Imaging of coronavirus disease 2019: A Chinese expert consensus statement

Challenges of mesenchymal stem cells in the clinical treatment of COVID-19

The 2019 coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has brought an enormous public health burden to the global society. The duration of the epidemic, the number of infected people, and the widespread of the epidemic are extremely rare in modern society. In the initial stage of infection, people generally show fever, cough, and dyspnea, which can lead to pneumonia, acute respiratory syndrome, kidney failure, and even death in severe cases. The strong infectivity and pathogenicity of SARS-CoV-2 make it more urgent to find an effective treatment. Mesenchymal stem cells (MSCs) are a kind of pluripotent stem cells with the potential for self-renewal and multi-directional differentiation. They are widely used in clinical experiments because of their low immunogenicity and immunomodulatory function. Mesenchymal stem cell-derived exosomes (MSC-Exo) can play a physiological role similar to that of stem cells. Since the COVID-19 pandemic, a series of clinical trials based on MSC therapy have been carried out. The results show that MSCs are safe and can significantly improve patients' respiratory function and prognosis of COVID-19. Here, the effects of MSCs and MSC-Exo in the treatment of COVID-19 are reviewed, and the clinical challenges that may be faced in the future are clarified.

Contrastive learning with token projection for Omicron pneumonia identification from few-shot chest CT images

Introduction

Deep learning-based methods can promote and save critical time for the diagnosis of pneumonia from computed tomography (CT) images of the chest, where the methods usually rely on large amounts of labeled data to learn good visual representations. However, medical images are difficult to obtain and need to be labeled by professional radiologists.

Methods

To address this issue, a novel contrastive learning model with token projection, namely CoTP, is proposed for improving the diagnostic quality of few-shot chest CT images. Specifically, (1) we utilize solely unlabeled data for fitting CoTP, along with a small number of labeled samples for fine-tuning, (2) we present a new Omicron dataset and modify the data augmentation strategy, i.e., random Poisson noise perturbation for the CT interpretation task, and (3) token projection is utilized to further improve the quality of the global visual representations.

Results

The ResNet50 pre-trained by CoTP attained accuracy (ACC) of 92.35%, sensitivity (SEN) of 92.96%, precision (PRE) of 91.54%, and the area under the receiver-operating characteristics curve (AUC) of 98.90% on the presented Omicron dataset. On the contrary, the ResNet50 without pre-training achieved ACC, SEN, PRE, and AUC of 77.61, 77.90, 76.69, and 85.66%, respectively.

Conclusion

Extensive experiments reveal that a model pre-trained by CoTP greatly outperforms that without pre-training. The CoTP can improve the efficacy of diagnosis and reduce the heavy workload of radiologists for screening of Omicron pneumonia.

Correlation of Computed Tomography (CT) Severity Score With Laboratory and Clinical Parameters and Outcomes in Coronavirus Disease 2019 (COVID-19)

BACKGROUND

 Coronavirus disease 2019 (COVID-19) is a potentially lethal respiratory illness caused by a newly identified coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the novelty of the virus, high caseloads, and increasing turnaround time for reverse transcriptase-polymerase chain reaction (RT-PCR) results, accurate information about the clinical course and prognosis of individual patients was largely unknown. This has forced physicians all over the world to brainstorm attempts to come up with reliable indicators like chest high-resolution computed tomography (HRCT) for any changes suggestive of COVID-19; surrogate laboratory parameters such as C-reactive protein (CRP), ferritin, D-dimer, lactate dehydrogenase (LDH), or interleukin-6 (IL-6) for assessing the severity of the disease; and other organ-specific tests to identify the multiorgan involvement in severe-to-critical COVID-19. Chest computed tomography (CT) scans play a significant role in the management of COVID-19 disease and serve as an indicator of disease severity and its possible outcome, which might help in the early identification of patients who might need critical care and earlier prognostication.

METHODS

A retrospective observational study was conducted at a single center in a level 3 critical care unit (CCU) of a 750-bed teaching hospital in Hyderabad, Telangana, India, over a period of six months. All RT-PCR-positive COVID-19 patients admitted to the CCU with CT chest performed within 24 hours of admission were screened for eligibility for this study. CT severity scoring was based on chest HRCT or CT.

RESULTS

Of the 110 patients, a majority (36.36%) were aged between 61 and 70 years. The mean age of our study population was 59.65±11.88 years. Of the 110 patients, the majority were admitted to the hospital for 22-28 days (24.55%), followed by 8-14 days (22.72%), and 21.82% were admitted for one day. Of the 110 patients, a majority were admitted to the CCU for seven days (41.82%), followed by 15-21 days (24.55%); and 19.09% were admitted for 8-14 days. Most of the patients were discharged (65.45%), and we had a 34.55% mortality rate in our study. We found a significant association between chest CT severity score (CTSS) and the age of the patient, duration of hospital stay, and duration of CCU stay using multivariate regression analysis.

CONCLUSION

CTSS could be greatly helpful for the screening and early identification of the disease, especially in those patients awaiting an RT-PCR report or with negative RT-PCR, which would lead to appropriate isolation and treatment measures. Early detection could also help assess the progression of the disease, alter the course of management at the earliest point possible, and improve the prognostication of COVID-19 patients.

Effectiveness of RCTs Pooling Evidence on Mesenchymal Stem Cell (MSC) Therapeutic Applications During COVID-19 Epidemic: A Systematic Review

Background

Global pandemic identified as coronavirus disease 2019 (COVID-19) has resulted in a variety of clinical symptoms, from asymptomatic carriers to those with severe acute respiratory distress syndrome (SARS) and moderate upper respiratory tract symptoms (URTS). This systematic review aimed to determine effectiveness of stem cell (SC) applications among COVID-19 patients.

Methods

Multiple databases of PubMed, EMBASE, Science Direct, Google Scholar, Scopus, Web of Science, and Cochrane Library were used. Studies were screened, chosen, and included in this systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flowchart diagram and PRISMA checklist. Included studies' quality was assessed employing Critical Appraisal Skills Programme (CASP) quality evaluation criteria for 14 randomized controlled trials (RCTs).

Results

Fourteen RCTs were performed between the years of 2020 to 2022, respectively, with a sample size n = 574 (treatment group (n = 318); control group (n = 256)) in multiple countries of Indonesia, Iran, Brazil, Turkey, China, Florida, UK, and France. The greatest sample size reported from China among 100 COVID-19 patients, while the lowest sample of 9 COVID-19 patients from Jakarta, Indonesia, and the patient's age ranges from 18 to 69 years. Studies applied to the type of SC were "Umbilical cord MSCs, MSCs secretome, MSCs, Placenta-derived MSCs, Human immature dental pulp SC, DW-MSC infusion, Wharton Jelly-derived MSCs". The injected therapeutic dose was 1 × 10⁶ cells/kg, 1 × 10⁷ cells/kg, 1 × 10⁵ cells/kg, and 1 million cells/kg as per the evidence from the different studies. Studies focused on demographic variables, clinical symptoms, laboratory tests, Comorbidities, respiratory measures, concomitant therapies, Sequential Organ Failure Assessment score, mechanical ventilation, body mass index, adverse events, inflammatory markers, and PaO₂/FiO₂ ratio were all recorded as study characteristics.

Conclusion

Clinical evidence on MSC's therapeutic applications during COVID-19 pandemic has proven to be a promising therapy for COVID-19 patient recovery with no consequences and applied as a routine treatment for challenging ailments.

Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital

BACKGROUND

Chest radiographic scoring systems for COVID-19 pneumonia have been developed. However, little is published on the utilityof these scoring systems in low- and middle-income countries.

OBJECTIVES

To perform risk stratification of COVID-19 pneumonia in Johannesburg, South Africa (SA), by comparing the Brixia score withclinical parameters, disease course and clinical outcomes. To assess inter-rater reliability and developing predictive models of the clinicaloutcome using the Brixia score and clinical parameters.

METHODS

Retrospective investigation was conducted of adult participants with established COVID-19 pneumonia admitted at a tertiaryinstitution from 1 May to 30 June 2020. Two radiologists, blinded to clinical data, assigned Brixia scores. Brixia scores were compared withclinical parameters, length of stay and clinical outcomes (discharge/death). Inter-rater agreement was determined. Multivariable logisticregression extracted variables predictive of in-hospital demise.

RESULTS

The cohort consisted of 263 patients, 51% male, with a median age of 47 years (interquartile range (IQR) = 20; 95% confidenceinterval (CI) 46.5 - 49.9). Hypertension (38.4%), diabetes (25.1%), obesity (19.4%) and HIV (15.6%) were the most common comorbidities.The median length of stay for 258 patients was 7.5 days (IQR = 7; 95% CI 8.2 - 9.7) and 6.5 days (IQR = 8; 95% CI 6.5 - 12.5) for intensivecare unit stay. Fifty (19%) patients died, with a median age of 55 years (IQR = 23; 95% CI 50.5 - 58.7) compared with survivors, of medianage 46 years (IQR = 20; 95% CI 45 - 48.6) (p=0.01). The presence of one or more comorbidities resulted in a higher death rate (23% v. 9.2%;p=0.01) than without comorbidities. The median Brixia score for the deceased was higher (14.5) than for the discharged patients (9.0)(p<0.001). Inter-rater agreement for Brixia scores was good (intraclass correlation coefficient 0.77; 95% CI 0.6 - 0.85; p<0.001). A modelcombining Brixia score, age, male gender and obesity (sensitivity 84%; specificity 63%) as well as a model with Brixia score and C-reactiveprotein (CRP) count (sensitivity 81%; specificity 63%) conferred the highest risk for in-hospital mortality.

CONCLUSION

We have demonstrated the utility of the Brixia scoring system in a middle-income country setting and developed the first SArisk stratification models incorporating comorbidities and a serological marker. When used in conjunction with age, male gender, obesityand CRP, the Brixia scoring system is a promising and reliable risk stratification tool. This may help inform the clinical decision pathway inresource-limited settings like ours during future waves of COVID-19.

Interobserver Variability in CT Severity Scoring System in COVID-19 Positive Patients

Background: Chest CT scans are done in cases of coronavirus disease 2019 (COVID-19)-positive patients to understand the severity of the disease and plan treatment accordingly. Severity is determined according to a 25-point scoring system, however, there could be interobserver variability in using this scoring system thus leading to the different categorization of patients. We tried to look for this interobserver variability and thus find out its reliability.

Methods: The study was retrospective and was done in a designated COVID center. Some 100 patients were involved in the study who tested positive for COVID-19 disease. The research was conducted over six months (January 2021 to June 2021). Images were given to three radiologists with a minimum of 10 years of experience in thoracic imaging working in different setups at different places for interpretation and scoring further and their scores were compared. Before the study, the local ethics committee granted its approval.

Results: There was no significant variability in the interobserver scoring system thus proving its reliability. The standard deviation between different observers was less than three. There was almost perfect agreement amongst all the observers (Fleiss’ K=0.99 [95% confidence interval, CI: 0.995-0.998]). Maximum variations were observed in the moderate class. 

Conclusion: There was minimum inter-observer variability in the 25-point scoring system thus proving its reliability in categorizing patients according to severity. There was no change in the class of the patient according to its severity. A 25-point scoring system hence can be used by clinicians to plan treatment and thus improve a patient's prognosis.

Evaluation of cognitive, mental, and sleep patterns of post-acute COVID-19 patients and their correlation with thorax CT

Objective

In this study, we have evaluated the cognitive, mental, and sleep patterns of post-COVID patients 2 months after their hospitalization, and after scoring their hospitalization thorax CTs, we have compared the degree of the lung involvement with cognitive and mental states of the patients.

Materials and methods

Forty post-COVID patients were included in our study. Patients who were hospitalized due to COVID-19 and who had thorax CT scan at the admission were included in the study. Thorax CT scans of the patients were scored using chest severity scoring (CT-SS). The Mini-Mental State Examination test (MMSE), the Montreal Cognitive Assessment Test (MoCA), the Pittsburgh Sleep Quality Index, and the Hamilton Depression and Hamilton Anxiety scales of all the participants were evaluated by the same person.

Results

Early stage cognitive impairment was detected in 15% of post-COVID patients in the MMSE test and mean MMSE test score was 26.9 ± 2.1. The MoCA test detected cognitive impairment in 55% of the patients, and the mean MoCA score was 19.6 ± 5.2. Furthermore, all patients showed depressive symptoms in Hamilton Depression Scoring System and 57.5% of the patients showed anxiety symptoms in the Hamilton Anxiety Scoring System. The mean Pittsburg Sleep Quality Index of the patients was 10.7 ± 3.1, and it was found to be higher than normal. The mean CT-SS scores, which used to evaluate the lung involvement, of the patients were 4.7 ± 5.6. We did not find any correlation between patients’ cognitive tests and CT-SS scores.

Conclusion

When these results are taken into consideration, our study has shown that the neuropsychiatric symptoms of the patients who had COVID-19 continued even after 2 months of their illness. Therefore, long-term rehabilitation of these patients, including cognitive education and psychological services, should be continued.

Novel COVID-19 Diagnosis Delivery App Using Computed Tomography Images Analyzed with Saliency-Preprocessing and Deep Learning

This app project was aimed to remotely deliver diagnoses and disease-progression information to COVID-19 patients to help minimize risk during this and future pandemics. Data collected from chest computed tomography (CT) scans of COVID-19-infected patients were shared through the app. In this article, we focused on image preprocessing techniques to identify and highlight areas with ground glass opacity (GGO) and pulmonary infiltrates (PIs) in CT image sequences of COVID-19 cases. Convolutional neural networks (CNNs) were used to classify the disease progression of pneumonia. Each GGO and PI pattern was highlighted with saliency map fusion, and the resulting map was used to train and test a CNN classification scheme with three classes. In addition to patients, this information was shared between the respiratory triage/radiologist and the COVID-19 multidisciplinary teams with the application so that the severity of the disease could be understood through CT and medical diagnosis. The three-class, disease-level COVID-19 classification results exhibited a macro-precision of more than 94.89% in a two-fold cross-validation. Both the segmentation and classification results were comparable to those made by a medical specialist.

Diaphragm and Phrenic Nerve Ultrasound in COVID-19 Patients and Beyond: Imaging Technique, Findings, and Clinical Applications

The diaphragm, the principle muscle of inspiration, is an under-recognized contributor to respiratory disease. Dysfunction of the diaphragm can occur secondary to lung disease, prolonged ventilation, phrenic nerve injury, neuromuscular disease, and central nervous system pathology. In light of the global pandemic of coronavirus disease 2019 (COVID-19), there has been growing interest in the utility of ultrasound for evaluation of respiratory symptoms including lung and diaphragm sonography. Diaphragm ultrasound can be utilized to diagnose diaphragm dysfunction, assess severity of dysfunction, and monitor disease progression. This article reviews diaphragm and phrenic nerve ultrasound and describes clinical applications in the context of COVID-19.

A 2-month post-COVID-19 follow-up study on patients with dyspnea

BACKGROUND AND AIMS

Dyspnea is one of the most common symptoms associated with the COVID-19 caused by novel coronavirus SARS-CoV-2. This study aimed to assess the prevalence of dyspnea, observe co-variables, and find predictors of dyspnea after 2 months of recovery from COVID-19.

METHODS

A total of 377 patients were included in the study based on their responses and clinical findings during initial admission to the hospital with COVID-19. After excluding five deceased patients, a total of 327 patients were interviewed through telephone using a 12-point dyspnea scale and using relevant questions to gauge the patient clinically. Interviews were carried out by trained physicians, and responses were recorded and stored. All analyses were carried out using the statistical programming language R.

RESULTS

Of the total 327 participants in the study, 34% had stated that they were suffering from respiratory symptoms even after 2 months of COVID-19. The study demonstrated that patient oxygen saturation level SpO2 (P = .03), D-dimer (P = .001), serum ferritin (P = .006), and the presence and severity of dyspnea are significantly correlated. In addition to that, patient smoking history (P = .012) and comorbidities such as chronic obstructive pulmonary disease (COPD) (P = .021) were found to be statistically significant among groups.

CONCLUSION

These findings of this study can be useful for predicting and managing long-term complications of COVID-19.

The Role and Challenges of Clinical Imaging During COVID-19 Outbreak

Objective:

The Radiology department played a crucial role in detecting and following up with the COVID-19 disease during the pandemic. The purpose of this review was to highlight and discuss the role of each imaging modality, in the radiology department, that can help in the current pandemic and to determine the challenges faced by staff and how to overcome them.

Materials and Methods:

A literature search was performed using different databases, including PubMed, Google scholar, and the college electronic library to access 2020 published related articles.

Results:

A chest computed tomogram (CT) was found to be superior to a chest radiograph, with regards to the early detection of COVID-19. Utilizing lung point of care ultrasound (POCUS) with pediatric patients, demonstrated excellent sensitivity and specificity, compared to a chest radiography. In addition, lung ultrasound (LUS) showed a high correlation with the disease severity assessed with CT. However, magnetic resonance imaging (MRI) has some limiting factors with regard to its clinical utilization, due to signal loss. The reported challenges that the radiology department faced were mainly related to infection control, staff workload, and the training of students.

Conclusion:

The choice of an imaging modality to provide a COVID-19 diagnosis is debatable. It depends on several factors that should be carefully considered, such as disease stage, mobility of the patient, and ease of applying infection control procedures. The pros and cons of each imaging modality were highlighted, as part of this review. To control the spread of the infection, precautionary measures such as the use of portable radiographic equipment and the use of personal protective equipment (PPE) must be implemented.

Diagnostic value of chest computed tomography imaging for COVID-19 based on reverse transcription-polymerase chain reaction: a meta-analysis

Background

The computed tomography (CT) diagnostic value of COVID-19 is controversial. We summarized the value of chest CT in the diagnosis of COVID-19 through a meta-analysis based on the reference standard.

Methods

All Chinese and English studies related to the diagnostic value of CT for COVID-19 across multiple publication platforms, was searched for and collected. Studies quality evaluation and plotting the risk of bias were estimated. A heterogeneity test and meta-analysis, including plotting sensitivity (Sen), specificity (Spe) forest plots, pooled positive likelihood ratio (+LR), negative likelihood ratio (-LR), dignostic odds ratio (DOR) values and 95% confidence interval (CI), were estimated. If there was a threshold effect, summary receiver operating characteristic curves (SROC) was further plotted. Pooled area under the receiver operating characteristic curve (AUROC) and 95% CI were also calculated.

Results

Twenty diagnostic studies that represented a total of 9004 patients were included from 20 pieces of literatures after assessing all the aggregated studies. The reason for heterogeneity was caused by the threshold effect, so the AUROC = 0.91 (95% CI: 0.89–0.94) for chest CT of COVID-19. Pooled sensitivity, specificity, +LR, -LR from 20 studies were 0.91 (95% CI: 0.88–0.94), 0.71 (95% CI: 0.59–0.80), 3.1(95% CI: 2.2–4.4), 0.12 (95% CI: 0.09–0.17), separately. The I² was 85.6% (P = 0.001) by Q-test.

Conclusions

The results of this study showed that CT diagnosis of COVID-19 was close to the reference standard. The diagnostic value of chest CT may be further enhanced if there is a unified COVID-19 diagnostic standard. However, please pay attention to rational use of CT.

Graphic Abstract

Is chest X-ray severity scoring for COVID-19 pneumonia reliable?

Purpose

To explore whether chest X-ray severity scoring (CX-SS) could be reliable to assess the severity of pulmonary parenchymal disease in COVID-19 patients.

Material and methods

The study consisted of 325 patients whose COVID-19 was confirmed by RT-PCR test and who underwent chest X-ray and computed tomography (CT) studies to assess parenchymal disease severity. Only 195 cases included in the final analysis after exclusion of cases with previous chest disease and cases having more than 24 hours interval between their X-ray and CT chest studies. Both chest X-ray and CT severity scores (CT-SS) were recorded by 2 experienced radiologists and were compared to the clinical severity. Interobserver agreement was assessed for CX-SS and CT-SS.

Results

In relation to the clinical severity, the sensitivity of the CX-SS for diagnosis of moderate to severe parenchymal disease was high (90.4% and 100%) and low for mild cases (66.2%), while the specificity was high for mild to moderate parenchymal disease (100%) compared to severe cases (86.7%). The sensitivity, specificity, and diagnostic accuracy of the CT-SS were higher than CX-SS. Pearson correlation coefficient demonstrated a strong positive correlation between CX-SS and CT-SS (rs = 0.88, p < 0.001). The inter-observer agreement for CX-SS was good (k = 0.79, p = 0.001), and it was excellent for CT-SS (k = 0.85, p = 0.001).

Conclusions

CX-SS is reliable to assess the severity of COVID-19 pulmonary parenchymal disease, especially in moderate and severe cases, with the tendency of overestimation of severe cases.

New challenges for management of COVID-19 patients: Analysis of MDCT based "Automated pneumonia analysis program"

PURPOSE

The aim of this study is to define the role of an "Automated Multi Detector Computed Tomography (MDCT) Pneumonia Analysis Program'' as an early outcome predictor for COVID-19 pneumonia in hospitalized patients.

MATERIALS AND METHODS

A total of 96 patients who had RT-PCR proven COVID-19 pneumonia diagnosed by non-contrast enhanced chest MDCT and hospitalized were enrolled in this retrospective study. An automated CT pneumonia analysis program was used for each patient to see the extent of disease. Patients were divided into two clinical subgroups upon their clinical status as good and bad clinical course. Total opacity scores (TOS), intensive care unit (ICU) entry, and mortality rates were measured for each clinical subgroups and also laboratory values were used to compare each subgroup.

RESULTS

Left lower lobe was the mostly effected side with a percentage of 78.12 % and followed up by right lower lobe with 73.95 %. TOS, ICU entry, and mortality rates were higher in bad clinical course subgroup. TOS values were also higher in patients older than 60 years and in patients with comorbidities including, Hypertension (HT), Diabetes Mellitus (DM), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF) and malignancy.

CONCLUSION

Automated MDCT analysis programs for pneumonia are fast and an objective way to define the disease extent in COVID-19 pneumonia and it is highly correlated with the disease severity and clinical outcome thus providing physicians with valuable knowledge from the time of diagnosis.

The Characteristics, Manifestations and Cardiopulmonary Imaging (CT/MRI) of COVID-19 in SARS-CoV-2 Infection

The World Health Organization (WHO) declared the transmission of SARS-CoV-2 a Public Health Emergency of International Concern. The novel coronavirus has diverse manifestations, usually similar to a common cold or influenza. The majority of patients with coronavirus disease have typical imaging features. The typical CT characteristics of patients with COVID-19 pneumonia are ground-glass opacities and consolidative lesions with a peripheral and posterior distribution. Noninvasive imaging methods are precise and rapid means of diagnosing pneumonia and cardiovascular complications caused by COVID-19 infection. Therefore, it is important for clinicians to understand the implications of this pandemic and to be familiar with the different imaging aspects of the novel coronavirus disease. This review focuses on the most commonly reported imaging findings of COVID-19 infection in different patients from different countries, the expert recommendations, and the cardiac manifestations of SARS-CoV-2 infection.

Long-Term Pulmonary Damage From SARS-CoV-2 in an Infant With Brief Unexplained Resolved Events: A Case Report

A brief unexplained resolved event (BRUE) is an event observed in a child under 1 year of age in which the observer witnesses a sudden, brief but resolved episode of change in skin color, lack of breathing, weakness or poor responsiveness. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-2019 (COVID-19). We report the case of a previously healthy, full-term infant infected with SARS-CoV-2 when he was 8 months old. Previous to this event, both his grandfather and great-uncle had died of severe pneumonia and his mother had developed respiratory symptoms and fever. Over the following month he was seen five times in the emergency room and was hospitalized twice for recurrent BRUE. At the first hospital admission, after the second emergency room visit, he twice tested positive for COVID-19 after nasopharyngeal swab tests. During his second hospital admission, after the fifth emergency room visit, chest computed tomography revealed typical SARS-CoV-2 pneumonia. During a follow-up examination 6 months later, mild respiratory distress required administration of inhaled oxygen (0.5 L/min) and chest computed tomography disclosed a slight improvement in pulmonary involvement. The clinical manifestation of pulmonary complications from COVID-19 infection was unusual. This is the first report of an infant at high-risk for BRUE, which was the only manifestation of long-term lung involvement due to SARS-CoV-2 pneumonia.

Quality of and Recommendations for Relevant Clinical Practice Guidelines for COVID-19 Management: A Systematic Review and Critical Appraisal

Background: The morbidity and mortality of coronavirus disease 2019 (COVID-19) are still increasing. This study aimed to assess the quality of relevant COVID-19 clinical practice guidelines (CPGs) and to compare the similarities and differences between recommendations. Methods: A comprehensive search was conducted using electronic databases (PubMed, Embase, and Web of Science) and representative guidelines repositories from December 1, 2019, to August 11, 2020 (updated to April 5, 2021), to obtain eligible CPGs. The Appraisal of Guidelines for Research and Evaluation (AGREE II) tool was used to evaluate the quality of CPGs. Four authors extracted relevant information and completed data extraction forms. All data were analyzed using R version 3.6.0 software. Results: In total, 39 CPGs were identified and the quality was not encouragingly high. The median score (interquartile range, IQR) of every domain from AGREE II for evidence-based CPGs (EB-CPGs) versus (vs.) consensus-based CPG (CB-CPGs) was 81.94% (75.00-84.72) vs. 58.33% (52.78-68.06) in scope and purpose, 59.72% (38.89-75.00) vs. 36.11% (33.33-36.11) in stakeholder involvement, 64.58% (32.29-71.88) vs. 22.92% (16.67-26.56) in rigor of development, 75.00% (52.78-86.81) vs. 52.78% (50.00-63.89) in clarity of presentation, 40.63% (22.40-62.50) vs. 20.83% (13.54-25.00) in applicability, and 58.33% (50.00-100.00) vs. 50.00% (50.00-77.08) in editorial independence, respectively. The methodological quality of EB-CPGs were significantly superior to the CB-CPGs in the majority of domains (P < 0.05). There was no agreement on diagnosis criteria of COVID-19. But a few guidelines show Remdesivir may be beneficial for the patients, hydroxychloroquine +/- azithromycin may not, and there were more consistent suggestions regarding discharge management. For instance, after discharge, isolation management and health status monitoring may be continued. Conclusions: In general, the methodological quality of EB-CPGs is greater than CB-CPGs. However, it is still required to be further improved. Besides, the consistency of COVID-19 recommendations on topics such as diagnosis criteria is different. Of them, hydroxychloroquine +/- azithromycin may be not beneficial to treat patients with COVID-19, but remdesivir may be a favorable risk-benefit in severe COVID-19 infection; isolation management and health status monitoring after discharge may be still necessary. Chemoprophylaxis, including SARS-CoV 2 vaccines and antiviral drugs of COVID-19, still require more trials to confirm this.

Chest Computed Tomography (CT) as a Predictor of Clinical Course in Coronavirus Disease

BACKGROUND Chest imaging may be taken into consideration in detecting viral lung infections, especially if there are no tests available or there is a need for a prompt diagnosis. Imaging modalities enable evaluation of the character and extent of pulmonary lesions and monitoring of the disease course. The aim of this study was to verify the prognostic value of chest CT in COVID-19 patients. MATERIAL AND METHODS We conducted a retrospective review of clinical data and CT scans of 156 patients with SARS-CoV-2 infection confirmed by real-time reverse-transcription polymerase-chain-reaction (rRT-PCR) assay hospitalized in the Central Clinical Hospital of the Ministry of the Interior in Warsaw and in the Medical Centre in Łańcut, Poland. The total severity score (TSS) was used to quantify the extent of lung opacification in CT scans. RESULTS The dominant pattern in discharged patients was ground-glass opacities, whereas in the non-survivors, the dominant pulmonary changes were consolidations. The non-survivors were more likely to have pleural effusion, pleural thickening, lymphadenopathy, air bronchogram, and bronchiolectasis. There were no statistically significant differences among the 3 analyzed groups (non-survivors, discharged patients, and patients who underwent prolonged hospitalization) in the presence of fibrotic lesions, segmental or subsegmental pulmonary vessel enlargement, subpleural lines, air bubble sign, and halo sign. CONCLUSIONS Lung CT is a diagnostic tool with prognostic utility in COVID-19 patients. The correlation of the available clinical data with semi-quantitative radiological features enables evaluation of disease severity. The occurrence of specific radiomics shows a positive correlation with prognosis.

Frequency and Risk Factors for Spontaneous Pneumomediastinum in COVID-19 Patients

Background: Spontaneous pneumomediastinum (SPM) is an uncommon condition in COVID-19 patients. No information about outcome or risk factors is available at the time. The aim of this research is to report on the frequency and risk factors of spontaneous pneumomediastinum in COVID-19 patients. Materials and Methods: An unmatched case-control study was carried out in a tertiary health-care facility for patients with COVID-19. Electronic files were reviewed to identify patients with confirmed COVID-19 infection by RT-PCR. Univariate analysis was used to describe demographic data. Mean differences were calculated using the Mann-Whitney test. Frequency and odds ratios were calculated by standard operations. Results: A total of 271 patients were included in the study. Nine patients showed spontaneous pneumomediastinum and four of them presented associated spontaneous pneumothorax. The most common risk factors associated with poor outcomes in COVID-19 patients were not considered as risk factors for spontaneous pneumomediastinum development. Conclusion: Spontaneous pneumomediastinum is an uncommon clinical feature in COVID-19 patients. More research is necessary to formulate statements regarding prevalence, risk factors, and outcome.

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.

Effect of human umbilical cord-derived mesenchymal stem cells on lung damage in severe COVID-19 patients: a randomized, double-blind, placebo-controlled phase 2 trial

Treatment of severe Coronavirus Disease 2019 (COVID-19) is challenging. We performed a phase 2 trial to assess the efficacy and safety of human umbilical cord-mesenchymal stem cells (UC-MSCs) to treat severe COVID-19 patients with lung damage, based on our phase 1 data. In this randomized, double-blind, and placebo-controlled trial, we recruited 101 severe COVID-19 patients with lung damage. They were randomly assigned at a 2:1 ratio to receive either UC-MSCs (4 × 107 cells per infusion) or placebo on day 0, 3, and 6. The primary endpoint was an altered proportion of whole lung lesion volumes from baseline to day 28. Other imaging outcomes, 6-minute walk test (6-MWT), maximum vital capacity, diffusing capacity, and adverse events were recorded and analyzed. In all, 100 COVID-19 patients were finally received either UC-MSCs (n = 65) or placebo (n = 35). UC-MSCs administration exerted numerical improvement in whole lung lesion volume from baseline to day 28 compared with the placebo (the median difference was -13.31%, 95% CI -29.14%, 2.13%, P = 0.080). UC-MSCs significantly reduced the proportions of solid component lesion volume compared with the placebo (median difference: -15.45%; 95% CI -30.82%, -0.39%; P = 0.043). The 6-MWT showed an increased distance in patients treated with UC-MSCs (difference: 27.00 m; 95% CI 0.00, 57.00; P = 0.057). The incidence of adverse events was similar in the two groups. These results suggest that UC-MSCs treatment is a safe and potentially effective therapeutic approach for COVID-19 patients with lung damage. A phase 3 trial is required to evaluate effects on reducing mortality and preventing long-term pulmonary disability. (Funded by The National Key R&D Program of China and others. ClinicalTrials.gov number, NCT04288102.

Reporting radiographers' interpretation and use of the British Society of Thoracic Imaging's coding system when reporting COVID-19 chest x-rays

INTRODUCTION

The United Kingdom (UK) has experienced one of the worst initial waves of the COVID-19 pandemic. Clinical signs help guide initial diagnosis, though definitive diagnosis is made using the laboratory technique reverse transcription polymerase chain reaction (RT-PCR). The chest x-ray (CXR) is used as the primary imaging investigation in the United Kingdom (UK) for patients with suspected COVID-19. In some hospitals these CXRs may be reported by a radiographer.

METHODS

Retrospective review of CXR reports by radiographers for suspected COVID-19 patients attending the Emergency Department (ED) of a hospital in the UK. Interpretation and use of the British Society of Thoracic Imaging (BSTI) coding system was assessed. Report description and code use were cross-checked. Report and code usage were checked against the RT-PCR result to determine accuracy. Report availability was checked against the availability of the RT-PCR result. A confusion matrix was utilised to determine performance. The data were analysed manually using Excel.

RESULTS

Sample size was 320 patients; 54.1% male patients (n = 173), 45.9% female patients (n = 147). The correct code matched report descriptions in 316 of the 320 cases (98.8%). In 299 of the 320 cases (93.4%), the reports were available before the RT-PCR swab result. CXR sensitivity for detecting COVID-19 was 85% compared to 93% for the initial RT-PCR.

CONCLUSION

Reporting radiographers can adequately utilise and apply the BSTI classification system when reporting COVID-19 CXRs. They can recognise the classic CXR appearances of COVID-19 and those with normal appearances. Future best practice includes checking laboratory results when reporting CXRs with ambiguous appearances.

IMPLICATIONS FOR PRACTICE

Utilisation of reporting radiographers to report CXRs in any future respiratory pandemic should be considered a service-enabling development.

High-risk chest radiographic features associated with COVID-19 disease severity

Objectives

High-risk CXR features in COVID-19 are not clearly defined. We aimed to identify CXR features that correlate with severe COVID-19.

Methods

All confirmed COVID-19 patients admitted within the study period were screened. Those with suboptimal baseline CXR were excluded. CXRs were reviewed by three independent radiologists and opacities recorded according to zones and laterality. The primary endpoint was defined as hypoxia requiring supplemental oxygen, and CXR features were assessed for association with this endpoint to identify high-risk features. These features were then used to define criteria for a high-risk CXR, and clinical features and outcomes of patients with and without baseline high-risk CXR were compared using logistic regression analysis.

Results

109 patients were included. In the initial analysis of 40 patients (36.7%) with abnormal baseline CXR, presence of bilateral opacities, multifocal opacities, or any upper or middle zone opacity were associated with supplemental oxygen requirement. Of the entire cohort, 29 patients (26.6%) had a baseline CXR with at least one of these features. Having a high-risk baseline CXR was significantly associated with requiring supplemental oxygen in univariate (odds ratio 14.0, 95% confidence interval 3.90–55.60) and multivariate (adjusted odds ratio 8.38, 95% CI 2.43–28.97, P = 0.001) analyses.

Conclusion

We identified several high-risk CXR features that are significantly associated with severe illness. The association of upper or middle zone opacities with severe illness has not been previously emphasized. Recognition of these specific high-risk CXR features is important to prioritize limited healthcare resources for sicker patients.

Recommendations for SARS-CoV-2/COVID-19 testing: a scoping review of current guidance

BACKGROUND

Testing used in screening, diagnosis and follow-up of COVID-19 has been a subject of debate. Several organisations have developed formal advice about testing for COVID-19 to assist in the control of the disease. We collated, delineated and appraised current worldwide recommendations about the role and applications of tests to control SARS-CoV-2/COVID-19.

METHODS

We searched for documents providing recommendations for COVID-19 testing in PubMed, EMBASE, LILACS, the Coronavirus Open Access Project living evidence database and relevant websites such as TRIP database, ECRI Guidelines Trust, the GIN database, from inception to 21 September 2020. Two reviewers applied the eligibility criteria to potentially relevant citations without language or geographical restrictions. We extracted data in duplicate, including assessment of methodological quality using the Appraisal of Guidelines for Research and Evaluation-II tool.

RESULTS

We included 47 relevant documents and 327 recommendations about testing. Regarding the quality of the documents, we found that the domains with the lowest scores were 'Editorial independence' (Median=4%) and 'Applicability' (Median=6%). Only six documents obtained at least 50% score for the 'Rigour of development' domain. An important number of recommendations focused on the diagnosis of suspected cases (48%) and deisolation measures (11%). The most frequently recommended test was the reverse transcription-PCR (RT-PCR) assay (87 recommendations) and the chest CT (38 recommendations). There were 22 areas of agreement among guidance developers, including the use of RT-PCR for SARS-Cov-2 confirmation, the limited role of bronchoscopy, the use chest CT and chest X-rays for grading severity and the co-assessment for other respiratory pathogens.

CONCLUSION

This first scoping review of recommendations for COVID-19 testing showed many limitations in the methodological quality of included guidance documents that could affect the confidence of clinicians in their implementation. Future guidance documents should incorporate a minimum set of key methodological characteristics to enhance their applicability for decision making.

Lessons learned in transitioning to AI in the medical imaging of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc across the world. It also created a need for the urgent development of efficacious predictive diagnostics, specifically, artificial intelligence (AI) methods applied to medical imaging. This has led to the convergence of experts from multiple disciplines to solve this global pandemic including clinicians, medical physicists, imaging scientists, computer scientists, and informatics experts to bring to bear the best of these fields for solving the challenges of the COVID-19 pandemic. However, such a convergence over a very brief period of time has had unintended consequences and created its own challenges. As part of Medical Imaging Data and Resource Center initiative, we discuss the lessons learned from career transitions across the three involved disciplines (radiology, medical imaging physics, and computer science) and draw recommendations based on these experiences by analyzing the challenges associated with each of the three associated transition types: (1) AI of non-imaging data to AI of medical imaging data, (2) medical imaging clinician to AI of medical imaging, and (3) AI of medical imaging to AI of COVID-19 imaging. The lessons learned from these career transitions and the diffusion of knowledge among them could be accomplished more effectively by recognizing their associated intricacies. These lessons learned in the transitioning to AI in the medical imaging of COVID-19 can inform and enhance future AI applications, making the whole of the transitions more than the sum of each discipline, for confronting an emergency like the COVID-19 pandemic or solving emerging problems in biomedicine.

Does CT help in reducing RT-PCR false negative rate for COVID-19?

BACKGROUND

Early detection is the key to contain the ongoing pandemic. The current gold standard to detect SARS CoV2 is RT-PCR. However, it has a high false negative rate and long turnaround time.

PURPOSE

In view of the high sensitivity of CT in detection of lower respiratory tract pathologies, a study of 2581 patients comparing RT-PCR status with CT findings was undertaken to see if it augments the diagnostic performance.

MATERIALS AND METHODS

A multi centre prospective study of consecutive cases was conducted. All CT studies suggestive of COVID 19 pneumonia were collated and evaluated independently by three Radiologists to confirm the imaging diagnosis of COVID-19 pneumonia. The RT-PCR values were retrospectively obtained, based on the RT-PCR values, CT studies were categorised into three subgroups, positive, negative and unknown. CT features from all three groups were compared to evaluate any communality or discordance.

RESULTS

Out of the 2581 patients with positive CT findings for COVID pneumonia, 825 were females and 1,756 were males in a wide age group of 28-90 years. Predominant CT features observed in all the subgroups were Ground glass densities 94.8%, in mixed distribution (peripheral and central) (59.12%), posterior segments in 92% and multilobar involvement in 70.9%. The CT features across the three subgroups were statistically significant with a P value <0.001.

CONCLUSION

There was a communality of CT findings regardless of RT-PCR status. In a pandemic setting ground glass densities in a subpleural, posterior and basal distribution are indicative of COVID 19. Thus CT chest in conjunction to RT PCR augments the diagnosis of COVID 19 pneumonia; utilization of CT chest may just be the missing link in closing this pandemic.

Back to the basics: Study of portable chest radiographic findings in 116 COVID-19 positive patients in an Indian tertiary care hospital

CONTEXT

Paucity of literature of portable CXR findings in COVID-19.

AIMS

Evaluate radiographic findings in COVID-19 patients and calculate sensitivity of radiographs with RT-PCR as gold standard.

SUBJECTS AND METHODS

Total 116 COVID-19 patients underwent portable CXR between April-June, 2020. Two radiologists reviewed radiographs with respect to laterality, craniocaudal, mediolateral distribution, shape, density, unifocality/multifocality and number of lung zones. Sensitivity of radiography was calculated with RT-PCR as gold standard.

STATISTICAL ANALYSIS USED

IBM SPSS Statistics Subscription software (IBM, New York, USA).

RESULTS

Many patients 67.2% (78/116) were asymptomatic. Cough (21.5%, 25/116) and fever (17.6%, 20/116) were the most frequent symptoms. 36.2% (42/116) patients revealed COVID-19 pneumonia-like abnormalities on CXR. Sensitivity of CXR with RT-PCR as gold standard was 36.2% (CI: Confidence interval = 27.46% - 44.95%). More patients in symptomatic group (68.4%, 26/38) had abnormal CXR compared to asymptomatic group (20.5%, 16/78) [P < 0.0001]. Radiographs revealed both unilateral (57.1%, 24/42), bilateral (42.8%, 18/42), GGO (80.9%, 34/42), or consolidation (11/42, 26.1%) in a middle (57.1%, 24/42), lower zone (83.3%, 35/42) and peripheral distribution (78.5%, 33/42). Lesions were commonly patchy (88%, 37/42) and multifocal (59.5%, 25/42). Majority had single (40.4%, 17/42) or two zone (35.7%, 15/42) involvement.

CONCLUSIONS

Significant number of COVID-19 patients were asymptomatic. Over 1/3rd of patients showed radiographic abnormalities. Symptomatic patients were more likely to show radiographic findings than asymptomatic patients. If radiographs identify pneumonia in appropriate clinical setting, CT can be avoided. Common radiographic abnormalities among COVID 19 patients were bilateral/unilateral, patchy, multifocal, ground glass opacity or consolidation in peripheral and middle/lower zone distribution.

Radiologic diagnosis of patients with COVID-19

The pandemia caused by the SARS-CoV-2 virus has triggered an unprecedented health and economic crisis. Although the diagnosis of infection with SARS-CoV-2 is microbiological, imaging techniques play an important role in supporting the diagnosis, grading the severity of disease, guiding treatment, detecting complications, and evaluating the response to treatment. The lungs are the main organ involved, and chest X-rays, whether obtained in conventional X-ray suites or with portable units, are the first-line imaging test because they are widely available and economical. Chest CT is more sensitive than plain chest X-rays, and CT studies make it possible to identify complications in addition to pulmonary involvement, as well as to suggestive alternative diagnoses. The most common radiologic findings in COVID-19 are airspace opacities (consolidations and/or ground-glass opacities), which are typically bilateral, peripheral, and located primarily in the lower fields.

Radiologic diagnosis of patients with COVID-19

The pandemia caused by the SARS-CoV-2 virus has triggered an unprecedented health and economic crisis. Although the diagnosis of infection with SARS-CoV-2 is microbiological, imaging techniques play an important role in supporting the diagnosis, grading the severity of disease, guiding treatment, detecting complications, and evaluating the response to treatment. The lungs are the main organ involved, and chest X-rays, whether obtained in conventional X-ray suites or with portable units, are the first-line imaging test because they are widely available and economical. Chest CT is more sensitive than plain chest X-rays, and CT studies make it possible to identify complications in addition to pulmonary involvement, as well as to suggestive alternative diagnoses. The most common radiologic findings in COVID-19 are airspace opacities (consolidations and/or ground-glass opacities), which are typically bilateral, peripheral, and located primarily in the lower fields.

Lung ultrasound in the emergency department - a valuable tool in the management of patients presenting with respiratory symptoms during the SARS-CoV-2 pandemic

BACKGROUND

Typical lung ultrasound (LUS) findings in patients with a COVID-19 infection were reported early on. During the global SARS-CoV-2 pandemic, LUS was propagated as a useful instrument in triage and monitoring. We evaluated LUS as a rapid diagnostic triage tool for the management of patients with suspected COVID-19 in the emergency department (ED).

METHODS

The study retrospectively enrolled patients with suspected COVID-19, who were admitted from 1st April to 25th of April 2020 to the ED of a tertiary care center in Germany. During clinical work-up, patients underwent LUS and polymerase chain reaction (PCR) testing for SARS-CoV-2. The recorded ultrasound findings were analyzed and judged regarding typical signs of viral pneumonia, blinded for clinical information of the patients. The results were compared with PCR test and chest computed tomography (CT).

RESULTS

2236 patients were treated in the ED during the study period. 203 were tested for SARS-CoV-2 using PCR, 135 (66.5%) underwent LUS and 39 (28.9%) of the patients were examined by chest CT scan. 39 (28.9%) of the 135 patients were tested positive for SARS-CoV-2 with PCR. In 52 (38.5%) COVID-19 was suspected from the finding of the LUS, resulting in a sensitivity of 76.9% and a specificity of 77.1% compared with PCR results. The negative predictive value reached 89.2%. The findings of the LUS had - compared to a positive chest CT scan for COVID-19 - a sensitivity of 70.6% and a specificity of 72.7%.

CONCLUSIONS

LUS is a rapid and useful triage tool in the work-up of patients with suspected COVID-19 infection during a pandemic scenario. Still, the results of the LUS depend on the physician's experience and skills.

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography

INTRODUCTION

Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease.

METHODS

A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated.

RESULTS

The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates.

CONCLUSION

Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.

COVID-19 infection: epidemiological, clinical, and radiological expression among adult population

Background

High-resolution computed tomography (HRCT) has proved to be an important diagnostic tool throughout the COVID-19 pandemic outbreaks. Increasing number of the infected personnel and shortage of real-time transcriptase polymerase chain reaction (RT-PCR) as well as its lower sensitivity made the CT a backbone in diagnosis, assessment of severity, and follow-up of the cases.

Results

Two hundred forty patients were evaluated retrospectively for clinical, laboratory, and radiological expression in COVID-19 infection. One hundred eighty-six non-severe cases with home isolation and outpatient treatment and 54 severe cases needed hospitalization and oxygen support. Significant difference between both groups was encountered regarding the age, male gender, > 38° fever, dyspnea, chest pain, hypertension, ≤ 93 oxygen saturation, intensive care unit (ICU) admission, elevated D-dimer, high serum ferritin and troponin levels, and high CT-severity score (CT-SS) of the severe group. CT-SS showed a negative correlation with O₂ saturation and patients’ outcome (r − 0.73/p 0.001 and r − 0.56/p 0.001, respectively). Bilateral peripherally distributed ground glass opacities (GGOs) were the commonest imaging feature similar to the literature.

Conclusion

Older age, male gender, smoking, hypertension, low O₂ saturation, increased CT score, high serum ferritin, and high D-dimer level are the most significant risk factors for severe COVID-19 pneumonia. Follow-up of the recovered severe cases is recommended to depict possible post COVID-19 lung fibrosis.

Tomographic findings in patients with COVID-19 according to evolution of the disease

Background

The tomographic findings in COVID-19, its classification, a brief overview of the application of artificial intelligence, and the stages during the course of the disease in patients with moderate COVID-19

Main body

Chest CT allows us to follow the course of COVID-19 in an objective way; each phase has characteristic imaging findings and, consequently, takes the corresponding measures.

A search was made in the PubMed database with the keywords extracted from the DeCs and the combinations of these. Only articles published between December 2019 and June 2020 were included. The search was limited to the English language.

Conclusions

CT serves to monitor the course of the disease since it assesses the severity of lung involvement. The most frequent finding is bilateral ground glass opacities with a subpleural distribution. The progression occurs in two phases: one slow and one fast. At discharge, the patient may have ground glass opacities or areas that will later become fibrosis, leaving sequelae for life.

Anxiety and depression levels in Covid-19 disease and their relation to hypertension

Objectives: The study aimed to assess the relation of anxiety and depression levels with hypertension in COVID-19 outbreak. The analysis of the association of selected socio-demographic and clinical parameters on the presence and severity of psychological distress was also performed. Methods: The study involved 91 patients applying with a medical history supportive of COVID-19 infection. According to the hospitalization criteria and diagnostic result of SARS-CoV-2 nucleic acid test certainty of the disease, three groups were created. Patients with positive SARS-CoV-2 nucleic acid test results were consisted of 31 hospitalized subjects. To assess the applicant psychological state, a specially developed questionnaire was used, as the presence and severities of the symptoms were assessed using the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI). Results: Statistically, a significantly higher average level of depression and a higher incidence of anxiety were demonstrated among applicants in the Covid-19 pandemic (% 24 and % 44). Also a higher level of anxiety was demonstrated in hospitalized patients compared with the outpatient group. Different from the presence of depression symptoms, the presence of anxiety symptoms was associated independently with hypertension in our study group OR 2.6 (95% CI, 0.99-6.78) P = .04). Conclusions: In the aftermath of COVID-19 outbreak both anxiety and depression are common psychological disorders. Also, different from the symptoms of depression, the symptoms of anxiety are associated independently with hypertension. The described socio-demographic parameters and clinical characteristics had no impact on the symptoms of depression and anxiety irrespective of hospitalized status in the investigated groups.

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments

Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.

Chest x-ray in the COVID-19 pandemic: Radiologists' real-world reader performance

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Chest x-ray had a 89 % sensitivity detecting COVID-19 pneumonia during pandemic peak.

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Experienced radiologists had higher specificity than less-experienced ones.

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Overall and per-group sensitivity in detecting COVID-19 pneumonia increased over time.

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Overall and per-group accuracy in detecting COVID-19 pneumonia increased over time.

Purpose

To report real-world diagnostic performance of chest x-ray (CXR) readings during the COVID-19 pandemic.

Methods

In this retrospective observational study we enrolled all patients presenting to the emergency department of a Milan-based university hospital from February 24th to April 8th 2020 who underwent nasopharyngeal swab for reverse transcriptase-polymerase chain reaction (RT-PCR) and anteroposterior bedside CXR within 12 h. A composite reference standard combining RT-PCR results with phone-call-based anamnesis was obtained. Radiologists were grouped by CXR reading experience (Group-1, >10 years; Group-2, <10 years), diagnostic performance indexes were calculated for each radiologist and for the two groups.

Results

Group-1 read 435 CXRs (77.0 % disease prevalence): sensitivity was 89.0 %, specificity 66.0 %, accuracy 83.7 %. Group-2 read 100 CXRs (73.0 % prevalence): sensitivity was 89.0 %, specificity 40.7 %, accuracy 76.0 %. During the first half of the outbreak (195 CXRs, 66.7 % disease prevalence), overall sensitivity was 80.8 %, specificity 67.7 %, accuracy 76.4 %, Group-1 sensitivity being similar to Group-2 (80.6 % versus 81.5 %, respectively) but higher specificity (74.0 % versus 46.7 %) and accuracy (78.4 % versus 69.0 %). During the second half (340 CXRs, 81.8 % prevalence), overall sensitivity increased to 92.8 %, specificity dropped to 53.2 %, accuracy increased to 85.6 %, this pattern mirrored in both groups, with decreased specificity (Group-1, 58.0 %; Group-2, 33.3 %) but increased sensitivity (92.7 % and 93.5 %) and accuracy (86.5 % and 81.0 %, respectively).

Conclusions

Real-world CXR diagnostic performance during the COVID-19 pandemic showed overall high sensitivity with higher specificity for more experienced radiologists. The increase in accuracy over time strengthens CXR role as a first line examination in suspected COVID-19 patients.

Baseline electrolyte abnormalities would be related to poor prognosis in hospitalized coronavirus disease 2019 patients

Electrolyte abnormalities are not uncommon in coronavirus disease 2019 (COVID-19). Several studies have suggested that various electrolyte imbalances seem to have an impact on disease prognosis. However, no study has primarily focused on the effect of baseline electrolyte abnormalities on disease outcome. In this study, we assessed the validity of the hypothesis that baseline electrolyte imbalances may be related to unfavourable outcomes in hospitalized COVID-19 patients. Design of the study was retrospective and observational. We included 408 hospitalized individuals with COVID-19 over 18 years old. Baseline levels of sodium, potassium, calcium and chloride were assessed and the effects of abnormalities in these electrolytes on requirement for intensive care unit and mechanical ventilation, hospitalization duration and treatment outcome were evaluated. Patients were clustered based on electrolyte levels and clusters were compared according to outcome variables. Frequency of other severe disease indices was compared between the clusters. Lastly, we evaluated the independent factors related to COVID-19-associated deaths with multivariate analyses. In all, 228 (55.8%) of the patients had at least one electrolyte imbalance at baseline. Hyponatraemia was the most frequent electrolyte abnormality. Patients with hyponatraemia, hypochloraemia or hypocalcaemia had, respectively, more frequent requirement for intensive care unit and mechanical ventilation, higher mortality rate and longer hospitalization. The clusters associated with electrolyte abnormalities had unfavourable outcomes. Also, Clinical and laboratory features associated with severe disease were detected more often in those clusters. Hyponatraemia was an independent factor related to death from COVID-19 (OR 10.33; 95% CI 1.62-65.62; p 0.01). Furthermore, baseline electrolyte imbalances, primarily hyponatraemia, were related to poor prognosis in COVID-19 and baseline electrolyte assessment would be beneficial for evaluating the risk of severe COVID-19.

Coronavirus disease 2019-associated rapidly progressive organizing pneumonia with fibrotic feature: Two case reports

INTRODUCTION

Pneumonia is one of the most important characteristics of coronavirus disease 2019 (COVID-19) and imaging findings of COVID-19 pneumonia are diverse and change over disease course. However, the detailed clinical course of organizing pneumonia (OP) caused by COVID-19 has not been clarified.

PATIENT CONCERNS

A 60-year-old man and a 61-year-old woman diagnosed with mild COVID-19 were admitted to our hospital. Their respiratory symptoms were deteriorating even after initiating treatment with antiviral drugs.

DIAGNOSIS

Chest X-rays and computed tomography scan showed a rapid progression of linear consolidation with reversed halo sign, distributed in subpleural and peri-bronchial regions. They also presented with pulmonary fibrosis findings, including traction bronchiectasis and marked lung volume reduction. They were diagnosed with rapidly progressing OP.

INTERVENTIONS

They were treated with systemic corticosteroids.

OUTCOMES

The patients' imaging findings and respiratory conditions improved rapidly without any adverse effects.

CONCLUSION

Physicians should carefully monitor patients with COVID-19, as they can develop rapidly progressive and fibrotic OP, which respond to corticosteroids.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and coronavirus disease 19 (COVID-19) - anatomic pathology perspective on current knowledge

BACKGROUND

The world is currently witnessing a major devastating pandemic of Coronavirus disease-2019 (COVID-19). This disease is caused by a novel coronavirus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). It primarily affects the respiratory tract and particularly the lungs. The virus enters the cell by attaching its spike-like surface projections to the angiotensin-converting enzyme-2 (ACE-2) expressed in various tissues. Though the majority of symptomatic patients have mild flu-like symptoms, a significant minority develop severe lung injury with acute respiratory distress syndrome (ARDS), leading to considerable morbidity and mortality. Elderly patients with previous cardiovascular comorbidities are particularly susceptible to severe clinical manifestations. BODY: Currently, our limited knowledge of the pathologic findings is based on post-mortem biopsies, a few limited autopsies, and very few complete autopsies. From these reports, we know that the virus can be found in various organs but the most striking tissue damage involves the lungs resulting almost always in diffuse alveolar damage with interstitial edema, capillary congestion, and occasional interstitial lymphocytosis, causing hypoxia, multiorgan failure, and death. A few pathology studies have also reported intravascular microthrombi and pulmonary thrombembolism. Although the clinical presentation of this disease is fairly well characterized, knowledge of the pathologic aspects remains comparatively limited.

CONCLUSION

In this review, we discuss clinical, pathologic, and genomic features of COVID-19, review current hypotheses regarding the pathogenesis, and briefly discuss the clinical characteristics. We also compare the salient features of COVID-19 with other coronavirus-related illnesses that have posed significant public health issues in the past, including SARS and the Middle East Respiratory Syndrome (MERS).

The Global Emergency of Novel Coronavirus (SARS-CoV-2): An Update of the Current Status and Forecasting

Over the past two decades, there have been two major outbreaks where the crossover of animal Betacoronaviruses to humans has resulted in severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). In December 2019, a global public health concern started with the emergence of a new strain of coronavirus (SARS-CoV-2 or 2019 novel coronavirus, 2019-nCoV) which has rapidly spread all over the world from its origin in Wuhan, China. SARS-CoV-2 belongs to the Betacoronavirus genus, which includes human SARS-CoV, MERS and two other human coronaviruses (HCoVs), HCoV-OC43 and HCoV-HKU1. The fatality rate of SARS-CoV-2 is lower than the two previous coronavirus epidemics, but it is faster spreading and the large number of infected people with severe viral pneumonia and respiratory illness, showed SARS-CoV-2 to be highly contagious. Based on the current published evidence, herein we summarize the origin, genetics, epidemiology, clinical manifestations, preventions, diagnosis and up to date treatments of SARS-CoV-2 infections in comparison with those caused by SARS-CoV and MERS-CoV. Moreover, the possible impact of weather conditions on the transmission of SARS-CoV-2 is also discussed. Therefore, the aim of the present review is to reconsider the two previous pandemics and provide a reference for future studies as well as therapeutic approaches.

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 of Comorbidities with Coronavirus Disease 2019: A Review

The novel Coronavirus disease 2019 (COVID-19) pandemic started with few cases of pneumonia of unknown origin in Wuhan, China. It has now become one of the significant public health emergencies of all time. Within 5 months of its existence, it has led to a significant impact on national and international policies. Apart from being a medical emergency, it is also affecting the global economy, and without proper measures, it may have severely impact the socioeconomic statuses of individuals. It has profoundly challenged the healthcare infrastructure, particularly in low- and middle-income nations. Every nation is trying to safeguard its population and the health workers as adequately as possible. While we still wait for the development of an absolute cure in the form of a vaccine, preventive measures have taken the lead in reducing the disease spread and breaking the chain of transmission. The knowledge gained from the clinical characteristics of patients has suggested markers or comorbid conditions that may aid in the risk assessment. This narrative review aims to provide an update on SARS-CoV-2, the causative virus of COVID-19, its pathogenesis, the clinical and laboratory features, and its association with several comorbid conditions that may influence the prognosis of this disease.

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.