SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results. (November 2021)

• Record Type: Journal Article
• Authors: Leiner, Johannes; Pellissier, Vincent; Nitsche, Anne; König, Sebastian; Hohenstein, Sven; Nachtigall, Irit; Hindricks, Gerhard; Kutschker, Christoph; Rolinski, Boris; Gebauer, Julian; Prantz, Anja; Schubert, Joerg; Patzschke, Joerg; Bollmann, Andreas; Wolz, Martin
• Is Part Of: International journal of infectious diseases. Volume 112(2021)
• Page Start: 117
• Page End: 123
• Publication Date: 2021-11
• Subjects: SARS-CoV-2 -- COVID-19 -- rapid antigen test -- false negative -- prediction models -- healthcare; AST Aspartate aminotransferase -- AUC Receiver operating characteristic area under the curve -- BF Bayes Factor -- COI Cut-Off-Index -- COVID-19 Coronavirus disease 2019 -- CRP C-reactive protein -- Ct Cycle threshold -- CT Computed tomography -- FIA Fluorescence-immunoassays -- FN False negative -- HIS Hospital information system -- ICU Intensive care unit -- LDH Lactate dehydrogenase -- ML Machine learning -- NPV Negative predictive value -- PCR Polymerase chain reaction -- PoC Point-of-care -- PPV Positive predictive value -- RAT Rapid antigen test -- RT-PCR Reverse transcription polymerase chain reaction -- RKI Robert-Koch-Institute -- ROC Receiver operating characteristic -- SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 -- Standard F Standard F COVID-19 Ag FIA (SD Biosensor Inc.) -- TN True negative -- WHO World Health Organization; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2021.09.008
• Languages: English
• ISSNs: 1201-9712

Abstract

Highlights: SARS-CoV-2 rapid antigen tests provide fast identification of infectious patients Prediction models for identification of false negative test results were developed One investigated clinical model reached an area under the curve of 0.971 Prediction models can be routinely applied in the healthcare sector The prevention of nosocomial infections positively influences the pandemic's course 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. Graphical Abstract: Image, graphical abstract