Chest computed tomography versus RT-PCR in early diagnostics of COVID-19 - a systematic review with meta-analysis

Evaluation of the therapeutic action of original antiviral drug in SARS-CoV-2

Purpose of this article is to study the possible direct antiviral effect of "Armenikum" on SARS-CoV-2, conduct an in vitro study on the SARS-CoV-2 encephalomocarditis virus, and an in vivo study on the Syrian hamster model. Human coronavirus SARS-CoV-2 (delta strain) was used as the virus. Two groups of four-specimen hamsters were used to study the therapeutic activity of the drug during 48 h after infecting. One group of hamsters served as positive control and was infected with the virus at a similar dose as experimental one and was used as a control of pathology induced by the viral infection till the end of the experiment. Another group of hamsters (four of them) was injected physiological solution and was used as a control. The Syrian hamsters underwent a clinical blood test and computed tomography. "Armenikum" in the form of an injection has a significant antiviral effect on the human coronavirus SARS-CoV-2, credibly reducing the titers of the virus and the time of its elimination from the Syrian hamsters, significantly mitigating the viral infection. "Armenikum" in the form of an injection drug almost completely removes the pathological effect of the virus in the lungs of the hamsters.

Auxiliary screening COVID-19 by computed tomography

Background

The 2019 novel coronavirus (COVID-19) pandemic remains rampant in many countries/regions. Improving the positive detection rate of COVID-19 infection is an important measure for the control and prevention of this pandemic. This meta-analysis aims to systematically summarize the current characteristics of the computed tomography (CT) auxiliary screening methods for COVID-19 infection in the real world.

Methods

Web of Science, Cochrane Library, Embase, PubMed, CNKI, and Wanfang databases were searched for relevant articles published prior to 1 September 2022. Data on specificity, sensitivity, positive/negative likelihood ratio, area under curve (AUC), and diagnostic odds ratio (dOR) were calculated purposefully.

Results

One hundred and fifteen studies were included with 51,500 participants in the meta-analysis. Among these studies, the pooled estimates for AUC of CT in confirmed cases, and CT in suspected cases to predict COVID-19 diagnosis were 0.76 and 0.85, respectively. The CT in confirmed cases dOR was 5.51 (95% CI: 3.78-8.02). The CT in suspected cases dOR was 13.12 (95% CI: 11.07-15.55).

Conclusion

Our findings support that CT detection may be the main auxiliary screening method for COVID-19 infection in the real world.

Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study

Background

The high mortality rate of COVID-19 makes it necessary to seek early identification of high-risk patients with poor prognoses. Although the association between CT-SS and mortality of COVID-19 patients was reported, its prognosis significance in combination with other prognostic parameters was not evaluated yet.

Methods

This retrospective single-center study reviewed a total of 6854 suspected patients referred to Imam Khomeini hospital, Ilam city, west of Iran, from February 9, 2020 to December 20, 2020. The prognostic performances of k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and J48 decision tree algorithms were evaluated based on the most important and relevant predictors. The metrics derived from the confusion matrix were used to determine the performance of the ML models.

Results

After applying exclusion criteria, 815 hospitalized cases were entered into the study. Of these, 447(54.85%) were male and the mean (± SD) age of participants was 57.22(± 16.76) years. The results showed that the performances of the ML algorithms were improved when they are fed by the dataset with CT-SS data. The kNN model with an accuracy of 94.1%, sensitivity of 100. 0%, precision of 89.5%, specificity of 88.3%, and AUC around 97.2% had the best performance among the other three ML techniques.

Conclusions

The integration of CT-SS data with demographics, risk factors, clinical manifestations, and laboratory parameters improved the prognostic performances of the ML algorithms. An ML model with a comprehensive collection of predictors could identify high-risk patients more efficiently and lead to the optimal use of hospital resources.

Prognostic accuracy of visual lung damage computed tomography score for mortality prediction in patients with COVID-19 pneumonia: a systematic review and meta-analysis

Background

Chest computed tomography (CT) findings provide great added value in characterizing the extent of disease and severity of pulmonary involvements. Chest CT severity score (CT-SS) could be considered as an appropriate prognostic factor for mortality prediction in patients with COVID-19 pneumonia. In this study, we performed a meta-analysis evaluating the prognostic accuracy of CT-SS for mortality prediction in patients with COVID-19 pneumonia.

Methods

A systematic search was conducted on Web of Science, PubMed, Embase, Scopus, and Google Scholar databases between December 2019 and September 2021. The meta-analysis was performed using the random-effects model, and sensitivity and specificity (with 95%CIs) of CT-SS were calculated using the study authors’ pre-specified threshold.

Results

Sensitivity estimates ranged from 0.32 to 1.00, and the pooled estimate of sensitivity was 0.67 [95%CI (0.59–0.75)]. Specificity estimates ranged from 0.53 to 0.95 and the pooled estimate of specificity was 0.79 [95%CI (0.74–0.84)]. Results of meta-regression analysis showed that radiologist experiences did not affect the sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients (P = 0.314 and 0.283, respectively). The test for subgroup differences suggests that study location significantly modifies sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients. The area under the summary receiver operator characteristic (ROC) curve was 0.8248.

Conclusion

Our results have shown that CT-SS has acceptable prognostic accuracy for mortality prediction in COVID-19 patients. This simple scoring method could help to improve the management of high-risk patients with COVID-19.

COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study

In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.

Comparative study of six SARS-CoV-2 serology assays: Diagnostic performance and antibody dynamics in a cohort of hospitalized patients for moderate to critical COVID-19

BACKGROUND

To overcome the COVID-19 pandemic, serology assays are needed to identify past and ongoing infections. In this context, we evaluated the diagnostic performance of 6 immunoassays on samples from hospitalized patients for moderate to critical COVID-19.

METHODS

701 serum samples obtained from 443 COVID-19 patients (G1: 356 positive RT-PCR patients and G2: 87 negative RT-PCR cases) and 108 pre-pandemic sera from blood donors were tested with 6 commercial immunoassays: (1) Elecsys Anti-SARS-CoV-2, Roche (Nucleocapsid, N), (2) Elecsys Anti-SARS-CoV-2 S, Roche (Spike, S), (3) Vidas SARS-COV-2 IgM/IgG, BioMérieux (S), (4) SARS-CoV-2 IgG, Abbott (N), (5) Access SARS-CoV-2 IgG, Beckman Coulter (Receptor Binding Domain), and (6) Standard F COVID-19 IgM/IgG Combo FIA, SD Biosensor (N).

RESULTS

Global sensitivities of the evaluated assays were as follows: (1) Roche anti-N = 74.5% [69.6-79.3], (2) Roche anti-S = 92.7% [84.7-100], (3) Vidas IgM = 74.9% [68.6-81.2], (4) Vidas IgG = 73.9% [67.6-80.1], (5) Abbott = 78.6% [63.4-93.8], (6) Beckman Coulter = 74.5% [62-86.9], (7) SD Biosensor IgM = 73.1% [61-85.1], and (8) SD Biosensor IgG = 76.9% [65.4-88.4]. Sensitivities increased gradually from week 1 to week 3 as follow: (1) Roche anti-N: 63.3%, 81% and 82.1%; (2) Vidas IgM: 68.2%, 83.2% and 85.9%; and (3) Vidas IgG: 66.7%, 79.1% and 86.6%. All immunoassays showed a specificity of 100%. Seropositivity was significantly associated with a higher frequency of critical COVID-19 (50.8% vs. 38.2%), p = 0.018, OR [95% CI] = 1.668 [1.09-2.553]. Inversely, death occurred more frequently in seronegative patients (28.7% vs. 13.6%), p=3.02 E-4, OR [95% CI] = 0.392 [0.233-0.658].

CONCLUSION

Evaluated serology assays exhibited good sensitivities and excellent specificities. Sensitivities increased gradually after symptoms onset. Even if seropositivity is more frequent in patients with critical COVID-19, it may predict a recovery outcome.