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

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 docum...

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Autores principales:	Johannes Leiner, MD, Vincent Pellissier, PhD, Anne Nitsche, PhD, Sebastian König, MD, Sven Hohenstein, PhD, Irit Nachtigall, MD, Gerhard Hindricks, MD, Christoph Kutschker, MD, Boris Rolinski, MD, Julian Gebauer, MD, Anja Prantz, MD, Joerg Schubert, MDPhD, Joerg Patzschke, MD, Andreas Bollmann, MDPhD, Martin Wolz, MD
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	SARS-CoV-2 COVID-19 rapid antigen test false negative prediction models healthcare Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/2706f93753074671a11b974f47777c15
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spelling	oai:doaj.org-article:2706f93753074671a11b974f47777c152021-11-30T04:14:08ZSARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results1201-971210.1016/j.ijid.2021.09.008https://doaj.org/article/2706f93753074671a11b974f47777c152021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1201971221007177https://doaj.org/toc/1201-9712Objectives: 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 1st 2020 and January 31st 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.Johannes Leiner, MDVincent Pellissier, PhDAnne Nitsche, PhDSebastian König, MDSven Hohenstein, PhDIrit Nachtigall, MDGerhard Hindricks, MDChristoph Kutschker, MDBoris Rolinski, MDJulian Gebauer, MDAnja Prantz, MDJoerg Schubert, MDPhDJoerg Patzschke, MDAndreas Bollmann, MDPhDMartin Wolz, MDElsevierarticleSARS-CoV-2COVID-19rapid antigen testfalse negativeprediction modelshealthcareInfectious and parasitic diseasesRC109-216ENInternational Journal of Infectious Diseases, Vol 112, Iss , Pp 117-123 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	SARS-CoV-2 COVID-19 rapid antigen test false negative prediction models healthcare Infectious and parasitic diseases RC109-216
spellingShingle	SARS-CoV-2 COVID-19 rapid antigen test false negative prediction models healthcare Infectious and parasitic diseases RC109-216 Johannes Leiner, MD Vincent Pellissier, PhD Anne Nitsche, PhD Sebastian König, MD Sven Hohenstein, PhD Irit Nachtigall, MD Gerhard Hindricks, MD Christoph Kutschker, MD Boris Rolinski, MD Julian Gebauer, MD Anja Prantz, MD Joerg Schubert, MDPhD Joerg Patzschke, MD Andreas Bollmann, MDPhD Martin Wolz, MD SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results
description	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 1st 2020 and January 31st 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.
format	article
author	Johannes Leiner, MD Vincent Pellissier, PhD Anne Nitsche, PhD Sebastian König, MD Sven Hohenstein, PhD Irit Nachtigall, MD Gerhard Hindricks, MD Christoph Kutschker, MD Boris Rolinski, MD Julian Gebauer, MD Anja Prantz, MD Joerg Schubert, MDPhD Joerg Patzschke, MD Andreas Bollmann, MDPhD Martin Wolz, MD
author_facet	Johannes Leiner, MD Vincent Pellissier, PhD Anne Nitsche, PhD Sebastian König, MD Sven Hohenstein, PhD Irit Nachtigall, MD Gerhard Hindricks, MD Christoph Kutschker, MD Boris Rolinski, MD Julian Gebauer, MD Anja Prantz, MD Joerg Schubert, MDPhD Joerg Patzschke, MD Andreas Bollmann, MDPhD Martin Wolz, MD
author_sort	Johannes Leiner, MD
title	SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results
title_short	SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results
title_full	SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results
title_fullStr	SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results
title_full_unstemmed	SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results
title_sort	sars-cov-2 rapid antigen testing in the healthcare sector: a clinical prediction model for identifying false negative results
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/2706f93753074671a11b974f47777c15
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SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results

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