Dataset Information

SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.

ABSTRACT:

Objectives

SARS-CoV-2 rapid antigen tests (RAT) provide fast identification of infectious patients when RT-PCR results are not immediately available. We aimed to develop a prediction model for identification of false negative (FN) RAT results.

Methods

In this multicenter trial, patients with documented paired results of RAT and RT-PCR between October 1^st 2020 and January 31^st 2021 were retrospectively analyzed regarding clinical findings. Variables included demographics, laboratory values and specific symptoms. Three different models were evaluated using Bayesian logistic regression.

Results

The initial dataset contained 4,076 patients. Overall sensitivity and specificity of RAT was 62.3% and 97.6%. 2,997 cases with negative RAT results (FN: 120; true negative: 2,877; reference: RT-PCR) underwent further evaluation after removal of cases with missing data. The best-performing model for predicting FN RAT results containing 10 variables yielded an area under the curve of 0.971. Sensitivity, specificity, PPV and NPV for 0.09 as cut-off value (probability for FN RAT) were 0.85, 0.99, 0.7 and 0.99.

Conclusion

FN RAT results can be accurately identified through ten routinely available variables. Implementation of a prediction model in addition to RAT testing in clinical care can provide decision guidance for initiating appropriate hygiene measures and therefore helps avoiding nosocomial infections.

SUBMITTER: Leiner J

PROVIDER: S-EPMC8431843 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.

Leiner Johannes J Pellissier Vincent V Nitsche Anne A König Sebastian S Hohenstein Sven S Nachtigall Irit I Hindricks Gerhard G Kutschker Christoph C Rolinski Boris B Gebauer Julian J Prantz Anja A Schubert Joerg J Patzschke Joerg J Bollmann Andreas A Wolz Martin M

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 20210910

<h4>Objectives</h4>SARS-CoV-2 rapid antigen tests (RAT) provide fast identification of infectious patients when RT-PCR results are not immediately available. We aimed to develop a prediction model for identification of false negative (FN) RAT results.<h4>Methods</h4>In this multicenter trial, patients with documented paired results of RAT and RT-PCR between October 1<sup>st</sup> 2020 and January 31<sup>st</sup> 2021 were retrospectively analyzed regarding clinical findings. Variables included d ...[more]

PMID: 34517045

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Project description:Objectives: With the worldwide spread of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), various antibody detection kits have been developed to test for SARS-CoV-2- specific IgG, IgM, and total antibody. However, the use of different testing methods under various heat-inactivation conditions might affect the COVID-19 detection results. Methods: Seven different antibody detection kits produced by four manufacturers for detection of SARS-CoV-2 IgG, IgM, and total antibody were tested at Wuhan Huoshenshan Hospital, China. Most of the kits used the indirect immunity, capture, and double-antigen sandwich methods. The effects of various heat-inactivation conditions on SARS-CoV-2-specific IgG, IgM, and total antibody detection were analyzed for the different test methods. Results: Using the indirect immunity method, values for SARS-CoV-2 IgG antibody significantly increased and those for IgM antibody decreased with increasing temperature of heat-inactivation using indirect immunity method. However, values for SARS-CoV-2 IgM and total antibody showed no change when the capture and double-antigen sandwich methods were used. The changes in IgG and IgM antibody values with the indirect immunity method indicated that heat-inactivation could affect COVID-19 detection results obtained using this method. In particular, 18 (22.2%) SARS-CoV-2 IgM positive samples were detected as negative with heat-inactivation at 65°C for 30 min, and one (25%) IgG negative sample was detected as positive after heat-inactivation at 56°C for 60 min and 60°C for 30 min. Conclusions: Heat-inactivation could increase SARS-CoV-2 IgG antibody values, and decrease IgM antibody values, causing potential false-positive or false-negative results for COVID-19 antibody detection using the indirect immunity method. Thus, before conducting antibody testing, the testing platforms should be evaluated in accordance with the relevant requirements to ensure accurate COVID-19 detection results.

Dataset Information

SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.

Objectives

Methods

Results

Conclusion

Publications

SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.

Similar Datasets

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