Multicenter Evaluation of the QIAstat-Dx Respiratory Panel for Detection of Viruses and Bacteria in Nasopharyngeal Swab Specimens.
J Clin Microbiol
; 58(5)2020 04 23.
Article
in English
| MEDLINE | ID: covidwho-209223
ABSTRACT
The QIAstat-Dx Respiratory Panel (QIAstat-Dx RP) is a multiplex in vitro diagnostic test for the qualitative detection of 20 pathogens directly from nasopharyngeal swab (NPS) specimens. The assay is performed using a simple sample-to-answer platform with results available in approximately 69 min. The pathogens identified are adenovirus, coronavirus 229E, coronavirus HKU1, coronavirus NL63, coronavirus OC43, human metapneumovirus A and B, influenza A, influenza A H1, influenza A H3, influenza A H1N1/2009, influenza B, parainfluenza virus 1, parainfluenza virus 2, parainfluenza virus 3, parainfluenza virus 4, rhinovirus/enterovirus, respiratory syncytial virus A and B, Bordetella pertussis, Chlamydophila pneumoniae, and Mycoplasma pneumoniae This multicenter evaluation provides data obtained from 1,994 prospectively collected and 310 retrospectively collected (archived) NPS specimens with performance compared to that of the BioFire FilmArray Respiratory Panel, version 1.7. The overall percent agreement between QIAstat-Dx RP and the comparator testing was 99.5%. In the prospective cohort, the QIAstat-Dx RP demonstrated a positive percent agreement of 94.0% or greater for the detection of all but four analytes coronaviruses 229E, NL63, and OC43 and rhinovirus/enterovirus. The test also demonstrated a negative percent agreement of ≥97.9% for all analytes. The QIAstat-Dx RP is a robust and accurate assay for rapid, comprehensive testing for respiratory pathogens.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Bacteria
/
Viruses
/
Nasopharynx
/
Molecular Diagnostic Techniques
/
Multiplex Polymerase Chain Reaction
Type of study:
Cohort study
/
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Qualitative research
Limits:
Humans
Language:
English
Year:
2020
Document Type:
Article
Affiliation country:
JCM.00155-20
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