Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days

Objectives

This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT).

Methods

From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results.

Results

Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful.

Conclusion

The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

Interest of Chest CT to Assess the Prognosis of SARS-CoV-2 Pneumonia: An In-Hospital-Based Experience in Sub-Saharan Africa

Methods

We included all patients with respiratory symptoms (dyspnea, fever, and cough) and/or respiratory failure admitted to the SOS Médecins de nuit SARL hospital, DR Congo, during the 2nd and 3rd waves of the COVID-19 pandemic. The diagnosis of COVID-19 was established based on RT-PCR anti-SARS-CoV-2 tests (G1 (RT-PCR positive) vs. G2 (RT-PCR negative)), and all patients had a chest CT on the day of admission. We retrieved the digital files of patients, precisely the clinical, biological, and chest CT parameters of the day of admission as well as the vital outcome (survival or death). Chest CT were read by a very high-definition console using Advantage Windows software and exported to the hospital network using the RadiAnt DICOM viewer. To determine the threshold for the percentage of lung lesions associated with all-cause mortality, we used ROC curves. Factors associated with death, including chest CT parameters, were investigated using logistic regression analysis.

Results

The study included 200 patients (average age 56.2 ± 15.2 years; 19% diabetics and 4.5% obese), and COVID-19 was confirmed among 56% of them (G1). Chest CT showed that ground glass (72.3 vs. 39.8%), crazy paving (69.6 vs. 17.0%), and consolidation (83.9 vs. 22.7%), with bilateral and peripheral locations (68.8 vs. 30.7%), were more frequent in G1 vs. G2 (p < 0.001). No case of pulmonary embolism and fibrosis had been documented. The lung lesions affecting 30% of the parenchyma were informative in predicting death (area under the ROC curve at 0.705, p = 0.017). In multivariate analysis, a percentage of lesions affecting 50% of the lung parenchyma increased the risk of dying by 7.194 (1.656-31.250).

Conclusion

The chest CT demonstrated certain characteristic lesions more frequently in patients in whom the diagnosis of COVID-19 was confirmed. The extent of lesions affecting at least half of the lung parenchyma from the first day of admission to hospital increases the risk of death by a factor of 7.

Use of serum KL-6 and chest radiographic severity grade to predict 28-day mortality in COVID-19 patients with pneumonia: a retrospective cohort study

BACKGROUND

Coronavirus disease 2019 (COVID-19) has had a global social and economic impact. An easy assessment procedure to handily identify the mortality risk of inpatients is urgently needed in clinical practice. Therefore, the aim of this study was to develop a simple nomogram model to categorize patients who might have a poor short-term outcome.

METHODS

A retrospective cohort study of 189 COVID-19 patients was performed at Shanghai Ren Ji Hospital from December 12, 2022 to February 28, 2023. Chest radiography and biomarkers, including KL-6 were assessed. Risk factors of 28-day mortality were selected by a Cox regression model. A nomogram was developed based on selected variables by SMOTE strategy. The predictive performance of the derived nomogram was evaluated by calibration curve.

RESULTS

In total, 173 patients were enrolled in this study. The 28-day mortality event occurred in 41 inpatients (23.7%). Serum KL-6 and radiological severity grade (RSG) were selected as the final risk factors. A nomogram model was developed based on KL-6 and RSG. The calibration curve suggested that the nomogram model might have potential clinical value. The AUCs for serum KL-6, RSG, and the combined score in the development group and validation group were 0.885 (95% CI: 0.804-0.952), 0.818 (95% CI: 0.711-0.899), 0.868 (95% CI: 0.776-0.942) and 0.932 (95% CI: 0.862-0.997), respectively.

CONCLUSIONS

Our results suggested that the nomogram based on KL-6 and RSG might be a potential method for evaluating 28-day mortality in COVID-19 patients. A high combined score might indicate a poor outcome in COVID-19 patients with pneumonia.

Predictive factors of mortality in patients admitted to the emergency department for SARS-Cov2 pneumonia

INTRODUCTION

The overcrowding of intensive care units during the corona virus pandemic increased the number of patients managed in the emergency department (ED). The detection timely of the predictive factors of mortality and bad outcomes improve the triage of those patients.

AIM

To define the predictive factors of mortality at 30 days among patients admitted on ED for covid-19 pneumonia.

METHODS

This was a prospective, monocentric, observational study for 6 months. Patients over the age of 16 years admitted on the ED for hypoxemic pneumonia due to confirmed SARS-COV 2 infection by real-time reverse-transcription polymerase chain reaction (rRT-PCR) were included. Multivariate logistic regression was performed to investigate the predictive factors of mortality at 30 days.

RESULTS

463 patients were included. Mean age was 65±14 years, Sex-ratio=1.1. Main comorbidities were hypertension (49%) and diabetes (38%). Mortality rate was 33%. Patients who died were older (70±13 vs. 61±14;p<0.001), and had more comorbidities: hypertension (57% vs. 43%, p=0.018), chronic heart failure (8% vs. 3%, p=0.017), and coronary artery disease (12% vs. 6%, p=0.030). By multivariable analysis, factors independently associated with 30-day mortality were age ≥65 years aOR: 6.9, 95%CI 1.09-44.01;p=0.04) SpO2<80% (aOR: 26.6, 95%CI 3.5-197.53;p=0.001) and percentage of lung changes on CT scan>70% (aOR: 5.6% 95%CI .01-31.29;p=0.04).

CONCLUSION

Mortality rate was high among patients admitted in the ED for covid-19 pneumonia. The identification of predictive factors of mortality would allow better patient management.

Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers

Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.

Chest computed tomography findings of patients infected with Covid-19 and their association with disease evolution stages

INTRODUCTION

To describe CT findings in patients with confirmed Covid-19 infection and correlate them with the disease evolution stages.

METHODS

This is a historical cohort observational analytical study carried out with outpatients, inpatients, and emergency patients from a private hospital in Maceió/AL, Brazil. The final sample consisted of 390 patients with positive RT-PCR for Covid-19 with available laboratory tests and chest CT results.

RESULTS

The most frequent initial symptoms were cough, fever, dyspnea and headache. The most commonly found comorbidities were hypertension, diabetes mellitus and obesity. A total of 22% of the CT scans showed no alterations; ground-glass opacity was the most frequently found one. There was a significant association between age, comorbidities, pulmonary involvement, ground-glass opacity, mosaic attenuation and percentage of pulmonary involvement with death. The analysis of the disease stages showed a significant association with laboratory data (CRP and platelet levels), ground-glass opacity and mosaic attenuation with the disease evolution stages in relation to the days since symptom onset.

CONCLUSION

The disease evolution of Covid-19 occurs in stages, and this study describes tomographic findings in patients with confirmed Covid-19 infection and shows they vary depending on the disease evolution stages.

IMPLICATIONS FOR PRACTICE

This paper provides important addition to the various records that have been accumulated through the Covid-19 pandemic.

Lung Ultrasound in Predicting Outcomes in Patients with COVID-19 Treated with Extracorporeal Membrane Oxygenation

Pulmonary involvement due to SARS-CoV-2 infection can lead to acute respiratory distress syndrome in patients with COVID-19. Consequently, pulmonary imaging is crucial for management of COVID-19. This study aimed to evaluate the prognostic value of lung ultrasound (LUS) with a handheld ultrasound device (HHUD) in patients with COVID-19 treated with extracorporeal membrane oxygenation (ECMO). Therefore, patients underwent LUS with a HHUD every two days until they were either discharged from the intensive care unit or died. The study was conducted at the University Hospital of Bonn's anesthesiological intensive care ward from December 2020 to August 2021. A total of 33 patients (median [IQR]: 56.0 [53-60.5] years) were included. A high LUS score was associated with a decreased P/F ratio (repeated measures correlation [rmcorr]: -0.26; 95% CI: -0.34, -0.15; p < 0.001), increased extravascular lung water, defined as fluid accumulation in the pulmonary interstitium and alveoli (rmcorr: 0.11; 95% CI: 0.01, 0.20; p = 0.030), deteriorated electrolyte status (base excess: rmcorr: 0.14; 95% CI: 0.05, 0.24; p = 0.004; pH: rmcorr: 0.12; 95% CI: 0.03, 0.21; p = 0.001), and decreased pulmonary compliance (rmcorr: -0.10; 95% CI: -0.20, -0.01; p = 0.034). The maximum LUS score was lower in survivors (median difference [md]: -0.35; 95% CI: -0.55, -0.06; p = 0.006). A cutoff value for non-survival was calculated at a LUS score of 2.63. At the time of maximum LUS score, P/F ratio (md: 1.97; 95% CI: 1.12, 2.76; p < 0.001) and pulmonary compliance (md: 18.67; 95% CI: 3.33, 37.15; p = 0.018) were higher in surviving patients. In conclusion, LUS with a HHUD enables continuous evaluation of cardiopulmonary function in COVID-19 patients receiving ECMO support therapy and provides prognostic value in determining the patients' likelihood of survival.

High Fever, Wide Distribution of Viral Pneumonia, and Pleural Effusion are More Critical Findings at the First Visit in Predicting the Prognosis of COVID-19: A Single Center, retrospective, Propensity Score-Matched Case-Control Study

Introduction

Currently, infection control measures for SARS-COV2 are being relaxed, and it is important in daily clinical practice to decide which findings to focus on when managing patients with similar background factors.

Methods

We retrospectively evaluated 66 patients who underwent blood tests (complete blood count, blood chemistry tests, and coagulation tests) and thin slice CT between January 1 and May 31, 2020, and performed a propensity score-matched case-control study. Cases and controls were a severe respiratory failure group (non-rebreather mask, nasal high-flow, and positive-pressure ventilation) and a non-severe respiratory failure group, matched at a ratio of 1:3 by propensity scores constructed by age, sex, and medical history. We compared groups for maximum body temperature up to diagnosis, blood test findings, and CT findings in the matched cohort. Two-tailed P-values <0.05 were considered statistically significant.

Results

Nine cases and 27 controls were included in the matched cohort. Significant differences were seen in maximum body temperature up to diagnosis (p=0.0043), the number of shaded lobes (p=0.0434), amount of ground-glass opacity (GGO) in the total lung field (p=0.0071), amounts of GGO (p=0.0001), and consolidation (p=0.0036) in the upper lung field, and pleural effusion (p=0.0117).

Conclusion

High fever, the wide distribution of viral pneumonia, and pleural effusion may be prognostic indicators that can be easily measured at diagnosis in COVID-19 patients with similar backgrounds.

Relation of impulse oscillometry and spirometry with quantitative thorax computed tomography after COVID-19 pneumonia

OBJECTIVE

This study aimed to investigate if there is any correlation between the quantitative computed tomography and the impulse oscillometry or spirometry results of post-COVID-19 patients.

METHODS

The study comprised 47 post-COVID-19 patients who had spirometry, impulse oscillometry, and high-resolution computed tomography examinations at the same time. The study group consisted of 33 patients with quantitative computed tomography involvement, while the control group included 14 patients who did not have CT findings. The quantitative computed tomography technology was used to calculate percentages of density range volumes. The relationship between percentages of density range volumes for different quantitative computed tomography density ranges and impulse oscillometry-spirometry findings was statistically analyzed.

RESULTS

In quantitative computed tomography, the percentage of relatively high-density lung parenchyma, including fibrotic areas, was 1.76±0.43 and 5.65±3.73 in the control and study groups, respectively. The percentages of primarily ground-glass parenchyma areas were found to be 7.60±2.86 and 29.25±16.50 in the control and study groups, respectively. In the correlation analysis, the forced vital capacity% predicted in the study group was correlated with DRV%[(-750)-(-500)] (volume of the lung parenchyma that has density between (-750)-(-500) Hounsfield units), but no correlation with DRV%[(-500)-0] was detected. Also, reactance area and resonant frequency were correlated with DRV%[(-750)-(-500)], while X5 was correlated with both DRV%[(-500)-0] and DRV%[(-750)-(-500)] density. Modified Medical Research Council score was correlated with predicted percentages of forced vital capacity and X5.

CONCLUSION

After COVID-19, forced vital capacity, reactance area, resonant frequency, and X5 correlated with the percentages of density range volumes of ground-glass opacity areas in the quantitative computed tomography. X5 was the only parameter correlated with density ranges consistent with both ground-glass opacity and fibrosis. Furthermore, the percentages of forced vital capacity and X5 were shown to be associated with the perception of dyspnea.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION

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

A CT-Based Radiomics Model for Prediction of Prognosis in Patients with Novel Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Study

This study aimed to develop a computed tomography (CT)-based radiomics model to predict the outcome of COVID-19 pneumonia. In total of 44 patients with confirmed diagnosis of COVID-19 were retrospectively enrolled in this study. The radiomics model and subtracted radiomics model were developed to assess the prognosis of COVID-19 and compare differences between the aggravate and relief groups. Each radiomic signature consisted of 10 selected features and showed good performance in differentiating between the aggravate and relief groups. The sensitivity, specificity, and accuracy of the first model were 98.1%, 97.3%, and 97.6%, respectively (AUC = 0.99). The sensitivity, specificity, and accuracy of the second model were 100%, 97.3%, and 98.4%, respectively (AUC = 1.00). There was no significant difference between the models. The radiomics models revealed good performance for predicting the outcome of COVID-19 in the early stage. The CT-based radiomic signature can provide valuable information to identify potential severe COVID-19 patients and aid clinical decisions.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia

BACKGROUND

The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.

METHODS

LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.

RESULTS

Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.

CONCLUSIONS

Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts.

KEY POINTS

We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Automatic Diagnosis of Stage of COVID-19 Patients using an Ensemble of Transfer Learning with Convolutional Neural Networks Based on Computed Tomography Images

Background

Diagnosis of the stage of COVID-19 patients using the chest computed tomography (CT) can help the physician in making decisions on the length of time required for hospitalization and adequate selection of patient care. This diagnosis requires very expert radiologists who are not available everywhere and is also tedious and subjective. The aim of this study is to propose an advanced machine learning system to diagnose the stages of COVID-19 patients including normal, early, progressive, peak, and absorption stages based on lung CT images, using an automatic deep transfer learning ensemble.

Methods

Different strategies of deep transfer learning were used which were based on pretrained convolutional neural networks (CNNs). Pretrained CNNs were fine-tuned on the chest CT images, and then, the extracted features were classified by a softmax layer. Finally, we built an ensemble method based on majority voting of the best deep transfer learning outputs to further improve the recognition performance.

Results

The experimental results from 689 cases indicate that the ensemble of three deep transfer learning outputs based on EfficientNetB4, InceptionResV3, and NasNetlarge has the highest results in diagnosing the stage of COVID-19 with an accuracy of 91.66%.

Conclusion

The proposed method can be used for the classification of the stage of COVID-19 disease with good accuracy to help the physician in making decisions on patient care.

The value of lung ultrasound in COVID-19 pneumonia, verified by high resolution computed tomography assessed by artificial intelligence

BACKGROUND

Lung ultrasound (LUS) is an increasingly popular imaging method in clinical practice. It became particularly important during the COVID-19 pandemic due to its mobility and ease of use compared to high-resolution computed tomography (HRCT). The objective of this study was to assess the value of LUS in quantifying the degree of lung involvement and in discrimination of lesion types in the course of COVID-19 pneumonia as compared to HRCT analyzed by the artificial intelligence (AI).

METHODS

This was a prospective observational study including adult patients hospitalized due to COVID-19 in whom initial HRCT and LUS were performed with an interval < 72 h. HRCT assessment was performed automatically by AI. We evaluated the correlations between the inflammation volume assessed both in LUS and HRCT, between LUS results and the HRCT structure of inflammation, and between LUS and the laboratory markers of inflammation. Additionally we compared the LUS results in subgroups depending on the respiratory failure throughout the hospitalization.

RESULTS

Study group comprised 65 patients, median 63 years old. For both lungs, the median LUS score was 19 (IQR-interquartile range 11-24) and the median CT score was 22 (IQR 16-26). Strong correlations were found between LUS and CT scores (for both lungs r = 0.75), and between LUS score and percentage inflammation volume (PIV) (r = 0.69). The correlations remained significant, if weakened, for individual lung lobes. The correlations between LUS score and the value of the percentage consolidation volume (PCV) divided by percentage ground glass volume (PGV), were weak or not significant. We found significant correlation between LUS score and C-reactive protein (r = 0.55), and between LUS score and interleukin 6 (r = 0.39). LUS score was significantly higher in subgroups with more severe respiratory failure.

CONCLUSIONS

LUS can be regarded as an accurate method to evaluate the extent of COVID-19 pneumonia and as a promising tool to estimate its clinical severity. Evaluation of LUS in the assessment of the structure of inflammation, requires further studies in the course of the disease.

TRIAL REGISTRATION

The study has been preregistered 13 Aug 2020 on clinicaltrials.gov with the number NCT04513210.

Feasibility of Dynamic Inhaled Gas MRI-Based Measurements Using Acceleration Combined with the Stretched Exponential Model

Dynamic inhaled gas (³He/¹²⁹Xe/¹⁹F) MRI permits the acquisition of regional fractional-ventilation which is useful for detecting gas-trapping in lung-diseases such as lung fibrosis and COPD. Deninger's approach used for analyzing the wash-out data can be substituted with the stretched-exponential-model (SEM) because signal-intensity is attenuated as a function of wash-out-breath in ¹⁹F lung imaging. Thirteen normal-rats were studied using ³He/¹²⁹Xe and ¹⁹F MRI and the ventilation measurements were performed using two 3T clinical-scanners. Two Cartesian-sampling-schemes (Fast-Gradient-Recalled-Echo/X-Centric) were used to test the proposed method. The fully sampled dynamic wash-out images were retrospectively under-sampled (acceleration-factors (AF) of 10/14) using a varying-sampling-pattern in the wash-out direction. Mean fractional-ventilation maps using Deninger's and SEM-based approaches were generated. The mean fractional-ventilation-values generated for the fully sampled k-space case using the Deninger method were not significantly different from other fractional-ventilation-values generated for the non-accelerated/accelerated data using both Deninger and SEM methods (p > 0.05 for all cases/gases). We demonstrated the feasibility of the SEM-based approach using retrospective under-sampling, mimicking AF = 10/14 in a small-animal-cohort from the previously reported dynamic-lung studies. A pixel-by-pixel comparison of the Deninger-derived and SEM-derived fractional-ventilation-estimates obtained for AF = 10/14 (≤16% difference) has confirmed that even at AF = 14, the accuracy of the estimates is high enough to consider this method for prospective measurements.

Validation of the North America expert consensus statement on reporting CT findings for COVID-19 in individuals with lung cancer

The aim of our study was to validate the use of the standardized Radiological Society of North America (RSNA) reporting system in individuals with known lung cancer who presented to the emergency department with suspected COVID-19. We included patients aged 18 years or older from the Cancer Institute of the State of São Paulo (ICESP) with a confirmed diagnosis of lung cancer, admitted to the emergency department and undergoing chest computed tomography (CT) for suspicion of COVID-19. Comparison between SARS-CoV2 RT-PCR across RSNA categories was performed in all patients and further stratified by diagnosis of lung cancer progression. Among 58 individuals included in the analysis (65±9 years, 43% men), 20 had positive RT-PCR. Less than a half (43%) had no new lung findings in the CT. Positive RT-PCR was present in 75% of those with typical findings according to RSNA and in only 9% when these findings were classified as atypical or negative (P<0.001). Diagnostic accuracy was even higher when stratified by the presence or absence of progressive disease (PD). Extent of pulmonary inflammatory changes was strongly associated with higher mortality, reaching a lethality of 83% in patients with >25% of lung involvement and 100% when there was >50% of lung involvement. The lung involvement score was also highly predictive of prognosis in this population as was reported for non-lung cancer individuals. Collectively, our results demonstrated that diagnostic and prognostic values of chest CT findings in COVID-19 are robust to the presence of lung abnormalities related to lung cancer.

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

Background

Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering.

Material and method

We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm.

Results

Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity.

Conclusions

Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

CT-based pathological lung opacities volume as a predictor of critical illness and inflammatory response severity in patients with COVID-19

Objective

The aim of the study was to assess the impact of CT-based lung pathological opacities volume on critical illness and inflammatory response severity of patients with COVID-19.

Methods

A retrospective, single center, single arm study was performed over a 30-day period. In total, 138 patients (85.2%) met inclusion criteria. All patients were evaluated with non-contrast enhanced chest CT scan at hospital admission. CT-based lung segmentation was performed to calculate pathological lung opacities volume (LOV). At baseline, complete blood count (CBC) and inflammation response biomarkers were obtained. The primary endpoint of the study was the occurrence of critical illness, as defined as, the need of mechanical ventilation and/or ICU admission. Mann-Whitney U test was performed for univariate analysis. Logistic regression analysis was performed to determine independent predictors of critical illness. Spearman analysis was performed to assess the correlation between inflammatory response biomarkers serum concentrations and LOV.

Results

Median LOV was 28.64% (interquartile range [IQR], 6.33-47.22%). Correlation analysis demonstrated that LOV was correlated with higher levels of D-dimer (r = 0.51, p < 0.01), procalcitonin (r = 0.47, p < 0.01) and IL6 (r = 0.48, p < 0.01). Critical illness occurred in 51 patients (37%). Univariate analysis demonstrated that inflammatory response biomarkers and LOV were associated with critical illness (p < 0.05). However, multivariate analysis demonstrated that only D-dimer and LOV were independent predictors of critical illness. Furthermore, a ROC analysis demonstrated that a LOV equal or greater than 60% had a sensitivity of 82.1% and specificity of 70.2% to determine critical illness with an odds ratio of 19.4 (95% CI, 4.2-88.9).

Conclusion

Critical illness may occur in up to 37% of the patients with COVID-19. Among patients with critical illness, higher levels of inflammatory response biomarkers with larger LOVs were observed. Furthermore, multivariate analysis demonstrated that pathological lung opacities volume was an independent predictor of critical illness. In fact, patients with a pathological lung opacities volume equal or greater than 60% had 19.4-fold increased risk of critical illness.

Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients

Objectives

We evaluated the contribution of lung lesion quantification on chest CT using a clinical Artificial Intelligence (AI) software in predicting death and intensive care units (ICU) admission for COVID-19 patients.

Methods

For 349 patients with positive COVID-19-PCR test that underwent a chest CT scan at admittance or during hospitalization, we applied the AI for lung and lung lesion segmentation to obtain lesion volume (LV), and LV/Total Lung Volume (TLV) ratio. ROC analysis was used to extract the best CT criterion in predicting death and ICU admission. Two prognostic models using multivariate logistic regressions were constructed to predict each outcome and were compared using AUC values. The first model ("Clinical") was based on patients' characteristics and clinical symptoms only. The second model ("Clinical+LV/TLV") included also the best CT criterion.

Results

LV/TLV ratio demonstrated best performance for both outcomes; AUC of 67.8% (95% CI: 59.5 - 76.1) and 81.1% (95% CI: 75.7 - 86.5) respectively. Regarding death prediction, AUC values were 76.2% (95% CI: 69.9 - 82.6) and 79.9% (95%IC: 74.4 - 85.5) for the "Clinical" and the "Clinical+LV/TLV" models respectively, showing significant performance increase (+ 3.7%; p-value<0.001) when adding LV/TLV ratio. Similarly, for ICU admission prediction, AUC values were 74.9% (IC 95%: 69.2 - 80.6) and 84.8% (IC 95%: 80.4 - 89.2) respectively corresponding to significant performance increase (+ 10%: p-value<0.001).

Conclusions

Using a clinical AI software to quantify the COVID-19 lung involvement on chest CT, combined with clinical variables, allows better prediction of death and ICU admission.

Correlation between vitamin D level and severity of prognostic markers in Egyptian COVID-19 patients: a cohort study

The outbreak of coronavirus disease 2019 (COVID-19), which is caused by the highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was announced a pandemic in March 2020 by the World Health Organization. The disease can be diagnosed on the basis of clinical symptoms, polymerase chain reaction positivity, and the presence of ground-glass opacities on computed tomography (CT) scans.

Recent studies have focused on the role of serum inflammatory markers that predict COVID-19, such as lymphocyte counts and C-reactive protein (CRP), homocysteine, and D-dimer levels. Vitamin D is thought to reduce the risk of viral infections through several mechanisms.

Our aim was to evaluate the correlation between serum vitamin D level and inflammatory markers and severity in Egyptian patients with COVID-19 infection. Serum vitamin D level had a positive correlation with hemoglobin level and lymphocytes.

As results, serum vitamin D had a negative correlation with serum ferritin, CRP, and D-dimer and was not correlated with CORAD scoring in the CT chest.

In conclusion, serum vitamin D was inversely correlated with inflammatory markers (ferritin, CRP, and D-dimer) which mean that participants with symptoms of COVID-19 had a high level of inflammatory markers and a low level of vitamin D.

Participants without symptoms of COVID-19 had normal inflammatory markers and normal vitamin D level.

Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.

Radiological comparison of the Wuhan and B.1.1.7 variant COVID-19 infection; are there any differences in chest CT scans?

Aim: In September 2020, a variant of the SARS-CoV-2 virus was detected in England and it became the dominant type in most of the countries. The clinical behavior of the B.1.1.7 variant COVID-19 infectionis different from the Wuhan type.So we aimed to investigate whether there are any differences in computed tomography (CT) imaging findings of pneumonia caused by COVID-19 variants. Material and Method: 340 patients who admitted to the emergency departmentwith symptoms of dyspnea and chest pain suspecting COVID-19 pneumonia and pulmonary embolism were included in the study. Oncology (n:12) and pediatric (n:8) patients, patients with negative PCR test (n:56), and patients infected with different variant (n:6) were excluded leaving 258 patients grouped into two (B.1.1.7 and Wuhan type) for evaluation of CT findings such as pleural thickening,pleural and pericardial effusion, consolidation, GGO presence and distribution, upper lobe involvement, pulmonary embolism, tree in bud pattern, centrilobuler nodule, revers halo sign, and hepatosteatosis. Results: A statistically significant difference was obtained between the two groups in terms of pleural thickening (p=0.020), upper lobe involvement (p=0.037), localization of GGO (p=0.001), presence of pleural effusion (p=0.025), embolism (p=0.011) and presence of consolidation (p=0.042). However, no significant difference was found for the development of hepatosteatosis (p=0.520). Conclusion: There aredifferences in radiological findings between B.1.1.7 variant and Wuhan type. In our study atypical radiological findings are more common in B.1.1.7 type. In addition, radiological findings that seen in severe COVID-19 pneumonia are more common in B.1.1.7.

Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.

A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia

RATIONALE AND OBJECTIVES

Hard data labels for automated algorithm training are binary and cannot incorporate uncertainty between labels. We proposed and evaluated a soft labeling methodology to quantify opacification and percent well-aerated lung (%WAL) on chest CT, that considers uncertainty in segmenting pulmonary opacifications and reduces labeling burden.

MATERIALS AND METHODS

We retrospectively sourced 760 COVID-19 chest CT scans from five international centers between January and June 2020. We created pixel-wise labels for >27,000 axial slices that classify three pulmonary opacification patterns: pure ground-glass, crazy-paving, consolidation. We also quantified %WAL as the total area of lung without opacifications. Inter-user hard label variability was quantified using Shannon entropy (range=0-1.39, low-high entropy/variability). We incorporated a soft labeling and modeling cycle following an initial model with hard labels and compared performance using point-wise accuracy and intersection-over-union of opacity labels with ground-truth, and correlation with ground-truth %WAL.

RESULTS

Hard labels annotated by 12 radiologists demonstrated large inter-user variability (3.37% of pixels achieved complete agreement). Our soft labeling approach increased point-wise accuracy from 60.0% to 84.3% (p=0.01) compared to hard labeling at predicting opacification type and area involvement. The soft label model accurately predicted %WAL (R=0.900) compared to the hard label model (R=0.856), but the improvement was not statistically significant (p=0.349).

CONCLUSION

Our soft labeling approach increased accuracy for automated quantification and classification of pulmonary opacification on chest CT. Although we developed the model on COVID-19, our intent is broad application for pulmonary opacification contexts and to provide a foundation for future development using soft labeling methods.

Comparison of CT findings of coronavirus disease 2019 (COVID-19) pneumonia caused by different major variants

Purpose

To explore the CT findings and pneumonnia progression pattern of the Alpha and Delta variants of SARS-CoV-2 by comparing them with the pre-existing wild type.

Method

In this retrospective comparative study, a total of 392 patients with COVID-19 were included: 118 patients with wild type (70 men, 56.8 ± 20.7 years), 137 with Alpha variant (93 men, 49.4 ± 17.0 years), and 137 with Delta variant (94 men, 45.4 ± 12.4). Chest CT evaluation included opacities and repairing changes as well as lesion distribution and laterality. Chest CT severity score was also calculated. These parameters were statistically compared across the variants.

Results

Ground glass opacity (GGO) with consolidation and repairing changes were more frequent in the order of Delta variant, Alpha variant, and wild type throughout the disease course. Delta variant showed GGO with consolidation more conspicuously than did the other two on days 1–4 (vs. wild type, Bonferroni corrected p = 0.01; vs. Alpha variant, Bonferroni corrected p = 0.003) and days 5–8 (vs. wild type, Bonferroni corrected p < 0.001; vs. Alpha variant, Bonferroni corrected-p = 0.003). Total lung CT severity scores of Delta variant were higher than those of wild type on days 1–4 and 5–8 (Bonferroni corrected p = 0.01 and Bonferroni corrected p = 0.005, respectively) and that of Alpha variant on days 1–4 (Bonferroni corrected p = 0.002). There was no difference in the CT findings between wild type and Alpha variant.

Conclusions

Pneumonia progression of Delta variant may be more rapid and severe in the early stage than in the other two.

Pulmonary CT scans of white rabbits using the selective photon shield technique of the third-generation dual-source CT

This study aims to optimise the protocol for the low-dose pulmonary computed tomography (CT) scanning of infants by studying the effects of the selective photon shield (SPS) technique of the third-generation dual-source CT (DSCT) on the image quality and radiation dose of a chest CT in white rabbits under different tube currents. Twelve white rabbits of a similar weight to an infant were selected and randomly divided into an experimental group and a control group. The experimental groups (A1-A5) were scanned at low dose by the third-generation DSCT using SPS under different tube current × time (60, 50, 40, 30, and 20 mAs). The control group (B) was scanned under a conventional tube voltage (100 kV) and current × time (20 mAs). Advanced model iterative reconstruction at strength three was used for the objective and subjective evaluation of the image quality and radiation dose of the lung and mediastinal windows. With the standard deviation of the air in the trachea as image noise, the signal-to-noise ratio (SNR), contrast-to-noise ratio, and CT values of each site were evaluated. Radiation doses were compared using the volume CT dose index, dose length product, and effective dose. The differences in subjective image quality between groups A2 and B were not statistically significant (P= 0.34). The differences in the SNRs of the lung and mediastinal windows between groups A2 and B were not statistically significant (P> 0.05). The radiation dose of group A2 was 83.2% lower than that of group B. The SPS of the third-generation DSCT under 50 mAs might be applied in the pulmonary CT examination of infants.

Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study

(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the “first wave” of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51–69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1–4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.

Lung Ultrasound in Patients With SARS-COV-2 Pneumonia: Correlations With Chest Computed Tomography, Respiratory Impairment, and Inflammatory Cascade

OBJECTIVES

Lung ultrasound (LUS) might be comparable to chest computed tomography (CT) in detecting parenchymal and pleural pathology, and in monitoring interstitial lung disease. We aimed to describe LUS characteristics of patients during the hospitalization for COVID-19 pneumonia, and to compare the extent of lung involvement at LUS and chest-CT with inflammatory response and the severity of respiration impairment.

METHODS

During a 2-week period, we performed LUS and chest CT in hospitalized patients affected by COVID-19 pneumonia. Dosages of high sensitivity C-reactive protein (HS-CRP), d-dimer, and interleukin-6 (IL-6) were also obtained. The index of lung function (P/F ratio) was calculated from the blood gas test. LUS and CT scoring were assessed using previously validated scores.

RESULTS

Twenty-six consecutive patients (3 women) underwent LUS 34 ± 14 days from the early symptoms. Among them, 21 underwent CT on the same day of LUS. A fair association was found between LUS and CT scores (R = 0.45, P = .049), which became stronger if the B-lines score on LUS was not considered (R = 0.57, P = .024). LUS B-lines score correlated with IL-6 levels (R = 0.75, P = .011), and the number of involved lung segments detected by LUS correlated with the P/F ratio (R = 0.60, P = .019) but not with HS-CRP and d-Dimer levels. No correlations were found between CT scores and inflammations markers or P/F.

CONCLUSION

In patients with COVID-19 pneumonia, LUS was correlated with both the extent of the inflammatory response and the P/F ratio.

Factors Predicting Progression to Severe COVID-19: A Competing Risk Survival Analysis of 1753 Patients in Community Isolation in Wuhan, China

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19-rather than to recovery-among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients (n = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04-1.58, p = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24-2.01, p < 0.001), and CT signs of ground-glass opacity (HR = 1.39, 95% CI 1.04-1.86, p = 0.024) and infiltrating shadows (HR = 1.84, 95% CI 1.22-2.78, p = 0.004). The risk of progression was found to be lower among patients with nausea or vomiting (HR = 0.53, 95% CI 0.30-0.96, p = 0.036) and headaches (HR = 0.54, 95% CI 0.29-0.99, p = 0.046). Our results suggest that several factors that can be easily measured even in resource-poor settings (dyspnea, sex, and age) can be used to identify mild COVID-19 patients who are at increased risk of disease progression. Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings. Common and unspecific symptoms (headaches, nausea, and vomiting) are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate. Future public health and clinical guidelines should build on this evidence to improve the screening, triage, and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms.

An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images

The lungs are COVID-19's most important focus, as it induces inflammatory changes in the lungs that can lead to respiratory insufficiency. Reducing the supply of oxygen to human cells negatively impacts humans, and multiorgan failure with a high mortality rate may, in certain circumstances, occur. Radiological pulmonary evaluation is a vital part of patient therapy for the critically ill patient with COVID-19. The evaluation of radiological imagery is a specialized activity that requires a radiologist. Artificial intelligence to display radiological images is one of the essential topics. Using a deep machine learning technique to identify morphological differences in the lungs of COVID-19-infected patients could yield promising results on digital images of chest X-rays. Minor differences in digital images that are not detectable or apparent to the human eye may be detected using computer vision algorithms. This paper uses machine learning methods to diagnose COVID-19 on chest X-rays, and the findings have been very promising. The dataset includes COVID-19-enhanced X-ray images for disease detection using chest X-ray images. The data were gathered from two publicly accessible datasets. The feature extractions are done using the gray level co-occurrence matrix methods. K-nearest neighbor, support vector machine, linear discrimination analysis, naïve Bayes, and convolutional neural network methods are used for the classification of patients. According to the findings, convolutional neural networks' efficiency linked to imaging modalities with fewer human involvements outperforms other traditional machine learning approaches.

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.

Lymphopenia in patients affected by SARS-CoV-2 infection is caused by margination of lymphocytes in large bowel: an [18F]FDG PET/CT study

BACKGROUND

To investigate the cause of lymphopenia in patients with newly diagnosed COVID-19, we measured [¹⁸F]FDG uptake in several tissues, including the ileum, right colon, and caecum at diagnosis and after recovery and correlated these measurements with haematological parameters.

METHODS

We studied, by [¹⁸F]FDG PET/CT, 18 newly diagnosed patients with COVID-19. Regions of interest were drawn over major organs and in the terminal ileum, caecum, and right colon, where the bowel wall was evaluable. Five patients were re-examined after recovery, and three of them also performed a white blood cell scan with ^99mTc-HMPAO-WBC on both occasions. Complete blood count was performed on both occasions, and peripheral blood lymphocyte subsets were measured at diagnosis. Data were analysed by a statistician.

RESULTS

Patients had moderate severity COVID-19 syndrome. Basal [¹⁸F]FDG PET/CT showed focal lung uptake corresponding to hyperdense areas at CT. We also found high spleen, ileal, caecal, and colonic activity as compared to 18 control subjects. At recovery, hypermetabolic tissues tended to normalize, but activity in the caecum remained higher than in controls. Regression analyses showed an inverse correlation between CD4 + lymphocytes and [¹⁸F]FDG uptake in the caecum and colon and a direct correlation between CD8 + lymphocytes and [¹⁸F]FDG uptake in lungs and bone marrow. WBC scans showed the presence of leukocytes in the caecum and colon that disappeared at recovery.

CONCLUSIONS

These findings indicate that lymphopenia in COVID-19 patients is associated with large bowel inflammation supporting the hypothesis that CD4 + lymphocytes migrate to peripheral lymphoid tissues in the bowel.

A Novel Threshold-Based Segmentation Method for Quantification of COVID-19 Lung Abnormalities

Since December 2019, the novel coronavirus disease 2019 (COVID-19) has claimed the lives of more than 3.75 million people worldwide. Consequently, methods for accurate COVID-19 diagnosis and classification are necessary to facilitate rapid patient care and terminate viral spread. Lung infection segmentations are useful to identify unique infection patterns that may support rapid diagnosis, severity assessment, and patient prognosis prediction, but manual segmentations are time-consuming and depend on radiologic expertise. Deep learning-based methods have been explored to reduce the burdens of segmentation; however, their accuracies are limited due to the lack of large, publicly available annotated datasets that are required to establish ground truths. For these reasons, we propose a semi-automatic, threshold-based segmentation method to generate region of interest (ROI) segmentations of infection visible on lung computed tomography (CT) scans. Infection masks are then used to calculate the percentage of lung abnormality (PLA) to determine COVID-19 severity and to analyze the disease progression in follow-up CTs. Compared with other COVID-19 ROI segmentation methods, on average, the proposed method achieved improved precision ( 47.49 % ) and specificity ( 98.40 % ) scores. Furthermore, the proposed method generated PLAs with a difference of ± 3.89 % from the ground-truth PLAs. The improved ROI segmentation results suggest that the proposed method has potential to assist radiologists in assessing infection severity and analyzing disease progression in follow-up CTs.

The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov-Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83% accuracy and 93.39% kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67% accuracy and 86.67% kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest.

CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis

Quick screening patients with COVID-19 is the most important way of controlling transmission by isolation and medical treatment. Chest computed tomography (CT) has been widely used during the initial screening process, including pneumonia diagnosis, severity assessment, and differential diagnosis of COVID-19. The course of COVID-19 changes rapidly. Serial CT imaging could observe the distribution, density, and range of lesions dynamically, monitor the changes, and then guide towards appropriate treatment. The aim of the review was to explore the chest CT findings and dynamic CT changes of COVID-19 using systematic evaluation methods, instructing the clinical imaging diagnosis. A systematic literature search was performed. The quality of included literature was evaluated with a quality assessment tool, followed by data extraction and meta-analysis. Homogeneity and publishing bias were analyzed. A total of 109 articles were included, involving 2908 adults with COVID-19. The lesions often occurred in bilateral lungs (74%) and were multifocal (77%) with subpleural distribution (81%). Lesions often showed ground-glass opacity (GGO) (68%), followed by GGO with consolidation (48%). The thickening of small vessels (70%) and thickening of intralobular septum (53%) were also common. The dynamic changes of chest CT manifestations showed that lesions were absorbed and improved gradually after reaching the peak (80%), had progressive deterioration (55%), were absorbed and improved gradually (46%), fluctuated (22%), or remained stable (26%). The review showed the common and key CT features and the dynamic imaging change patterns of COVID-19, helping with timely management during COVID-19 pandemic.

The correlation between severity scores in computed tomography lung scans and viral load in the severity of novel coronavirus 2019 progression

BACKGROUND

This study aimed to find the correlation between severe computed tomography (CT) lung scores and nasopharyngeal viral load (Ct value) in the severity of COVID-19 disease progression.

METHOD

In this study, 37 patients diagnosed with COVID-19 were categorized into severely ill and not severely ill samples. Their Ct values, epidemiological data, lung CT, and laboratory test results were collected three times, respectively, on the first day of their hospital admission, 3-5 days thereafter, and prior to hospital discharge. Among the 37 patients, 8 progressed from not severely ill to severely ill; we also paid attention and observed changes in clinical parameters of COVID-19 patients who entered our city from other cities (imported cases) and the infected local residents who contacted these imported patients (non-imported cases).

RESULTS

Among the 37 patients, the Ct values and lung severity scores (LSSs) were similar in imported and non-imported cases (F = 0.59 and 2.56; p = 0.45 and 0.12, respectively) but the proportion of severely ill imported patients was significantly higher compared with non-imported patients (F = 7.77; p = 0.01). Additionally, 21.6% of patients' illness worsened; lymphocyte counts and Ct values were significantly lowered, and C-reactive protein and LSS significantly increased during COVID-19 disease progression. Furthermore, LSS negatively correlated with lymphocyte and mononuclear cell counts, as well as Ct values (Pearson's rank = -0.763, -0.824, and -0.588; p = 0.028, 0.012, and 0.003, respectively).

CONCLUSION

In the severity of COVID-19 disease progression, nasopharyngeal viral load and lung CT severity were closely related, and LSS negatively correlated with lymphocyte and mononuclear cell counts, as well as Ct values.

COVID 19 Pandemic and biomedical waste management practices in healthcare system

The whole world was shaken with the pandemic of Coronavirus Disease (COVID-19) in end of the year 2019. Due to its novel origin, it was required to follow all precautions possible. Dealing with the massive amount of infectious healthcare waste became an enormous challenge. This review identifies the impacts of the pandemic on biomedical waste management. This systematic review was made by using keywords "biomedical waste" and "COVID 19" in open access databases like PubMed, Science Direct, Scopus, Google Scholers etc. 2124 articles downloaded and 765 found duplicate and 634 not related to the topic. after scrutiny with inclusion criteria 102 articles were considered to analyze the practices related to biomedical waste management during pandemic using PRISMA guideline.. The COVID-19 waste segregation, collection, storage, transportation, and disposal are a big challenge with all stakeholders. In order to control the virus spread, strict monitoring of the complete waste management cycle is required. Adoption of appropriate guidelines is paramount to worker safety and containment of infection. Sustainable recycling methods are needed to deal with the ever-increasing plastic waste resulting from mandatory personal protective equipment (PPE) usage. The situation also demands a rethinking of the healthcare system. Overall, there was an increase in BMW generation, and municipal waste had increased globally. Pandemic preparedness requires a global public health strategy and long-term investments. This will be vital for making a robust community capable enough to fight against any public health pressures in the future, as well as the pandemic tremors. Systematized efforts from all stakeholders, at all levels, not only refines epidemic preparation but also helps to attain a sustainable development of health for a healthier future.

[Specific features of the pathology of the respiratory system in SARS-CoV-2 (Coronaviridae: Coronavirinae: Betacoronavirus: Sarbecovirus) infected Syrian hamsters (Mesocricetus auratus)]

INTRODUCTION

Verification of histological changes in respiratory system using Syrian (golden) hamsters (Mesocricetus auratus) as experimental model is an important task for preclinical studies of drugs intended for prevention and treatment of the novel coronavirus infection COVID-19.The aim of this work was to study pathological changes of pulmonary tissue in SARS-CoV-2 (Coronaviridae: Coronavirinae: Betacoronavirus; Sarbecovirus) experimental infection in Syrian hamsters.

MATERIAL AND METHODS

Male Syrian hamsters weighting 80-100 g were infected by intranasal administration of culture SARS-CoV-2 at dose 4 × 104 TCID50/ml (TCID is tissue culture infectious dose). Animals were euthanatized on 3, 7 and 14 days after infection, with gravimetric registration. The viral load in lungs was measured using the polymerase chain reaction (PCR). Right lung and trachea tissues were stained with hematoxylin-eosin and according to Mallory.

RESULTS AND DISCUSSION

The highest viral replicative activity in lungs was determined 3 days after the infection. After 7 days, on a background of the decrease of the viral load in lungs, a pathologically significant increase of the organ's gravimetric parameters was observed. Within 3 to 14 days post-infection, the lung histologic pattern had been showing the development of inflammation with a succession of infiltrative-proliferative, edematousmacrophagal and fibroblastic changes. It was found that initial changes in respiratory epithelium can proceed without paranecrotic interstitial inflammation, while in the formation of multiple lung parenchyma lesions, damage to the epithelium of bronchioles and acinar ducts can be secondary. The appearance of epithelioid large-cell metaplastic epithelium, forming pseudoacinar structures, was noted as a pathomorphological feature specific to SARS-CoV-2 infection in Syrian hamsters.

CONCLUSION

As a result of the study, the specific features of the pathology of the respiratory system in SARSCoV-2 infected Syrian hamsters were described. These findings are of practical importance as reference data that can be used for preclinical studies to assess the effectiveness of vaccines and potential drugs.

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

SARS-CoV-2: A Professional and social gamechanger - Medical and dental aspects

This study reports the SARS-CoV-2 outbreak and its impact on dental practice and education in Brazil. A literature review involving medical and dental interests was performed based on recent general findings about the infection (research and relevant guidelines). COVID-19 is a high transmissible, unpredictable systemic disease, involving a viral replication phase, followed by an inflammatory phase that can evolve into hyperinflammation that leads to a cytokine storm and other serious issues including sepsis, shock and multiple organ failure. The dentists are directly impacted by the new coronavirus as they work with the oral cavity that is irrigated by the saliva and receive the respiratory aerosols and droplets from the patient. In conclusion, the world is facing a completely new situation that deserves the comprehension of the population and close attention of the authorities. Following protocols to attend patients can prevent the dissemination of the virus, cross-infection, and the contamination of health care professionals. New strategies need to be developed to enhance the existing teaching and learning protocols in Universities and to allow research to continue.

Observational study of clinico-radiological follow-up of COVID-19 pneumonia: a district general hospital experience in the UK

BACKGROUND

The British Thoracic Society (BTS) recommends that all patients admitted with COVID-19 pneumonia should have a chest X-ray (CXR) and clinical follow-up at 6 or 12 weeks, depending on the disease severity. Little data is available on long-term CXR follow-up for moderate and severe COVID-19 pneumonia. This study aims to evaluate compliance with clinico-radiological follow-up of patients recovering from COVID-19 pneumonia at a local hospital in the UK, as per the BTS guidance, and to analyse radiological changes at clinical follow-up at 12 weeks, in order to risk-stratify and improve patient outcomes.

METHODS

This is a single-centre retrospective audit of 255 consecutive COVID-19 positive patients admitted to a local hospital in the UK over 5 months between May and October 2020. All CXRs and clinic follow-up at 12 ± 8 weeks were checked on an electronic database.

RESULTS

Over one in two (131/255) patients had CXR evidence of COVID-19 pneumonia during the initial hospital admission. Half of the patients (60/131) died before CXR or clinic follow-up. Fifty-eight percent (41/71) of the surviving patients had a follow-up CXR, and only two developed respiratory complications- one had residual lung fibrosis, another a pulmonary embolism. Eighty-eight percent (36/41) of the patients had either resolution or improved radiological changes at follow-up. Most patients who had abnormal follow-up CXR were symptomatic (6/8), and many asymptomatic patients at follow-up had a normal CXR (10/12).

CONCLUSIONS

Although there were concerns about interstitial lung disease (ILD) incidence in patients with COVID-19 pneumonia, most of our patients with COVID-19 pneumonia had no pulmonary complications at follow-up with CXR. This emphasises that CXR, a cost-effective investigation, can be used to risk-stratify patients for long term pulmonary complications following their COVID-19 pneumonia. However, we acknowledge the limitations of a low CXR and clinic follow-up rate in our cohort.

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT

Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression,i.e.,from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, andF1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management

Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.

Early CT features of COVID-19 pneumonia, association with patients’ age and duration of presenting complaint

Background

Coronavirus disease (COVID-19) is a respiratory syndrome with a variable degree of severity. Imaging is a vital component of disease monitoring and follow-up in coronavirus pulmonary syndromes. The study of temporal changes of CT findings of COVID-19 pneumonia can help in better understanding of disease pathogenesis and prediction of disease prognosis. In this study, we aim to determine the typical and atypical CT imaging features of COVID-19 and discuss the association of typical CT imaging features with the duration of the presenting complaint and patients’ age.

Results

The lesions showed unilateral distribution in 20% of cases and bilateral distribution in 80% of cases. The lesions involved the lower lung lobes in 30% of cases and showed diffuse involvement in 58.2% of cases. The lesions showed peripheral distribution in 74.5% of cases. The most common pattern was multifocal ground glass opacity found in 72.7% of cases. Atypical features like cavitation and pleural effusion can occur early in the disease course. There was significant association between increased number of the lesions, bilaterality, diffuse pattern of lung involvement and older age group (≥ 50 years old) and increased duration of presenting complaint (≥ 4 days). There was significant association between crazy-paving pattern and increased duration of presenting complaint. No significant association could be detected between any CT pattern and increased patient age.

Conclusion

The most common CT feature of COVID-19 was multifocal ground glass opacity. Atypical features like cavitation and pleural effusion can occur early in the course of the disease. Our cases showed more extensive lesions with bilateral and diffuse patterns of distribution in the older age group and with increased duration of presenting complaint. There was a significant association between crazy-paving pattern and increased duration of presenting complaint. No significant association could be detected between any CT pattern and increased patient age.

Role of CT angiography in detecting acute pulmonary embolism associated with COVID-19 pneumonia

PURPOSE

Recently coronavirus disease (COVID-19) caused a global pandemic, characterized by acute respiratory distress syndrome (ARDS). The aim of our study was to detect pulmonary embolism (PE) in patients with severe form of COVID-19 infection using pulmonary CT angiography, and its associations with clinical and laboratory parameters.

METHODS

From March to December 2020, we performed a prospective monocentric study collecting data from 374 consecutive patients with confirmed SARS-CoV-2 infection, using real-time reverse-transcriptase polymerase-chain-reaction (rRT-PCR) assay of nasopharyngeal swab specimens. We subsequently selected patients with at least two of the following inclusion criteria: (1) severe acute respiratory symptoms (such as dyspnea, persistent cough, fever > 37.5 °C, fatigue, etc.); (2) arterial oxygen saturation ≤ 93% at rest; (3) elevated D-dimer (≥ 500 ng/mL) and C-reactive protein levels (≥ 0.50 mg/dL); and (4) presence of comorbidities. A total of 63/374 (17%) patients met the inclusion criteria and underwent CT angiography during intravenous injection of iodinated contrast agent (Iomeprol 400 mgI/mL). Statistical analysis was performed using Wilcoxon rank-sum and Chi-square tests.

RESULTS

About, 26/60 patients (40%) were found positive for PE at chest CT angiography. In these patients, D-dimer and CRP values were significantly higher, while a reduction in SaO2 < 93% was more common than in patients without PE (P < 0.001). Median time between illness onset and CT scan was significantly longer (15 days; P < 0.001) in patients with PE. These were more likely to be admitted to the Intensive Care Unit (19/26 vs. 11/34 patients; P < 0.001) and required mechanical ventilation more frequently than those without PE (15/26 patients vs. 9/34 patients; P < 0.001). Vascular enlargement was significantly more frequent in patients with PE than in those without (P = 0.041).

CONCLUSIONS

Our results pointed out that patients affected by severe clinical features of COVID-19 associated with comorbidities and significant increase of D-dimer levels developed acute mono- or bi-lateral pulmonary embolism in 40% of cases. Therefore, the use of CT angiography rather than non-contrast CT should be considered in these patients, allowing a better evaluation, that can help the management and improve the outcomes.

Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment

The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environment. The accuracy rate of diagnosis (classification) based on laboratory findings can be improved via light ML models. Three ML models, namely, naive Bayes (NB), Random Forest (RF), and support vector machine (SVM), were trained and tested on the basis of laboratory datasets. Three main methodological scenarios of COVID-19 diagnoses, such as diagnoses based on original and normalized datasets and those based on feature selection, were presented. Compared with benchmark studies, our proposed SVM model obtained the most substantial diagnosis performance (up to 95%). The proposed model based on ML and IoT can be served as a clinical decision support system. Furthermore, the outcomes could reduce the workload for doctors, tackle the issue of patient overcrowding, and reduce mortality rate during the COVID-19 pandemic.

Spontaneous Pneumomediastinum in Patients Diagnosed with COVID-19: A Case Series with Review of Literature

Background

Spontaneous pneumomediastinum (SPM) is a rare condition defined by the presence of air in the mediastinum in the absence of traumatic or iatrogenic injury. Although the imaging findings and complications of SARS-CoV-2 infection have been reported many times, there are few reports of the prevalence and outcomes of patients with SPM.

Purpose

In this paper, we aimed to illustrate the different manifestations, management, and outcome of three cases of SPM in COVID-19 patients and provide an extensive review available literature.

Materials and Methods

Detailed report of patients' demographics, clinical presentation, management, and outcome of three cases of COVID-19 induced SPM seen in our institution was provided. Additionally, literature search was employed through March 2021 using Pubmed and Google scholar databases where a total of 22 articles consisting of 35 patients were included.

Results

Statistical analysis of the reviewed articles showed that SPM in COVID-19 occurs in patients with a mean age of 55.6 ± 16.7 years. Furthermore, 80% of the 35 patients are males and almost 60% have comorbidities. Intriguingly, SPM in COVID-19 is associated with a 28.5% mortality rate. These findings are consistent with our case series and are different from previous reports of SPM in non-COVID-19 cases where it most commonly occurs in younger individuals and has a self-limiting course with a good outcome.

Conclusion

Therefore, SPM in COVID-19 patients occurs in older patients and is potentially associated with a higher mortality rate. Further studies are necessary to assess its role as a prognostic marker of poor outcome.

The COVID-19 Pandemic: Are There Any Impacts on Sustainability?

This paper sets out to explain and describe the potential ways to control COVID-19′s impact on the environment and what controllable strategies and anticipations emerge from rethinking sustainable production. The rapid and devastating spread of this disease has made millions of people throughout the world cover themselves, wear gloves, and use hand sanitizers and other medical applications. However, it means that a huge amount of clinical waste is being dumped into landfills or the oceans, and such activity may simply worsen the infection’s transmission and the sustainability of the environment, the socio-economy, and sustainable productions. This disease has greatly changed the way people live and has caused considerable occupational job losses and misfortunes, sending sustainable businesses and other organizations to the wall. Virtually every country is trying to stop the infection transmission by testing patients and isolating people, but the environmental effects of the pandemic and sustainable business have not previously been analyzed. The study suggests that the current options for sustainable production must be measured and also further researched.