A meta-analysis to evaluate the effectiveness of real-time PCR for diagnosing novel coronavirus infections.
Identifieur interne : 001376 ( Main/Exploration ); précédent : 001375; suivant : 001377A meta-analysis to evaluate the effectiveness of real-time PCR for diagnosing novel coronavirus infections.
Auteurs : C. Lin [République populaire de Chine] ; R. Ye [République populaire de Chine] ; Y L Xia [République populaire de Chine]Source :
- Genetics and molecular research : GMR [ 1676-5680 ] ; 2015.
Descripteurs français
- KwdFr :
- MESH :
- diagnostic : Infections à coronavirus.
- génétique : Coronavirus.
- virologie : Infections à coronavirus.
- Biais de publication, Coronavirus, Humains, Odds ratio, Réaction de polymérisation en chaine en temps réel, Sensibilité et spécificité, Études cas-témoins.
English descriptors
- KwdEn :
- MESH :
- classification : Coronavirus.
- diagnosis : Coronavirus Infections.
- genetics : Coronavirus.
- methods : Real-Time Polymerase Chain Reaction.
- virology : Coronavirus Infections.
- Case-Control Studies, Humans, Odds Ratio, Publication Bias, Sensitivity and Specificity.
Abstract
Novel coronavirus (nCoV) belongs to the Coronaviridae family, which includes the virus that causes SARS, or severe acute respiratory syndrome. However, infection source, transmission route, and host of nCoV have not yet been thoroughly characterized. In some cases, nCoV presented a limited person-to-person transmission. Therefore, early diagnosis of nCoV may be of importance for reducing the spread of disease in public. Methods for nCoV diagnosis involve smear dyeing inspection, culture identification, and real-time PCR detection, all of which are proved highly effective. Here, we performed a meta-analysis to evaluate the effectiveness of real-time PCR for diagnosing nCoV infection. Fifteen articles conformed to the inclusion and exclusion criteria for further meta-analysis on the basis of a wide range of publications searched from databases involving PubMed, EMBASE, Web of Science, Medline, ISI. We analyzed the stability and publication bias as well as examined the heterogeneity inspection of real-time PCR detection in contrast to smear staining and culture identification. The fixed-effect model was adopted in our meta-analysis. Our result demonstrated that the combination of real-time PCR and smear diagnostics yielded an odds ratio (OR) = 1.91, 95% confidence interval (CI) = 1.51-2.41, Z = 5.43, P < 0.05, while the combination of real-time PCR and culture identification yielded OR = 2.44, 95%CI = 1.77-3.37, Z = 5.41, P < 0.05. Therefore, we propose real-time PCR as an efficient method that offers an auxiliary support for future nCoV diagnosis.
DOI: 10.4238/2015.December.1.15
PubMed: 26634531
Affiliations:
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Lin</name><affiliation wicri:level="1"><nlm:affiliation>School of Basic Medical Sciences, Fudan University, Shanghai, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Basic Medical Sciences, Fudan University, Shanghai</wicri:regionArea><wicri:noRegion>Shanghai</wicri:noRegion></affiliation></author><author><name sortKey="Ye, R" sort="Ye, R" uniqKey="Ye R" first="R" last="Ye">R. Ye</name><affiliation wicri:level="1"><nlm:affiliation>School of Basic Medical Sciences, Fudan University, Shanghai, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Basic Medical Sciences, Fudan University, Shanghai</wicri:regionArea><wicri:noRegion>Shanghai</wicri:noRegion></affiliation></author><author><name sortKey="Xia, Y L" sort="Xia, Y L" uniqKey="Xia Y" first="Y L" last="Xia">Y L Xia</name><affiliation wicri:level="1"><nlm:affiliation>Department of Pediatrics, First Hospital of Jiaxing, Jiaxing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Pediatrics, First Hospital of Jiaxing, Jiaxing</wicri:regionArea><wicri:noRegion>Jiaxing</wicri:noRegion></affiliation></author></analytic><series><title level="j">Genetics and molecular research : GMR</title><idno type="eISSN">1676-5680</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Case-Control Studies</term><term>Coronavirus (classification)</term><term>Coronavirus (genetics)</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (virology)</term><term>Humans</term><term>Odds Ratio</term><term>Publication Bias</term><term>Real-Time Polymerase Chain Reaction (methods)</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Biais de publication</term><term>Coronavirus ()</term><term>Coronavirus (génétique)</term><term>Humains</term><term>Infections à coronavirus (diagnostic)</term><term>Infections à coronavirus (virologie)</term><term>Odds ratio</term><term>Réaction de polymérisation en chaine en temps réel ()</term><term>Sensibilité et spécificité</term><term>Études cas-témoins</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Coronavirus</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Coronavirus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Coronavirus</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Real-Time Polymerase Chain Reaction</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Infections à coronavirus</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Case-Control Studies</term><term>Humans</term><term>Odds Ratio</term><term>Publication Bias</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Biais de publication</term><term>Coronavirus</term><term>Humains</term><term>Odds ratio</term><term>Réaction de polymérisation en chaine en temps réel</term><term>Sensibilité et spécificité</term><term>Études cas-témoins</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Novel coronavirus (nCoV) belongs to the Coronaviridae family, which includes the virus that causes SARS, or severe acute respiratory syndrome. However, infection source, transmission route, and host of nCoV have not yet been thoroughly characterized. In some cases, nCoV presented a limited person-to-person transmission. Therefore, early diagnosis of nCoV may be of importance for reducing the spread of disease in public. Methods for nCoV diagnosis involve smear dyeing inspection, culture identification, and real-time PCR detection, all of which are proved highly effective. Here, we performed a meta-analysis to evaluate the effectiveness of real-time PCR for diagnosing nCoV infection. Fifteen articles conformed to the inclusion and exclusion criteria for further meta-analysis on the basis of a wide range of publications searched from databases involving PubMed, EMBASE, Web of Science, Medline, ISI. We analyzed the stability and publication bias as well as examined the heterogeneity inspection of real-time PCR detection in contrast to smear staining and culture identification. The fixed-effect model was adopted in our meta-analysis. Our result demonstrated that the combination of real-time PCR and smear diagnostics yielded an odds ratio (OR) = 1.91, 95% confidence interval (CI) = 1.51-2.41, Z = 5.43, P < 0.05, while the combination of real-time PCR and culture identification yielded OR = 2.44, 95%CI = 1.77-3.37, Z = 5.41, P < 0.05. Therefore, we propose real-time PCR as an efficient method that offers an auxiliary support for future nCoV diagnosis.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Lin, C" sort="Lin, C" uniqKey="Lin C" first="C" last="Lin">C. Lin</name></noRegion><name sortKey="Xia, Y L" sort="Xia, Y L" uniqKey="Xia Y" first="Y L" last="Xia">Y L Xia</name><name sortKey="Ye, R" sort="Ye, R" uniqKey="Ye R" first="R" last="Ye">R. 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