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      Systematic review with meta-analysis of the accuracy of diagnostic tests for COVID-19

      , , , , ,
      American Journal of Infection Control
      Elsevier BV

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          Abstract

          Highlights • RT-PCR followed by CT shows high sensitivity for detecting COVID-19 • Immunological tests should use a combination of IgG and IgM • The genes E and RdRp present high analytical sensitivity to detect the virus • Assays for molecular diagnosis should employ two-target systems • Studies of diagnostic tests for COVID-19 are of moderate methodological quality

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          Most cited references38

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            Is Open Access

            Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR

            Background The ongoing outbreak of the recently emerged novel coronavirus (2019-nCoV) poses a challenge for public health laboratories as virus isolates are unavailable while there is growing evidence that the outbreak is more widespread than initially thought, and international spread through travellers does already occur. Aim We aimed to develop and deploy robust diagnostic methodology for use in public health laboratory settings without having virus material available. Methods Here we present a validated diagnostic workflow for 2019-nCoV, its design relying on close genetic relatedness of 2019-nCoV with SARS coronavirus, making use of synthetic nucleic acid technology. Results The workflow reliably detects 2019-nCoV, and further discriminates 2019-nCoV from SARS-CoV. Through coordination between academic and public laboratories, we confirmed assay exclusivity based on 297 original clinical specimens containing a full spectrum of human respiratory viruses. Control material is made available through European Virus Archive – Global (EVAg), a European Union infrastructure project. Conclusion The present study demonstrates the enormous response capacity achieved through coordination of academic and public laboratories in national and European research networks.
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              QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

              In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
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                Author and article information

                Journal
                American Journal of Infection Control
                American Journal of Infection Control
                Elsevier BV
                01966553
                January 2021
                January 2021
                : 49
                : 1
                : 21-29
                Article
                10.1016/j.ajic.2020.07.011
                38634aba-122e-4b7c-8d1f-fff87e41066a
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

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