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SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results
- Source :
- International Journal of Infectious Diseases, Vol 112, Iss, Pp 117-123 (2021), International Journal of Infectious Diseases
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
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 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 a decision guidance for initiating appropriate hygiene measures and therefore helps avoiding nosocomial infections.<br />Graphical abstract Image, graphical abstract
- Subjects :
- Health Care Sector
Infectious and parasitic diseases
RC109-216
COI, Cut-Off-Index
Health care
PPV, Positive predictive value
Medicine
rapid antigen test
Antigen testing
COVID-19, Coronavirus disease 2019
HIS, Hospital information system
RKI, Robert-Koch-Institute
Area under the curve
healthcare
false negative
General Medicine
Prognosis
PoC, Point-of-care
ICU, Intensive care unit
Infectious Diseases
Rapid antigen test
CRP, C-reactive protein
CT, Computed tomography
AST, Aspartate aminotransferase
FIA, Fluorescence-immunoassays
ML, Machine learning
Microbiology (medical)
medicine.medical_specialty
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
ROC, Receiver operating characteristic
TN, True negative
Sensitivity and Specificity
Article
WHO, World Health Organization
Internal medicine
Multicenter trial
Humans
NPV, Negative predictive value
Retrospective Studies
LDH, Lactate dehydrogenase
SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2
Models, Statistical
business.industry
SARS-CoV-2
RAT, Rapid antigen test
COVID-19
prediction models
Bayes Theorem
BF, Bayes Factor
RT-PCR, Reverse transcription polymerase chain reaction
Ct, Cycle threshold
Missing data
FN, False negative
Standard F, Standard F COVID-19 Ag FIA (SD Biosensor Inc.)
AUC, Receiver operating characteristic area under the curve
PCR, Polymerase chain reaction
business
Predictive modelling
Subjects
Details
- Language :
- English
- ISSN :
- 12019712
- Volume :
- 112
- Database :
- OpenAIRE
- Journal :
- International Journal of Infectious Diseases
- Accession number :
- edsair.doi.dedup.....68f0764e4c0c06f1be51b1bccda4d9da