ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2021
SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.
Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection.The Cochrane database of systematic reviews · 2025Pooled it
- Clinical accuracy of instrument-based SARS-CoV-2 antigen diagnostic tests: a systematic review and meta-analysis.Virology journal · 2024Pooled it
- Prediction model to identify infectious COVID-19 patients in the emergency department.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2024Article
- Accuracy of Rapid Antigen Screening Tests for SARS-CoV-2 Infection at Correctional Facilities in Korea: March - May 2022.Infection & chemotherapy · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesSARS-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.
methodsIn this multicenter trial, patients with documented paired results of RAT and RT-PCR between October 1
resultsThe 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.
conclusionFN 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.
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