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

A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis

BACKGROUND

Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.

METHOD

A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.

FINDINGS

In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.

CONCLUSION

RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

COVID-19 Detection and Diagnosis Model on CT Scans Based on AI Techniques

The end of 2019 could be mounted in a rudimentary framing of a new medical problem, which globally introduces into the discussion a fulminant outbreak of coronavirus, consequently spreading COVID-19 that conducted long-lived and persistent repercussions. Hence, the theme proposed to be solved arises from the field of medical imaging, where a pulmonary CT-based standardized reporting system could be addressed as a solution. The core of it focuses on certain impediments such as the overworking of doctors, aiming essentially to solve a classification problem using deep learning techniques, namely, if a patient suffers from COVID-19, viral pneumonia, or is healthy from a pulmonary point of view. The methodology's approach was a meticulous one, denoting an empirical character in which the initial stage, given using data processing, performs an extraction of the lung cavity from the CT scans, which is a less explored approach, followed by data augmentation. The next step is comprehended by developing a CNN in two scenarios, one in which there is a binary classification (COVID and non-COVID patients), and the other one is represented by a three-class classification. Moreover, viral pneumonia is addressed. To obtain an efficient version, architectural changes were gradually made, involving four databases during this process. Furthermore, given the availability of pre-trained models, the transfer learning technique was employed by incorporating the linear classifier from our own convolutional network into an existing model, with the result being much more promising. The experimentation encompassed several models including MobileNetV1, ResNet50, DenseNet201, VGG16, and VGG19. Through a more in-depth analysis, using the CAM technique, MobilneNetV1 differentiated itself via the detection accuracy of possible pulmonary anomalies. Interestingly, this model stood out as not being among the most used in the literature. As a result, the following values of evaluation metrics were reached: loss (0.0751), accuracy (0.9744), precision (0.9758), recall (0.9742), AUC (0.9902), and F1 score (0.9750), from 1161 samples allocated for each of the three individual classes.

Multiplexed CRISPR-based Methods for Pathogen Nucleic Acid Detection

Bacterial and viral pathogens are devastating to human health and well-being. In many regions, dozens of pathogen species and variants co-circulate. Thus, it is important to detect many different species and variants of pathogens in a given sample through multiplexed detection methods. CRISPR-based nucleic acid detection has shown to be a promising step towards an easy-to-use sensitive, specific, and high-throughput method to detect nucleic acids from DNA and RNA viruses and bacteria. Here, we review the current state of multiplexed nucleic acid detection methods with a focus on CRISPR-based methods. We also look toward the future of multiplexed point-of-care diagnostics.

Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey

Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.

Impedimetric Nanobiosensor for the Detection of SARS-CoV-2 Antigens and Antibodies

Detection of antigens and antibodies (Abs) is of great importance in determining the infection and immunity status of the population, as they are key parameters guiding the handling of pandemics. Current point-of-care (POC) devices are a convenient option for rapid screening; however, their sensitivity requires further improvement. We present an interdigitated gold nanowire-based impedance nanobiosensor to detect COVID-19-associated antigens (receptor-binding domain of S1 protein of the SARS-CoV-2 virus) and respective Abs appearing during and after infection. The electrochemical impedance spectroscopy technique was used to assess the changes in measured impedance resulting from the binding of respective analytes to the surface of the chip. After 20 min of incubation, the sensor devices demonstrate a high sensitivity of about 57 pS·sn per concentration decade and a limit of detection (LOD) of 0.99 pg/mL for anti-SARS-CoV-2 Abs and a sensitivity of around 21 pS·sn per concentration decade and an LOD of 0.14 pg/mL for the virus antigen detection. Finally, the analysis of clinical plasma samples demonstrates the applicability of the developed platform to assist clinicians and authorities in determining the infection or immunity status of the patients.

Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections

Testing remains a key tool for managing health care and making health policy during the coronavirus pandemic, and it will probably be important in future pandemics. Because of false negative and false positive tests, the observed fraction of positive tests-the surface positivity-is generally different from the fraction of infected individuals (the incidence rate of the disease). In this paper a previous method for translating surface positivity to a point estimate for incidence rate, then to an appropriate range of values for the incidence rate consistent with the model and data (the test range), and finally to the risk (the probability of including one infected individual) associated with groups of different sizes is illustrated. The method is then extended to include asymptomatic infections. To do so, the process of testing is modeled using both analysis and Monte Carlo simulation. Doing so shows that it is possible to determine point estimates for the fraction of infected and symptomatic individuals, the fraction of uninfected and symptomatic individuals, and the ratio of infected asymptomatic individuals to infected symptomatic individuals. Inclusion of symptom status generalizes the test range from an interval to a region in the plane determined by the incidence rate and the ratio of asymptomatic to symptomatic infections; likelihood methods can be used to determine the contour of the rest region. Points on this contour can be used to compute the risk (defined as the probability of including one asymptomatic infected individual) in groups of different sizes. These results have operational implications that include: positivity rate is not incidence rate; symptom status at testing can provide valuable information about asymptomatic infections; collecting information on time since putative virus exposure at testing is valuable for determining point estimates and test ranges; risk is a graded (rather than binary) function of group size; and because the information provided by testing becomes more accurate with more tests but at a decreasing rate, it is possible to over-test fixed spatial regions. The paper concludes with limitations of the method and directions for future work.

The value of chest X-ray and CT severity scoring systems in the diagnosis of COVID-19: A review

Coronavirus disease 2019 (COVID-19) is caused by a coronavirus family member known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The main laboratory test to confirm the quick diagnosis of COVID-19 infection is reverse transcription-polymerase chain reaction (RT-PCR) based on nasal or throat swab sampling. A small percentage of false-negative RT-PCR results have been reported. The RT-PCR test has a sensitivity of 50-72%, which could be attributed to a low viral load in test specimens or laboratory errors. In contrast, chest CT has shown 56-98% of sensitivity in diagnosing COVID-19 at initial presentation and has been suggested to be useful in correcting false negatives from RT-PCR. Chest X-rays and CT scans have been proposed to predict COVID-19 disease severity by displaying the score of lung involvement and thus providing information about the diagnosis and prognosis of COVID-19 infection. As a result, the current study provides a comprehensive overview of the utility of the severity score index using X-rays and CT scans in diagnosing patients with COVID-19 when compared to RT-PCR.

CO-RADS score and its correlation with clinical and laboratory parameters in patients with COVID-19

Background

Polymerase chain reaction (PCR) based SARS-CoV-2 RNA detection and serological antibody tests give a proof of Coronavirus Disease 2019 (COVID-19) infection. Several variables can influence the consequences of these tests. Inflammatory markers among mild and severe patients of COVID-19 showed dissimilarity in inflammatory markers while computed tomography (CT) in patients infected with COVID-19 used to evaluate infection severity. The aim of this study is to investigate the application of the COVID-19 Reporting and Data System (CO-RADS) classification in COVID-19 patients and its relation to clinical and laboratory finding.

Results

One hundred patients suspected to have COVID-19 infection were involved. Their age was 49.6 ± 14.7. Fever and cough were the frequent presenting symptoms. Patients with positive PCR were significantly associated with dyspnea and higher inflammatory markers. Lymphopenia had sensitivity of 63.6% and specificity of 91.7%. Combination of PCR and lymphopenia increased both sensitivity and specificity. CT findings in relation to PCR showed sensitivity of 90.5% and specificity of 25%. CO-RADS score showed positive correlation with age and inflammatory biomarkers and negative correlation with absolute lymphocyte count (ALC).

Conclusions

CT finding was more prominent in older patients with COVID-19 and associated with higher inflammatory biomarkers and lower ALC which were correlated with CO-RADS score. Patients with positive PCR had more symptoms and inflammatory marker. Combination of PCR with either lymphopenia or CT finding had more sensitivity, specificity and accuracy in diagnosis

Clinical features and outcomes of COVID-19 patients hospitalized for psychiatric disorders: a French multi-centered prospective observational study

BACKGROUND

Patients with psychiatric disorders are exposed to high risk of COVID-19 and increased mortality. In this study, we set out to assess the clinical features and outcomes of patients with current psychiatric disorders exposed to COVID-19.

METHODS

This multi-center prospective study was conducted in 22 psychiatric wards dedicated to COVID-19 inpatients between 28 February and 30 May 2020. The main outcomes were the number of patients transferred to somatic care units, the number of deaths, and the number of patients developing a confusional state. The risk factors of confusional state and transfer to somatic care units were assessed by a multivariate logistic model. The risk of death was analyzed by a univariate analysis.

RESULTS

In total, 350 patients were included in the study. Overall, 24 (7%) were transferred to medicine units, 7 (2%) died, and 51 (15%) patients presented a confusional state. Severe respiratory symptoms predicted the transfer to a medicine unit [odds ratio (OR) 17.1; confidence interval (CI) 4.9-59.3]. Older age, an organic mental disorder, a confusional state, and severe respiratory symptoms predicted mortality in univariate analysis. Age >55 (OR 4.9; CI 2.1-11.4), an affective disorder (OR 4.1; CI 1.6-10.9), and severe respiratory symptoms (OR 4.6; CI 2.2-9.7) predicted a higher risk, whereas smoking (OR 0.3; CI 0.1-0.9) predicted a lower risk of a confusional state.

CONCLUSION

COVID-19 patients with severe psychiatric disorders have multiple somatic comorbidities and have a risk of developing a confusional state. These data underline the need for extreme caution given the risks of COVID-19 in patients hospitalized for psychiatric disorders.

Increased KL-6 levels in moderate to severe COVID-19 infection

BACKGROUND

The global coronavirus disease 2019 (COVID-19) has presented significant challenges and created concerns worldwide. Besides, patients who have experienced a SARS-CoV-2 infection could present post-viral complications that can ultimately lead to pulmonary fibrosis. Serum levels of Krebs von den Lungen 6 (KL-6), high molecular weight human MUC1 mucin, are increased in the most patients with various interstitial lung damage. Since its production is raised during epithelial damages, KL-6 could be a helpful non-invasive marker to monitor COVID-19 infection and predict post-infection sequelae.

METHODS

We retrospectively evaluated KL-6 levels of 222 COVID-19 infected patients and 70 healthy control. Serum KL-6, fibrinogen, lactate dehydrogenase (LDH), platelet-lymphocytes ratio (PLR) levels and other biological parameters were analyzed. This retrospective study also characterized the relationships between serum KL-6 levels and pulmonary function variables.

RESULTS

Our results showed that serum KL-6 levels in COVID-19 patients were increased compared to healthy subjects (470 U/ml vs 254 U/ml, P <0.00001). ROC curve analysis enabled us to identify that KL-6 > 453.5 U/ml was associated with COVID-19 (AUC = 0.8415, P < 0.0001). KL-6 level was positively correlated with other indicators of disease severity such as fibrinogen level (r = 0.1475, P = 0.0287), LDH level (r = 0,31, P = 0,004) and PLR level (r = 0.23, P = 0.0005). However, KL-6 levels were not correlated with pulmonary function tests (r = 0.04, P = 0.69).

CONCLUSIONS

KL-6 expression was correlated with several disease severity indicators. However, the association between mortality and long-term follow-up outcomes needs further investigation. More extensive trials are required to prove that KL-6 could be a marker of disease severity in COVID-19 infection.

How accurate are radiography and computed tomography in the diagnosis of COVID-19?-A Bayesian approach

Background

The role of radiology in patients with clinical suspicion of COVID-19 is evolving with scientific evidence, but there are differences in opinion on when and how the technique should be used for clinical diagnosis.

Purpose

To estimate the pre-test and post-test probability that a patient has COVID-19 in the event of a positive and/or negative result from chest X-ray and chest computed tomography (CT) radiological studies, comparing with those of real time polymerase chain reaction (RT-PCR) tests.

Methods

The literature on the sensitivity and specificity of the chest X-ray, chest CT, and RT-PCR was reviewed. Based on these reported data, the likelihood ratios (LR) were estimated and the pre-test probabilities were related to the post-test probabilities after positive or negative results.

Results

The chest X-ray has only a confirmatory value in cases of high suspicion. Chest CT analyses showed that when it is used as a general study, it has almost confirmatory value under high clinical suspicion. A chest CT classified with CO-RADS ≥ 4 has almost a diagnostic certainty of COVID-19 even with moderate or low clinical presumptions, and the CO-RADS 5 classification is almost pathognomonic before any clinical presumption. To rule out COVID-19 completely is only possible in very low clinical assumptions with negative RT-PCR and/or CT.

Conclusions

Chest X-ray and especially CT are fast studies that have the capacity to report high probability of COVID-19, being a real contribution to the concept of "probable case" and allowing support to be installed in an early and timely manner.

Diffusion-Induced Ingress of Angiotensin-Converting Enzyme 2 into the Charge Conducting Path of a Pentacene Channel for Efficient Detection of SARS-CoV-2 in Saliva Samples

Rapid and accurate identification of a pathogen is crucial for disease control and prevention of the epidemic of emerging infectious like SARS-CoV-2. However, no foolproof gold standard assay exists to date. Nucleic acid-based molecular diagnostic tests have been established for identifying COVID-19. However, viral RNAs are highly unstable in handling with poor laboratory procedures, leading to a false negative that accelerates the spread of the disease. Detection of the spike protein (S1) of the SARS-CoV-2 virus through a proper receptor, commonly used in antigen-based rapid testing kits, also suffers from false-negative predictions due to decreasing viral titers in clinical specimens. Organic field-effect transistor (OFET)-based sensors can be highly sensitive upon properly integrating receptors in the conducting channel. This work demonstrates how angiotensin-converting enzyme 2 (ACE2) molecules can be used as receptor molecules of the SARS-CoV-2 virus in the OFET platform. Integration of ACE2 molecules into pentacene grain boundaries has been studied through the statistical analysis of rough surfaces in terms of lateral correlation length and interface width. The uniform coating of ACE2 molecules has been confirmed through growth studies to achieve better ingress of the receptors into the conducting channel at the semiconductor/dielectric interface of OFETs. We have observed less than a minute detection time with 94% sensitivity, which is the highest reported value. The sensor works with a saliva sample, requiring no sample preparation or virus transfer medium. A prototype module developed for remote monitoring confirms the suitability for point-of-care (POC) application at large-scale testing in more crowded areas like airports, railway stations, shopping malls, etc.

Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images. We developed a novel computationally light and optimized deep Convolutional Neural Networks (CNNs) based framework for chest X-ray analysis. We proposed a new COV-Net to learn COVID-specific patterns from chest X-rays and employed several machine learning classifiers to enhance the discrimination power of the presented framework. Systematic exploitation of max-pooling operations facilitates the proposed COV-Net in learning the boundaries of infected patterns in chest X-rays and helps for multi-class classification of two diverse infection types along with normal images. The proposed framework has been evaluated on a publicly available benchmark dataset containing X-ray images of coronavirus-infected, pneumonia-infected, and normal patients. The empirical performance of the proposed method with developed COV-Net and support vector machine is compared with the state-of-the-art deep models which show that the proposed deep hybrid learning-based method achieves 96.69% recall, 96.72% precision, 96.73% accuracy, and 96.71% F-score. For multi-class classification and binary classification of COVID-19 and pneumonia, the proposed model achieved 99.21% recall, 99.22% precision, 99.21% F-score, and 99.23% accuracy.

Analytical performances of different diagnostic methods for SARS-CoV-2 virus - A review

Covid-19 is a dreadful pandemic of the 21st century that has created fear among people, affected the whole world, and taken thousands of lives. It infects the respiratory system and causes flu-type symptoms. According to the WHO reports, 2,082,745 deaths and 96,267,473 confirmed cases were perceived all around the globe till January 22, 2021. The significant roots of transmission are inhalation and direct contact with the infected surface. Its incubation period is 2-14 days and remains asymptomatic in most people. However, no treatment and vaccine are available for the people, so preventive measures like social distancing, wearing personal protective equipment (PPE), and frequent hand-washing are the practical and only options for cure. It has affected every sector of the world, whether it is trade or health all around the world. There is high demand for diagnostic tools as high-scale and expeditious testing is crucial for controlling disease spread; thus, detection methods play an essential role. Like flu, Covid-19 is also detected through RT-PCR, as the World Health Organization (WHO) suggested, but it is time taking and expensive method that many countries cannot afford. A vaccine is a crucial aspect of eradicating disease, and for SARS-CoV-2), plasma therapy and antibiotics therapy are used in the early spreading phase. The later stage involves forming a vaccine based on spike protein, N-protein, and whole-viral antigen that effectively immunizes the population worldwide until herd immunity can be achieved. In this review, we will discuss all possible and developed techniques for identifying SARS-CoV-2 and make a comparison of their specificity, selectivity, and cost; thus, we choose an appropriate method for fast, reliable, and pocket-friendly detection.

An Umbrella Review With Meta-Analysis of Chest Computed Tomography for Diagnosis of COVID-19: Considerations for Trauma Patient Management

Background

RT-PCR testing is the standard for diagnosis of COVID-19, although it has its suboptimal sensitivity. Chest computed tomography (CT) has been proposed as an additional tool with diagnostic value, and several reports from primary and secondary studies that assessed its diagnostic accuracy are already available. To inform recommendations and practice regarding the use of chest CT in the in the trauma setting, we sought to identify, appraise, and summarize the available evidence on the diagnostic accuracy of chest CT for diagnosis of COVID-19, and its application in emergency trauma surgery patients; overcoming limitations of previous reports regarding chest CT accuracy and discussing important considerations regarding its role in this setting.

Methods

We conducted an umbrella review using Living Overview of Evidence platform for COVID-19, which performs regular automated searches in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and more than 30 other sources. The review was conducted following the JBI methodology for systematic reviews. The Grading of Recommendations, Assessment, Development, and Evaluation approach for grading the certainty of the evidence is reported (registered in International Prospective Register of Systematic Reviews, CRD42020198267).

Results

Thirty studies that fulfilled selection criteria were included; 19 primary studies provided estimates of sensitivity (0.91, 95%CI = [0.88-0.93]) and specificity (0.73, 95%CI = [0.61; 0.82]) of chest CT for COVID-19. No correlation was found between sensitivities and specificities (ρ = 0.22, IC95% [-0.33; 0.66]). Diagnostic odds ratio was estimated at: DOR = 27.5, 95%CI (14.7; 48.5). Evidence for sensitivity estimates was graded as MODERATE, and for specificity estimates it was graded as LOW.

Conclusion

The value of chest CT appears to be that of an additional screening tool that can easily detect PCR false negatives, which are reportedly highly frequent. Upon the absence of PCR testing and impossibility to perform RT-PCR in trauma patients, chest CT can serve as a substitute with increased value and easy implementation.

Systematic Review Registration

[www.crd.york.ac.uk/prospero], identifier [CRD42020198267].

Update on the limited sensitivity of computed tomography relative to RT-PCR for COVID-19: a systematic review

Purpose

The global and ongoing COVID-19 outbreak has compelled the need for timely and reliable methods of detection for SARS-CoV-2 infection. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely accepted as a reference standard for COVID-19 diagnosis, several early studies have suggested the superior sensitivity of computed tomography (CT) in identifying SARS-CoV-2 infection. In a previous systematic review, we stratified studies based on risk for bias to evaluate the true sensitivity of CT for detecting SARS-CoV-2 infection. This study revisits our prior analysis, incorporating more current data to assess the sensitivity of CT for COVID-19.

Material and methods

The PubMed and Google Scholar databases were searched for relevant articles published between 1 January 2020, and 25 April 2021. Exclusion criteria included lack of specification regarding whether the study cohort was adult or paediatric, whether patients were symptomatic or asymptomatic, and not identifying the source of RT-PCR specimens. Ultimately, 62 studies were included for systematic review and were subsequently stratified by risk for bias using the QUADAS-2 quality assessment tool. Sensitivity data were extracted for random effects meta-analyses.

Results

The average sensitivity for COVID-19 reported by the high-risk-of-bias studies was 68% [CI: 58, 80; range: 38-96%] for RT-PCR and 91% [CI: 87, 96; range: 47-100%] for CT. The average sensitivity reported by the low-risk-of-bias studies was 84% [CI: 0.75, 0.94; range: 70-97%] for RT-PCR and 78% [CI: 71, 0.86; range: 44-92%] for CT.

Conclusions

On average, the high-risk-of bias studies underestimated the sensitivity of RT-PCR and overestimated the sensitivity of CT for COVID-19. Given the incorporation of recently published low-risk-of-bias articles, the sensitivities according to low-risk-of-bias studies for both RT-PCR and CT were higher than previously reported.

Epidemiology, Clinico-Pathological Characteristics, and Comorbidities of SARS-CoV-2-Infected Pakistani Patients

SARS-CoV-2 is a causative agent for COVID-19 disease, initially reported from Wuhan, China. The infected patients experienced mild to severe symptoms, resulting in several fatalities due to a weak understanding of its pathogenesis, which is the same even to date. This cross-sectional study has been designed on 452 symptomatic mild-to-moderate and severe/critical patients to understand the epidemiology and clinical characteristics of COVID-19 patients with their comorbidities and response to treatment. The mean age of the studied patients was 58 ± 14.42 years, and the overall male to female ratio was 61.7 to 38.2%, respectively. In total, 27.3% of the patients had a history of exposure, and 11.9% had a travel history, while for 60% of patients, the source of infection was unknown. The most prevalent signs and symptoms in ICU patients were dry cough, myalgia, shortness of breath, gastrointestinal discomfort, and abnormal chest X-ray (p < 0.001), along with a high percentage of hypertension (p = 0.007) and chronic obstructive pulmonary disease (p = 0.029) as leading comorbidities. The complete blood count indicators were significantly disturbed in severe patients, while the coagulation profile and D-dimer values were significantly higher in mild-to-moderate (non-ICU) patients (p < 0.001). The serum creatinine (1.22 μmol L^-1; p = 0.016) and lactate dehydrogenase (619 μmol L^-1; p < 0.001) indicators were significantly high in non-ICU patients, while raised values of total bilirubin (0.91 μmol L^-1; p = 0.054), C-reactive protein (84.68 mg L^-1; p = 0.001), and ferritin (996.81 mg L^-1; p < 0.001) were found in ICU patients. The drug dexamethasone was the leading prescribed and administrated medicine to COVID-19 patients, followed by remdesivir, meropenem, heparin, and tocilizumab, respectively. A characteristic pattern of ground glass opacities, consolidation, and interlobular septal thickening was prominent in severely infected patients. These findings could be used for future research, control, and prevention of SARS-CoV-2-infected patients.

Diagnostic Performance of Antigen Rapid Diagnostic Tests, Chest Computed Tomography, and Lung Point-of-Care-Ultrasonography for SARS-CoV-2 Compared with RT-PCR Testing: A Systematic Review and Network Meta-Analysis

(1) Background: The comparative performance of various diagnostic methods for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remains unclear. This study aimed to investigate the comparison of the 3 index test performances of rapid antigen diagnostic tests (RDTs), chest computed tomography (CT), and lung point-of-care-ultrasonography (US) with reverse transcription-polymerase chain reaction (RT-PCR), the reference standard, to provide more evidence-based data on the appropriate use of these index tests. (2) Methods: We retrieved data from electronic literature searches of PubMed, Cochrane Library, and EMBASE from 1 January 2020, to 1 April 2021. Diagnostic performance was examined using bivariate random-effects diagnostic test accuracy (DTA) and Bayesian network meta-analysis (NMA) models. (3) Results: Of the 3992 studies identified in our search, 118 including 69,445 participants met our selection criteria. Among these, 69 RDT, 38 CT, and 15 US studies in the pairwise meta-analysis were included for DTA with NMA. CT and US had high sensitivity of 0.852 (95% credible interval (CrI), 0.791-0.914) and 0.879 (95% CrI, 0.784-0.973), respectively. RDT had high specificity, 0.978 (95% CrI, 0.960-0.996). In accuracy assessment, RDT and CT had a relatively higher than US. However, there was no significant difference in accuracy between the 3 index tests. (4) Conclusions: This meta-analysis suggests that, compared with the reference standard RT-PCR, the 3 index tests (RDTs, chest CT, and lung US) had similar and complementary performances for diagnosis of SARS-CoV-2 infection. To manage and control COVID-19 effectively, future large-scale prospective studies could be used to obtain an optimal timely diagnostic process that identifies the condition of the patient accurately.

Thoracic imaging tests for the diagnosis of COVID-19

BACKGROUND

Our March 2021 edition of this review showed thoracic imaging computed tomography (CT) to be sensitive and moderately specific in diagnosing COVID-19 pneumonia. This new edition is an update of the review.

OBJECTIVES

Our objectives were to evaluate the diagnostic accuracy of thoracic imaging in people with suspected COVID-19; assess the rate of positive imaging in people who had an initial reverse transcriptase polymerase chain reaction (RT-PCR) negative result and a positive RT-PCR result on follow-up; and evaluate the accuracy of thoracic imaging for screening COVID-19 in asymptomatic individuals. The secondary objective was to assess threshold effects of index test positivity on accuracy.

SEARCH METHODS

We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 17 February 2021. We did not apply any language restrictions.

SELECTION CRITERIA

We included diagnostic accuracy studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19. Studies had to assess chest CT, chest X-ray, or ultrasound of the lungs for the diagnosis of COVID-19, use a reference standard that included RT-PCR, and report estimates of test accuracy or provide data from which we could compute estimates. We excluded studies that used imaging as part of the reference standard and studies that excluded participants with normal index test results.

DATA COLLECTION AND ANALYSIS

The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using QUADAS-2. We presented sensitivity and specificity per study on paired forest plots, and summarized pooled estimates in tables. We used a bivariate meta-analysis model where appropriate.

MAIN RESULTS

We included 98 studies in this review. Of these, 94 were included for evaluating the diagnostic accuracy of thoracic imaging in the evaluation of people with suspected COVID-19. Eight studies were included for assessing the rate of positive imaging in individuals with initial RT-PCR negative results and positive RT-PCR results on follow-up, and 10 studies were included for evaluating the accuracy of thoracic imaging for imagining asymptomatic individuals. For all 98 included studies, risk of bias was high or unclear in 52 (53%) studies with respect to participant selection, in 64 (65%) studies with respect to reference standard, in 46 (47%) studies with respect to index test, and in 48 (49%) studies with respect to flow and timing. Concerns about the applicability of the evidence to: participants were high or unclear in eight (8%) studies; index test were high or unclear in seven (7%) studies; and reference standard were high or unclear in seven (7%) studies. Imaging in people with suspected COVID-19 We included 94 studies. Eighty-seven studies evaluated one imaging modality, and seven studies evaluated two imaging modalities. All studies used RT-PCR alone or in combination with other criteria (for example, clinical signs and symptoms, positive contacts) as the reference standard for the diagnosis of COVID-19. For chest CT (69 studies, 28285 participants, 14,342 (51%) cases), sensitivities ranged from 45% to 100%, and specificities from 10% to 99%. The pooled sensitivity of chest CT was 86.9% (95% confidence interval (CI) 83.6 to 89.6), and pooled specificity was 78.3% (95% CI 73.7 to 82.3). Definition for index test positivity was a source of heterogeneity for sensitivity, but not specificity. Reference standard was not a source of heterogeneity. For chest X-ray (17 studies, 8529 participants, 5303 (62%) cases), the sensitivity ranged from 44% to 94% and specificity from 24 to 93%. The pooled sensitivity of chest X-ray was 73.1% (95% CI 64. to -80.5), and pooled specificity was 73.3% (95% CI 61.9 to 82.2). Definition for index test positivity was not found to be a source of heterogeneity. Definition for index test positivity and reference standard were not found to be sources of heterogeneity. For ultrasound of the lungs (15 studies, 2410 participants, 1158 (48%) cases), the sensitivity ranged from 73% to 94% and the specificity ranged from 21% to 98%. The pooled sensitivity of ultrasound was 88.9% (95% CI 84.9 to 92.0), and the pooled specificity was 72.2% (95% CI 58.8 to 82.5). Definition for index test positivity and reference standard were not found to be sources of heterogeneity. Indirect comparisons of modalities evaluated across all 94 studies indicated that chest CT and ultrasound gave higher sensitivity estimates than X-ray (P = 0.0003 and P = 0.001, respectively). Chest CT and ultrasound gave similar sensitivities (P=0.42). All modalities had similar specificities (CT versus X-ray P = 0.36; CT versus ultrasound P = 0.32; X-ray versus ultrasound P = 0.89). Imaging in PCR-negative people who subsequently became positive For rate of positive imaging in individuals with initial RT-PCR negative results, we included 8 studies (7 CT, 1 ultrasound) with a total of 198 participants suspected of having COVID-19, all of whom had a final diagnosis of COVID-19. Most studies (7/8) evaluated CT. Of 177 participants with initially negative RT-PCR who had positive RT-PCR results on follow-up testing, 75.8% (95% CI 45.3 to 92.2) had positive CT findings. Imaging in asymptomatic PCR-positive people For imaging asymptomatic individuals, we included 10 studies (7 CT, 1 X-ray, 2 ultrasound) with a total of 3548 asymptomatic participants, of whom 364 (10%) had a final diagnosis of COVID-19. For chest CT (7 studies, 3134 participants, 315 (10%) cases), the pooled sensitivity was 55.7% (95% CI 35.4 to 74.3) and the pooled specificity was 91.1% (95% CI 82.6 to 95.7).

AUTHORS' CONCLUSIONS

Chest CT and ultrasound of the lungs are sensitive and moderately specific in diagnosing COVID-19. Chest X-ray is moderately sensitive and moderately specific in diagnosing COVID-19. Thus, chest CT and ultrasound may have more utility for ruling out COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. The uncertainty resulting from high or unclear risk of bias and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results.

Probing the Immune System Dynamics of the COVID-19 Disease for Vaccine Designing and Drug Repurposing Using Bioinformatics Tools

The pathogenesis of COVID-19 is complicated by immune dysfunction. The impact of immune-based therapy in COVID-19 patients has been well documented, with some notable studies on the use of anti-cytokine medicines. However, the complexity of disease phenotypes, patient heterogeneity and the varying quality of evidence from immunotherapy studies provide problems in clinical decision-making. This review seeks to aid therapeutic decision-making by giving an overview of the immunological responses against COVID-19 disease that may contribute to the severity of the disease. We have extensively discussed theranostic methods for COVID-19 detection. With advancements in technology, bioinformatics has taken studies to a higher level. The paper also discusses the application of bioinformatics and machine learning tools for the diagnosis, vaccine design and drug repurposing against SARS-CoV-2.

PERFORMANCE EVALUATION OF SIX DIFFERENT SARS-CoV-2 ANTIBODY IMMUNOASSAYS: DISEASE SEVERITY AND SERUM SAMPLING TIME AFFECT THE SENSITIVITY

Comparative validation data and clinical performance data are essential for the reliable interpretation of SARS-CoV-2 antibody test results. This study aimed to assess the performance of six SARS-CoV-2 IgG immunoassays in different disease severity settings. Four automated chemiluminescence immunoassays Access (Beckman Coulter), Architect (Abbott), Atellica-IM (Siemens) and Elecsys (Roche) and two ELISA assays (SARS-CoV-2 IgG-S1-based and NCP IgG, Euroimmun) were evaluated in 143 patients and 50 pre-pandemic control sera. Accuracy and precision tests were performed for validation. Overall sensitivity differed between 73.38-88.65%, being higher in spike protein-based assays. Specificity was ≥ 98% in all immunoassays. IgG response was lower for the samples taken <20 days post-symptom onset (87.30%) than for the samples taken ≥20 days post-symptom onset (94.80%). Higher rate of antibody was detected in the clinically moderate disease group. In the asymptomatic and mild group more antibody positivity was detected with spike protein-based assays. Clinical performance of the immunoassays differs according to disease severity and antigen targeted; moderate disease leading to highest rate of IgG response. All the assays tested were eligible for the detection of SARS-CoV-2 IgG however, spike-based assays revealed relatively higher sensitivity than the nucleoprotein-based assays particularly in the asymptomatic and mild disease severity.

A rapid RNA extraction-free lateral flow assay for molecular point-of-care detection of SARS-CoV-2 augmented by chemical probes

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the major shortcoming of healthcare systems globally in their inability to diagnose the disease rapidly and accurately. At present, the molecular approaches for diagnosing COVID-19 primarily use reverse transcriptase polymerase chain reaction (RT-PCR) to create and amplify cDNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA. Although molecular tests are reported to be specific, false negatives are quite common. Furthermore, literally all these tests require a step involving RNA isolation which does not make them point-of-care (POC) in the true sense. Here, we report a lateral flow strip-based RNA extraction and amplification-free nucleic acid test (NAT) for rapid diagnosis of positive COVID-19 cases at POC. The assay uses highly specific 6-carboxyfluorescein (6-FAM) and biotin labeled antisense oligonucleotides (ASOs) as probes those are designed to target N-gene sequence of SARS-CoV-2. Additionally, we utilized cysteamine capped gold-nanoparticles (Cyst-AuNPs) to augment the signal further for enhanced sensitivity. Without any large-stationary equipment and highly trained staffers, the entire sample-to-answer approach in our case would take less than 30 min from a patient swab sample collection to final diagnostic result. Moreover, when evaluated with 60 clinical samples and verified with an FDA-approved TaqPath RT-PCR kit for COVID-19 diagnosis, the assay obtained almost 99.99% accuracy and specificity. We anticipate that the newly established low-cost amplification-free detection of SARS-CoV-2 RNA will aid in the development of a platform technology for rapid and POC diagnosis of COVID-19 and other pathogens.

[Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

Effectiveness of tests to detect the presence of SARS-CoV-2 virus, and antibodies to SARS-CoV-2, to inform COVID-19 diagnosis: a rapid systematic review

OBJECTIVES

We undertook a rapid systematic review with the aim of identifying evidence that could be used to answer the following research questions: (1) What is the clinical effectiveness of tests that detect the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to inform COVID-19 diagnosis? (2) What is the clinical effectiveness of tests that detect the presence of antibodies to the SARS-CoV-2 virus to inform COVID-19 diagnosis?

DESIGN AND SETTING

Systematic review and meta-analysis of studies of diagnostic test accuracy. We systematically searched for all published evidence on the effectiveness of tests for the presence of SARS-CoV-2 virus, or antibodies to SARS-CoV-2, up to 4 May 2020, and assessed relevant studies for risks of bias using the QUADAS-2 framework.

MAIN OUTCOME MEASURES

Measures of diagnostic accuracy (sensitivity, specificity, positive/negative predictive value) were the main outcomes of interest. We also included studies that reported influence of testing on subsequent patient management, and that reported virus/antibody detection rates where these facilitated comparisons of testing in different settings, different populations or using different sampling methods.

RESULTS

38 studies on SARS-CoV-2 virus testing and 25 studies on SARS-CoV-2 antibody testing were identified. We identified high or unclear risks of bias in the majority of studies, most commonly as a result of unclear methods of patient selection and test conduct, or because of the use of a reference standard that may not definitively diagnose COVID-19. The majority were in hospital settings, in patients with confirmed or suspected COVID-19 infection. Pooled analysis of 16 studies (3818 patients) estimated a sensitivity of 87.8% (95% CI 81.5% to 92.2%) for an initial reverse-transcriptase PCR test. For antibody tests, 10 studies reported diagnostic accuracy outcomes: sensitivity ranged from 18.4% to 96.1% and specificity 88.9% to 100%. However, the lack of a true reference standard for SARS-CoV-2 diagnosis makes it challenging to assess the true diagnostic accuracy of these tests. Eighteen studies reporting different sampling methods suggest that for virus tests, the type of sample obtained/type of tissue sampled could influence test accuracy. Finally, we searched for, but did not identify, any evidence on how any test influences subsequent patient management.

CONCLUSIONS

Evidence is rapidly emerging on the effectiveness of tests for COVID-19 diagnosis and management, but important uncertainties about their effectiveness and most appropriate application remain. Estimates of diagnostic accuracy should be interpreted bearing in mind the absence of a definitive reference standard to diagnose or rule out COVID-19 infection. More evidence is needed about the effectiveness of testing outside of hospital settings and in mild or asymptomatic cases. Implementation of public health strategies centred on COVID-19 testing provides opportunities to explore these important areas of research.

Estimate false-negative RT-PCR rates for SARS-CoV-2. A systematic review and meta-analysis

BACKGROUND

Molecular-based tests used to identify symptomatic or asymptomatic patients infected by SARS-CoV-2 are characterized by high specificity but scarce sensitivity, generating false-negative results. We aimed to estimate, through a systematic review of the literature, the rate of RT-PCR false negatives at initial testing for COVID-19.

METHODS

We systematically searched Pubmed, Embase and CENTRAL as well as a list of reference literature. We included observational studies that collected samples from respiratory tract to detect SARS-CoV-2 RNA using RT-PCR, reporting the number of false-negative subjects and the number of final patients with a COVID-19 diagnosis. Reported rates of false negatives were pooled in a meta-analysis as appropriate. We assessed the risk of bias of included studies and graded the quality of evidence according to the GRADE method. All information in this article is current up to February 2021.

RESULTS

We included 32 studies, enrolling more than 18,000 patients infected by SARS-CoV-2. The overall false-negative rate was 0.12 (95%CI from 0.10 to 0.14) with very low certainty of evidence. The impact of misdiagnoses was estimated according to disease prevalence; a range between 2 and 58/1,000 subjects could be misdiagnosed with a disease prevalence of 10%, increasing to 290/1,000 misdiagnosed subjects with a disease prevalence of 50%.

CONCLUSIONS

This systematic review showed that up to 58% of COVID-19 patients may have initial false-negative RT-PCR results, suggesting the need to implement a correct diagnostic strategy to correctly identify suspected cases, thereby reducing false-negative results and decreasing the disease burden among the population.

A novel use for routine CBCT imaging during radiotherapy to detect COVID-19

INTRODUCTION

Thoracic CT is a useful tool in the early diagnosis of patients with COVID-19. Typical appearances include patchy ground glass shadowing. Thoracic radiotherapy uses daily cone beam CT imaging (CBCT) to check for changes in patient positioning and anatomy prior to treatment through a qualitative assessment of lung appearance by radiographers. Observation of changes related to COVID-19 infection during this process may facilitate earlier testing improving patient management and staff protection.

METHODS

A tool was developed to create overview reports for all CBCTs for each patient throughout their treatment. Reports contain coronal maximum intensity projection (MIP's) of all CBCTs and plots of lung density over time. A single therapeutic radiographer undertook a blinded off-line audit that reviewed 150 patient datasets for tool optimisation in which medical notes were compared to image findings. This cohort included 75 patients treated during the pandemic and 75 patients treated between 2014 and 2017. The process was repeated retrospectively on a subset of the 285 thoracic radiotherapy patients treated between January-June 2020 to assess the efficiency of the tool and process.

RESULTS

Three patients in the n = 150 optimisation cohort had confirmed COVID-19 infections during their radiotherapy. Two of these were detected by the reported image assessment process. The third case was not detected on CBCT due to minimal density changes in the visible part of the lungs. Within the retrospective cohort four patients had confirmed COVID-19 based on RT-PCR tests, three of which were retrospectively detected by the reported process.

CONCLUSION

The preliminary results indicate that the presence of COVID-19 can be detected on CBCT by therapeutic radiographers.

IMPLICATIONS FOR PRACTICE

This process has now been extended to clinical service with daily assessments of all thoracic CBCTs. Changes noted are referred for oncologist review.

User experience of home-based AbC-19 SARS-CoV-2 antibody rapid lateral flow immunoassay test

The urgent need to scale up testing capacity during the COVID-19 pandemic has prompted the rapid development of point-of-care diagnostic tools such as lateral flow immunoassays (LFIA) for large-scale community-based rapid testing. However, studies of how the general public perform when using LFIA tests in different environmental settings are scarce. This user experience (UX) study of 264 participants in Northern Ireland aimed to gather a better understanding of how self-administered LFIA tests were performed by the general public at home. The UX performance was assessed via analysis of a post-test questionnaire including 30 polar questions and 11 7-point Likert scale questions, which covers the multidimensional aspects of UX in terms of ease of use, effectiveness, efficiency, accuracy and satisfaction. Results show that 96.6% of participants completed the test with an overall average UX score of 95.27% [95% confidence interval (CI) 92.71–97.83%], which suggests a good degree of user experience and effectiveness. Efficiency was assessed based on the use of physical resources and human support received, together with the mental effort of self-administering the test measured via NASA Task Load Index (TLX). The results for six TLX subscales show that the participants scored the test highest for mental demand and lowest for physical demand, but the average TLX score suggests that the general public have a relatively low level of mental workload when using LFIA self-testing at home. Five printed LFIA testing results (i.e. the ‘simulated’ results) were used as the ground truth to assess the participant’s performance in interpreting the test results. The overall agreement (accuracy) was 80.63% [95% CI 75.21–86.05%] with a Kappa score 0.67 [95% CI 0.58–0.75] indicating substantial agreement. The users scored lower in confidence when interpreting test results that were weak positive cases (due to the relatively low signal intensity in the test-line) compared to strong positive cases. The end-users also found that the kit was easier to use than they expected (p < 0.001) and 231 of 264 (87.5%) reported that the test kit would meet their requirements if they needed an antibody testing kit. The overall findings provide an insight into the opportunities for improving the design of self-administered SARS-CoV-2 antibody testing kits for the general public and to inform protocols for future UX studies of LFIA rapid test kits.

Taguchi optimization of integrated flow microfluidic biosensor for COVID-19 detection

In this research, Taguchi's method was employed to optimize the performance of a microfluidic biosensor with an integrated flow confinement for rapid detection of the SARS-CoV-2. The finite element method was used to solve the physical model which has been first validated by comparison with experimental results. The novelty of this study is the use of the Taguchi approach in the optimization analysis. An L 8 2 7 orthogonal array of seven critical parameters-Reynolds number (Re), Damköhler number (Da), relative adsorption capacity ( σ ), equilibrium dissociation constant (K_D), Schmidt number (Sc), confinement coefficient (α) and dimensionless confinement position (X), with two levels was designed. Analysis of variance (ANOVA) methods are also used to calculate the contribution of each parameter. The optimal combination of these key parameters was Re = 10^-2, Da = 1000, σ = 0.5, K _D  = 5, Sc = 10⁵, α = 2 and X = 2 to achieve the lowest dimensionless response time (0.11). Among the all-optimization factors, the relative adsorption capacity ( σ ) has the highest contribution (37%) to the reduction of the response time, while the Schmidt number (Sc) has the lowest contribution (7%).

A Large Retrospective Study of Epidemiological Characteristics of COVID-19 Patients in the North of Iran: Association between SARS-CoV-2 RT-PCR Ct Values with Demographic Data

OBJECTIVES

To avoid worsening from mild, moderate, and severe diseases and to reduce mortality, it is necessary to identify the subpopulation that is more vulnerable to the development of COVID-19 unfavorable consequences. This study aims to investigate the demographic information, prevalence rates of common comorbidities among negative and positive real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) patients, and the association between SARS-CoV-2 cycle threshold (Ct) at hospital admission, demographic data, and outcomes of the patients in a large population in Northern Iran.

METHODS

This large retrospective cross-sectional study was performed from 7 March to 20 December 2020. Demographic data, including gender, age, underlying diseases, clinical outcomes, and Ct values, were obtained from 8,318 cases suspected of COVID-19, who were admitted to four teaching hospitals affiliated to Babol University of Medical Sciences (MUBABOL), in the north of Iran.

RESULTS

Since 7 March 2020, the data were collected from 8,318 cases suspected of COVID-19 (48.5% female and 51.5% male) with a mean age of 53 ± 25.3 years. Among 8,318 suspected COVID-19 patients, 3,250 (39.1%) had a positive rRT-PCR result; 1,632 (50.2%) patients were male and 335 (10.3%) patients died during their hospital stay. The distribution of positive rRT-PCR revealed that most patients (464 (75.7%)) had a Ct between 21 and 30 (Group B).

CONCLUSION

Elderly patients, lower Ct, patients having at least one comorbidity, and male cases were significantly associated with increased risk for COVID-19-related mortality. Moreover, mortality was significantly higher in patients with diabetes, kidney disease, and respiratory disease.

Diagnosis of COVID-19 in symptomatic patients: An updated review

A group of pneumonia patients was detected in Hubei Province, in China in December 2019. The etiology of the disease was unknown. Later, the researchers diagnosed the novel Coronavirus as the causal agent of this respiratory disease. On February 12th 2020, the World Health Organization (WHO) officially named this disease Coronavirus disease 2019 (COVID-19). Consequently, the disease spread globally and became a pandemic. As there is no specific treatment for the symptomatic patients and several vaccines are approved by WHO, the efficacy and effectiveness of these vaccines are not fully understood yet and the availability of these vaccines are very limited. In addition, new variants and mutants of SARS-CoV-2 are thought to be able to evade the immune system of the host. So, diagnosis and isolation of infected individuals is advised. Currently, real-time reverse transcription-polymerase chain reaction (RT-PCR) is considered the gold standard method to detect novel Coronavirus, however, there are few limitations associated with RT-PCR such as false-negative results. This demanded another diagnostic tool to detect and isolate COVID-19 early and accurately. Chest computed tomography (CT) became another option to diagnose COVID-19 patients accurately (about 98% sensitivity). However, it did not apply to the asymptomatic carriers and sometimes the results were misinterpreted as from other groups of Coronavirus infection. The combination of RT-PCR and chest CT might be the best option in detecting novel Coronavirus infection early and accurately thereby allowing adaptation of measures for the prevention and control of the COVID-19.

A Cold Chain-Independent Specimen Collection and Transport Medium Improves Diagnostic Sensitivity and Minimizes Biosafety Challenges of COVID-19 Molecular Diagnosis

Equitable and timely access to COVID-19-related care has emerged as a major challenge, especially in developing and low-income countries. In India, ∼65% of the population lives in villages where infrastructural constraints limit the access to molecular diagnostics of COVID-19 infection. Especially, the requirement of a cold chain transport for sustained sample integrity and associated biosafety challenges pose major bottlenecks to the equitable access. Here, we developed an innovative clinical specimen collection medium, named SupraSens microbial transport medium (SSTM). SSTM allowed a cold chain-independent transport at a wide temperature range (15°C to 40°C) and directly inactivated SARS-CoV-2 (<15 min). Evaluation of SSTM compared to commercial viral transport medium (VTM) in field studies (n = 181 patients) highlighted that, for the samples from same patients, SSTM could capture more symptomatic (∼26.67%, 4/15) and asymptomatic (52.63%, 10/19) COVID-19 patients. Compared to VTM, SSTM yielded significantly lower quantitative PCR (qPCR) threshold cycle (Ct) values (mean ΔCt > -3.50), thereby improving diagnostic sensitivity of SSTM (18.79% [34/181]) versus that of VTM (11.05% [20/181]). Overall, SSTM had detection of COVID-19 patients 70% higher than that of VTM. Since the logistical and infrastructural constraints are not unique to India, our study highlights the invaluable global utility of SSTM as a key to accurately identify those infected and control COVID-19 transmission. Taken together, our data provide a strong justification to the adoption of SSTM for sample collection and transport during the pandemic. IMPORTANCE Approximately forty-four percent of the global population lives in villages, including 59% in Africa (https://unhabitat.org/World%20Cities%20Report%202020). The fast-evolving nature of SARS-CoV-2 and its extremely contagious nature warrant early and accurate COVID-19 diagnostics across rural and urban population as a key to prevent viral transmission. Unfortunately, lack of adequate infrastructure, including the availability of biosafety-compliant facilities and an end-to-end cold chain availability for COVID-19 molecular diagnosis, limits the accessibility of testing in these countries. Here, we fulfill this urgent unmet need by developing a sample collection and transport medium, SSTM, that does not require cold chain, neutralizes the virus quickly, and maintains the sample integrity at broad temperature range without compromising sensitivity. Further, we observed that use of SSTM in field studies during pandemic improved the diagnostic sensitivity, thereby establishing the feasibility of molecular testing even in the infrastructural constraints of remote, hilly, or rural communities in India and elsewhere.

Diagnostic performance of thorax CT in mildly symptomatic COVID-19 patients: The importance of atypical CT findings

OBJECTIVE:

Computed tomography of the thorax (Thorax CT) is frequently used to diagnose viral pneumonia in moderate to severe COVID-19 patients, but its diagnostic performance in mildly symptomatic COVID-19 patients is still unclear. Assessing the diagnostic performance of thorax CT in mildly symptomatic COVID-19 patients was the purpose of our study.

METHODS:

Mildly symptomatic and clinically stable, suspected COVID-19 patients scanned with Thorax CTs between March 11, 2020, and April 13, 2020, were included in this study. The sensitivity, specificity, positive and negative likelihood ratios, positive and negative predictive values, and the respective accuracies were calculated for diagnostic purposes.

RESULTS:

Among the 1119 patients enrolled in our study, abnormal thorax CT scans were 527 out of which 363/527 (68.9%) had typical CT features for COVID-19. According to analysis of typical COVID findings, sensitivity, specificity, positive predictive values, negative predictive value, and the accuracy of Thorax CTs with were 51.45%, 86.07%, 78.24%, 64.55%, and 68.99%, respectively. When typical CT findings and atypical CT findings were combined for the statistical analysis, the sensitivity, specificity, and accuracy observed 68.84%, 74%, and 71.49%.

CONCLUSION:

Diagnosing pneumonia can be challenging in mildly symptomatic COVID-19 patients since the Reverse Transcription Polymerase Chain Reaction test results, when compared with symptoms are not always evident. According to our study, thorax CT sensitivity was higher when atypical COVID-19 CT findings were included compared to those with typical COVID-19 CT findings alone. Our study which included the largest number of patients among all other similar studies indicates that not only typical but also atypical CT findings should be considered for an accured diagnosis of COVID-19 pneumonia.

Chest CT in COVID-19 pneumonia: correlations of imaging findings in clinically suspected but repeatedly RT-PCR test-negative patients

Background

To emphasize the importance of CT in the diagnosis of COVID-19 disease by comparing the thoracic CT findings of COVID-19 patients with positive RT-PCR results and patients with clinical suspicion of COVID-19 but with negative RT-PCR results.

Results

In our study, COVID-19 patients with positive RT-PCR results (RT-PCR (+) group) and patients with clinical suspicion of COVID-19 but negative RT-PCR results (RT-PCR (−) group) were compared in terms of CT findings. In CT images, ground-glass opacity and ground-glass opacity + patchy consolidation were the most common lesion patterns in both groups. No statistically significant differences in the rates and types of lesion patterns were observed between the two groups. In both groups, lesion distributions and distribution patterns were similarly frequent in the bilateral, peripheral, and lower lobe distributions. Among the 39 patients who underwent follow-up CT imaging in the first or second month, a regression in lesion number and density was detected in 18 patients from both groups. Consolidations were completely resorbed in 16 of these patients, and five patients had newly developed fibrotic changes. The follow-up CT examination of 16 patients was normal.

Conclusions

Due to the false-negative rate of RT-PCR tests caused by various reasons, clinically suspected COVID-19 patients with a contact history should be examined with CT scans, even if RT-PCR tests are negative. If the CT findings are positive, these patients should not be removed from isolation.

Olfactory dysfunction as a screening tool for mild and moderate cases of COVID-19: a single-center prevalence study of 646 patients in flu clinic

Background

To evaluate the prevalence of olfactory dysfunction (OD) in the Indian population and to establish olfactory dysfunction as a screening tool in COVID-19-positive patients. Data was collected using a questionnaire from laboratory-confirmed COVID-19 patients. The patient’s demographic and clinical details were analyzed to calculate the prevalence of olfactory dysfunction, general symptoms like fever, cough, malaise, diarrhea, along with the sinonasal symptoms. All the symptoms were self-reported, and no objective tests were carried out.

Results

Out of 646 laboratory-confirmed cases of COVID-19 infection, olfactory dysfunction was self-reported by 465 (72%) patients and gustatory dysfunction (GD) was seen in 406 (62.8%) patients. The affected males (416) were proportionately more than females (230), with the mean age of our study population being 39.47 ± 13.85 (range 18–85 years). The most common symptoms were myalgia (n = 494, 76.5%), cough (n = 471, 72.9%), and fever (n = 444, 68.7%). Out of 465 patients with olfactory dysfunction, only 108 (23.2%) reported nasal obstruction. Five hundred thirty-three (82.5%) RT-PCR-positive patients did not give a history of smoking; however, co-morbidity was reported by 163 patients, of which 117 were found to have olfactory dysfunction. One hundred seventy (26.3%) patients gave a positive contact history.

13.6% reported olfactory dysfunction as their first symptom. A positive association was seen between olfactory dysfunction and gustatory dysfunction

Conclusions

Our study demonstrates a high prevalence of 72% in the Indian population. We recommend that anosmia be used as a screening tool to identify mild to moderate cases of COVID-19.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.

Chest CT manifestations with emphasis on the role of CT scoring and serum ferritin/lactate dehydrogenase in prognosis of coronavirus disease 2019 (COVID-19)

Background

In March 2020, the World Health Organization announced coronavirus disease 2019 (COVID-19) a pandemic, and because of the primary pulmonary manifestations of the disease, chest CT is essential in the evaluation of those patients. The aim of the study was to evaluate the role of chest CT findings and chest CT scoring along with serum ferritin and LDH in the prognosis of COVID-19 patients in a cohort of the Egyptian population.

Results

This retrospective study included 250 patients with positive RT-PCR for COVID-19, 138 males [55.2%] and 112 females [44.8%], age range 17–82 years with median 49.5. Two hundred patients had a positive significant correlation between age, serum ferritin, serum LDH, and CT score. Bilateral affection was 88% while unilaterality was 12%, and peripheral chest CT findings were stratified as follows: mild [score from 1 to 10], 114 patients [57%]; moderate [score from 11 to 19], 65 patients [32.5%]; and severe [score from 20 to 25], 21 patients [10.5%]. In severe cases, males constitute 85.7% while females were only 14.3%. Statistical and central distribution was 67%, peripheral was 31%, and central was 2%. Ground glass opacity (GGO) was the highest pattern 39.2%, consolidation 31.2%, fibrosis 15.2%, and CP 13.7%, with lymph nodes only 0.6%. Fifteen cases [6%] were critical; all showed severe scores ranging from 21 to 23 with three times increase in serum ferritin and four times increase in LDH. A follow-up study done to 8 cases [3.2%] showed an increase in CT scoring, serum ferritin, and serum LDH.

Conclusion

Chest CT findings are crucial for early diagnosis of COVID-19 disease especially for asymptomatic patients with old age and male sex considered risk factors for poor prognosis. Chest CT score, serum ferritin, and serum LDH help in predicting the short-term outcome of the patients aiming to decrease both morbidity and mortality.

Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

Clinical methods are used for diagnosing COVID-19 infected patients, but reports posit that, several people who were initially tested positive of COVID-19, and who had some underlying diseases, turned out having negative results after further tests. Therefore, the performance of clinical methods is not always guaranteed. Moreover, chest X-ray image data of COVID-19 infected patients are mostly used in the computational models for COVID-19 diagnosis, while the use of common symptoms, such as fever, cough, fatigue, muscle aches, headache, etc. in computational models is not yet reported. In this study, we employed seven classification algorithms to empirically test and verify their efficacy when applied to diagnose COVID-19 using the aforementioned symptoms. We experimented with Logistic Regression (LR), Support Vector Machine (SVM), Naïve Byes (NB), Decision Tree (DT), Multilayer Perceptron (MLP), Fuzzy Cognitive Map (FCM) and Deep Neural Network (DNN) algorithms. The techniques were subjected to random undersampling and oversampling. Our results showed that with class imbalance, MLP and DNN outperform others. However, without class imbalance, MLP, FCM and DNN outperform others with the use of random undersampling, but DNN has the best performance by utilizing random oversampling. This study identified MLP, FCM and DNN as better classifiers over LR, NB, DT and SVM, so that healthcare software system developers can adopt them to develop intelligence-based expert systems which both medical personnel and patients can use for differential diagnosis of COVID-19 based on the aforementioned symptoms. However, the test of performance must not be limited to the traditional performance metrics.

Recent findings and applications of biomedical engineering for COVID-19 diagnosis: a critical review

COVID-19 is one of the most severe global health crises that humanity has ever faced. Researchers have restlessly focused on developing solutions for monitoring and tracing the viral culprit, SARS-CoV-2, as vital steps to break the chain of infection. Even though biomedical engineering (BME) is considered a rising field of medical sciences, it has demonstrated its pivotal role in nurturing the maturation of COVID-19 diagnostic technologies. Within a very short period of time, BME research applied to COVID-19 diagnosis has advanced with ever-increasing knowledge and inventions, especially in adapting available virus detection technologies into clinical practice and exploiting the power of interdisciplinary research to design novel diagnostic tools or improve the detection efficiency. To assist the development of BME in COVID-19 diagnosis, this review highlights the most recent diagnostic approaches and evaluates the potential of each research direction in the context of the pandemic.

Spectrum of CT appearance and CT severity index of COVID-19 pulmonary infection in correlation with age, sex, and PCR test: an Iraqi experience

Background

Since June 2020, an explosion in number of new COVID-19 patients has been reported in Iraq with a steady increment in new daily reported cases over the next 3 months. The limited number of PCR kits in the country and the increment in the number of new COVID-19 cases makes the role of CT scan examinations rising and becoming essential in aiding the health institutions in diagnosing and isolating infected patients and those in close contacts. This study will review the spectrum of CT pulmonary changes due to COVID-19 infection and estimate the CT severity score index and its relation to age, sex, and PCR test results.

Results

The ground glass opacities were the most common encountered pattern of pulmonary changes and were seen in (79%). There was strong positive correlation between higher CT severity score and male gender (p value = 0.0002, R² = 0.9). Also, there was significant correlation of CT severity score and increasing age (p value less than 0.00018). Significant correlation was seen between CT scan percentage of lung involvement and positive PCR test results (p value = 0.001917), as the CT severity index is increasing, the PCR test is more likely to be positive.

Conclusions

Chest CT is an important and fast imaging tool for the diagnosis of COVID-19-infected patients especially in developing countries. In addition, chest CT can predict the disease severity by showing the percentage of lung involvement and hence give an idea about the prognosis of the disease. Higher CT severity score is significantly correlated with male gender, older age group patients and likely positive PCR test.

The estimated risk of SARS-CoV- 2 infection via cornea transplant in Canada

In late 2019 the respiratory illness, Corona Virus Disease-19 caused by the SARS-CoV-2 virus emerged in China and quickly spread to other countries. The primary mode of transmission is person-to-person via respiratory droplets. SARS-CoV-2 has been identified in conjunctiva. Transmission by cornea transplant has not been reported but is theoretically possible. We aimed to estimate the possible risk of transmission in Canada via cornea transplant during the first wave of the pandemic, and the potential risk reduction from testing decedents. We constructed a deterministic model in which the risk of transmission was estimated as the product of three proportions: decedents with SARS-CoV-2 infection, corneas that are NAT positive, and NAT positive corneas presumed to transmit. Risk was estimated according to 3 scenarios: most likely, optimistic and pessimistic. At the peak of the first wave of the pandemic risk was estimated to be 1 in 63,031 cornea transplants in Canada but could be as low as 1 in 175,821 or as high as 1 in 10,129. It would take 16 years at the peak infection of the first wave of the pandemic to observe 1 transmission. Testing would reduce the risk of 1 in 63,031 to 1 in 210,104 assuming 70% test sensitivity. The theoretical risk of SARS-CoV-2 transmission by cornea transplant is extremely low and decedent testing is unlikely to be beneficial.

Systematic review and meta-analysis of chest radiograph (CXR) findings in COVID-19

Chest radiography (CXR) is most likely to be the utilized modality for diagnosing COVID-19 and following up on any lung-associated abnormalities. This review provides a meta-analysis of the current literature on CXR imaging findings to determine the most common appearances of lung abnormalities in COVID-19 patients in order to equip medical researchers and healthcare professionals in their efforts to combat this pandemic. Twelve studies met the inclusion criteria and were analyzed. The inclusion criteria consisted of: (1) published in English literature; (2) original research study; (3) sample size of at least 5 patients; (4) reporting clinical characteristics of COVID-19 patients as well as CXR imaging features; and (5) noting the number of patients with each corresponding imaging feature. A total of 1948 patients were included in this study. To perform the meta-analysis, a random-effects model calculated the pooled prevalence and 95% confidence intervals of abnormal CXR imaging findings. Seventy-four percent (74%) (95% CI: 51-92%) of patients with COVID-19 had an abnormal CXR at the initial time of diagnosis or sometime during the disease course. While there was no single feature on CXR that was diagnostic of COVID-19 viral pneumonia, a characteristic set of findings were obvious. The most common abnormalities were consolidation (28%, 95% CI: 8-54%) and ground-glass opacities (29%, 95% CI: 10-53%). The distribution was most frequently bilateral (43%, 95% CI: 27-60%), peripheral (51%, 95% CI: 36-66%), and basal zone (56%, 95% CI: 37-74%) predominant. Contrary to parenchymal abnormalities, pneumothorax (1%, 95% CI: 0-3%) and pleural effusions (6%, 95% CI: 1-16%) were rare.

Correlation Between Low-Dose Chest Computed Tomography and RT-PCR Results for the Diagnosis of COVID-19: A Report of 27,824 Cases in Tehran, Iran

RATIONALE AND OBJECTIVES

Real-time polymerase chain reaction (RT-PCR) remains the gold standard for confirmation of Coronavirus Disease 2019 (COVID-19) despite having many disadvantages. Here, we investigated the diagnostic performance of chest computed tomography (CT) as an alternative to RT-PCR in patients with clinical suspicion of COVID-19 infection.

METHODS

In this descriptive cross-sectional study, 27,824 patients with clinical suspicion of COVID-19 infection who underwent unenhanced low-dose chest CT from 20 February, 2020 to 21 May, 2020 were evaluated. Patients were recruited from seven specifically designated hospitals for patients with COVID-19 infection affiliated to Shahid Beheshti University of Medical Sciences. In each hospital, images were interpreted by two independent radiologists. CT findings were considered as positive/negative for COVID-19 infection based on RSNA diagnostic criteria. Then, the correlation between the number of daily positive chest CT scans and number of daily PCR-confirmed cases and COVID-19-related deaths in Tehran province during this three-month period was assessed. The trends of admission rate and patients with positive CT scans were also evaluated.

RESULTS

A strong positive correlation between the numbers of daily positive CT scans and daily PCR-confirmed COVID-19 cases (r = 0.913, p < 0.001) was observed. Furthermore, in hospitals located in regions with a lower socioeconomic status, the admission rate and number of positive cases within this three-month period was higher as compared to other hospitals.

CONCLUSION

Low-dose chest CT is a safe, rapid and reliable alternative to RT-PCR for the diagnosis of COVID-19 in high-prevalence regions. In addition, our study provides further evidence for considering patients' socioeconomic status as an important risk factor for COVID-19.

Dual electrochemical sensing of spiked virus and SARS-CoV-2 using natural bed-receptor (MV-gal1)

It has been necessary to use methods that can detect the specificity of a virus during virus screening. In this study, we use a dual platform to identify any spiked virus and specific SARS-CoV-2 antigen, sequentially. We introduce a natural bed-receptor surface as Microparticle Vesicle-Galactins1 (MV-gal1) with the ability of glycan binding to screen every spiked virus. MV are the native vesicles which may have the gal-1 receptor. Gal-1 is the one of lectin receptor which can bind to glycan. After dropping the MV-gal1 on the SCPE/GNP, the sensor is turned on due to the increased electrochemical exchange with [Fe(CN)6]-3/-4 probe. Dropping the viral particles of SARS-CoV-2 cause to turn off the sensor with covering the sugar bond (early screening). Then, with the addition of Au/Antibody-SARS-CoV-2 on the MV-gal1@SARS-CoV-2 Antigen, the sensor is turned on again due to the electrochemical amplifier of AuNP (specific detection).For the first time, our sensor has the capacity of screening of any spike virus, and the specific detection of COVID-19 (LOD: 4.57 × 102 copies/mL) by using the natural bed-receptor and a specific antibody in the point of care test.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Diagnosis of COVID-19, vitality of emerging technologies and preventive measures

Coronavirus diseases-2019 (COVID-19) is becoming increasing serious and major threat to public health concerns. As a matter of fact, timely testing enhances the life-saving judgments on treatment and isolation of COVID-19 infected individuals at possible earliest stage which ultimately suppresses spread of infectious diseases. Many government and private research institutes and manufacturing companies are striving to develop reliable tests for prompt quantification of SARS-CoV-2. In this review, we summarize existing diagnostic methods as manual laboratory-based nucleic acid assays for COVID-19 and their limitations. Moreover, vitality of rapid and point of care serological tests together with emerging biosensing technologies has been discussed in details. Point of care tests with characteristics of rapidity, accurateness, portability, low cost and requiring non-specific devices possess great suitability in COVID-19 diagnosis and detection. Besides, this review also sheds light on several preventive measures to track and manage disease spread in current and future outbreaks of diseases.

On Analyzing Capnogram as a Novel Method for Screening COVID-19: A Review on Assessment Methods for COVID-19

In November 2019, the novel coronavirus disease COVID-19 was reported in Wuhan city, China, and was reported in other countries around the globe. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Strategies such as contact tracing and a vaccination program have been imposed to keep COVID-19 under control. Furthermore, a fast, noninvasive and reliable testing device is needed urgently to detect COVID-19, so that contact can be isolated and ringfenced before the virus spreads. Although the reverse transcription polymerase chain reaction (RT-PCR) test is considered the gold standard method for the diagnosis of SARS-CoV-2 infection, this test presents some limitations which cause delays in detecting the disease. The antigen rapid test (ART) test, on the other hand, is faster and cheaper than PCR, but is less sensitive, and may limit SARS-CoV-2 detection. While other tests are being developed, accurate, noninvasive and easy-to-use testing tools are in high demand for the rapid and extensive diagnosis of the disease. Therefore, this paper reviews current diagnostic methods for COVID-19. Following this, we propose the use of expired carbon dioxide (CO2) as an early screening tool for SARS-CoV-2 infection. This system has already been developed and has been tested on asthmatic patients. It has been proven that expired CO2, also known as capnogram, can help differentiate between respiratory conditions and, therefore, could be used to detect SARS-CoV-2 infection, as it causes respiratory tract-related diseases.

How did radiologists' diagnostic performance has changed in COVID-19 pneumonia: A single-centre retrospective study

AIMS

Delay and false positivity in PCR test results have necessitated accurate chest CT reporting for the management of patients with COVID-19-suspected symptoms. Pandemic related workload and level of experience on covid-dedicated chest CT scans might have affected the diagnostic performance of on-call radiologists. The aim of this study was to reveal the interpretation errors (IEs) in chest CT reports of COVID-19-suspected patients admitted to the Emergency Room (ER).

METHODS

Chest CT scans between March and June 2020 were re-evaluated and compared with the former reports and PCR test results. CT scan results were classified into four groups. Parenchymal involvement ratios, radiology departments' workload, COVID-19-related educational activities have been examined.

RESULTS

Out of 5721 Chest CT scans, 783 CTs belonging to 664 patients (340 female, 324 male) were included in this study. PCR test was positive in 398; negative in 385 cases. PCR positivity was found to be highest in "normal" and "typical for covid" groups whereas lowest in "atypical for covid" and "not covid" groups. 5%-25% parenchymal involvement ratio was found in 84.2% of the cases. Regarding the number of chest CT scans performed, radiologists' workload has found to be increased six-folds. With the re-evaluation, a total of 145 IEs (18.5%) have been found. IEs were mostly precipitated in the first two months (88.3%) and mostly in the "not covid" class (60%) regardless of PCR positivity. COVID-19 and radiology entitled educational activities along with the ER admission rates within the first two months of the pandemic have seemed to be related to the decline of IEs within time.

CONCLUSION

COVID-19 pandemic made a great impact on radiology departments with an inevitable burden of daily chest CT reporting. This workload and concomitant factors have effects on diagnostic challenges in COVID-19 pneumonia.

Rapid Screening of COVID-19 Directly from Clinical Nasopharyngeal Swabs Using the MasSpec Pen

The outbreak of COVID-19 has created an unprecedent global crisis. While the polymerase chain reaction (PCR) is the gold standard method for detecting active SARS-CoV-2 infection, alternative high-throughput diagnostic tests are of a significant value to meet universal testing demands. Here, we describe a new design of the MasSpec Pen technology integrated to electrospray ionization (ESI) for direct analysis of clinical swabs and investigate its use for COVID-19 screening. The redesigned MasSpec Pen system incorporates a disposable sampling device refined for uniform and efficient analysis of swab tips via liquid extraction directly coupled to an ESI source. Using this system, we analyzed nasopharyngeal swabs from 244 individuals including symptomatic COVID-19 positive, symptomatic negative, and asymptomatic negative individuals, enabling rapid detection of rich lipid profiles. Two statistical classifiers were generated based on the lipid information acquired. Classifier 1 was built to distinguish symptomatic PCR-positive from asymptomatic PCR-negative individuals, yielding a cross-validation accuracy of 83.5%, sensitivity of 76.6%, and specificity of 86.6%, and validation set accuracy of 89.6%, sensitivity of 100%, and specificity of 85.3%. Classifier 2 was built to distinguish symptomatic PCR-positive patients from negative individuals including symptomatic PCR-negative patients with moderate to severe symptoms and asymptomatic individuals, yielding a cross-validation accuracy of 78.4%, specificity of 77.21%, and sensitivity of 81.8%. Collectively, this study suggests that the lipid profiles detected directly from nasopharyngeal swabs using MasSpec Pen-ESI mass spectrometry (MS) allow fast (under a minute) screening of the COVID-19 disease using minimal operating steps and no specialized reagents, thus representing a promising alternative high-throughput method for screening of COVID-19.