

Radio-Histological Correlation of Lung Features in Severe COVID-19 Through CT-Scan and Lung Ultrasound Evaluation

Background

Patients with coronavirus disease 2019 (COVID-19) can develop severe bilateral pneumonia leading to respiratory failure. Lung histological samples were scarce due to the high risk of contamination during autopsies. We aimed to correlate histological COVID-19 features with radiological findings through lung ultrasound (LU)-guided postmortem core needle biopsies (CNBs) and computerized tomography (CT) scans.

Methodology

We performed an observational prospective study, including 30 consecutive patients with severe COVID-19. The thorax was divided into 12 explorations regions to correlate LU and CT-scan features. Histological findings were also related to radiological features through CNBs.

Results

Mean age was 62.56 ± 13.27 years old, with 96.7% male patients. Postmortem LU-guided CNBs were performed in 13 patients. Thirty patients were evaluated with both thoracic LU and chest CT scan, representing a total of 279 thoracic regions explored. The most frequent LU finding was B2-lines (49.1%). The most CT-scan finding was ground-glass opacity (GGO, 29%). Pathological CT-scan findings were commonly observed when B2-lines or C-lines were identified through LU (positive predictive value, PPV, 87.1%). Twenty-five postmortem echo-guided histological samples were obtained from 12 patients. Histological samples showed diffuse alveolar damage (DAD) (75%) and chronic interstitial inflammation (25%). The observed DAD was heterogeneous, showing multiple evolving patterns of damage, including exudative (33.3%), fibrotic (33.3%), and organizing (8.3%) phases. In those patients with acute or exudative pattern, two lesions were distinguished: classic hyaline membrane; fibrin "plug" in alveolar space (acute fibrinous organizing pneumonia, AFOP). C-profile was described in 33.3% and presented histological signs of DAD and lung fibrosis. The predominant findings were collagen deposition (50%) and AFOP (50%). B2-lines were identified in 66.7%; the presence of hyaline membrane was the predominant finding (37.5%), then organizing pneumonia (12.5%) and fibrosis (37.5%). No A-lines or B1-lines were observed in these patients.

Conclusion

LU B2-lines and C-profile are predominantly identified in patients with severe COVID-19 with respiratory worsening, which correspond to different CT patterns and histological findings of DAD and lung fibrosis.

A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system

Starting from December 2019, the COVID-19 pandemic has globally strained medical resources and caused significant mortality. It is commonly recognized that the severity of SARS-CoV-2 disease depends on both the comorbidity and the state of the patient's immune system, which is reflected in several biomarkers. The development of early diagnosis and disease severity prediction methods can reduce the burden on the health care system and increase the effectiveness of treatment and rehabilitation of patients with severe cases. This study aims to develop and validate an ensemble machine-learning model based on clinical and immunological features for severity risk assessment and post-COVID rehabilitation duration for SARS-CoV-2 patients. The dataset consisting of 35 features and 122 instances was collected from Lviv regional rehabilitation center. The dataset contains age, gender, weight, height, BMI, CAT, 6-minute walking test, pulse, external respiration function, oxygen saturation, and 15 immunological markers used to predict the relationship between disease duration and biomarkers using the machine learning approach. The predictions are assessed through an area under the receiver-operating curve, classification accuracy, precision, recall, and F1 score performance metrics. A new hybrid ensemble feature selection model for a post-COVID prediction system is proposed as an automatic feature cut-off rank identifier. A three-layer high accuracy stacking ensemble classification model for intelligent analysis of short medical datasets is presented. Together with weak predictors, the associative rules allowed improving the classification quality. The proposed ensemble allows using a random forest model as an aggregator for weak repressors' results generalization. The performance of the three-layer stacking ensemble classification model (AUC 0.978; CA 0.920; F1 score 0.921; precision 0.924; recall 0.920) was higher than five machine learning models, viz. tree algorithm with forward pruning; Naïve Bayes classifier; support vector machine with RBF kernel; logistic regression, and a calibrated learner with sigmoid function and decision threshold optimization. Aging-related biomarkers, viz. CD3+, CD4+, CD8+, CD22+ were examined to predict post-COVID rehabilitation duration. The best accuracy was reached in the case of the support vector machine with the linear kernel (MAPE = 0.0787) and random forest classifier (RMSE = 1.822). The proposed three-layer stacking ensemble classification model predicted SARS-CoV-2 disease severity based on the cytokines and physiological biomarkers. The results point out that changes in studied biomarkers associated with the severity of the disease can be used to monitor the severity and forecast the rehabilitation duration.

Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect

Purpose

Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease onset to CT (i.e., time until CT) in this setting.

Materials and methods

From March to May 2020, 32 COVID-19 patients underwent initial chest CT before enrollment were evaluated in this study. Eighteen patients were randomized to start favipiravir on day 1 (early treatment group), and 14 patients on day 6 of study participation (late treatment group). In this study, percentages of ground-glass opacity (GGO), reticulation, consolidation, emphysema, honeycomb, and nodular lesion volumes were calculated as quantitative indexes by means of the software, while CT-determined disease severity was also visually scored. Next, univariate and stepwise regression analyses were performed to determine relationships between quantitative indexes and time until CT. Moreover, patient outcomes determined as viral clearance in the first 6 days and duration of fever were compared for those who started therapy within 4, 5, or 6 days as time until CT and those who started later by means of the Kaplan–Meier method followed by Wilcoxon’s signed-rank test.

Results

% GGO and % consolidation showed significant correlations with time until CT (p < 0.05), and stepwise regression analyses identified both indexes as significant descriptors for time until CT (p < 0.05). When divided all patients between time until CT of 4 days and that of more than 4 days, accuracy of the combined quantitative method (87.5%) was significantly higher than that of the CT disease severity score (62.5%, p = 0.008).

Conclusion

ML-based CT texture analysis is equally or more useful for predicting time until CT for favipiravir treatment on COVID-19 patients than CT disease severity score.

Seroprevalence of COVID-19 in Riyadh City during the early increase of COVID-19 infections in Saudi Arabia, June 2020

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) emerged in December 2019 and caused a global pandemic of the Coronavirus Disease 2019 (COVID-19). More than 170 million cases have been reported worldwide with mortality rate of 1–3%. The detection of SARS-CoV-2 by molecular testing is limited to acute infections, therefore serological studies provide a better estimation of the virus spread in a population. This study aims to evaluate the seroprevalence of SARS-CoV-2 in the major city of Riyadh, Saudi Arabia during the sharp increase of the pandemic, in June 2020. Serum samples from non-COVID patients (n = 432), patients visiting hospitals for other complications and confirmed negative for COVID-19, and healthy blood donors (n = 350) were collected and evaluated using an in-house enzyme-linked immunosorbent assay (ELISA). The overall percentage of positive samples was 7.80% in the combined two populations (n = 782). The seroprevalence was lower in the blood donors (6%) than non-COVID-19 patients (9.25%), p = 0.0004. This seroprevalence rate is higher than the documented cases, indicating asymptomatic or mild unreported COVID-19 infections in these two populations. This warrants further national sero-surveys and highlights the importance of real-time serological surveillance during pandemics.

Serum extracellular vesicles profiling is associated with COVID-19 progression and immune responses

Coronavirus disease 2019 (COVID‐19) has transformed very quickly into a world pandemic with severe and unexpected consequences on human health. Concerted efforts to generate better diagnostic and prognostic tools have been ongoing. Research, thus far, has primarily focused on the virus itself or the direct immune response to it. Here, we propose extracellular vesicles (EVs) from serum liquid biopsies as a new and unique modality to unify diagnostic and prognostic tools for COVID‐19 analyses. EVs are a novel player in intercellular signalling particularly influencing immune responses. We herein show that innate and adaptive immune EVs profiling, together with SARS‐CoV‐2 Spike S1⁺ EVs provide a novel signature for SARS‐CoV‐2 infection. It also provides a unique ability to associate the co‐existence of viral and host cell signatures to monitor affected tissues and severity of the disease progression. And provide a phenotypic insight into COVID‐associated EVs.

Statistical analysis of COVID-19 infection severity in lung lobes from chest CT

Detection of the COVID 19 virus is possible through the reverse transcription-polymerase chain reaction (RT-PCR) kits and computed tomography (CT) images of the lungs. Diagnosis via CT images provides a faster diagnosis than the RT-PCR method does. In addition to low false-negative rate, CT is also used for prognosis in determining the severity of the disease and the proposed treatment method. In this study, we estimated a probability density function (PDF) to examine the infections caused by the virus. We collected 232 chest CT of suspected patients and had them labeled by two radiologists in 6 classes, including a healthy class and 5 classes of different infection severity. To segment the lung lobes, we used a pre-trained U-Net model with an average Dice similarity coefficient (DSC) greater than 0.96. First, we extracted the PDF to grade the infection of each lobe and selected five specific thresholds as feature vectors. We then assigned this feature vector to a support vector machine (SVM) model and made the final prediction of the infection severity. Using the T-Test statistics, we calculated the p-value at different pixel thresholds and reported the significant differences in the pixel values. In most cases, the p-value was less than 0.05. Our developed model was developed on roughly labeled data without any manual segmentation, which estimated lung infection involvements with the area under the curve (AUC) in the range of [0.64, 0.87]. The introduced model can be used to generate a systematic automated report for individual patients infected by COVID-19.

Reduced COVID-19 mortality linked with early antibodies against SARS-CoV-2, irrespective of age

BACKGROUND

COVID-19 pandemic has generated a million deaths worldwide. The efficiency of the immune system can modulate individual vulnerability with variable outcomes. However, the relationships between disease severity and the titer of antibodies produced against SARS-CoV-2 in non-vaccinated, recently infected subjects need to be fully elucidated.

METHODS

A total of 99 patients admitted to a COVID-unit underwent clinical assessment and measurement of serum levels of anti-spike protein (S1) IgM, and anti-nucleocapsid protein IgG. Patients were stratified according to the clinical outcome (i.e., discharged at home or in-hospital death).

RESULTS

Following hospitalization, 18 died during the hospital stay. They were older, had lymphopenia, a higher co-morbidity rate, and longer hospital stay than 81 patients who were discharged after healing. Patients in this latter group had, at hospital admittance, 7.9-fold higher serum concentration of IgM, and 2.4-fold higher IgG levels. Multivariate Cox regression models indicated age and anti-nucleocapsid protein IgG concentration at admission as independently associated with the risk of in-hospital death.

CONCLUSIONS

An efficient immunological response during the early phase of COVID-19 protects from mortality, irrespective of age. Advanced age is a critical risk factor for poor outcome in infected subjects. Further studies must explore potential therapeutic strategies able to restore a valid functional humoral immunity in elderly patients with poor antibody response during the early stage of COVID-19 infection.

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid‐19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X‐ray) and Covid‐19 (real‐time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid‐19 can be detected instantly from chest X‐rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid‐19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community‐acquired pneumonia, viral pneumonia, and Covid‐19 from images of chest X‐rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one‐stop solution to beginners and current researchers interested in this field.

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.

Prevalence of comorbidities and symptoms stratified by severity of illness amongst adult patients with COVID-19: a systematic review

Introduction

We performed a systematic review of comorbidities and symptoms of adult patients with coronavirus disease 2019 (COVID-19) to evaluate comorbidities, symptoms, and severity.

Material and methods

We searched databases and extracted comorbidities and symptoms from the included studies. We stratified the similar signs and symptoms in groups and on the basis of severity and compared them with stratified analysis. Individual case reports and case series with < 5 patients were excluded.

Results

A total of 163 studies with 43,187 patients were included. Mean age was 54.6 years. There were significantly fewer women in the study (43.9% vs. 56.1%, p < 0.0001). Prevalent cardiovascular comorbidities were hypertension (31.9%), obesity (27.9%), hyperlipidemia (26.4%), smoking (18.9%), diabetes mellitus (17.2%), atherosclerotic disease (9.2%) and arrhythmia (5.0%). The most frequently reported constitutional symptoms of COVID-19 were fever (73.9%), fatigue (33.4%), malaise (29.9%), myalgia and/or arthralgia (19.2%), generalized weakness (19.0%), and chills (11.3%). For the cardiovascular system, chest pain and/or tightness were most often reported (19.6%), followed by palpitations (5.2%). Hypertension and diabetes were common in severe disease. Obesity and congestive heart failure were not observed in any non-severe cases. Severe cases compared to non-severe cases more frequently had fever (87.8% vs. 58.5%, p < 0.001), shortness of breath (47.4% vs. 20.6%, p < 0.001), cough (66.8% vs. 62.9%, p < 0.001), sputum production (35.4% vs. 26.5%, p < 0.001) and rhinorrhea (32.2% vs. 7.3%, p < 0.001).

Conclusions

Hypertension, diabetes, and atherosclerotic diseases are common comorbidities across the world, with obesity as the second most common in the US and more common in men.

Hymecromone: a clinical prescription hyaluronan inhibitor for efficiently blocking COVID-19 progression

Currently, there is no effective drugs for treating clinically COVID-19 except dexamethasone. We previously revealed that human identical sequences of SARS-CoV-2 promote the COVID-19 progression by upregulating hyaluronic acid (HA). As the inhibitor of HA synthesis, hymecromone is an approved prescription drug used for treating biliary spasm. Here, we aimed to investigate the relation between HA and COVID-19, and evaluate the therapeutic effects of hymecromone on COVID-19. Firstly, HA was closely relevant to clinical parameters, including lymphocytes (n = 158; r = −0.50; P < 0.0001), C-reactive protein (n = 156; r = 0.55; P < 0.0001), D-dimer (n = 154; r = 0.38; P < 0.0001), and fibrinogen (n = 152; r = 0.37; P < 0.0001), as well as the mass (n = 78; r = 0.43; P < 0.0001) and volume (n = 78; r = 0.41; P = 0.0002) of ground-glass opacity, the mass (n = 78; r = 0.48; P < 0.0001) and volume (n = 78; r = 0.47; P < 0.0001) of consolidation in patient with low level of hyaluronan (HA < 48.43 ng/mL). Furthermore, hyaluronan could directly cause mouse pulmonary lesions. Besides, hymecromone remarkably reduced HA via downregulating HAS2/HAS3 expression. Moreover, 89% patients with hymecromone treatment had pulmonary lesion absorption while only 42% patients in control group had pulmonary lesion absorption (P < 0.0001). In addition, lymphocytes recovered more quickly in hymecromone-treated patients (n = 8) than control group (n = 5) (P < 0.05). These findings suggest that hymecromone is a promising drug for COVID-19 and deserves our further efforts to determine its effect in a larger cohort.

Perspective of AI system for COVID-19 detection using chest images: a review

Coronavirus Disease 2019 (COVID-19) is an evolving communicable disease caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which has led to a global pandemic since December 2019. The virus has its origin from bat and is suspected to have transmitted to humans through zoonotic links. The disease shows dynamic symptoms, nature and reaction to the human body thereby challenging the world of medicine. Moreover, it has tremendous resemblance to viral pneumonia or Community Acquired Pneumonia (CAP). Reverse Transcription Polymerase Chain Reaction (RT-PCR) is performed for detection of COVID-19. Nevertheless, RT-PCR is not completely reliable and sometimes unavailable. Therefore, scientists and researchers have suggested analysis and examination of Computing Tomography (CT) scans and Chest X-Ray (CXR) images to identify the features of COVID-19 in patients having clinical manifestation of the disease, using expert systems deploying learning algorithms such as Machine Learning (ML) and Deep Learning (DL). The paper identifies and reviews various chest image features using the aforementioned imaging modalities for reliable and faster detection of COVID-19 than laboratory processes. The paper also reviews and compares the different aspects of ML and DL using chest images, for detection of COVID-19.

Frequency of typical and atypical computed tomography findings of COVID-19 and their effect on hospitalization

Objective:

This study aimed to determine the frequency of typical and atypical computed tomography (CT) findings of COVID-19 and their effect on hospitalization.

Methods:

We retrospectively assessed 414 patients who were diagnosed with COVID-19 by real-time reverse transcription-polymerase chain reaction and who had lung involvement in their admission chest CT. We evaluated the frequency of typical and atypical chest CT findings and analyzed the relationship between typical and atypical findings of COVID-19 in patients treated in ambulatory versus inpatient settings.

Results:

Ground-glass opacities were the most common typical finding of COVID-19 chest CT scans. The frequencies of other typical findings, including consolidation, air bronchogram, pulmonary vascular enlargement (PVE), airway changes, crazy paving pattern, and reticular pattern, were similar to those reported in the literature. Atypical findings were less common and found at varying frequencies. Crazy paving pattern, air bronchogram, reticular pattern, and PVE were significantly more common in hospitalized patients (p<0.001). The frequencies of other typical and atypical findings were not significantly different between ambulatory and hospitalized patients.

Conclusion:

Increased recognition of typical and atypical findings of COVID-19 and their frequencies, as well as knowledge of admission chest CT findings that are associated with hospitalization, will facilitate medical care during the pandemic.

Endocrine, metabolic, and hepatic dysfunction in patients with COVID-19 pneumonia with severe and critically ill status

Background

Management of endocrine and hepatic disorders is very important for better management of patient with COVID-19 infections. Hepatic and endocrine dysfunction needs clinical assessment, continued monitoring, and specific treatment. It is important to understand clearly the potential mechanisms causing hepatic and endocrine injury

Objectives

To assess the endocrine and hepatic dysfunctions in patient with COVID-19 pneumonia with severe and critically ill status.

Methods

This retrospective analytical study was performed on 75 patients admitted to intensive care or high dependency units (ICU/HDU) in Obaidullah Hospital, Ministry of Health, United Arab Emirates. All patients were subjected to the following on admission: history taking, general and local examinations, routine laboratory studies (CBC, liver function tests, and kidney function tests), and other laboratory tests: C-reactive protein (CRP), D-dimer, Chest X-ray. Endocrine and hepatic function tests and other laboratory studies are repeated daily to show the progress of endocrine and hepatic functions.

Results

The age range of the studied group was between 35 years and 107 years with mean age 59.98 ± 12.88. The sex distribution was (42 male, 56%) and (33 female, 44%). The associated comorbidities were 4 patients had bronchial asthma (5.3%), 12 patients had chronic kidney disease (16%), 30 patients had diabetes mellitus (40%), 26 patients had hypertension (34.7%), 2 patient had hypothyroidism (2.7%), 12 patient had ischemic heart disease (16%), and 21 patients did not have any comorbidities (28%). There was significant difference between glucose level, thyroid stimulating hormone and free t4 before and after admission (P values are 0.001, 0.04, and 0.023 respectively). There was significant difference between serum electrolytes before and after admission (P value = 0.001). There was significant difference between liver function test before and after admission (P values are 0.004, 0.001, and 0.001)

Conclusions

Hepatic, pancreatic, thyroid functions, and electrolytes are affected by COVID-19 infection. These could act as surrogate biomarkers for better management of hepatic, pancreatic, thyroid functions and electrolytes disturbances in patients with COVID-19 infection.

Combined Model of Quantitative Evaluation of Chest Computed Tomography and Laboratory Values for Assessing the Prognosis of Coronavirus Disease 2019

PURPOSE

 To assess the prognostic power of quantitative analysis of chest CT, laboratory values, and their combination in COVID-19 pneumonia.

MATERIALS AND METHODS

 Retrospective analysis of patients with PCR-confirmed COVID-19 pneumonia and chest CT performed between March 07 and November 13, 2020. Volume and percentage (PO) of lung opacifications and mean HU of the whole lung were quantified using prototype software. 13 laboratory values were collected. Negative outcome was defined as death, ICU admittance, mechanical ventilation, or extracorporeal membrane oxygenation. Positive outcome was defined as care in the regular ward or discharge. Logistic regression was performed to evaluate the prognostic value of CT parameters and laboratory values. Independent predictors were combined to establish a scoring system for prediction of prognosis. This score was validated on a separate validation cohort.

RESULTS

 89 patients were included for model development between March 07 and April 27, 2020 (mean age: 60.3 years). 38 patients experienced a negative outcome. In univariate regression analysis, all quantitative CT parameters as well as C-reactive protein (CRP), relative lymphocyte count (RLC), troponin, and LDH were associated with a negative outcome. In a multivariate regression analysis, PO, CRP, and RLC were independent predictors of a negative outcome. Combination of these three values showed a strong predictive value with a C-index of 0.87. A scoring system was established which categorized patients into 4 groups with a risk of 7 %, 30 %, 67 %, or 100 % for a negative outcome. The validation cohort consisted of 28 patients between May 5 and November 13, 2020. A negative outcome occurred in 6 % of patients with a score of 0, 50 % with a score of 1, and 100 % with a score of 2 or 3.

CONCLUSION

 The combination of PO, CRP, and RLC showed a high predictive value for a negative outcome. A 4-point scoring system based on these findings allows easy risk stratification in the clinical routine and performed exceptionally in the validation cohort.

KEY POINTS

  · A high PO is associated with an unfavorable outcome in COVID-19. · PO, CRP, and RLC are independent predictors of an unfavorable outcome, and their combination has strong predictive power. · A 4-point scoring system based on these values allows quick risk stratification in a clinical setting.

CITATION FORMAT

· Scharf G, Meiler S, Zeman F et al. Combined Model of Quantitative Evaluation of Chest Computed Tomography and Laboratory Values for Assessing the Prognosis of Coronavirus Disease 2019. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1731-7905.