CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)

Imaging of COVID-19 pneumonia: Patterns, pathogenesis, and advances

COVID-19 pneumonia is a newly recognized lung infection. Initially, CT imaging was demonstrated to be one of the most sensitive tests for the detection of infection. Currently, with broader availability of polymerase chain reaction for disease diagnosis, CT is mainly used for the identification of complications and other defined clinical indications in hospitalized patients. Nonetheless, radiologists are interpreting lung imaging in unsuspected patients as well as in suspected patients with imaging obtained to rule out other relevant clinical indications. The knowledge of pathological findings is also crucial for imagers to better interpret various imaging findings. Identification of the imaging findings that are commonly seen with the disease is important to diagnose and suggest confirmatory testing in unsuspected cases. Proper precautionary measures will be important in such unsuspected patients to prevent further spread. In addition to understanding the imaging findings for the diagnosis of the disease, it is important to understand the growing set of tools provided by artificial intelligence. The goal of this review is to highlight common imaging findings using illustrative examples, describe the evolution of disease over time, discuss differences in imaging appearance of adult and pediatric patients and review the available literature on quantitative CT for COVID-19. We briefly address the known pathological findings of the COVID-19 lung disease that may help better understand the imaging appearance, and we provide a demonstration of novel display methodologies and artificial intelligence applications serving to support clinical observations.

Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis

INTRODUCTION

Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a world-wide pandemic with an enormous medical and societal-economic toll. Thus, our aim was to gather all available information regarding comorbidities, clinical signs and symptoms, outcomes, laboratory findings, imaging features, and treatments in patients with coronavirus disease 2019 (COVID-19).

METHODS

EMBASE, PubMed/Medline, Scopus, and Web of Science were searched for studies published in any language between December 1st, 2019 and March 28th, 2020. Original studies were included if the exposure of interest was an infection with SARS-CoV-2 or confirmed COVID-19. The primary outcome was the risk ratio of comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatments, outcomes, and complications associated with COVID-19 morbidity and mortality. We performed random-effects pairwise meta-analyses for proportions and relative risks, I2, T2, and Cochrane Q, sensitivity analyses, and assessed publication bias.

RESULTS

148 studies met the inclusion criteria for the systematic review and meta-analysis with 12'149 patients (5'739 female) and a median age of 47.0 [35.0-64.6] years. 617 patients died from COVID-19 and its complication. 297 patients were reported as asymptomatic. Older age (SMD: 1.25 [0.78-1.72]; p < 0.001), being male (RR = 1.32 [1.13-1.54], p = 0.005) and pre-existing comorbidity (RR = 1.69 [1.48-1.94]; p < 0.001) were identified as risk factors of in-hospital mortality. The heterogeneity between studies varied substantially (I2; range: 1.5-98.2%). Publication bias was only found in eight studies (Egger's test: p < 0.05).

CONCLUSIONS

Our meta-analyses revealed important risk factors that are associated with severity and mortality of COVID-19.

Association Between Pulmonary Embolism and COVID-19 in Emergency Department Patients Undergoing Computed Tomography Pulmonary Angiogram: The PEPCOV International Retrospective Study

BACKGROUND

There have been reports of procoagulant activity in patients with COVID-19. Whether there is an association between pulmonary embolism (PE) and COVID-19 in the emergency department (ED) is unknown. The aim of this study was to assess whether COVID-19 is associated with PE in ED patients who underwent a computed tomographic pulmonary angiogram (CTPA).

METHODS

A retrospective study in 26 EDs from six countries. ED patients in whom a CTPA was performed for suspected PE during a 2-month period covering the pandemic peak. The primary endpoint was the occurrence of a PE on CTPA. COVID-19 was diagnosed in the ED either on CT or reverse transcriptase-polymerase chain reaction. A multivariable binary logistic regression was built to adjust with other variables known to be associated with PE. A sensitivity analysis was performed in patients included during the pandemic period.

RESULTS

A total of 3,358 patients were included, of whom 105 were excluded because COVID-19 status was unknown, leaving 3,253 for analysis. Among them, 974 (30%) were diagnosed with COVID-19. Mean (±SD) age was 61 (±19) years and 52% were women. A PE was diagnosed on CTPA in 500 patients (15%). The risk of PE was similar between COVID-19 patients and others (15% in both groups). In the multivariable binary logistic regression model, COVID-19 was not associated with higher risk of PE (adjusted odds ratio = 0.98, 95% confidence interval = 0.76 to 1.26). There was no association when limited to patients in the pandemic period.

CONCLUSION

In ED patients who underwent CTPA for suspected PE, COVID-19 was not associated with an increased probability of PE diagnosis. These results were also valid when limited to the pandemic period. However, these results may not apply to patients with suspected COVID-19 in general.

From SARS and MERS to COVID-19: a brief summary and comparison of severe acute respiratory infections caused by three highly pathogenic human coronaviruses

Within two decades, there have emerged three highly pathogenic and deadly human coronaviruses, namely SARS-CoV, MERS-CoV and SARS-CoV-2. The economic burden and health threats caused by these coronaviruses are extremely dreadful and getting more serious as the increasing number of global infections and attributed deaths of SARS-CoV-2 and MERS-CoV. Unfortunately, specific medical countermeasures for these hCoVs remain absent. Moreover, the fast spread of misinformation about the ongoing SARS-CoV-2 pandemic uniquely places the virus alongside an annoying infodemic and causes unnecessary worldwide panic. SARS-CoV-2 shares many similarities with SARS-CoV and MERS-CoV, certainly, obvious differences exist as well. Lessons learnt from SARS-CoV and MERS-CoV, timely updated information of SARS-CoV-2 and MERS-CoV, and summarized specific knowledge of these hCoVs are extremely invaluable for effectively and efficiently contain the outbreak of SARS-CoV-2 and MERS-CoV. By gaining a deeper understanding of hCoVs and the illnesses caused by them, we can bridge knowledge gaps, provide cultural weapons for fighting and controling the spread of MERS-CoV and SARS-CoV-2, and prepare effective and robust defense lines against hCoVs that may emerge or reemerge in the future. To this end, the state-of-the-art knowledge and comparing the biological features of these lethal hCoVs and the clinical characteristics of illnesses caused by them are systematically summarized in the review.

From SARS and MERS to COVID-19: a brief summary and comparison of severe acute respiratory infections caused by three highly pathogenic human coronaviruses

Within two decades, there have emerged three highly pathogenic and deadly human coronaviruses, namely SARS-CoV, MERS-CoV and SARS-CoV-2. The economic burden and health threats caused by these coronaviruses are extremely dreadful and getting more serious as the increasing number of global infections and attributed deaths of SARS-CoV-2 and MERS-CoV. Unfortunately, specific medical countermeasures for these hCoVs remain absent. Moreover, the fast spread of misinformation about the ongoing SARS-CoV-2 pandemic uniquely places the virus alongside an annoying infodemic and causes unnecessary worldwide panic. SARS-CoV-2 shares many similarities with SARS-CoV and MERS-CoV, certainly, obvious differences exist as well. Lessons learnt from SARS-CoV and MERS-CoV, timely updated information of SARS-CoV-2 and MERS-CoV, and summarized specific knowledge of these hCoVs are extremely invaluable for effectively and efficiently contain the outbreak of SARS-CoV-2 and MERS-CoV. By gaining a deeper understanding of hCoVs and the illnesses caused by them, we can bridge knowledge gaps, provide cultural weapons for fighting and controling the spread of MERS-CoV and SARS-CoV-2, and prepare effective and robust defense lines against hCoVs that may emerge or reemerge in the future. To this end, the state-of-the-art knowledge and comparing the biological features of these lethal hCoVs and the clinical characteristics of illnesses caused by them are systematically summarized in the review.

Social Group Optimization-Assisted Kapur's Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning-based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19-affected CTI using social group optimization-based Kapur's entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis-based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.

COVID-19 pneumonia: Diagnostic and prognostic role of CT based on a retrospective analysis of 214 consecutive patients from Paris, France

OBJECTIVES

To evaluate the diagnostic and prognostic performance of CT in patients referred for COVID19 suspicion to a French university hospital, depending on symptoms and date of onset.

METHODS

From March 1st to March 28th, 214 patients having both chest CT scan and reverse transcriptase polymerase chain reaction (RT- PCT) within 24 h were retrospectively evaluated. Sensitivity, specificity, negative and positive predictive values of first and expert readings were calculated together with inter reader agreement, with results of RT-PCR as standard of reference and according to symptoms and onset date. Patient characteristics and disease extent on CT were correlated to short-term outcome (death or intubation at 3 weeks follow-up).

RESULTS

Of the 214 patients (119 men, mean age 59 ± 19 years), 129 had at least one positive RT-PCR result. Sensitivity, specificity, negative and positive predictive values were 79 % (95 % CI: 71-86 %), 84 %(74-91 %), 72 %(63-81 %) and 88 % (81-93 %) for initial CT reading and 81 %(74-88 %), 91 % (82-96 %), 76 % (67-84 %) and 93 % (87-97 %), for expert reading, with strong inter-reader agreement (kappa index: 0.89). Considering the 123 patients with symptoms for more than 5 days, the corresponding figures were 90 %, 78 %, 80 % and 89 % for initial reading and 93 %, 88 %, 86 % and 94 % for the expert. Disease extent exceeded 25 % for 68 % and 26 % of severe and non-severe patients, respectively (p < 0.001).

CONCLUSION

CT sensitivity increased after 5 days of symptoms. A disease extent > 25 % was associated with poorer outcome.

Clinical characteristics and risk factors of patients with severe COVID-19 in Jiangsu province, China: a retrospective multicentre cohort study

BACKGROUND

Coronavirus Disease-2019 (COVID-19) pandemic has become a major health event that endangers people health throughout China and the world. Understanding the factors associated with COVID-19 disease severity could support the early identification of patients with high risk for disease progression, inform prevention and control activities, and potentially reduce mortality. This study aims to describe the characteristics of patients with COVID-19 and factors associated with severe or critically ill presentation in Jiangsu province, China.

METHODS

Multicentre retrospective cohort study of all individuals with confirmed Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infections diagnosed at 24 COVID-19-designated hospitals in Jiangsu province between the 10th January and 15th March 2020. Demographic, clinical, laboratory, and radiological data were collected at hospital admission and data on disease severity were collected during follow-up. Patients were categorised as asymptomatic/mild/moderate, and severe/critically ill according to the worst level of COVID-19 recorded during hospitalisation.

RESULTS

A total of 625 patients, 64 (10.2%) were severe/critically ill and 561 (89.8%) were asymptomatic/mild/moderate. All patients were discharged and no patients died. Patients with severe/critically ill COVID-19 were more likely to be older, to be single onset (i.e. not belong to a cluster of cases in a family/community, etc.), to have a medical history of hypertension and diabetes; had higher temperature, faster respiratory rates, lower peripheral capillary oxygen saturation (SpO2), and higher computer tomography (CT) image quadrant scores and pulmonary opacity percentage; had increased C-reactive protein, fibrinogen, and D-dimer on admission; and had lower white blood cells, lymphocyte, and platelet counts and albumin on admission than asymptomatic/mild/moderate cases. Multivariable regression showed that odds of being a severe/critically ill case were associated with age (year) (OR 1.06, 95%CI 1.03-1.09), lymphocyte count (109/L) (OR 0.25, 95%CI 0.08-0.74), and pulmonary opacity in CT (per 5%) on admission (OR 1.31, 95%CI 1.15-1.51).

CONCLUSIONS

Severe or critically ill patients with COVID-19 is about one-tenths of patients in Jiangsu. Age, lymphocyte count, and pulmonary opacity in CT on admission were associated with risk of severe or critically ill COVID-19.

Outcomes Associated With Use of a Kinin B2 Receptor Antagonist Among Patients With COVID-19

This case-control study examines the association between receipt of the bradykinin 2 (B2) receptor antagonist icatibant and improved oxygenation in patients with coronavirus disease 2019 (COVID-19).

Clinical Course, Imaging Features, and Outcomes of COVID-19 in Kidney Transplant Recipients

Coronavirus disease 2019 (COVID-19) is a novel and highly contagious disease caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Older adults and patients with comorbidities and immunosuppressive conditions may experience severe signs and symptoms that can lead to death. This case series assesses the clinical course, imaging features, and outcomes for 12 patients with COVID-19 and a history of kidney transplantation. Patients were evaluated for symptoms, laboratory data, imaging findings, and outcomes from February 2020 to April 2020. Fever, cough, and dyspnea were the most common clinical symptoms, noted in 75% (nine/12), 75% (nine/12), and 41.7% (five/12) of the patients, respectively. Most of the patients had a normal white blood cell count, while 33.3% (four/12) had leukopenia and 8.3% (one/12) had leukocytosis. A combination of consolidation and ground glass opacity was the most predominant (75%) pattern of lung involvement on computed tomography (CT). Eight patients died of severe COVID-19 pneumonia and acute respiratory distress syndrome and four were discharged. All recovered cases had a unilateral peripheral pattern of involvement limited to only one zone on initial chest CT. It seems that CT imaging has an important role in predicting COVID-19 outcomes for solid organ transplant recipients. Future studies with long-term follow up and more cases are needed to elucidate COVID-19 diagnosis, outcome, and management strategies for these patients.

Spectrum of chest computed tomographic (CT) findings in coronavirus disease-19 (COVID-19) patients in India

PURPOSE

To report the spectrum of chest computed tomographic (CT) imaging findings in coronavirus disease-19 (COVID-19) infected Indian patients.

METHODS

This was a prospective descriptive study comprising 147 consecutive reverse transcriptase polymerase chain reaction (RT-PCR) positive patients who underwent CT chest. Prevalence, distribution, extent and type of abnormal lung findings were recorded.

RESULTS

Among the total study cohort of 147 patients, 104 (70.7 %) were males and 43 (29.3 %) were females with mean age of 40.9 ± 17.2 years (range 24-71 years). We observed lung parenchymal abnormalities in 51 (34.7 %) cases whereas 96 (65.3 %) RT-PCR positive cases had a normal chest CT. Only 12.2 % of the patients were dyspneic, 6.1 % had desaturation, 7.4 % had increased respiratory rate and 10.9 % had comorbidities. Among the patients with abnormal CT findings bilateral 39/51 (76.5 %), multilobar (88.2 %) lung involvement with a predominant peripheral and posterior distribution was commonly observed. With regards to the type of opacity, ground glass opacity (GGO) was the dominant abnormality found in all 51 (100 %) cases. Pure GGO was observed in 15 (29.4 %), GGO with crazy paving pattern was seen in 15 (29.4 %) and GGO mixed with consolidation was noted in 21(41.2 %). Peri-lesional or intralesional segmental or subsegmental pulmonary vessel enlargement was observed in 36 (70.6 %) cases.

CONCLUSION

In this study population predominantly with mild symptoms and few comorbidities, two-thirds of RT-PCR positive patients had a normal chest CT; whereas the remaining patients showed typical findings of predominant GGOs with a bilateral distribution and peripheral predominance.

Usefulness of Mobile Computed Tomography in Patients with Coronavirus Disease 2019 Pneumonia: A Case Series

The coronavirus disease (COVID-19) outbreak has reached global pandemic status as announced by the World Health Organization, which currently recommends reverse transcription polymerase chain reaction (RT-PCR) as the standard diagnostic tool. However, although the RT-PCR test results may be found negative, there are cases that are found positive for COVID-19 pneumonia on computed tomography (CT) scan. CT is also useful in assessing the severity of COVID-19 pneumonia. When clinicians desire a CT scan of a patient with COVID-19 to monitor treatment response, a safe method for patient transport is necessary. To address the engagement of medical resources necessary to transport a patient with COVID-19, our institution has implemented the use of mobile CT. Therefore, we report two cases of COVID-19 pneumonia evaluated by using mobile cone-beam CT. Although mobile cone-beam CT had some limitations regarding its image quality such as scatter noise, motion and streak artifacts, and limited field of view compared with conventional multi-detector CT, both cases had acceptable image quality to establish the diagnosis of COVID-19 pneumonia. We report the usefulness of mobile cone-beam CT in patients with COVID-19 pneumonia.

Well-controlled vs poorly-controlled diabetes in patients with COVID-19: Are there any differences in outcomes and imaging findings?

AIMS

We aimed to compare the clinical outcomes and imaging findings between COVID-19 patients with well-controlled diabetes and those with poorly-controlled diabetes.

METHODS

In this retrospective single-center study, 117 patients with coexistent COVID-19 and type 2 diabetes mellitus were included. Patients were divided into two groups based on HbA1c values. Clinical data and laboratory parameters were collected from patients' medical records. Also, the chest computed tomography (CT) score was defined by the summation of individual scores from 5 lung lobes: scores of 0, 1, 2, 3, 4 and 5 were respectively assigned for each lobe if pulmonary involvement was 0%, less than 5%, 5%-25%, 26%-49%, 50%-75%, or more than 75% of each region.

RESULTS

Among all patients with diabetes, 93 (79.5%) patients had poorly-controlled diabetes and 24 (20.5%) had well-controlled diabetes; 66 (56.4%) patients were male and the median age was 66 years (IQR, 55-75 years). The chest CT severity scores were not significantly different between patients with well-controlled diabetes and those with poorly-controlled diabetes (p = 0.33). Also, the mortality and recovery rates were similar between the two groups (p = 0.54 and p = 0.85, respectively).

CONCLUSION

Based on the results, clinical outcomes and chest CT severity scores are similar between patients with well-controlled and poorly-controlled diabetes among the Iranian population with COVID-19.

Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia

Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine prognosis in patients with COVID-19 pneumonia. Materials and Methods Imaging of patients admitted at the emergency department between February 17 and March 10, 2020 who underwent chest CT were retrospectively analyzed. Patients with negative results of reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 at nasal-pharyngeal swabbing, negative chest CT findings, and incomplete clinical data were excluded. CT images were analyzed for quantification of WAL visually (%V-WAL), with open-source software (%S-WAL), and with absolute volume (VOL-WAL). Clinical parameters included patient characteristics, comorbidities, symptom type and duration, oxygen saturation, and laboratory values. Logistic regression was used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit [ICU] admission or death vs no ICU admission or death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (59 of 123 [25%] were female; median age, 68 years). A %V-WAL less than 73% (odds ratio [OR], 5.4; 95% confidence interval [CI]: 2.7, 10.8; P < .001), %S-WAL less than 71% (OR, 3.8; 95% CI: 1.9, 7.5; P < .001), and VOL-WAL less than 2.9 L (OR, 2.6; 95% CI: 1.2, 5.8; P < .01) were predictors of ICU admission or death. In comparison with clinical models containing only clinical parameters (AUC = 0.83), all three quantitative models showed better diagnostic performance (AUC = 0.86 for all models). The models containing %V-WAL less than 73% and VOL-WAL less than 2.9 L were superior in terms of performance as compared with the models containing only clinical parameters (P = .04 for both models). Conclusion In patients with confirmed coronavirus disease 2019 pneumonia, visual or software quantification of the extent of CT lung abnormality were predictors of intensive care unit admission or death. ©  RSNA, 2020 Online supplemental material is available for this article.

Assessment of the Severity of Coronavirus Disease: Quantitative Computed Tomography Parameters versus Semiquantitative Visual Score

OBJECTIVE

To compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visual score in evaluating clinical classification of severity of coronavirus disease (COVID-19).

MATERIALS AND METHODS

We retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji Medical College from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data were collected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical). A semiquantitative visual score was used to estimate the lesion extent. A three-dimensional slicer was used to precisely quantify the volume and CT value of the lung and lesions. Correlation coefficients of the quantitative CT parameters, semiquantitative visual score, and clinical classification were calculated using Spearman's correlation. A receiver operating characteristic curve was used to compare the accuracies of quantitative and semi-quantitative methods.

RESULTS

There were 59 patients in group 1 and 128 patients in group 2. The mean age and sex distribution of the two groups were not significantly different. The lesions were primarily located in the subpleural area. Compared to group 1, group 2 had larger values for all volume-dependent parameters (p < 0.001). The percentage of lesions had the strongest correlation with disease severity with a correlation coefficient of 0.495. In comparison, the correlation coefficient of semiquantitative score was 0.349. To classify the severity of COVID-19, area under the curve of the percentage of lesions was the highest (0.807; 95% confidence interval, 0.744-0.861: p < 0.001) and that of the quantitative CT parameters was significantly higher than that of the semiquantitative visual score (p = 0.001).

CONCLUSION

The classification accuracy of quantitative CT parameters was significantly superior to that of semiquantitative visual score in terms of evaluating the severity of COVID-19.

Management strategies for children with COVID-19: ESPR practical recommendations

During the outbreak of the COVID-19 pandemic, guidelines have been issued by international, national and local authorities to address management and the need for preparedness. Children with COVID-19 differ from adults in that they are less often and less severely affected. Additional precautions required in the management of children address their increased radiosensitivity, need for accompanying carers, and methods for dealing with children in a mixed adult-paediatric institution. In this guidance document, our aim is to define a pragmatic strategy for imaging children with an emphasis on proven or suspected COVID-19 cases. Children suspected of COVID-19 should not be imaged routinely. Imaging should be performed only when expected to alter patient management, depending on symptoms, preexisting conditions and clinical evolution. In order to prevent disease transmission, it is important to manage the inpatient caseload effectively by triaging children and carers outside the hospital, re-scheduling nonurgent elective procedures and managing symptomatic children and carers as COVID-19 positive until proven otherwise. Within the imaging department one should consider conducting portable examinations with COVID-19 machines or arranging dedicated COVID-19 paediatric imaging sessions and performing routine nasopharyngeal swab testing before imaging under general anaesthesia. Finally, regular personal hygiene, appropriate usage of personal protective equipment, awareness of which procedures are considered aerosol generating and information on how to best disinfect imaging machinery after examinations should be highlighted to all staff members.

Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method

Background

So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia.

Purpose

To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease.

Methods

We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis.

Results

189 patients were analyzed. PaO₂/FIO₂ ratio and oxygen saturation (SaO₂) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p < 0.05). Lung volume involvement was significantly higher in critically ill patients (38.5 % vs. 5.8 %, p < 0.05). A cut-off of 23.0 % of lung involvement showed 96 % sensitivity and 96 % specificity in distinguishing critically ill patients from patients with less severe disease. The fraction of involved lung was related to lactate dehydrogenase (LDH) levels, PaO₂/FIO₂ ratio and SaO₂ (p < 0.05).

Conclusion

Lung disease extension, assessed using quantitative CT, has a significant relationship with clinical severity and may predict the need for invasive ventilation in patients with COVID-19.

Association between cytokine profiles and lung injury in COVID-19 pneumonia

Background

Coronavirus disease 2019 (COVID-19) is a new respiratory and systemic disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The purpose of the present study was to investigate the association between cytokine profiles and lung injury in COVID-19 pneumonia.

Methods

This retrospective study was conducted in COVID-19 patients. Demographic characteristics, symptoms, signs, underlying diseases, and laboratory data were collected. The patients were divided into COVID-19 with pneumonia and without pneumonia. CT severity score and PaO₂/FiO₂ ratio were used to assess lung injury.

Results

106 patients with 12 COVID-19 without pneumonia and 94 COVID-19 with pneumonia were included. Compared with COVID-19 without pneumonia, COVID-19 with pneumonia had significantly higher serum interleukin (IL)-2R, IL-6, and tumor necrosis factor (TNF)-α. Correlation analysis showed that CT severity score and PaO₂/FiO₂ were significantly correlated with age, presence of any coexisting disorder, lymphocyte count, procalcitonin, IL-2R, and IL-6. In multivariate analysis, log IL6 was the only independent explanatory variables for CT severity score (β = 0.397, p < 0.001) and PaO₂/FiO₂ (β = − 0.434, p = 0.003).

Conclusions

Elevation of circulating cytokines was significantly associated with presence of pneumonia in COVID-19 and the severity of lung injury in COVID-19 pneumonia. Circulating IL-6 independently predicted the severity of lung injury in COVID-19 pneumonia.

CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia

Purpose

To compare prediction of disease outcome, severity, and patient triage in coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' interpretation, and clinical variables.

Materials and Methods

This institutional review board-approved retrospective study included 315 adult patients (mean age, 56 years [range, 21-100 years], 190 men, 125 women) with COVID-19 pneumonia who underwent noncontrast chest CT. All patients (inpatients, n = 210; outpatients, n = 105) were followed-up for at least 2 weeks to record disease outcome. Clinical variables, such as presenting symptoms, laboratory data, peripheral oxygen saturation, and comorbid diseases, were recorded. Two radiologists assessed each CT in consensus and graded the extent of pulmonary involvement (by percentage of involved lobe) and type of opacities within each lobe. Radiomics were obtained for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as outputs were performed.

Results

Most patients (276/315, 88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died, and 3/315 patients (1%) remained admitted in the hospital. Radiomics differentiated chest CT in outpatient versus inpatient with an AUC of 0.84 (P < .005), while radiologists' interpretations of disease extent and opacity type had an AUC of 0.69 (P < .0001). Whole lung radiomics were superior to the radiologists' interpretation for predicting patient outcome in terms of intensive care unit (ICU) admission (AUC: 0.75 vs 0.68) and death (AUC: 0.81 vs 0.68) (P < .002). The addition of clinical variables to radiomics improved the AUC to 0.84 for predicting ICU admission.

Conclusion

Radiomics from noncontrast chest CT were superior to radiologists' assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.© RSNA, 2020.

Computed Tomography (CT) Imaging Features of Patients with COVID-19: Systematic Review and Meta-Analysis

INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious disease, and its first outbreak was reported in Wuhan, China. A coronavirus disease (COVID-19) causes severe respiratory distress (ARDS). Due to the primary involvement of the respiratory system, chest CT is strongly recommended in suspected COVID-19 cases, for both initial evaluation and follow-up.

OBJECTIVE

The aim of this review was to systematically analyze the existing literature on CT imaging features of patients with COVID-19 pneumonia.

METHODS

A systematic search was conducted on PubMed, Embase, Cochrane Library, Open Access Journals (OAJ), and Google Scholar databases until April 15, 2020. All articles with a report of CT findings in COVID-19 patients published in English from the onset of COVID-19 outbreak to April 20, 2020, were included in the study.

RESULT

From a total of 5041 COVID-19-infected patients, about 98% (4940/5041) had abnormalities in chest CT, while about 2% have normal chest CT findings. Among COVID-19 patients with abnormal chest CT findings, 80% (3952/4940) had bilateral lung involvement. Ground-glass opacity (GGO) and mixed GGO with consolidation were observed in 2482 (65%) and 768 (18%) patients, respectively. Consolidations were detected in 1259 (22%) patients with COVID-19 pneumonia. CT images also showed interlobular septal thickening in about 691 (27%) patients.

CONCLUSION

Frequent involvement of bilateral lung infections, ground-glass opacities, consolidation, crazy paving pattern, air bronchogram signs, and intralobular septal thickening were common CT imaging features of patients with COVID-19 pneumonia.

The rationale of using mesenchymal stem cells in patients with COVID-19-related acute respiratory distress syndrome: What to expect

The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2)‐caused coronavirus disease 2019 (COVID‐19) pandemic has become a global health crisis with an extremely rapid progress resulting in thousands of patients who may develop acute respiratory distress syndrome (ARDS) requiring intensive care unit (ICU) treatment. So far, no specific antiviral therapeutic agent has been demonstrated to be effective for COVID‐19; therefore, the clinical management is largely supportive and depends on the patients' immune response leading to a cytokine storm followed by lung edema, dysfunction of air exchange, and ARDS, which could lead to multiorgan failure and death. Given that human mesenchymal stem cells (MSCs) from various tissue sources have revealed successful clinical outcomes in many immunocompromised disorders by inhibiting the overactivation of the immune system and promoting endogenous repair by improving the microenvironment, there is a growing demand for MSC infusions in patients with COVID‐19‐related ARDS in the ICU. In this review, we have documented the rationale and possible outcomes of compassionate use of MSCs, particularly in patients with SARS‐CoV‐2 infections, toward proving or disproving the efficacy of this approach in the near future. Many centers have registered and approved, and some already started, single‐case or phase I/II trials primarily aiming to rescue their critical patients when no other therapeutic approach responds. On the other hand, it is also very important to mention that there is a good deal of concern about clinics offering unproven stem cell treatments for COVID‐19. The reviewers and oversight bodies will be looking for a balanced but critical appraisal of current trials.

COVID-19 severity scoring systems in radiological imaging - a review

The current reference standard to make a definitive diagnosis of SARS-CoV-2 infection is the reverse transcription- polymerase chain reaction assay (rt-PCR). However, radiological imaging plays a crucial role in evaluating the course of COVID-19 and in choosing proper management of infected patients. Chest X-ray (CXR) is generally considered not to be sensitive for the detection of pulmonary abnormalities in the early stage of the disease. However, in the emergency setting CXR can be a useful diagnostic tool for monitoring the rapid progression of lung involvement in COVID-19, especially in patients admitted to intensive care units. The rapid course of SARS-CoV-2 infection and the severity and progression of lung aberrations require a method of radiological evaluation to implement and manage the appropriate treatment for infected patients. Computed tomography (CT) imaging is considered to be the most effective method for the detection of lung abnormalities, especially in the early stage of the disease. Moreover, serial chest CT imaging with different time intervals is also effective in estimating the evolution of the disease from initial diagnosis to discharge from hospital. Despite having low specificity in distinguishing abnormalities in viral infections, the high sensitivity of CT makes this method ideal for assessing the severity of the disease in patients with confirmed COVID-19. In this review, we present and discuss currently available scales that can be used to assess the severity of lung involvement in COVID-19 patients in everyday work, both for CXR and CT imaging.

The Role of Imaging Techniques in Management of COVID-19 in China: From Diagnosis to Monitoring and Follow-Up

In December 2019, an outbreak of coronavirus infection emerged in Wuhan, Hubei Province of China, which is now named Coronavirus Disease 2019 (COVID-19). The outbreak spread rapidly within mainland China and globally. This paper reviews the different imaging modalities used in the diagnosis and treatment process of COVID-19, such as chest radiography, computerized tomography (CT) scan, ultrasound examination, and positron emission tomography (PET/CT) scan. A chest radiograph is not recommended as a first-line imaging modality for COVID-19 infection due to its lack of sensitivity, especially in the early stages of infection. Chest CT imaging is reported to be a more reliable, rapid, and practical method for diagnosis of COVID-19, and it can assess the severity of the disease and follow up the disease time course. Ultrasound, on the other hand, is portable and involves no radiation, and thus can be used in critically ill patients to assess cardiorespiratory function, guide mechanical ventilation, and identify the presence of deep venous thrombosis and secondary pulmonary thromboembolism. Supplementary information can be provided by PET/CT. In the absence of vaccines and treatments for COVID-19, prompt diagnosis and appropriate treatment are essential. Therefore, it is important to exploit the advantages of different imaging modalities in the fight against COVID-19.

Chest CT practice and protocols for COVID-19 from radiation dose management perspective

The global pandemic of coronavirus disease 2019 (COVID-19) has upended the world with over 6.6 million infections and over 391,000 deaths worldwide. Reverse-transcription polymerase chain reaction (RT-PCR) assay is the preferred method of diagnosis of COVID-19 infection. Yet, chest CT is often used in patients with known or suspected COVID-19 due to regional preferences, lack of availability of PCR assays, and false-negative PCR assays, as well as for monitoring of disease progression, complications, and treatment response. The International Atomic Energy Agency (IAEA) organized a webinar to discuss CT practice and protocol optimization from a radiation protection perspective on April 9, 2020, and surveyed participants from five continents. We review important aspects of CT in COVID-19 infection from the justification of its use to specific scan protocols for optimizing radiation dose and diagnostic information.

Key Points

• Chest CT provides useful information in patients with moderate to severe COVID-19 pneumonia.

• When indicated, chest CT in most patients with COVID-19 pneumonia must be performed with non-contrast, low-dose protocol.

• Although chest CT has high sensitivity for diagnosis of COVID-19 pneumonia, CT findings are non-specific and overlap with other viral infections including influenza and H1N1.

COVID-19 Assessment with Bedside Lung Ultrasound in a Population of Intensive Care Patients Treated with Mechanical Ventilation and ECMO

The COVID-19 pandemic has increased the need for an accessible, point-of-care and accurate imaging modality for pulmonary assessment. COVID-19 pneumonia is mainly monitored with chest X-ray, however, lung ultrasound (LUS) is an emerging tool for pulmonary evaluation. In this study, patients with verified COVID-19 disease hospitalized at the intensive care unit and treated with ventilator and extracorporal membrane oxygenation (ECMO) were evaluated with LUS for pulmonary changes. LUS findings were compared to C-reactive protein (CRP) and ventilator settings. Ten patients were included and scanned the day after initiation of ECMO and thereafter every second day until, if possible, weaned from ECMO. In total 38 scans adding up to 228 cineloops were recorded and analyzed off-line with the use of a constructed LUS score. The study indicated that patients with a trend of lower LUS scores over time were capable of being weaned from ECMO. LUS score was associated to CRP (R = 0.34; p < 0.03) and compliance (R = 0.60; p < 0.0001), with the strongest correlation to compliance. LUS may be used as a primary imaging modality for pulmonary assessment reducing the use of chest X-ray in COVID-19 patients treated with ventilator and ECMO.

COVID-19 S: A new proposal for diagnosis and structured reporting of COVID-19 on computed tomography imaging

PURPOSE

Because of the widespread use of CT in the diagnosis of COVID 19, indeterminate presentations such as single, few or unilateral lesions amount to a considerable number. We aimed to develop a new classification and structured reporting system on CT imaging (COVID-19 S) that would facilitate the diagnosis of COVID-19 in the most accurate way.

METHODS

Our retrospective cohort included 803 patients with a chest CT scan upon suspicion of COVID 19. The patients' history, physical examination, CT findings, RT PCR, and other laboratory test results were reviewed, and a final diagnosis was made as COVID 19 or non-COVID 19. Chest CT scans were classified according to the COVID 19 S CT diagnosis criteria. Cohen's kappa analysis was used.

RESULTS

Final clinical diagnosis was COVID-19 in 98 patients (12%). According to the COVID-19 S CT diagnosis criteria, the number of patients in the normal, compatible with COVID 19, indeterminate and alternative diagnosis groups were 581 (72.3%), 97 (12.1%), 16 (2.0%) and 109 (13.6%). When the indeterminate group was combined with the group compatible with COVID 19, the sensitivity and specificity of COVID-19 S were 99.0% and 87.1%, with 85.8% positive predictive value (PPV) and 99.1% negative predictive value (NPV). When the indeterminate group was combined with the alternative diagnosis group, the sensitivity and specificity of COVID-19 S were 93.9% and 96.0%, with 94.8% PPV and 95.2% NPV.

CONCLUSION

COVID-19 S CT classification system may meet the needs of radiologists in distinguishing COVID-19 from pneumonia of other etiologies and help optimize patient management and disease control in this pandemic by the use of structured reporting.

Radiological approach to COVID-19 pneumonia with an emphasis on chest CT

Coronavirus disease 2019 (COVID-19) has recently become a worldwide outbreak with several millions of people infected and more than 160.000 deaths. A fast and accurate diagnosis in this outbreak is critical to isolate and treat patients. Radiology plays an important role in the diagnosis and management of the patients. Among various imaging modalities, chest CT has received attention with its higher sensitivity and specificity rates. Shortcomings of the real-time reverse transcriptase-polymerase chain reaction test, including inappropriate sample collection and analysis methods, initial false negative results, and limited availability has led to widespread use of chest CT in the diagnostic algorithm. This review summarizes the role of radiology in COVID-19 pneumonia, diagnostic accuracy of imaging, and chest CT findings of the disease.

Impact of coronavirus disease 2019 on pulmonary function in early convalescence phase

OBJECTIVE

This study investigated the influence of Coronavirus Disease 2019 (COVID-19) on lung function in early convalescence phase.

METHODS

A retrospective study of COVID-19 patients at the Fifth Affiliated Hospital of Sun Yat-sen University were conducted, with serial assessments including lung volumes (TLC), spirometry (FVC, FEV1), lung diffusing capacity for carbon monoxide (DLCO),respiratory muscle strength, 6-min walking distance (6MWD) and high resolution CT being collected at 30 days after discharged.

RESULTS

Fifty-seven patients completed the serial assessments. There were 40 non-severe cases and 17 severe cases. Thirty-one patients (54.3%) had abnormal CT findings. Abnormalities were detected in the pulmonary function tests in 43 (75.4%) of the patients. Six (10.5%), 5(8.7%), 25(43.8%) 7(12.3%), and 30 (52.6%) patients had FVC, FEV1, FEV1/FVC ratio, TLC, and DLCO values less than 80% of predicted values, respectively. 28 (49.1%) and 13 (22.8%) patients had PImax and PEmax values less than 80% of the corresponding predicted values. Compared with non-severe cases, severe patients showed higher incidence of DLCO impairment (75.6%vs42.5%, p = 0.019), higher lung total severity score (TSS) and R20, and significantly lower percentage of predicted TLC and 6MWD. No significant correlation between TSS and pulmonary function parameters was found during follow-up visit.

CONCLUSION

Impaired diffusing-capacity, lower respiratory muscle strength, and lung imaging abnormalities were detected in more than half of the COVID-19 patients in early convalescence phase. Compared with non-severe cases, severe patients had a higher incidence of DLCO impairment and encountered more TLC decrease and 6MWD decline.

Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome

BACKGROUND

Computed tomography (CT) enables quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, helping in outcome prediction.

METHODS

From 1 to 22 March 2020, patients with pneumonia symptoms, positive lung CT scan, and confirmed SARS-CoV-2 on reverse transcription-polymerase chain reaction (RT-PCR) were consecutively enrolled. Clinical data was collected. Outcome was defined as favourable or adverse (i.e., need for mechanical ventilation or death) and registered over a period of 10 days following CT. Volume of disease (VoD) on CT was calculated semi-automatically. Multiple linear regression was used to predict VoD by clinical/laboratory data. To predict outcome, important features were selected using a priori analysis and subsequently used to train 4 different models.

RESULTS

A total of 106 consecutive patients were enrolled (median age 63.5 years, range 26-95 years; 41/106 women, 38.7%). Median duration of symptoms and C-reactive protein (CRP) was 5 days (range 1-30) and 4.94 mg/L (range 0.1-28.3), respectively. Median VoD was 249.5 cm3 (range 9.9-1505) and was predicted by lymphocyte percentage (p = 0.008) and CRP (p < 0.001). Important variables for outcome prediction included CRP (area under the curve [AUC] 0.77), VoD (AUC 0.75), age (AUC 0.72), lymphocyte percentage (AUC 0.70), coronary calcification (AUC 0.68), and presence of comorbidities (AUC 0.66). Support vector machine had the best performance in outcome prediction, yielding an AUC of 0.92.

CONCLUSIONS

Measuring the VoD using a simple CT post-processing tool estimates SARS-CoV-2 burden. CT and clinical data together enable accurate prediction of short-term clinical outcome.

COVID-19 Screening with Chest CT in Acute Stroke Imaging: A Clinical Decision Model

BACKGROUND AND PURPOSE

Acute stroke patients may have undiagnosed coronavirus disease 2019 (COVID‐19) infection, transmissible to medical professionals involved in their care. Our aim was to determine the value of incorporating a chest computed tomography (CT) scan during acute stroke imaging, and the factors that influence this decision.

METHODS

We constructed a probabilistic decision tree of the value of acquiring a chest CT scan or not, expressed in quality‐adjusted life months (QALM) of patients and medical professionals. The model was based on the chance of detecting infection by chest CT scan, the case fatality rates of COVID‐19 infection, the risk of COVID‐19 infection after exposure, the expected proportion of medical professionals exposed, and the exposure reduction derived from early disease detection.

RESULTS

The decision to incorporate the chest CT scan was superior to not doing so (12.00 QALM vs 11.99 QALM, respectively), when the probability of patients having undetected COVID‐19 infection is 3.5%, potentially exposing 100% of medical professionals, and if early detection reduces exposure by 50%. The risk of developing symptomatic COVID‐19 infection following exposure casts uncertainty on the results, but this is offset by the potential for reducing exposure.

CONCLUSIONS

We identified a measurable benefit of incorporating a chest CT into the urgent imaging protocol of acute stroke patients in reducing exposure of medical professionals without appropriate precautions. The clinical impact of this benefit, however, may not be materially significant.

The value of chest CT as a COVID-19 screening tool in children

It is difficult to identify children infected with coronavirus disease 2019 (COVID-19) who have little or no respiratory symptoms. For routine clinical care in different circumstances, it is relevant to assess the COVID-19 status of patients. Routine PCR is recognised as the gold standard but can be falsely negative due to sampling errors. For diagnosing and monitoring adult COVID-19 patients, characteristic radiological lesions have been recognised [1, 2] and to assess the possibility of COVID-19 infection in adults scheduled for surgery in whom a PCR test is negative or missing, a non-enhanced chest computed tomography (CT) scan has been proposed as an option in the Netherlands [3] because: 1) patients may be pre-symptomatic in the incubation period of COVID-19 infection and subsequently develop symptoms post-operatively, implying a greater risk for adverse post-operative outcomes; and 2) patients may be asymptomatic or mildly symptomatic carriers and shedders of COVID-19, and place hospital workers and other patients at risk.

Chest CT is not a suitable screening tool to rule out COVID-19 in childrenhttps://bit.ly/2SBGzQm

ADL-dependency, D-Dimers, LDH and absence of anticoagulation are independently associated with one-month mortality in older inpatients with Covid-19

Background: To assess factors associated with one-month mortality among older inpatients with Covid-19.

Results: The mean age was 78 ± 7.8 years, 55.5% were men, CT scan lung damage was observed in 76% of the patients (mild 23%, moderate 38%, extensive 22%, and severe 7%). The mortality rate was 26%. Dependency/Activities of Daily Living (ADL) score ≤ 5/6, D-Dimers, LDH, and no anticoagulation by reference for curative were independently associated with one-month mortality. A score derived from the multivariate model showed good calibration and very good discrimination (Harrell’s C index [95%CI] = 0.83 [0.79-0.87]).

Conclusion: ADL-dependency, high serum levels of D-Dimers and LDH and the absence of anticoagulation were independently associated with one-month mortality among older inpatients with Covid-19.

Methods: 108 consecutive older inpatients aged 65 and over with Covid-19 confirmed by RT-PCR and/or typical CT chest scan were prospectively included in a French single-centre cohort study from March to April 2020. A systematic geriatric assessment was performed. Covariates were lymphocyte count, serum levels of albumin, C-Reactive Protein, D-Dimers and Lactate Dehydrogenase (LDH), anticoagulation level, and exposure to the hydroxychloroquine and azithromycin combined therapy. Cox uni- and multivariate proportional-hazard regressions were performed to identify predictors of one-month mortality.

Chest CT features of the novel coronavirus disease (COVID-19)

A new type of coronavirus (2019-nCoV) is rapidly spreading worldwide and causes pneumonia, respiratory distress, thromboembolic events, and death. Chest computed tomography (CT) plays an essential role in the diagnosis of viral pneumonia, monitoring disease progression, determination of disease severity, and evaluating therapeutic efficacy. Chest CT can show important clues of 2019-nCoV disease (also known as COVID-19) in patients with an appropriate clinic. Prompt diagnosis of COVID-19 is essential to prevent disease transmission and provides close clinical observation of patients with clinically severe disease. Therefore, radiologists and clinicians should be familiar with the CT imaging findings of COVID-19 pneumonia. Herein, we aimed to review the imaging findings of COVID-19 pneumonia and examine the critical points to be considered for imaging in cases with COVID-19 suspicion.

Lung Ultrasound in COVID-19 Pneumonia: Correlations with Chest CT on Hospital admission

Background

Lung ultrasound (LUS) is an accurate, safe, and cheap tool assisting in the diagnosis of several acute respiratory diseases. The diagnostic value of LUS in the workup of coronavirus disease-19 (COVID-19) in the hospital setting is still uncertain.

Objectives

The aim of this observational study was to explore correlations of the LUS appearance of COVID-19-related pneumonia with CT findings.

Methods

Twenty-six patients (14 males, age 64 ± 16 years) urgently hospitalized for COVID-19 pneumonia, who underwent chest CT and bedside LUS on the day of admission, were enrolled in this observational study. CT images were reviewed by expert chest radiologists, who calculated a visual CT score based on extension and distribution of ground-glass opacities and consolidations. LUS was performed by clinicians with certified competency in thoracic ultrasonography, blind to CT findings, following a systematic approach recommended by ultrasound guidelines. LUS score was calculated according to presence, distribution, and severity of abnormalities.

Results

All participants had CT findings suggestive of bilateral COVID-19 pneumonia, with an average visual scoring of 43 ± 24%. LUS identified 4 different possible ­abnormalities, with bilateral distribution (average LUS score 15 ± 5): focal areas of nonconfluent B lines, diffuse confluent B lines, small subpleural microconsolidations with pleural line irregularities, and large parenchymal consolidations with air bronchograms. LUS score was significantly correlated with CT visual scoring (r = 0.65, p < 0.001) and oxygen saturation in room air (r = −0.66, p < 0.001).

Conclusion

When integrated with clinical data, LUS could represent a valid diagnostic aid in patients with suspect COVID-19 pneumonia, which reflects CT findings.

Chest CT features of coronavirus disease-19 (COVID-19) pneumonia: which findings on initial CT can predict an adverse short-term outcome?

OBJECTIVE

To study the spectrum of chest CT features in coronavirus disease-19 (COVID-19) pneumonia and to identify the initial CT findings that may have the potential to predict a poor short-term outcome.

METHODS

This was a retrospective study comprising 211 reverse transcriptase-polymerase chain reaction (RT-PCR) positive patients who had undergone non-contrast chest CT. Prevalence, extent, pattern, distribution and type of abnormal lung findings were recorded. Patients with positive CT findings were divided into two groups; clinically stable (requiring in-ward hospitalization) and clinically unstable [requiring intensive care unit (ICU) admission or demised] based on short-term follow-up.

RESULTS

Lung parenchymal abnormalities were present in 42.2% (89/211) whereas 57.8% (122/211) cases had a normal chest CT. The mean age of clinically unstable patients (63.6 ± 8.3 years) was significantly different from the clinically stable group (44.6 ± 13.2 years) (p-value < 0.05). Bilaterality, combined involvement of central-peripheral and anteroposterior lung along with a higher percentage of the total lung involvement, presence of crazy paving, coalescent consolidations with air bronchogram and segmental pulmonary vessel enlargement were found in a significantly higher proportion of clinically unstable group (ICU/demised) compared to the stable group (in-ward hospitalization) with all p values < 0.05.

CONCLUSION

Certain imaging findings on initial CT have the potential to predict short-term outcome in COVID-19 pneumonia. Extensive pulmonary abnormalities, evaluated by combined anteroposterior, central-peripheral and a higher percentage of the total lung involvement, indicate a poor short-term outcome. Similarly, the presence of crazy paving pattern, consolidation with air bronchogram and segmental vascular changes are also indicators of poor short-term outcome.

ADVANCES IN KNOWLEDGE

Certain findings on initial CT can predict an adverse short-term prognosis in COVID-19 pneumonia.

Acute pulmonary embolism in non-hospitalized COVID-19 patients referred to CTPA by emergency department

Objectives

To evaluate the prevalence of acute pulmonary embolism (APE) in non-hospitalized COVID-19 patients referred to CT pulmonary angiography (CTPA) by the emergency department.

Methods

From March 14 to April 6, 2020, 72 non-hospitalized patients referred by the emergency department to CTPA for COVID-19 pneumonia were retrospectively identified. Relevant clinical and laboratory data and CT scan findings were collected for each patient. CTPA scans were reviewed by two radiologists to determinate the presence or absence of APE. Clinical classification, lung involvement of COVID-19 pneumonia, and CT total severity score were compared between APE group and non-APE group.

Results

APE was identified in 13 (18%) CTPA scans. The mean age and D-dimer of patients from the APE group were higher in comparison with those from the non-APE group (74.4 vs. 59.6 years, p = 0.008, and 7.29 vs. 3.29 μg/ml, p = 0.011). There was no significant difference between APE and non-APE groups concerning clinical type, COVID-19 pneumonia lung lesions (ground-glass opacity: 85% vs. 97%; consolidation: 69% vs. 68%; crazy paving: 38% vs. 37%; linear reticulation: 69% vs. 78%), CT severity score (6.3 vs. 7.1, p = 0.365), quality of CTPA (1.8 vs. 2.0, p = 0.518), and pleural effusion (38% vs. 19%, p = 0.146).

Conclusions

Non-hospitalized patients with COVID-19 pneumonia referred to CT scan by the emergency departments are at risk of APE. The presence of APE was not limited to severe or critical clinical type of COVID-19 pneumonia.

Key Points

• Acute pulmonary embolism was found in 18% of non-hospitalized COVID-19 patients referred by the emergency department to CTPA. Two (15%) patients had main, four (30%) lobar, and seven (55%) segmental acute pulmonary embolism.

• Five of 13 (38%) patients with acute pulmonary embolism had a moderate clinical type.

• Severity and radiological features of COVID-19 pneumonia showed no significant difference between patients with or without acute pulmonary embolism.

Early chest CT features of patients with 2019 novel coronavirus (COVID-19) pneumonia: relationship to diagnosis and prognosis

OBJECTIVE

To determine the consistency between CT findings and real-time reverse transcription-polymerase chain reaction (RT-PCR) and to investigate the relationship between CT features and clinical prognosis in COVID-19.

METHODS

The clinical manifestations, laboratory parameters, and CT imaging findings were analyzed in 34 COVID-19 patients, confirmed by RT-PCR from January 20 to February 4 in Hainan Province. CT scores were compared between the discharged patients and the ICU patients.

RESULTS

Fever (85%) and cough (79%) were most commonly seen. Ten (29%) patients demonstrated negative results on their first RT-PCR. Of the 34 (65%) patients, 22 showed pure ground-glass opacity. Of the 34 (50%) patients, 17 had five lobes of lung involvement, while the 23 (68%) patients had lower lobe involvement. The lesions of 24 (71%) patients were distributed mainly in the subpleural area. The initial CT lesions of ICU patients were distributed in both the subpleural area and centro-parenchyma (80%), and the lesions were scattered. Sixty percent of ICU patients had five lobes involved, while this was seen in only 25% of the discharged patients. The lesions of discharged patients were mainly in the subpleural area (75%). Of the discharged patients, 62.5% showed pure ground-glass opacities; 80% of the ICU patients were in the progressive stage, and 75% of the discharged patients were at an early stage. CT scores of the ICU patients were significantly higher than those of the discharged patients.

CONCLUSION

Chest CT plays a crucial role in the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. The initial features in CT may be associated with prognosis.

KEY POINTS

• Chest CT is valuable for the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. • The early CT findings of COVID-19 in ICU patients differed from those of discharged patients.

What every orthopaedic surgeon should know about COVID-19: A review of the current literature

The coronavirus (COVID-19) pandemic has severely affected the medical community and stopped the world in its tracks. This review aims to provide the basic information necessary for us, orthopaedic surgeons to prepare ourselves to face this pandemic together. Herein, we cover the background of COVID-19, presentation, investigations, transmission, infection control and touch upon emerging treatments. It is of paramount importance that we should stay vigilant for our patients, our families and ourselves. Adequate infection control measures are necessary during day-to-day clinical work.

Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort

The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6.

Update for Anaesthetists on Clinical Features of COVID-19 Patients and Relevant Management

When preparing for the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the coronavirus infection disease (COVID-19) questions arose regarding various aspects concerning the anaesthetist. When reviewing the literature it became obvious that keeping up-to-date with all relevant publications is almost impossible. We searched for and summarised clinically relevant topics that could help making clinical decisions. This is a subjective analysis of literature concerning specific topics raised in our daily practice (e.g., clinical features of COVID-19 patients; ventilation of the critically ill COVID-19 patient; diagnostic of infection with SARS-CoV-2; stability of the virus; Covid-19 in specific patient populations, e.g., paediatrics, immunosuppressed patients, patients with hypertension, diabetes mellitus, kidney or liver disease; co-medication with non-steroidal anti-inflammatory drugs (NSAIDs); antiviral treatment) and we believe that these answers help colleagues in clinical decision-making. With ongoing treatment of severely ill COVID-19 patients other questions will come up. While respective guidelines on these topics will serve clinicians in clinical practice, regularly updating all guidelines concerning COVID-19 will be a necessary, although challenging task in the upcoming weeks and months. All recommendations during the current extremely rapid development of knowledge must be evaluated on a daily basis, as suggestions made today may be out-dated with the new evidence available tomorrow.

A systematic review of chest imaging findings in COVID-19

Chest computed tomography (CT) is frequently used in diagnosing coronavirus disease 2019 (COVID-19) for detecting abnormal changes in the lungs and monitoring disease progression during the treatment process. Furthermore, CT imaging appearances are correlated with patients presenting with different clinical scenarios, such as early versus advanced stages, asymptomatic versus symptomatic patients, and severe versus nonsevere situations. However, its role as a screening and diagnostic tool in COVID-19 remains to be clarified. This article provides a systematic review and meta-analysis of the current literature on chest CT imaging findings with the aim of highlighting the contribution and judicious use of CT in the diagnosis of COVID-19. A search of PubMed/Medline, Web of Science, ScienceDirect, Google Scholar and Scopus was performed to identify studies reporting chest imaging findings in COVID-19. Chest imaging abnormalities associated with COVID-19 were extracted from the eligible studies and diagnostic value of CT in detecting these abnormal changes was compared between studies consisting of both COVID-19 and non-COVID-19 patients. A random-effects model was used to perform meta-analysis for calculation of pooled mean values and 95% confidence intervals (95% CI) of abnormal imaging findings. Fifty-five studies met the selection criteria and were included in the analysis. Pulmonary lesions more often involved bilateral lungs (78%, 95% CI: 45-100%) and were more likely to have a peripheral (65.35%, 95% CI: 25.93-100%) and peripheral plus central distribution (31.12%, 95% CI: 1.96-74.07%), but less likely to have a central distribution (3.57%, 95% CI: 0.99-9.80%). Ground glass opacities (GGO) (58.05%, 95% CI: 16.67-100%), consolidation (44.18%, 95% CI: 1.61-71.46%) and GGO plus consolidation (52.99%, 95% CI: 19.05-76.79%) were the most common findings reported in 94.5% (52/55) of the studies, followed by air bronchogram (42.50%, 95% CI: 7.78-80.39%), linear opacities (41.29%, 95% CI: 7.44-65.06%), crazy-paving pattern (23.57%, 95% CI: 3.13-91.67%) and interlobular septal thickening (22.91%, 95% CI: 0.90-80.49%). CT has low specificity in differentiating pneumonia-related lung changes due to significant overlap between COVID-19 and non-COVID-19 patients with no significant differences in most of the imaging findings between these two groups (P>0.05). Furthermore, normal CT (13.31%, 95% CI: 0.74-38.36%) was reported in 26 (47.3%) studies. Despite widespread use of CT in the diagnosis of COVID-19 patients based on the current literature, CT findings are not pathognomonic as it lacks specificity in differentiating imaging appearances caused by different types of pneumonia. Further, there is a relatively high percentage of normal CT scans. Use of CT as a first-line diagnostic or screening tool in COVID-19 is not recommended.

Quantitative analysis of chest CT imaging findings with the risk of ARDS in COVID-19 patients: a preliminary study

Background

The coronavirus disease 2019 (COVID-19) has rapidly become a pandemic worldwide. The value of chest computed tomography (CT) is debatable during the treatment of COVID-19 patients. Compared with traditional chest X-ray radiography, quantitative CT may supply more information, but its value on COVID-19 patients was still not proven.

Methods

An automatic quantitative analysis model based on a deep network called VB-Net for infection region segmentation was developed. A quantitative analysis was performed for patients diagnosed as severe COVID 19. The quantitative assessment included volume and density among the infectious area. The primary clinical outcome was the existence of acute respiratory distress syndrome (ARDS). A univariable and multivariable logistic analysis was done to explore the relationship between the quantitative results and ARDS existence.

Results

The VB-Ne model was sensitive and stable for pulmonary lesion segmentation, and quantitative analysis indicated that the total volume and average density of the lung lesions were not related to ARDS. However, lesions with specific density changes showed some influence on the risk of ARDS. The proportion of lesion density from −549 to −450 Hounsfield unit (HU) was associated with increased risk of ARDS, while the density was ranging from −149 to −50 HU was related to a lowered risk of ARDS.

Conclusions

The automatic quantitative model based on VB-Ne can supply useful information for ARDS risk stratification in COVID-19 patients during treatment.

Diagnostic performance between CT and initial real-time RT-PCR for clinically suspected 2019 coronavirus disease (COVID-19) patients outside Wuhan, China

INTRODUCTION

Chest CT is thought to be sensitive but less specific in diagnosing the 2019 coronavirus disease (COVID-19). The diagnostic value of CT is unclear. We aimed to compare the performance of CT and initial RT-PCR for clinically suspected COVID-19 patients outside the epicentre-Wuhan, China.

MATERIALS AND METHODS

Patients clinically suspected of COVID-19 infection who underwent initial RT-PCR and chest CT at the same time were retrospectively enrolled. Two radiologists with specific training reviewed the CT images independently and final diagnoses of the presence or absence of COVID-19 was reached by consensus. With serial RT-PCR as reference standard, the performance of initial RT-PCR and chest CT was analysed. A strategy of combining initial RT-PCR and chest CT was analysed to study the additional benefit.

RESULTS

82 patients admitted to hospital between Jan 10, 2020 to Feb 28, 2020 were enrolled. 34 COVID-19 and 48 non-COVID-19 patients were identified by serial RT-PCR. The sensitivity, specificity was 79% (27/34) and 100% (48/48) for initial RT-PCR and 77% (26/34) and 96% (46/48) for chest CT. The image readers had a good interobserver agreement with Cohen's kappa of 0.69. No statistical difference was found in the diagnostic performance between initial RT-PCR and chest CT. The comprehensive strategy had a higher sensitivity of 94% (32/34).

CONCLUSIONS

Initial RT-PCR and chest CT had comparable diagnostic performance in identification of suspected COVID-19 patients outside the epidemic center. To compensate potential risk of false-negative PCR, chest CT should be applied for clinically suspected patients with negative initial RT-PCR.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Current Status of Computed Tomography in Novel Coronavirus Disease 2019 Pneumonia

The novel coronavirus disease, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), has developed into a pandemic affecting more than three million people worldwide. It predominantly affects the respiratory system and patients present with fever, dry cough, dyspnea, and myalgia. The confirmatory diagnostic test is real-time reverse transcriptase polymerase chain reaction on blood or respiratory samples. Imaging with computed tomography, although not routinely recommended, may not only assist in making a diagnosis but also in assessing disease progression, assessing complications, and in prognostication. This review describes the objectives, techniques, imaging features, and reporting of computed tomography findings of SARS-CoV2 pneumonia.