Computed Tomographic Imaging of 3 Patients With Coronavirus Disease 2019 Pneumonia With Negative Virus Real-time Reverse-Transcription Polymerase Chain Reaction Test

A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices

The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral–temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral–temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral–temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral–temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral–temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral–temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.

COVID-19 Diagnosis: A Comprehensive Review of the RT-qPCR Method for Detection of SARS-CoV-2

The world is grappling with the coronavirus disease 2019 (COVID-19) pandemic, the causative agent of which is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 symptoms are similar to the common cold, including fever, sore throat, cough, muscle and chest pain, brain fog, dyspnoea, anosmia, ageusia, and headache. The manifestation of the disease can vary from being asymptomatic to severe life-threatening conditions warranting hospitalization and ventilation support. Furthermore, the emergence of mutecated variants of concern (VOCs) is paramount to the devastating effect of the pandemic. This highly contagious virus and its emergent variants challenge the available advanced viral diagnostic methods for high-accuracy testing with faster result yields. This review is to shed light on the natural history, pathology, molecular biology, and efficient diagnostic methods of COVID-19, detecting SARS-CoV-2 in collected samples. We reviewed the gold standard RT-qPCR method for COVID-19 diagnosis to confer a better understanding and application to combat the COVID-19 pandemic. This comprehensive review may further develop awareness about the management of the COVID-19 pandemic.

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

COVID-19 Spike Protein Induced Phononic Modification in Antibody-Coupled Graphene for Viral Detection Application

With an incubation time of about 5 days, early diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to control the spread of the coronavirus disease 2019 (COVID-19) that killed more than 3 million people in its first 1.5 years. Here, we report on the modification of the dopant density and the phononic energy of antibody-coupled graphene when it interfaces with SARS-CoV-2 spike protein. This graphene chemeo-phononic system was able to detect SARS-CoV-2 spike protein at the limit of detection of ∼3.75 and ∼1 fg/mL in artificial saliva and phosphate-buffered saline, respectively. It also exhibited selectivity over proteins in saliva and MERS-CoV spike protein. Since the change in graphene phononics is monitored instead of the phononic signature of the analyte, this optical platform can be replicated for other COVID variants and specific-binding-based biodetection applications.

Covid-19 imaging: A narrative review

Background

The 2019 novel coronavirus disease (COVID-19) imaging data is dispersed in numerous publications. A cohesive literature review is to be assembled.

Objective

To summarize the existing literature on Covid-19 pneumonia imaging including precautionary measures for radiology departments, Chest CT's role in diagnosis and management, imaging findings of Covid-19 patients including children and pregnant women, artificial intelligence applications and practical recommendations.

Methods

A systematic literature search of PubMed/med line electronic databases.

Results

The radiology department's staff is on the front line of the novel coronavirus outbreak. Strict adherence to precautionary measures is the main defense against infection's spread. Although nucleic acid testing is Covid-19's pneumonia diagnosis gold standard; kits shortage and low sensitivity led to the implementation of the highly sensitive chest computed tomography amidst initial diagnostic tools. Initial Covid-19 CT features comprise bilateral, peripheral or posterior, multilobar ground-glass opacities, predominantly in the lower lobes. Consolidations superimposed on ground-glass opacifications are found in few cases, preponderantly in the elderly. In later disease stages, GGO transformation into multifocal consolidations, thickened interlobular and intralobular lines, crazy paving, traction bronchiectasis, pleural thickening, and subpleural bands are reported. Standardized CT reporting is recommended to guide radiologists. While lung ultrasound, pulmonary MRI, and PET CT are not Covid-19 pneumonia's first-line investigative diagnostic modalities, their characteristic findings and clinical value are outlined. Artificial intelligence's role in strengthening available imaging tools is discussed.

Conclusion

This review offers an exhaustive analysis of the current literature on imaging role and findings in COVID-19 pneumonia.

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Chest computed tomography is a highly sensitive Covid −19 pneumonia's diagnostic tool.

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Initial Covid-19 CT features are bilateral, multifocal, peripheral or posterior ground-glass opacities, mainly in the lower lobes.

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Multifocal consolidations, bronchiectasis, pleural thickening, and subpleural bands are late disease stages features.

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Standardized CT reporting is recommended to guide radiologists.

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Artificial intelligence could strengthen available imaging tools.

Role of Chest Computed Tomography versus Real Time Reverse Transcription Polymerase Chain Reaction for Diagnosis of COVID-19: A Systematic Review and Meta-Analysis

Background

The current global pandemic of COVID-19 is considered a public health emergency. The diagnosis of COVID-19 depends on detection of the viral nucleic acid by real time reverse transcription polymerase chain reaction (RT-PCR). However, false-negative RT-PCR tests are reported and could hinder the control of the pandemic. Chest computed tomography could achieve a more reliable diagnosis and represent a complementary diagnostic tool.

Aim

To perform a meta-analysis and systematic review to find out the role of chest computed tomography versus RT-PCR for precise diagnosis of COVID-19 infection.

Methods

We searched three electronic databases (PubMed, ScienceDirect, and Scopus) from April 1 to April 20, 2020, to find out articles including the accuracy of chest computed tomography scan versus RT-PCR for diagnosis of SARS-CoV-2 infection. Observational studies, case series, and case reports were included.

Results

A total of 238 articles were retrieved from the search strategy. Following screening, 39 articles were chosen for full text assessment and finally 35 articles were included for qualitative and quantitative analysis. Chest computed tomography showed a wide range of sensitivity varied from 12%–100%.

Conclusion

Chest computed tomography is playing a key role for diagnosis and detection of COVID-19 infection. Computed tomography image findings may precede the initially positive RT-PCR assay.

Clinical Manifestations of COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third coronavirus causing an outbreak in the twenty-first century. It is related to a contagious coronavirus disease (COVID-19), which its high pace of spreading allowed it to lie to the whole world and be turned into a pandemic only a few months after the identification of the first case. Currently, the reverse transcription-polymerase chain reaction (RT-PCR) test of throat swap is the gold standard of diagnosis; however, several studies have reported false-negative results with non-ideal sensitivity. Because this pandemic constitutes a significant burden on global healthcare systems and due to the high transmission rate of the virus, an accurate diagnosis algorithm is needed to reduce the missing case number. A comprehensive clinical examination and taking a history of all systems (not just limited to the respiratory system) combined with hematologic laboratory tests and chest imaging can lead to a sensitive diagnosis, severity assessment, and RT-PCT test interpretation. This chapter focuses on clinical characteristics, hematologic laboratory, and chest imaging features in COVID-19.

COVID-19 Pandemic: Review of Contemporary and Forthcoming Detection Tools

Recent severe acute respiratory syndrome 2 (SARS-CoV-2) known as COVID-19, presents a deadly challenge to the global healthcare system of developing and developed countries, exposing the limitations of health facilities preparedness for emerging infectious disease pandemic. Opportune detection, confinement, and early treatment of infected cases present the first step in combating COVID-19. In this review, we elaborate on various COVID-19 diagnostic tools that are available or under investigation. Consequently, cell culture, followed by an indirect fluorescent antibody, is one of the most accurate methods for detecting SARS-CoV-2 infection. However, restrictions imposed by the regulatory authorities prevented its general use and implementation. Diagnosis via radiologic imaging and reverse transcriptase PCR assay is frequently employed, considered as standard procedures, whereas isothermal amplification methods are currently on the verge of clinical introduction. Notably, techniques such as CRISPR-Cas and microfluidics have added new dimensions to the SARS-CoV-2 diagnosis. Furthermore, commonly used immunoassays such as enzyme-linked immunosorbent assay (ELISA), lateral flow immunoassay (LFIA), neutralization assay, and the chemiluminescent assay can also be used for early detection and surveillance of SARS-CoV-2 infection. Finally, advancement in the next generation sequencing (NGS) and metagenomic analysis are smoothing the viral detection further in this global challenge.

Possible COVID-19 reinfection in a patient with X-linked agammaglobulinaemia

This report highlights the case of a patient with X-linked agammaglobulinaemia (XLA) and resultant bronchiectasis who was discharged from hospital after recovering from real-time reverse transcriptase-PCR positive COVID-19 infection having had a subsequent negative swab and resolution of symptoms, but was readmitted 3 weeks later with recrudescent symptoms and a further positive swab. Although there are reports of COVID-19 infection in XLA, for the first time we report a case of possible reinfection. Lessons learnt from this case include the potential for reinfection of COVID-19 in a patient with a weakened immune system and the importance of repeating COVID-19 swabs in inpatients. Extra caution needs to be taken when providing care in groups of patients who have a weakened or absent immune system.

Molecular (real-time reverse transcription polymerase chain reaction) diagnosis of SARS-CoV-2 infections: complexity and challenges

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first recorded in Wuhan, China. The World Health Organization initially classified COVID-19 as a public health emergency and subsequently declared the disease a global pandemic. COVID-19 can take at least three distinct forms: severe acute distress syndrome with a potentially fatal outcome, mild respiratory illness (pneumonia with eventual recovery) and asymptomatic infection. All three disease forms have the potential to transmit the infection to healthy contacts. At present, real-time reverse transcription polymerase chain reaction (RT-PCR) is the only available laboratory tool to confirm the presence of viral RNA in patient specimens. These assays are designed to detect one or more (at least 2) SARS-CoV-2 RNA gene targets allowing the detection of the virus. Commercially available RT-PCR assays employ various gene targets of the viral genome in their assay systems. Additionally, there are differences in primer selection for the same gene region of SARS-CoV-2. At present, it is unclear whether the results from different RT-PCR assays are comparable in detecting the spectrum of COVID-19 manifestations. The purpose of the present article is twofold: first, to briefly focus on the findings of these reports; and second, to emphasize the various challenges and flaws that can potentially impact the diagnostic accuracy of RT-PCR testing for SARS-CoV-2.

RSNA Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19: Interobserver Agreement Between Chest Radiologists

PURPOSE

To assess the interobserver variability between chest radiologists in the interpretation of the Radiological Society of North America (RSNA) expert consensus statement reporting guidelines in patients with suspected coronavirus disease 2019 (COVID-19) pneumonia in a setting with limited reverse transcription polymerase chain reaction testing availability.

METHODS

Chest computed tomography (CT) studies in 303 consecutive patients with suspected COVID-19 were reviewed by 3 fellowship-trained chest radiologists. Cases were assigned an impression of typical, indeterminate, atypical, or negative for COVID-19 pneumonia according to the RSNA expert consensus statement reporting guidelines, and interobserver analysis was performed. Objective CT features associated with COVID-19 pneumonia and distribution of findings were recorded.

RESULTS

The Fleiss kappa for all observers was almost perfect for typical (0.815), atypical (0.806), and negative (0.962) COVID-19 appearances (P < .0001) and substantial (0.636) for indeterminate COVID-19 appearance (P < .0001). Using Cramer V analysis, there were very strong correlations between all radiologists' interpretations, statistically significant for all (typical, indeterminate, atypical, and negative) COVID-19 appearances (P < .001). Objective CT imaging findings were recorded in similar percentages of typical cases by all observers.

CONCLUSION

The RSNA expert consensus statement on reporting chest CT findings related to COVID-19 demonstrates substantial to almost perfect interobserver agreement among chest radiologists in a relatively large cohort of patients with clinically suspected COVID-19. It therefore serves as a reliable reference framework for radiologists to accurately communicate their level of suspicion based on the presence of evidence-based objective findings.

The challenges of COVID-19 testing in Africa: the Ethiopian experience

Novel coronavirus disease (COVID-19) is spreading rapidly and creating a huge economic, social and public health challenge worldwide. Although currently an effective vaccine is ready, its distribution is limited, and hence the only currently available lever to reduce transmission is to identify and isolate individuals who are contagious. Thus, testing for SARS CoV-2 has a paramount importance. However, testing in many African countries including Ethiopia has multidimensional growing challenges. Here, we tried to identify, categorize and summarize the challenges of COVID-19 testing in Africa from Ethiopian experience.

Clinical, Laboratory, and Radiologic Characteristics of Patients With Initial False-Negative Severe Acute Respiratory Syndrome Coronavirus 2 Nucleic Acid Amplification Test Results

BACKGROUND

Concerns about false-negative (FN) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid amplification tests (NAATs) have prompted recommendations for repeat testing if suspicion for coronavirus disease 2019 (COVID-19) infection is moderate to high. However, the frequency of FNs and patient characteristics associated with FNs are poorly understood.

METHODS

We retrospectively reviewed test results from 15 011 adults who underwent ≥1 SARS-CoV-2 NAATs; 2699 had an initial negative NAAT and repeat testing. We defined FNs as ≥1 negative NAATs followed by a positive NAAT within 14 days during the same episode of illness. We stratified subjects with FNs by duration of symptoms before the initial FN test (≤5 days versus >5 days) and examined their clinical, radiologic, and laboratory characteristics.

RESULTS

Sixty of 2699 subjects (2.2%) had a FN result during the study period. The weekly frequency of FNs among subjects with repeat testing peaked at 4.4%, coinciding with peak NAAT positivity (38%). Most subjects with FNs had symptoms (52 of 60; 87%) and chest radiography (19 of 32; 59%) consistent with COVID-19. Of the FN NAATs, 18 of 60 (30%) were performed early (ie, ≤1 day of symptom onset), and 18 of 60 (30%) were performed late (ie, >7 days after symptom onset) in disease. Among 17 subjects with 2 consecutive FNs on NP NAATs, 9 (53%) provided lower respiratory tract (LRT) specimens for testing, all of which were positive.

CONCLUSIONS

Our findings support repeated NAATs among symptomatic patients, particularly during periods of higher COVID-19 incidence. The LRT testing should be prioritized to increase yield among patients with high clinical suspicion for COVID-19.

A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis

Nowadays there is an ongoing acute respiratory outbreak caused by the novel highly contagious coronavirus (COVID-19). The diagnostic protocol is based on quantitative reverse-transcription polymerase chain reaction (RT-PCR) and chests CT scan, with uncertain accuracy. This meta-analysis study determines the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. Three main databases; PubMed (MEDLINE), Scopus, and EMBASE were systematically searched for all published literature from January 1st, 2019, to the 21st May 2020 with the keywords "COVID19 virus", "2019 novel coronavirus", "Wuhan coronavirus", "2019-nCoV", "X-Ray Computed Tomography", "Polymerase Chain Reaction", "Reverse Transcriptase PCR", and "PCR Reverse Transcriptase". All relevant case-series, cross-sectional, and cohort studies were selected. Data extraction and analysis were performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5. Among 1022 articles, 60 studies were eligible for totalizing 5744 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan compared to RT-PCR were 87% (95% CI 85-90%), 46% (95% CI 29-63%), 69% (95% CI 56-72%), and 89% (95% CI 82-96%), respectively. It is important to rely on the repeated RT-PCR three times to give 99% accuracy, especially in negative samples. Regarding the overall diagnostic sensitivity of 87% for chest CT, the RT-PCR testing is essential and should be repeated to escape misdiagnosis.

Value of minimum intensity projections for chest CT in COVID-19 patients

PURPOSE

To investigate whether minimum intensity projection (MinIP) reconstructions enable more accurate depiction of pulmonary ground-glass opacity (GGO) compared to standard transverse sections and multiplanar reformat (MPR) series in patients with suspected coronavirus disease 2019 (COVID-19).

METHOD

In this multinational study, chest CT scans of 185 patients were retrospectively analyzed. Diagnostic accuracy, diagnostic confidence, image quality regarding the assessment of GGO, as well as subjective time-efficiency of MinIP and standard MPR series were analyzed based on the assessment of six radiologists. In addition, the suitability for COVID-19 evaluation, image quality regarding GGO and subjective time-efficiency in clinical routine was assessed by five clinicians.

RESULTS

The reference standard revealed a total of 149 CT scans with pulmonary GGO. MinIP reconstructions yielded significantly higher sensitivity (99.9 % vs 95.6 %), specificity (95.8 % vs 86.1 %) and accuracy (99.1 % vs 93.8 %) for assessing of GGO compared with standard MPR series. MinIP reconstructions achieved significantly higher ratings by radiologists concerning diagnostic confidence (medians, 5.00 vs 4.00), image quality (medians, 4.00 vs 4.00), contrast between GGO and unaffected lung parenchyma (medians, 5.00 vs 4.00) as well as subjective time-efficiency (medians, 5.00 vs 4.00) compared with MPR-series (all P < .001). Clinicians preferred MinIP reconstructions for COVID-19 assessment (medians, 5.00 vs 3.00), image quality regarding GGO (medians, 5.00 vs 3.00) and subjective time-efficiency in clinical routine (medians, 5.00 vs 3.00).

CONCLUSIONS

MinIP reconstructions improve the assessment of COVID-19 in chest CT compared to standard images and may be suitable for routine application.

Clinical usefulness of tomographic standards for COVID-19 pneumonia diagnosis: Experience from a Brazilian reference center

Background

COVID-19 is a new disease and the most common complication is pneumonia. The Radiological Society of North America (RSNA) proposed an expert consensus for imaging classification for COVID-19 pneumonia.

Objective

To evaluate sensitivity, specificity, accuracy, and reproducibility of chest CT standards in the beginning of the Brazilian COVID-19 outbreak.

Methods

Cross-sectional study performed from March 1st to April 14th, 2020. Patients with suspected COVID-19 pneumonia submitted to RT-PCR test and chest computed tomography (CT) were included. Two thoracic radiologists blinded for RT-PCR and clinical and laboratory results classified every patient scan according to the RSNA expert consensus. A third thoracic radiologist also evaluated in case of discordance, and consensus was reached among the three radiologists. A typical appearance was considered a positive chest CT for COVID-19 pneumonia. Sensitivity, specificity, positive and negative predictive values were calculated. Cohen’s kappa coefficient was used to evaluate intra- and inter-rater agreements.

Results

A total of 159 patients were included (mean age 57.9 ± 18.0 years; 88 [55.3%] males): 86 (54.1%) COVID-19 and 73 (45.9%) non-COVID-19 patients. Eighty (50.3%) patients had a positive CT for COVID-19 pneumonia. Sensitivity and specificity of typical appearance were 88.3% (95%CI, 79.9–93.5) and 94.5% (95%CI, 86.7–97.8), respectively. Intra- and inter-rater agreement were assessed (Cohen’s kappa = 0.924, P = 0.06; Cohen’s kappa=0.772, P = 0.05, respectively).

Conclusion

Chest CT categorical classification of COVID-19 findings is reproducible and demonstrates high level of agreement with clinical and RT-PCR diagnosis of COVID-19. In RT-PCR scarcity scenarios or in equivocal cases, it may be useful for attending physicians in the evaluation of suspected COVID-19 pneumonia patients attended at the emergency unit.

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

BACKGROUND

Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19.

OBJECTIVE

Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease.

METHODS

We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases.

RESULTS

A total of 10,504 patients with a clinical or polymerase chain reaction-confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care.

CONCLUSIONS

Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.

SARS-CoV-2 infections with emphasis on pediatric patients: a narrative review

This narrative review summarizes the main aspects underlying the new coronavirus SARS-CoV-2, its epidemiology, pathophysiology, pointing to differences of SARS-CoV-2 main receptors ACE2, in terms of expression and the amount of soluble ACE2 in the circulation of children, men and women, and also in those with risk factors such as the smokers and pregnant women or presenting with comorbidities (diabetes, obesity, hypertension and other cardiovascular diseases, renal and CNS pre-existing diseases). Clinical manifestations in adults and children were also described, emphasizing the particularities already seen in children, regarding signs, symptoms, viral excretion time and the involvement of all organs and systems. The COVID-19 in the pediatric population was divided into two sections: one dedicated to previously healthy children and adolescents with COVID-19, and the other to those who live with comorbidities and acquired COVID-19. A few paragraphs were reserved to the recently described severe multisystemic inflammatory syndrome associated with COVID-19 (MIS-C) that shares certain characteristics with Kawasaki disease. Some studies on the infection in pregnant and postpartum women, as well as neonates were shown. This review has also covered the laboratory diagnosis of COVID-19, passing through the imaging diagnosis made by the chest tomography revealing ground glass patching opacities, and results of non-specific exams such as the total blood with lymphopenia, the coagulation tests with increased prothrombin times, as well as marked increments of the D-dimer, troponin and proinflammatory cytokines. In the section devoted to the specific laboratory diagnosis of COVID-19, the most used RT-PCR protocols were described and some studies on the serological diagnosis with IgA, IgM and IgG detection were detailed, including the use of rapid immunochromatographic assays and discussing the ideal period after the onset of symptoms to perform each type of test. In the end, the management of pediatric patients with COVID-19 based mainly on supportive measures has been briefly commented.

Diagnosing COVID-19 in the Emergency Department: A Scoping Review of Clinical Examinations, Laboratory Tests, Imaging Accuracy, and Biases

Objective

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) emerged as a global pandemic in early 2020 with rapidly evolving approaches to diagnosing the clinical illness called coronavirus disease (COVID‐19). The primary objective of this scoping review is to synthesize current research of the diagnostic accuracy of history, physical examination, routine laboratory tests, real‐time reverse transcription–polymerase chain reaction (rRT‐PCR), immunology tests, and computed tomography (CT) for the emergency department (ED) diagnosis of COVID‐19. Secondary objectives included a synopsis of diagnostic biases likely with current COVID‐19 research as well as corresponding implications of false‐negative and false‐positive results for clinicians and investigators.

Methods

A Preferred Reporting Items for Systematic Reviews and Meta‐Analyses–Scoping Review (PRISMA‐ScR)–adherent synthesis of COVID‐19 diagnostic accuracy through May 5, 2020, was conducted. The search strategy was designed by a medical librarian and included studies indexed by PubMed and Embase since January 2020.

Results

A total of 1,907 citations were screened for relevance. Patients without COVID‐19 are rarely reported, so specificity and likelihood ratios were generally unavailable. Fever is the most common finding, while hyposmia and hypogeusia appear useful to rule in COVID‐19. Cough is not consistently present. Lymphopenia is the mostly commonly reported laboratory abnormality and occurs in over 50% of COVID‐19 patients. rRT‐PCR is currently considered the COVID‐19 criterion standard for most diagnostic studies, but a single test sensitivity ranges from 60% to 78%. Multiple reasons for false‐negatives rRT‐PCR exist, including sample site tested and disease stage during which sample was obtained. CT may increase COVID‐19 sensitivity in conjunction with rRT‐PCR, but guidelines for imaging patients most likely to benefit are emerging. IgM and IgG serology levels are undetectable in the first week of COVID‐19, but sensitivity (range = 82% to 100%) and specificity (range = 87% to 100%) are promising. Whether detectable COVID‐19 antibodies correspond to immunity remains unanswered. Current studies do not adhere to accepted diagnostic accuracy reporting standards and likely report significantly biased results if the same tests were to be applied to general ED populations with suspected COVID‐19.

Conclusions

With the exception of fever and disorders of smell/taste, history and physical examination findings are unhelpful to distinguish COVID‐19 from other infectious conditions that mimic SARS‐CoV‐2 like influenza. Routine laboratory tests are also nondiagnostic, although lymphopenia is a common finding and other abnormalities may predict severe disease. Although rRT‐PCR is the current criterion standard, more inclusive consensus‐based criteria will likely emerge because of the high false‐negative rate of PCR tests. The role of serology and CT in ED assessments remains undefined.

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

INTRODUCTION

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

OBJECTIVE

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

METHODS

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

RESULT

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

CONCLUSION

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

Chest CT for triage during COVID-19 on the emergency department: myth or truth?

PURPOSE

We aimed to investigate the diagnostic performance of chest CT compared with first RT-PCR results in adult patients suspected of COVID-19 infection in an ED setting. We also constructed a predictive machine learning model based on chest CT and additional data to improve the diagnostic accuracy of chest CT.

METHODS

This study's cohort consisted of 319 patients who underwent chest CT and RT-PCR testing at the ED. Patient characteristics, demographics, symptoms, vital signs, laboratory tests, and chest CT results (CO-RADS) were collected. With first RT-PCR as reference standard, the diagnostic performance of chest CT using the CO-RADS score was assessed. Additionally, a predictive machine learning model was constructed using logistic regression.

RESULTS

Chest CT, with first RT-PCR as a reference, had a sensitivity, specificity, PPV, and NPV of 90.2%, 88.2%, 84.5%, and 92.7%, respectively. The prediction model with CO-RADS, ferritin, leucocyte count, CK, days of complaints, and diarrhea as predictors had a sensitivity, specificity, PPV, and NPV of 89.3%, 93.4%, 90.8%, and 92.3%, respectively.

CONCLUSION

Chest CT, using the CO-RADS scoring system, is a sensitive and specific method that can aid in the diagnosis of COVID-19, especially if RT-PCR tests are scarce during an outbreak. Combining a predictive machine learning model could further improve the accuracy of diagnostic chest CT for COVID-19. Further candidate predictors should be analyzed to improve our model. However, RT-PCR should remain the primary standard of testing as up to 9% of RT-PCR positive patients are not diagnosed by chest CT or our machine learning model.

False-negative RT-PCR in SARS-CoV-2 disease: experience from an Italian COVID-19 unit

As of 25 May 2020, a total of 5 304 772 laboratory-confirmed cases and 342 029 deaths due to coronavirus diseases 2019 (COVID-19) have been reported worldwide [1], with the USA, Russia and Brazil being the most involved countries. In the absence of a specific treatment with established efficacy, and while waiting for the development of an effective and safe vaccine, prompt identification and immediate isolation of infected subjects still represent the most important countermeasures to stem the exponential rise in COVID-19 cases and related deaths.

False-negative cases of #COVID19 are being increasingly reported. Laboratory diagnosis through RT-PCR testing alone lacks adequate sensitivity to be recommended as the only valid criterion to confirm COVID-19 diagnosis.https://bit.ly/2BLFnEe

Corticosteroid therapy for patients with COVID-19 pneumonia: a before-after study

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Severe COVID-19 patients present overwhelming inflammatory reactions leading to lung injury.

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Corticosteroids may beneficially modulate the host immune response to COVID-19 pneumonia.

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Before–after study evaluating the effect of corticosteroid addition to COVID-19 pneumonia treatment on hospital mortality.

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In multivariate analysis, the ‘after’ period was associated with an adjusted lower risk of death (aHR = 0.47; P = 0.04).

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‘After’ period was also associated with lower risk of ICU admission or death before ICU admission (aHR = 0.37; P < 0.001).

Anti-inflammatory drugs such as corticosteroids may beneficially modulate the host inflammatory response to coronavirus disease 2019 (COVID-19) pneumonia. The aim of this study was to evaluate the impact of addition of corticosteroids to the hospital protocol for treatment of suspected or confirmed COVID-19 pneumonia on rates of death or intensive care unit (ICU) admission. A before–after study was performed to evaluate the effect of addition of corticosteroids to our institution's COVID-19 treatment protocol on hospital mortality. A total of 257 patients with a COVID-19 diagnosis were included in this study between 3 March 2020 and 14 April 2020. As corticosteroids were widely used after 27 March 2020, two periods were considered for the purposes of this study: the ‘before’ period from 3–20 March 2020 (n = 85); and the ‘after’ period from 26 March–14 April 2020 (n = 172). The ‘after’ period was associated with a lower risk of death [adjusted hazard ratio (aHR) = 0.47, 95% confidence interval (CI) 0.23–0.97; P = 0.04] and a lower risk of ICU admission or of death before ICU admission (aHR = 0.37, 95% CI 0.21–0.64; P = 0.0005) by multivariate analysis adjusted for age, National Early Warning score and institutionalisation status. In conclusion, addition of corticosteroids to our institution's COVID-19 treatment protocol was associated with a significant reduction in hospital mortality in the ‘after’ period.

Use of radiographic features in COVID-19 diagnosis: Challenges and perspectives

The rapid surge and wide spread of the coronavirus disease-2019 (COVID-19) overshadows the entire medical industries worldwide. The stringent medical resources hinder the diagnostic capacity globally, while 84 000 of new cases confirmed within a single day of April 14, 2020. Real-time reverse-transcription polymerase chain reaction (RT-PCR) with is the current first-line diagnosis, but the false-negative rate remains concerned. Radiographic technologies and tools, including computed tomography (CT) and chest X-ray, were applied for initial screening and follow-up, from which the tools provide detail diagnosis with specific pathologic features for staging and treatment arrangement. Although the radiographic imaging is found less sensitive, numerous CT-positive patients were not screened out by RT-PCR initially and later confirmed as COVID-19 positive. Besides, the shortage of sampling kits and the longer turn-over time of PCR examinations in some areas were noticed due to logistic issues and healthcare burden. In this review, we will discuss the challenges and the future perspectives of using radiographic modalities for COVID-19 diagnosis in view of securing human lives amid the crisis.

[COVID-19 and the kidneys]

Welche drastischen Folgen neue Infektionskrankheiten für Mensch und Gesellschaft haben können, wird aktuell an COVID-19 („coronavirus disease 2019“) deutlich. Seit seiner Erstbeschreibung im Dezember 2019 beherrscht SARS-CoV‑2 („severe acute respiratory syndrome coronavirus 2“) das aktuelle wissenschaftliche und öffentliche Interesse.

Challenges in Laboratory Diagnosis of the Novel Coronavirus SARS-CoV-2

The recent outbreak of the Coronavirus disease 2019 (COVID-19) has quickly spread worldwide since its discovery in Wuhan city, China in December 2019. A comprehensive strategy, including surveillance, diagnostics, research, clinical treatment, and development of vaccines, is urgently needed to win the battle against COVID-19. The past three unprecedented outbreaks of emerging human coronavirus infections at the beginning of the 21st century have highlighted the importance of readily available, accurate, and rapid diagnostic technologies to contain emerging and re-emerging pandemics. Real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) based assays performed on respiratory specimens remain the gold standard for COVID-19 diagnostics. However, point-of-care technologies and serologic immunoassays are rapidly emerging with high sensitivity and specificity as well. Even though excellent techniques are available for the diagnosis of symptomatic patients with COVID-19 in well-equipped laboratories; critical gaps still remain in screening asymptomatic people who are in the incubation phase of the virus, as well as in the accurate determination of live viral shedding during convalescence to inform decisions for ending isolation. This review article aims to discuss the currently available laboratory methods and surveillance technologies available for the detection of COVID-19, their performance characteristics and highlight the gaps in current diagnostic capacity, and finally, propose potential solutions. We also summarize the specifications of the majority of the available commercial kits (PCR, EIA, and POC) for laboratory diagnosis of COVID-19.