Comparison of the computed tomography findings in COVID-19 and other viral pneumonia in immunocompetent adults: a systematic review and meta-analysis

Chronic Chest Computed Tomography Findings Following COVID-19 Pneumonia

Respiratory symptoms are a frequent manifestation of patients with post-acute sequela of SARS-CoV-2 (PASC), also known as long-COVID. Many cohorts of predominantly hospitalized patients have shown that a significant subset may have persistent chest computed tomography findings for more than 12 months after the acute infection. Proper understanding of the evolving long-term imaging findings and terminology is crucial for accurate imaging interpretation and patient care. The goal of this article is to review the chronic chest computed tomography findings of patients with PASC and common pitfalls.

Evaluating Acute Pulmonary Changes of Coronavirus 2019: Comparative Analysis of the Pertinent Modalities

This review explores imaging's crucial role in acute Coronavirus Disease 2019 (COVID-19) assessment. High Resolution Computer Tomography is especially effective in detection of lung abnormalities. Chest radiography has limited utility in the initial stages of COVID-19 infection. Lung Ultrasound has emerged as a valuable, radiation-free tool in critical care, and Magnetic Resonance Imaging shows promise as a Computed Tomography alternative. Typical and atypical findings of COVID-19 by each of these modalities are discussed with emphasis on their prognostic value. Considerations for pediatric and immunocompromised cases are outlined. A comprehensive diagnostic approach is recommended, as radiological diagnosis remains challenging in the acute phase.

Bacterial Community- and Hospital-Acquired Pneumonia in Patients with Critical COVID-19-A Prospective Monocentric Cohort Study

The impact of bacterial pneumonia on patients with COVID-19 infection remains unclear. This prospective observational monocentric cohort study aims to determine the incidence of bacterial community- and hospital-acquired pneumonia (CAP and HAP) and its effect on mortality in critically ill COVID-19 patients admitted to the intensive care unit (ICU) at University Hospital Olomouc between 1 November 2020 and 31 December 2022. The secondary objectives of this study include identifying the bacterial etiology of CAP and HAP and exploring the capabilities of diagnostic tools, with a focus on inflammatory biomarkers. Data were collected from the electronic information hospital system, encompassing biomarkers, microbiological findings, and daily visit records, and subsequently evaluated by ICU physicians and clinical microbiologists. Out of 171 patients suffering from critical COVID-19, 46 (27%) had CAP, while 78 (46%) developed HAP. Critically ill COVID-19 patients who experienced bacterial CAP and HAP exhibited higher mortality compared to COVID-19 patients without any bacterial infection, with rates of 38% and 56% versus 11%, respectively. In CAP, the most frequent causative agents were chlamydophila and mycoplasma; Enterobacterales, which were multidrug-resistant in 71% of cases; Gram-negative non-fermenting rods; and Staphylococcus aureus. Notably, no strains of Streptococcus pneumoniae were detected, and only a single strain each of Haemophilus influenzae and Moraxella catarrhalis was isolated. The most frequent etiologic agents causing HAP were Enterobacterales and Gram-negative non-fermenting rods. Based on the presented results, commonly used biochemical markers demonstrated poor predictive and diagnostic accuracy. To confirm the diagnosis of bacterial CAP in our patient cohort, it was necessary to assess the initial values of inflammatory markers (particularly procalcitonin), consider clinical signs indicative of bacterial infection, and/or rely on positive microbiological findings. For HAP diagnostics, it was appropriate to conduct regular detailed clinical examinations (with a focus on evaluating respiratory functions) and closely monitor the dynamics of inflammatory markers (preferably Interleukin-6).

Respiratory Syncytial Virus: A Systematic Review and Meta-Analysis of Tomographic Findings (2000-2022)

Human respiratory syncytial virus (RSV) is a main cause of medical referrals and hospitalizations in all infants, particularly among newborns. Nevertheless, relatively limited evidence on chest tomography (CT) findings has been collected. According to the PRISMA statement, Pubmed, Embase, and medRxiv were searched for eligible observational studies published up to 31 December 2022. Cases were categorized in children and adolescents (age < 18 years), adults and elderly (age ≥ 18 years), and immunocompromised patients, and then pooled in a random-effects model. Heterogeneity was assessed using the I2 statistics, while reporting bias was assessed by means of funnel plots and regression analysis. A total of 10 studies (217 RSV cases) were retrieved (children, 37.3%; immunocompromised, 41.0%; adults, 21.7%). The most common features were signs of organizing pneumonia (33.65%, 95% confidence interval [95% CI] 22.39-47.27), followed by septal thickening (33.19%, 95% CI 21.76-47.03), ground glass opacities (GGOs; 28.03%, 95% CI 14.69-46.82), and tree-in-bud (TIB, 27.44%, 95% CI 15.04-44.68). Interestingly, up to 16.23% (95% CI 8.17-29.69) showed normal findings, while the large majority (76.06%, 95% CI 64.81-84.56) were characterized by bilateral involvement. Studies were highly heterogeneous without substantial reporting bias. Assuming children and adolescents as reference groups, healthy adults were characterized by a higher risk ratio [RR] for septal thickening (RR 3.878, 95% CI 1.253-12.000), nodular lesions (RR 20.197, 95% CI 1.286-317.082), and GGOs (RR 2.121, 95% CI 1.121-4.013). RSV cases are rarely assessed in terms of CT characteristics. Our study identified some specificities, suggesting that RSV infections evolve heterogeneous CT features in children/adolescents and adults, but the paucity of studies recommends a cautious appraisal.

Novel Corona Virus 2019 Disease: Assessment on High-Resolution Computed Tomography Thorax

Objectives This particular study was undertaken to assess the role of high-resolution computed tomography (HRCT) thorax in diagnosing patients with novel Corona virus-2019 disease and screening suspected COVID-19 cases. It also involves an assessment of the severity of bilateral lung involvement in proven and suspected cases of COVID-19 infection. Materials and methods Two hundred and fourteen symptomatic cases referred to the department of radio-diagnosis were evaluated in this study. HRCT thorax was performed on SIEMENS Somatom Emotion 16-slice spiral CT. Initially, a tomogram was taken, followed by sections in the lung window at B90s, kVp 130, with a pitch of 1.15. The images are then reconstructed into 1.0-mm-thin slices. Radiologists then interpreted the scans for features of COVID-19 disease. Various imaging features and the severity of the disease were analysed in all patients. Results We observed that the male population was more affected by the disease (72% of the total cases). The most consistent and common HRCT finding is that of ground-glass opacity (GGO), which was present in 172 cases, corresponding to 78.4% of the cases. Crazy pavement appearance was seen in 41.2 % of the cases. Other findings included consolidation, discrete nodules surrounded by ground-glass opacification, subpleural linear opacities, and tubular bronchiectasis. Conclusion HRCT thorax plays an ideal role in diagnosing COVID-19 disease with high sensitivity and also provides prompt results as compared to RT-PCR. It also helps in grading the severity of the disease based on various patterns and the extent of lung parenchyma involved. Therefore, because of the immediate results and the ability to grade the disease, HRCT became invaluable in directing the treatment of COVID-19 disease.

An Overview of the Impact of Bacterial Infections and the Associated Mortality Predictors in Patients with COVID-19 Admitted to a Tertiary Center from Eastern Europe

1.

BACKGROUND

Literature data on bacterial infections and their impact on the mortality rates of COVID-19 patients from Romania are scarce, while worldwide reports are contrasting. 2.

MATERIALS AND METHODS

We conducted a unicentric retrospective observational study that included 280 patients with SARS-CoV-2 infection, on whom we performed various microbiological determinations. Based on the administration or not of the antibiotic treatment, we divided the patients into two groups. First, we sought to investigate the rates and predictors of bacterial infections, the causative microbial strains, and the prescribed antibiotic treatment. Secondly, the study aimed to identify the risk factors associated with in-hospital death and evaluate the biomarkers' performance for predicting short-term mortality. 3.

RESULTS

Bacterial co-infections or secondary infections were confirmed in 23 (8.2%) patients. Acinetobacter baumannii was the pathogen responsible for most of the confirmed bacterial infections. Almost three quarters of the patients (72.8%) received empiric antibiotic therapy. Multivariate logistic regression has shown leukocytosis and intensive care unit admission as risk factors for bacterial infections and C-reactive protein, together with the length of hospital stay, as mortality predictors. The ROC curves revealed an acceptable performance for the erythrocyte sedimentation rate (AUC: 0.781), and C-reactive protein (AUC: 0.797), but a poor performance for fibrinogen (AUC: 0.664) in predicting fatal events. 4.

CONCLUSIONS

This study highlighted the somewhat paradoxical association of a low rate of confirmed infections with a high rate of empiric antibiotic therapy. A thorough assessment of the risk factors for bacterial infections, in addition to the acknowledgment of various mortality predictors, is crucial for identifying high-risk patients, thus allowing a timely therapeutic intervention, with a direct impact on improving patients' prognosis.

CT imaging findings in lung transplant recipients with COVID-19

OBJECTIVES

Our goal was to compare the chest computed tomography (CT) imaging findings of COVID-19 in lung transplant recipients (LTR) and a group of non-transplanted controls (NTC).

METHODS

This retrospective study included 51 consecutive LTR hospitalized with COVID-19 from two centers. A total of 75 NTC were included for comparison. Images were classified regarding the standardized RSNA category, main pattern of lung attenuation, and longitudinal and axial distribution. Quantitative CT (QCT) analysis was performed to evaluate percentage of high attenuation areas (%HAA, threshold -250 to -700 HU). CT scoring was used to measure severity of parenchymal abnormalities.

RESULTS

The imaging findings of COVID-19 in LTR were significantly different from controls regarding the RSNA classification and pattern of lung attenuation. LTR had a significantly higher proportion of patients with an indeterminate pattern on CT (0.31 vs. 0.11, p = 0.014). The most frequent pattern of attenuation in LTR was predominantly consolidation (0.39 vs. 0.22, p = 0.144) followed by a mixed pattern of ground-glass opacities (GGO) and consolidation (0.37 vs. 0.20, adjusted p = 0.102). On the other hand, the most common pattern in NTC was GGO predominant (0.58 vs. 0.24 of LTR, p = 0.001). LTR had significantly more severe parenchymal disease measured by CT score and %HAA by QCT (0.372 ± 0.08 vs. 0.148 ± 0.06, p < 0.001).

CONCLUSION

The most frequent finding of COVID-19 in LTR is a predominant pattern of consolidation. Compared to NTC, LTR more frequently demonstrated an indeterminate pattern according to the RSNA classification and more extensive lung abnormalities on QCT and semi-quantitative scoring.

KEY POINTS

• The most common CT finding of COVID-19 in LTR is a predominant pattern of consolidation followed by a mixed pattern of GGO and consolidation, while controls more often have a predominant pattern of GGO. • LTR more often presents with an indeterminate pattern of COVID-19 by RSNA classification than controls; therefore, molecular testing for COVID-19 is essential for LTR presenting with lower airway infection independently of imaging findings. • LTR had more extensive disease by semi-quantitative CT score and increased percentage areas of high attenuation on QCT.

Management of COVID-19: A comprehensive and practical approach

Since the beginning of Corona Virus Disease (COVID) pandemic, there has been lack of clarity about the management protocols in spite of frequently updated national and international guidelines. Irrational use of unproven therapies has not been helpful in improving treatment outcomes. Early use of high-dose steroids or late use of antiviral medicines might have caused more harm than the benefit. There is also lot of fear about post-COVID fibrosis leading to extended use of steroids and antifibrotics. We reviewed the available COVID guidelines and treatment protocols in the light of scientific evidence generated over last 2 years by a systematic literature search using various databases (PubMed, Google Scholar, MEDLINE, UpToDate, Embase, and Web of Science). This article presents a comprehensive approach to the diagnosis, appropriate investigations, their interpretations, and use of specific therapies according to the stage of disease.

Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study

Purpose

To compare temporal evolution of imaging features of coronavirus disease 2019 (COVID-19) and influenza in computed tomography and evaluate their predictive value for distinction.

Methods

In this retrospective, multicenter study 179 CT examinations of 52 COVID-19 and 44 influenza critically ill patients were included. Lung involvement, main pattern (ground glass opacity, crazy paving, consolidation) and additional lung and chest findings were evaluated by two independent observers. Additional findings and clinical data were compared patient-wise. A decision tree analysis was performed to identify imaging features with predictive value in distinguishing both entities.

Results

In contrast to influenza patients, lung involvement remains high in COVID-19 patients > 14 days after the diagnosis. The predominant pattern in COVID-19 evolves from ground glass at the beginning to consolidation in later disease. In influenza there is more consolidation at the beginning and overall less ground glass opacity (p = 0.002). Decision tree analysis yielded the following: Earlier in disease course, pleural effusion is a typical feature of influenza (p = 0.007) whereas ground glass opacities indicate COVID-19 (p = 0.04). In later disease, particularly more lung involvement (p < 0.001), but also less pleural (p = 0.005) and pericardial (p = 0.003) effusion favor COVID-19 over influenza. Regardless of time point, less lung involvement (p < 0.001), tree-in-bud (p = 0.002) and pericardial effusion (p = 0.01) make influenza more likely than COVID-19.

Conclusions

This study identified differences in temporal evolution of imaging features between COVID-19 and influenza. These findings may help to distinguish both diseases in critically ill patients when laboratory findings are delayed or inconclusive.

•

Decision tree analysis helps to distinguish COVID-19 and Influenza.

•

Pleural effusion is a typical feature of influenza in early disease.

•

Ground glass opacities indicate COVID-19 in early disease.

•

Lung involvement remains high in COVID-19 patients > 14 days after the diagnosis.

•

Pleural and pericardial effusion favor influenza over COVID-19 in later disease.

Molecular and serology methods in the diagnosis of COVID-19: An overview

Coronavirus disease-19 (COVID-19) has become a pandemic, being a global health concern since December 2019 when the first cases were reported. Severe acute respiratory syndrome coronavirus 2, the COVID-19 causal agent, is a β-coronavirus that has on its surface the spike protein, which helps in its virulence and pathogenicity towards the host. Thus, effective and applicable diagnostic methods to this disease come as an important tool for the management of the patients. The use of the molecular technique PCR, which allows the detection of the viral RNA through nasopharyngeal swabs, is considered the gold standard test for the diagnosis of COVID-19. Moreover, serological methods, such as enzyme-linked immunosorbent assays and rapid tests, are able to detect severe acute respiratory syndrome coronavirus 2-specific immunoglobulin A, immunoglobulin M, and immunoglobulin G in positive patients, being important alternative techniques for the diagnostic establishment and epidemiological surveillance. On the other hand, reverse transcription loop-mediated isothermal amplification also proved to be a useful diagnostic method for the infection, mainly because it does not require a sophisticated laboratory apparatus and has similar specificity and sensitivity to PCR. Complementarily, imaging exams provide findings of typical pneumonia, such as the ground-glass opacity radiological pattern on chest computed tomography scanning, which along with laboratory tests assist in the diagnosis of COVID-19.

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

Automated lung vessel segmentation reveals blood vessel volume redistribution in viral pneumonia

PURPOSE

It is known from histology studies that lung vessels are affected in viral pneumonia. However, their diagnostic potential as a chest CT imaging parameter has only rarely been exploited. The purpose of this study is to develop a robust method for automated lung vessel segmentation and morphology analysis and apply it to a large chest CT dataset.

METHODS

In total, 509 non-enhanced chest CTs (NECTs) and 563 CT pulmonary angiograms (CTPAs) were included. Sub-groups were patients with healthy lungs (group_NORM, n = 634) and those RT-PCR-positive for Influenza A/B (group_INF, n = 159) and SARS-CoV-2 (group_COV, n = 279). A lung vessel segmentation algorithm (LVSA) based on traditional image processing was developed, validated with a point-of-interest approach, and applied to a large clinical dataset. Total blood vessel volume in lung (TBV) and the blood vessel volume percentage (BV%) of three blood vessel size types were calculated and compared between groups: small (BV5%, cross-sectional area < 5 mm²), medium (BV5-10%, 5-10 mm²) and large (BV10%, >10 mm²).

RESULTS

Sensitivity of the LVSA was 84.6% (95 %CI: 73.9-95.3) for NECTs and 92.8% (95 %CI: 90.8-94.7) for CTPAs. In viral pneumonia, besides an increased TBV, the main finding was a significantly decreased BV5% in group_COV (n = 14%) and group_INF (n = 15%) compared to group_NORM (n = 18%) [p < 0.001]. At the same time, BV10% was increased (group_COV n = 15% and group_INF n = 14% vs. group_NORM n = 11%; p < 0.001).

CONCLUSION

In COVID-19 and Influenza, the blood vessel volume is redistributed from small to large vessels in the lung. Automated LSVA allows researchers and clinicians to derive imaging parameters for large amounts of CTs. This can enhance the understanding of vascular changes, particularly in infectious lung diseases.

Inflammation of the Pleural Cavity: A Review on Pathogenesis, Diagnosis and Implications in Tumor Pathophysiology

Pleural effusions are a common respiratory condition with many etiologies. Nonmalignant etiologies explain most pleural effusions and despite being nonmalignant, they can be associated with poor survival; thus, it is important to understand their pathophysiology. Furthermore, diagnosing a benign pleural pathology always harbors the uncertainty of a false-negative diagnosis for physicians and pathologists, especially for the group of non-specific pleuritis. This review aims to present the role of the inflammation in the development of benign pleural effusions, with a special interest in their pathophysiology and their association with malignancy.

COVID-19 pandemic in flu season. Chest computed tomography - what we know so far

Chest computed tomography (CT) is proven to have high sensitivity in COVID-19 diagnosis. It is available in most emergency wards, and in contrast to polymerase chain reaction (PCR) it can be obtained in several minutes. However, its imaging features change during the course of the disease and overlap with other viral pneumonias, including influenza pneumonia. In this brief analysis we review the recent literature about chest CT features, useful radiological scales, and COVID-19 differentiation with other viral infections.

COVID-19 in Immunocompromised Patients: A Systematic Review

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was first identified as a novel coronavirus in Wuhan, Hubei province, central China, in December 2019, and is responsible for the 2019-to-present pandemic. According to the most recent data released by the World Health Organization, more than 200 million people have been infected by SARS-CoV-2 so far, and more than 4 million people died worldwide. Although our knowledge on SARS-CoV-2 and COVID-19 is constantly growing, data on COVID-19 in immunocompromised patients are still limited. The aim of the present systematic review is to describe clinical picture, disease severity, proposed treatment regimen, and response to vaccination in patients with different types and severity of immunosuppression.

Increased frequency of pneumothorax and pneumomediastinum in COVID-19 patients admitted in the ICU: A multicentre study from Mumbai, India

BACKGROUND

There are limited data regarding the incidence of pneumothorax in COVID-19 patients as well as the impact of the same on patient outcomes.

METHODS

A retrospective review of the medical records at three large tertiary care hospitals in Mumbai was performed to identify patients hospitalised with COVID-19 from March 2020 to October 2020. The presence of pneumothorax and/or pneumomediastinum was noted when chest radiographs or CT scans were performed. Demographic and clinical characteristics of patients who developed air leak were recorded.

RESULTS

4,906 patients with COVID-19 were admitted, with 1,324 (27%) having severe COVID-19 disease. The overall incidence of pneumothorax and/or pneumomediastinum in patients with severe disease was 3.2% (42/1,324). Eighteen patients had pneumothorax, 16 had pneumomediastinum and 8 patients had both. Fourteen patients (33.3%) developed this complication breathing spontaneously, 28 patients (66.6%) developed it during mechanical ventilation. Overall mortality in this cohort was 74%, compared with 17% in the COVID-19 patients without pneumothorax (p<0.001).

CONCLUSIONS

Our study demonstrates that air leaks occur with a higher frequency in patients with COVID-19 than in other ICU patients. When present, such air leaks contributed to poor outcomes with almost 74% mortality rates in these patients.

Role of Imaging in Diagnosis and Management of COVID-19: A Multiorgan Multimodality Imaging Review

In this pandemic of Coronavirus disease 2019 (COVID-19), a vast proportion of healthcare resources, including imaging tools, have been dedicated to the management of affected patients; yet, the frequent reports of unknown presentations and complications of disease over time have been changing the usual standard of care and resource allocation in health centers. As of now, we have witnessed multisystemic symptoms requiring the collaboration of different clinical teams in COVID-19 patients' care. Compared to previous viral pandemics, imaging modalities are now playing an essential role in the diagnosis and management of patients. This widespread utility of imaging modalities calls for a deeper understanding of potential radiologic findings in this disease and identifying the most compatible imaging protocol with safety precautions. Although initially used for respiratory tract evaluation, imaging modalities have also been used for cardiovascular, neurologic, and gastrointestinal evaluation of patients with COVID-19. In this narrative review article, we provide multimodality and multisystemic review of imaging techniques and features that can aid in the diagnosis and management of COVID-19 patients.

Computed tomography findings in 3,557 COVID-19 infected children: a systematic review

Background

Although it was assumed in the early stages of the coronavirus disease 2019 (COVID-19) outbreak that the novel coronavirus infection was uncommon among children, the number of infected children has since been increasing significantly. Real-time polymerase chain reaction (RT-PCR) is the gold standard modality for the diagnosis of COVID-19 infection. In adults, chest CT is performed as an adjunct for identifying suspected COVID-19 cases along with patients' management and follow-up. However, CT findings in COVID-19 children studies have shown a diverse incidence of abnormal CT and finding patterns that made CT scan necessity to have remained controversial. The aim of the present review was to comprehensively determine the imaging findings of chest CT scans of confirmed COVID-19-infected pediatric patients through a systematic review of the available published studies.

Methods

A systematic literature search was performed in the PubMed, Embase, Scopus, and Web of Science core collection databases (four databases including SSCI, SCIE, AHCI, and ESCI) to find original articles containing chest CT findings in children with COVID-19 through May 7, 2021. This review included 81 articles published in English that in total included 3,557 pediatric patients.

Results

This review included 81 articles published in English that in total included 3,557 pediatric patients. Among the total confirmed coronavirus-infected cases (via RT-PCR test), two-thirds had abnormal chest CT findings; among these patients, 549 (37.8%) had bilateral lung involvement, and 475 (32.7%) had unilateral disease. Regarding the types of lung lesions, ground glass opacities were observed in 794 (54.7%) of patients, and consolidation was observed in 10.2%; moreover, halo sign, discrete pulmonary nodules, interstitial abnormalities or reticulations, and vascular thickening shadows were reported in 7.4%, 2.6%, 9.7% and 1.7% of the patients, respectively.

Discussion

This review revealed that chest CT scan manifestations in majority of COVID-19 positive children are mild, so regarding the risk of radiation exposure, it is reasonable to confine CT scan to individual cases that its benefits outweigh the risks.

Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs

Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

Antimicrobial stewardship in the ICU in COVID times: the known unknowns

Since the start of the COVID-19 pandemic, there has been concern about the concomitant rise of antimicrobial resistance. While bacterial co-infections seem rare in COVID-19 patients admitted to hospital wards and intensive care units (ICUs), an increase in empirical antibiotic use has been described. In the ICU setting, where antibiotics are already abundantly—and often inappropriately—prescribed, the need for an ICU-specific antimicrobial stewardship programme is widely advocated. Apart from essentially warning against the use of antibacterial drugs for the treatment of a viral infection, other aspects of ICU antimicrobial stewardship need to be considered in view of the clinical course and characteristics of COVID-19. First, the distinction between infectious and non-infectious (inflammatory) causes of respiratory deterioration during an ICU stay is difficult, and the much-debated relevance of fungal and viral co-infections adds to the complexity of empirical antimicrobial prescribing. Biomarkers such as procalcitonin for the decision to start antibacterial therapy for ICU nosocomial infections seem to be more promising in COVID-19 than non-COVID-19 patients. In COVID-19 patients, cytomegalovirus reactivation is an important factor to consider when assessing patients infected with SARS-CoV-2 as it may have a role in modulating the patient immune response. The diagnosis of COVID-19-associated invasive aspergillosis is challenging because of the lack of sensitivity and specificity of the available tests. Furthermore, altered pharmacokinetic/pharmacodynamic properties need to be taken into account when prescribing antimicrobial therapy. Future research should now further explore the ‘known unknowns’, ideally with robust prospective study designs.

Distinctive features of severe SARS-CoV-2 pneumonia

The coronavirus disease 2019 (COVID-19) pandemic is among the most important public health crises of our generation. Despite the promise of prevention offered by effective vaccines, patients with severe COVID-19 will continue to populate hospitals and intensive care units for the foreseeable future. The most common clinical presentation of severe COVID-19 is hypoxemia and respiratory failure, typical of the acute respiratory distress syndrome (ARDS). Whether the clinical features and pathobiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia differ from those of pneumonia secondary to other pathogens is unclear. This uncertainty has created variability in the application of historically proven therapies for ARDS to patients with COVID-19. We review the available literature and find many similarities between patients with ARDS from pneumonia attributable to SARS-CoV-2 versus other respiratory pathogens. A notable exception is the long duration of illness among patients with COVID-19, which could result from its unique pathobiology. Available data support the use of care pathways and therapies proven effective for patients with ARDS, while pointing to unique features that might be therapeutically targeted for patients with severe SARS-CoV-2 pneumonia.

Discrepancies in the clinical and radiological profiles of COVID-19: A case-based discussion and review of literature

The current gold standard for the diagnosis of coronavirus disease-19 (COVID-19) is a positive reverse transcriptase polymerase chain reaction (RT-PCR) test, on the background of clinical suspicion. However, RT-PCR has its limitations; this includes issues of low sensitivity, sampling errors and appropriate timing of specimen collection. As pulmonary involvement is the most common manifestation of severe COVID-19, early and appropriate lung imaging is important to aid diagnosis. However, gross discrepancies can occur between the clinical and imaging findings in patients with COVID-19, which can mislead clinicians in their decision making. Although chest X-ray (CXR) has a low sensitivity for the diagnosis of COVID-19 associated lung disease, especially in the earlier stages, a positive CXR increases the pre-test probability of COVID-19. CXR scoring systems have shown to be useful, such as the COVID-19 opacification rating score which helps to predict the need of tracheal intubation. Furthermore, artificial intelligence-based algorithms have also shown promise in differentiating COVID-19 pneumonia on CXR from other lung diseases. Although costlier than CXR, unenhanced computed tomographic (CT) chest scans have a higher sensitivity, but lesser specificity compared to RT-PCR for the diagnosis of COVID-19 pneumonia. A semi-quantitative CT scoring system has been shown to predict short-term mortality. The routine use of CT pulmonary angiography as a first-line imaging modality in patients with suspected COVID-19 is not justifiable due to the risk of contrast nephropathy. Scoring systems similar to those pioneered in CXR and CT can be used to effectively plan and manage hospital resources such as ventilators. Lung ultrasound is useful in the assessment of critically ill COVID-19 patients in the hands of an experienced operator. Moreover, it is a convenient tool to monitor disease progression, as it is cheap, non-invasive, easily accessible and easy to sterilise. Newer lung imaging modalities such as magnetic resonance imaging (MRI) for safe imaging among children, adolescents and pregnant women are rapidly evolving. Imaging modalities are also essential for evaluating the extra-pulmonary manifestations of COVID-19: these include cranial imaging with CT or MRI; cardiac imaging with ultrasonography (US), CT and MRI; and abdominal imaging with US or CT. This review critically analyses the utility of each imaging modality to empower clinicians to use them appropriately in the management of patients with COVID-19 infection.

PMX-DHP Therapy for Dyspnea and Deoxygenation in Severe COVID-19 Pneumonia: A Case Series

Hypercytokinemia induced by coronavirus disease-19 (COVID-19) is associated with severe pulmonary involvement, which may lead to respiratory failure. These conditions play an important role in the worsening of clinical symptoms in patients with severe COVID-19. There is no established treatment for hypercytokinemia. We report on two patients whose clinical symptoms improved after direct hemoperfusion using polymyxin B-immobilized fiber column (PMX-DHP), following the administration of the anti-inflammatory agent tocilizumab. Case A was a 70-year-old man diagnosed with COVID-19 pneumonia. Despite treatment with ciclesonide and favipiravir, supplemental oxygen was administered due to the worsening of dyspnea with tachypnea. Although tocilizumab was started on day 6, the patient deteriorated into deoxygenation, presenting with the PaO₂/F_IO₂ (P/F) ratio of 92. On days 8 and 10, the patient received PMX-DHP therapy. On day 11, his dyspnea improved. On day 13, his P/F ratio began to improve, and oxygen therapy was discontinued on day 18. The patient recovered without requiring mechanical ventilation. Case B was a 70-year-old man diagnosed with COVID-19 pneumonia and treated with favipiravir, starting on day 0. Despite starting ciclesonide inhalation and tocilizumab on day 2, his P/F ratio was 53. On day 5, he received PMX-DHP therapy. On day 6, his dyspnea improved, as did his P/F ratio, reaching 81 on day 8. Finally, his clinical symptoms resolved, and he was discharged from the intensive care unit without requiring mechanical ventilation. These cases indicate that PMX-DHP therapy might be a suitable treatment option for dyspnea and deoxygenation in COVID-19 pneumonia, especially in cases where an anti-inflammatory agent, such as tocilizumab, has failed to achieve the desired effect.

CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19

Background Lower muscle mass is a known predictor of unfavorable outcomes, but its prognostic impact on patients with COVID-19 is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in patients with COVID-19. Materials and Methods Clinical or laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed SARS-CoV-2 infection, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. The extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation by paravertebral muscles were measured on axial CT images at the T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation of odds ratios (ORs) with 95% CIs, were used to build four models to predict ICU admission and death, which were tested and compared by using receiver operating characteristic curve analysis. Results A total of 552 patients (364 men and 188 women; median age, 65 years [interquartile range, 54-75 years]) were included. In a CT-based model, lower-than-median T5 paravertebral muscle areas showed the highest ORs for ICU admission (OR, 4.8; 95% CI: 2.7, 8.5; P < .001) and death (OR, 2.3; 95% CI: 1.0, 2.9; P = .03). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle areas still showed the highest ORs for both ICU admission (OR, 4.3; 95%: CI: 2.5, 7.7; P < .001) and death (OR, 2.3; 95% CI: 1.3, 3.7; P = .001). At receiver operating characteristic analysis, the CT-based model and the model including clinical variables showed the same area under the receiver operating characteristic curve (AUC) for ICU admission prediction (AUC, 0.83; P = .38) and were not different in terms of predicting death (AUC, 0.86 vs AUC, 0.87, respectively; P = .28). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT images was independently associated with intensive care unit admission and in-hospital mortality. © RSNA, 2021 Online supplemental material is available for this article.

Diagnostic Performance of CO-RADS and the RSNA Classification System in Evaluating COVID-19 at Chest CT: A Meta-Analysis

PURPOSE

To determine the diagnostic performance of the COVID-19 Reporting and Data System (CO-RADS) and the Radiological Society of North America (RSNA) categorizations in patients with clinically suspected coronavirus disease 2019 (COVID-19) infection.

MATERIALS AND METHODS

In this meta-analysis, studies from 2020, up to August 24, 2020 were assessed for inclusion criteria of studies that used CO-RADS or the RSNA categories for scoring chest CT in patients with suspected COVID-19. A total of 186 studies were identified. After review of abstracts and text, a total of nine studies were included in this study. Patient information (n¸ age, sex), CO-RADS and RSNA scoring categories, and other study characteristics were extracted. Study quality was assessed with the QUADAS-2 tool. Meta-analysis was performed with a random effects model.

RESULTS

Nine studies (3283 patients) were included. Overall study quality was good, except for risk of non-performance of repeated reverse transcriptase polymerase chain reaction (RT-PCR) after negative initial RT-PCR and persistent clinical suspicion in four studies. Pooled COVID-19 frequencies in CO-RADS categories were: 1, 8.8%; 2, 11.1%; 3, 24.6%; 4, 61.9%; and 5, 89.6%. Pooled COVID-19 frequencies in RSNA classification categories were: negative 14.4%; atypical, 5.7%; indeterminate, 44.9%; and typical, 92.5%. Pooled pairs of sensitivity and specificity using CO-RADS thresholds were the following: at least 3, 92.5% (95% CI: 87.1, 95.7) and 69.2% (95%: CI: 60.8, 76.4); at least 4, 85.8% (95% CI: 78.7, 90.9) and 84.6% (95% CI: 79.5, 88.5); and 5, 70.4% (95% CI: 60.2, 78.9) and 93.1% (95% CI: 87.7, 96.2). Pooled pairs of sensitivity and specificity using RSNA classification thresholds for indeterminate were 90.2% (95% CI: 87.5, 92.3) and 75.1% (95% CI: 68.9, 80.4) and for typical were 65.2% (95% CI: 37.0, 85.7) and 94.9% (95% CI: 86.4, 98.2).

CONCLUSION

COVID-19 infection frequency was higher in patients categorized with higher CORADS and RSNA classification categories.

COVID-19 Associated Pneumonia: A review of chest radiograph and computed tomography findings

Medical imaging, including chest radiography and computed tomography, plays a major role in the diagnosis and follow-up of patients with COVID-19 associated pneumonia. This review aims to summarise current information on this topic based on the existing literature. A search of the Google Scholar (Google LLC, Mountain View, California, USA) and MEDLINE® (National Library of Medicine, Bethesda, Maryland, USA) databases was conducted for articles published until April 2020. A total of 30 articles involving 4,002 patients were identified. The most frequently reported imaging findings were bilateral ground glass and consolidative pulmonary opacities with a predominant lower lobe and peripheral subpleural distribution.

Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis

Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies. We propose a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity of 0.90±0.08 and a specificity of 0.96±0.04. To investigate the utility of the proposed method, we employ interpretability methods for the spatio-temporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity 0.806, specificity 0.962). The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for pulmonary diseases. Dataset and all code are publicly available at: https://github.com/BorgwardtLab/covid19_ultrasound.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach

BACKGROUND

Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19.

OBJECTIVE

We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection.

METHODS

In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia.

RESULTS

Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%).

CONCLUSIONS

Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

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.

COVID-19 Pandemic and Upcoming Influenza Season-Does an Expert's Computed Tomography Assessment Differentially Identify COVID-19, Influenza and Pneumonias of Other Origin?

(1) Background: Time-consuming SARS-CoV-2 RT-PCR suffers from limited sensitivity in early infection stages whereas fast available chest CT can already raise COVID-19 suspicion. Nevertheless, radiologists' performance to differentiate COVID-19, especially from influenza pneumonia, is not sufficiently characterized. (2) Methods: A total of 201 pneumonia CTs were identified and divided into subgroups based on RT-PCR: 78 COVID-19 CTs, 65 influenza CTs and 62 Non-COVID-19-Non-influenza (NCNI) CTs. Three radiology experts (blinded from RT-PCR results) raised pathogen-specific suspicion (separately for COVID-19, influenza, bacterial pneumonia and fungal pneumonia) according to the following reading scores: 0-not typical/1-possible/2-highly suspected. Diagnostic performances were calculated with RT-PCR as a reference standard. Dependencies of radiologists' pathogen suspicion scores were characterized by Pearson's Chi2 Test for Independence. (3) Results: Depending on whether the intermediate reading score 1 was considered as positive or negative, radiologists correctly classified 83-85% (vs. NCNI)/79-82% (vs. influenza) of COVID-19 cases (sensitivity up to 94%). Contrarily, radiologists correctly classified only 52-56% (vs. NCNI)/50-60% (vs. COVID-19) of influenza cases. The COVID-19 scoring was more specific than the influenza scoring compared with suspected bacterial or fungal infection. (4) Conclusions: High-accuracy COVID-19 detection by CT might expedite patient management even during the upcoming influenza season.

[Differential diagnosis of high altitude pulmonary edema and COVID-19 with computed tomography feature]

To investigate the computed tomography (CT) characteristics and differential diagnosis of high altitude pulmonary edema (HAPE) and COVID-19, CT findings of 52 cases of HAPE confirmed in Medical Station of Sanshili Barracks, PLA 950 Hospital from May 1, 2020 to May 30, 2020 were collected retrospectively. The size, number, location, distribution, density and morphology of the pulmonary lesions of these CT data were analyzed and compared with some already existed COVID-19 CT images which come from two files, "Radiological diagnosis of COVID-19: expert recommendation from the Chinese Society of Radiology (First edition)" and "A rapid advice guideline for the diagnosis and treatment of 2019 novel corona-virus (2019-nCoV) infected pneumonia (standard version)". The simple or multiple ground-glass opacity (GGO) lesions are located both in the HAPE and COVID-19 at the early stage, but only the thickening of interlobular septa, called "crazy paving pattern" belongs to COVID-19. At the next period, some increased cloudy shadows are located in HAPE, while lesions of COVID-19 are more likely to develop parallel to the direction of the pleura, and some of the lesions show the bronchial inflation. At the most serious stage, both the shadows in HAPE and COVID-19 become white, but the lesions of HAPE in the right lung are more serious than that of left lung. In summary, some cloudy shadows are the feature of HAPE CT image, and "crazy paving pattern" and "pleural parallel sign" belong to the COVID-19 CT, which can be used for differential diagnosis.

COVID-19 mimics on chest CT: a pictorial review and radiologic guide

Chest imaging is often used as a complementary tool in the evaluation of coronavirus disease 2019 (COVID-19) patients, helping physicians to augment their clinical suspicion. Despite not being diagnostic for COVID-19, chest CT may help clinicians to isolate high suspicion patients with suggestive imaging findings. However, COVID-19 findings on CT are also common to other pulmonary infections and non-infectious diseases, and radiologists and point-of-care physicians should be aware of possible mimickers. This state-of-the-art review goal is to summarize and illustrate possible etiologies that may have a similar pattern on chest CT as COVID-19. The review encompasses both infectious etiologies, such as non-COVID viral pneumonia, Mycoplasma pneumoniae, Pneumocystis jiroveci, and pulmonary granulomatous infectious, and non-infectious disorders, such as pulmonary embolism, fat embolism, cryptogenic organizing pneumonia, non-specific interstitial pneumonia, desquamative interstitial pneumonia, and acute and chronic eosinophilic pneumonia.

Pleural diseases and COVID-19: ubi fumus, ibi ignis

More than 45 000 articles in the PubMed database and around 3200 studies registered in ClinicalTrials.gov, of which greater than half are clinical trials, are the result of ongoing and relentless research into the global pandemic nature of an acute respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which made its initial appearance in December 2019 in China. As of 28 August 2020, the total confirmed cases of coronavirus disease 2019 (COVID-19) surpasses 24.5 million, with more than 830 000 global deaths [1]. An estimated 40% to 45% of persons infected with SARS-CoV-2 will remain asymptomatic, but they can transmit the virus to others for an extended period, perhaps longer than 14 days [2]. The primary presentation of symptomatic infection is that of an influenza-like illness or viral pneumonia, with about 20% of these patients developing severe or critical manifestations [3].

There is both direct and circumstantial evidence that SARS-CoV-2 is responsible for the generation of pleural effusions and secondary spontaneous pneumothorax/pneumomediastinumhttps://bit.ly/3gZqA7Z

Osmotic Adaptation by Na+-Dependent Transporters and ACE2: Correlation with Hemostatic Crisis in COVID-19

COVID-19 symptoms, including hypokalemia, hypoalbuminemia, ageusia, neurological dysfunctions, D-dimer production, and multi-organ microthrombosis reach beyond effects attributed to impaired angiotensin-converting enzyme 2 (ACE2) signaling and elevated concentrations of angiotensin II (Ang II). Although both SARS-CoV (Severe Acute Respiratory Syndrome Coronavirus) and SARS-CoV-2 utilize ACE2 for host entry, distinct COVID-19 pathogenesis coincides with the acquisition of a new sequence, which is homologous to the furin cleavage site of the human epithelial Na+ channel (ENaC). This review provides a comprehensive summary of the role of ACE2 in the assembly of Na+-dependent transporters of glucose, imino and neutral amino acids, as well as the functions of ENaC. Data support an osmotic adaptation mechanism in which osmotic and hemostatic instability induced by Ang II-activated ENaC is counterbalanced by an influx of organic osmolytes and Na+ through the ACE2 complex. We propose a paradigm for the two-site attack of SARS-CoV-2 leading to ENaC hyperactivation and inactivation of the ACE2 complex, which collapses cell osmolality and leads to rupture and/or necrotic death of swollen pulmonary, endothelial, and cardiac cells, thrombosis in infected and non-infected tissues, and aberrant sensory and neurological perception in COVID-19 patients. This dual mechanism employed by SARS-CoV-2 calls for combinatorial treatment strategies to address and prevent severe complications of COVID-19.

Chemoprophylaxis, diagnosis, treatments, and discharge management of COVID-19: An evidence-based clinical practice guideline (updated version)

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, coronavirus disease 2019 (COVID-19), affecting more than seventeen million people around the world. Diagnosis and treatment guidelines for clinicians caring for patients are needed. In the early stage, we have issued "A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)"; now there are many direct evidences emerged and may change some of previous recommendations and it is ripe for develop an evidence-based guideline. We formed a working group of clinical experts and methodologists. The steering group members proposed 29 questions that are relevant to the management of COVID-19 covering the following areas: chemoprophylaxis, diagnosis, treatments, and discharge management. We searched the literature for direct evidence on the management of COVID-19, and assessed its certainty generated recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Recommendations were either strong or weak, or in the form of ungraded consensus-based statement. Finally, we issued 34 statements. Among them, 6 were strong recommendations for, 14 were weak recommendations for, 3 were weak recommendations against and 11 were ungraded consensus-based statement. They covered topics of chemoprophylaxis (including agents and Traditional Chinese Medicine (TCM) agents), diagnosis (including clinical manifestations, reverse transcription-polymerase chain reaction (RT-PCR), respiratory tract specimens, IgM and IgG antibody tests, chest computed tomography, chest x-ray, and CT features of asymptomatic infections), treatments (including lopinavir-ritonavir, umifenovir, favipiravir, interferon, remdesivir, combination of antiviral drugs, hydroxychloroquine/chloroquine, interleukin-6 inhibitors, interleukin-1 inhibitors, glucocorticoid, qingfei paidu decoction, lianhua qingwen granules/capsules, convalescent plasma, lung transplantation, invasive or noninvasive ventilation, and extracorporeal membrane oxygenation (ECMO)), and discharge management (including discharge criteria and management plan in patients whose RT-PCR retesting shows SARS-CoV-2 positive after discharge). We also created two figures of these recommendations for the implementation purpose. We hope these recommendations can help support healthcare workers caring for COVID-19 patients.