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The comparative superiority of IgM-IgG antibody test to real-time reverse transcriptase PCR detection for SARS-CoV-2 infection diagnosis (preprint)
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.03.28.20045765
ABSTRACT

Background:

As the increasing number of Corona Virus Disease 2019 (COVID-19) patients caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2), which caused an outbreak initiated from Wuhan, China in December, 2019, the clinical features and treatment of COVID-19 patients have been understood. However, it is urgent to need the rapid and accurate detection for SARS-CoV-2 infection diagnosis. We aimed to evaluate the antibodies-based and nucleic acid-based tests (NAT) for SARS-CoV-2-infected patients.

Method:

We retrospectively and observationally studied 133 patients diagnosed with SARS-CoV-2 and admitted in Renmin Hospital of Wuhan University, China, from Feb 17 to Mar 1, 2020. Demographic data, symptoms, clinical examination, laboratory tests, and clinical outcomes were collected. Data were compared between IgM-IgG antibody test and real-time RT-PCR detection for COVID-19 patients.

Results:

Of 133 patients with SARS-CoV-2 infection, there were 44 moderate cases, 52 severe cases, and 37 critical cases with no significant difference of gender and age among three subgroups. Overall, the positive ratio in IgM antibody test was higher than in RT-PCR detection. In RT-PCR detection, the positive ratio was 65.91%, 71.15%, and 67.57% in moderate, severe, and critical cases, respectively. Whereas, the positive ratio of IgM/IgG antibody detection in patients was 79.55%/93.18%, 82.69%/100%, and 72.97%/97.30% in moderate, severe, and critical cases, respectively. Moreover, the concentrations of antibodies were also measured in three subgroups.

Conclusion:

The IgM-IgG antibodies-based test exhibited a comparative superiority to the NAT for COVID-19 diagnosis, which provides an effective complement to the false negative results from NAT for SARS-CoV-2 infection diagnosis.

Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document Type: Preprint