ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens
Quantitative real time PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, cut off...
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| Veröffentlicht in: | Emerging microbes & infections 2020-01, Vol.9 (1), p.1259-1268 |
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| creator | Suo, Tao Liu, Xinjin Feng, Jiangpeng Guo, Ming Hu, Wenjia Guo, Dong Ullah, Hafiz Yang, Yang Zhang, Qiuhan Wang, Xin Sajid, Muhanmmad Huang, Zhixiang Deng, Liping Chen, Tielong Liu, Fang Xu, Ke Liu, Yuan Zhang, Qi Liu, Yingle Xiong, Yong Chen, Guozhong Lan, Ke Chen, Yu |
| description | Quantitative real time PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 RNA from 77 patients, and compared with RT-PCR in terms of the diagnostic accuracy based on the results of follow-up survey. 26 patients of COVID-19 with negative RT-PCR reports were reported as positive by ddPCR. The sensitivity, specificity, PPV, NPV, negative likelihood ratio (NLR) and accuracy were improved from 40% (95% CI: 27-55%), 100% (95% CI: 54-100%), 100%, 16% (95% CI: 13-19%), 0.6 (95% CI: 0.48-0.75) and 47% (95% CI: 33-60%) for RT-PCR to 94% (95% CI: 83-99%), 100% (95% CI: 48-100%), 100%, 63% (95% CI: 36-83%), 0.06 (95% CI: 0.02-0.18), and 95% (95% CI: 84-99%) for ddPCR, respectively. Moreover, 6/14 (42.9%) convalescents were detected as positive by ddPCR at 5-12 days post discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the RT-PCR. |
| doi_str_mv | 10.1080/22221751.2020.1772678 |
| format | Article |
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However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 RNA from 77 patients, and compared with RT-PCR in terms of the diagnostic accuracy based on the results of follow-up survey. 26 patients of COVID-19 with negative RT-PCR reports were reported as positive by ddPCR. The sensitivity, specificity, PPV, NPV, negative likelihood ratio (NLR) and accuracy were improved from 40% (95% CI: 27-55%), 100% (95% CI: 54-100%), 100%, 16% (95% CI: 13-19%), 0.6 (95% CI: 0.48-0.75) and 47% (95% CI: 33-60%) for RT-PCR to 94% (95% CI: 83-99%), 100% (95% CI: 48-100%), 100%, 63% (95% CI: 36-83%), 0.06 (95% CI: 0.02-0.18), and 95% (95% CI: 84-99%) for ddPCR, respectively. Moreover, 6/14 (42.9%) convalescents were detected as positive by ddPCR at 5-12 days post discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the RT-PCR.</description><identifier>ISSN: 2222-1751</identifier><identifier>EISSN: 2222-1751</identifier><identifier>DOI: 10.1080/22221751.2020.1772678</identifier><identifier>PMID: 32438868</identifier><language>eng</language><publisher>United States: Taylor & Francis</publisher><subject>Betacoronavirus - genetics ; clinical detection ; Coronavirus Infections - diagnosis ; COVID-19 ; droplet digital PCR ; false negative ; False Negative Reactions ; Humans ; Limit of Detection ; Pandemics ; Pneumonia, Viral - diagnosis ; Real-Time Polymerase Chain Reaction - methods ; RNA, Viral - genetics ; RT-PCR ; SARS-CoV-2 ; Severe acute respiratory syndrome coronavirus 2 ; Viral Load - methods</subject><ispartof>Emerging microbes & infections, 2020-01, Vol.9 (1), p.1259-1268</ispartof><rights>2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Shanghai Shangyixun Cultural Communication Co., Ltd 2020</rights><rights>2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Shanghai Shangyixun Cultural Communication Co., Ltd. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Shanghai Shangyixun Cultural Communication Co., Ltd 2020 The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c628t-3b669e8683ea2032373babfb912ae9458a29c0d61049a89c13f0c78f9fcbe1c43</citedby><cites>FETCH-LOGICAL-c628t-3b669e8683ea2032373babfb912ae9458a29c0d61049a89c13f0c78f9fcbe1c43</cites><orcidid>0000-0003-2868-1816 ; 0000-0003-0678-4064 ; 0000-0003-1300-4652</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448897/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448897/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,27502,27924,27925,53791,53793,59143,59144</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32438868$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Suo, Tao</creatorcontrib><creatorcontrib>Liu, Xinjin</creatorcontrib><creatorcontrib>Feng, Jiangpeng</creatorcontrib><creatorcontrib>Guo, Ming</creatorcontrib><creatorcontrib>Hu, Wenjia</creatorcontrib><creatorcontrib>Guo, Dong</creatorcontrib><creatorcontrib>Ullah, Hafiz</creatorcontrib><creatorcontrib>Yang, Yang</creatorcontrib><creatorcontrib>Zhang, Qiuhan</creatorcontrib><creatorcontrib>Wang, Xin</creatorcontrib><creatorcontrib>Sajid, Muhanmmad</creatorcontrib><creatorcontrib>Huang, Zhixiang</creatorcontrib><creatorcontrib>Deng, Liping</creatorcontrib><creatorcontrib>Chen, Tielong</creatorcontrib><creatorcontrib>Liu, Fang</creatorcontrib><creatorcontrib>Xu, Ke</creatorcontrib><creatorcontrib>Liu, Yuan</creatorcontrib><creatorcontrib>Zhang, Qi</creatorcontrib><creatorcontrib>Liu, Yingle</creatorcontrib><creatorcontrib>Xiong, Yong</creatorcontrib><creatorcontrib>Chen, Guozhong</creatorcontrib><creatorcontrib>Lan, Ke</creatorcontrib><creatorcontrib>Chen, Yu</creatorcontrib><title>ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens</title><title>Emerging microbes & infections</title><addtitle>Emerg Microbes Infect</addtitle><description>Quantitative real time PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 RNA from 77 patients, and compared with RT-PCR in terms of the diagnostic accuracy based on the results of follow-up survey. 26 patients of COVID-19 with negative RT-PCR reports were reported as positive by ddPCR. The sensitivity, specificity, PPV, NPV, negative likelihood ratio (NLR) and accuracy were improved from 40% (95% CI: 27-55%), 100% (95% CI: 54-100%), 100%, 16% (95% CI: 13-19%), 0.6 (95% CI: 0.48-0.75) and 47% (95% CI: 33-60%) for RT-PCR to 94% (95% CI: 83-99%), 100% (95% CI: 48-100%), 100%, 63% (95% CI: 36-83%), 0.06 (95% CI: 0.02-0.18), and 95% (95% CI: 84-99%) for ddPCR, respectively. Moreover, 6/14 (42.9%) convalescents were detected as positive by ddPCR at 5-12 days post discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the RT-PCR.</description><subject>Betacoronavirus - genetics</subject><subject>clinical detection</subject><subject>Coronavirus Infections - diagnosis</subject><subject>COVID-19</subject><subject>droplet digital PCR</subject><subject>false negative</subject><subject>False Negative Reactions</subject><subject>Humans</subject><subject>Limit of Detection</subject><subject>Pandemics</subject><subject>Pneumonia, Viral - diagnosis</subject><subject>Real-Time Polymerase Chain Reaction - methods</subject><subject>RNA, Viral - genetics</subject><subject>RT-PCR</subject><subject>SARS-CoV-2</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Viral Load - methods</subject><issn>2222-1751</issn><issn>2222-1751</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNp9Uk1vFDEMHSEQrUp_AigSFy5T8jWThAOiWhWoVKlVC1wjJ-MpWc1MlmS2Vf892e62ajngSyz7-dmOX1W9ZfSIUU0_8mJMNeyIU15CSvFW6RfV_iZebxIvn_h71WHOS1pM0VYy-braE1wKrVu9X1103cXi8hMBMsaEBLxfJ5iRzDEOpI-JXB1fXtWL-KvmpMMZ_RziRMJEhnhLbkKCoXjQkbxCH0ac8pvqVQ9DxsPde1D9_HryY_G9Pjv_dro4Pqt9y_VcC9e2BssIAoFTwYUSDlzvDOOARjYauPG0axmVBrTxTPTUK92b3jtkXoqD6nTL20VY2lUKI6Q7GyHY-0BM1xbSHPyA1khovFNN71DKzhunm9LeCUeZ1GB84fq85Vqt3Yidx2kuiz0jfZ6Zwm97HW-sklJrowrBhx1Bin_WmGc7huxxGGDCuM6WS9oKRsvKBfr-H-gyrtNUvsryhmpVDtvSgmq2KJ9izgn7x2EYtRsJ2AcJ2I0E7E4Cpe7d000eqx4OXgBftoAwleuOcBvT0NkZ7oaY-gSTD9mK__f4C4S-vg0</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Suo, Tao</creator><creator>Liu, Xinjin</creator><creator>Feng, Jiangpeng</creator><creator>Guo, Ming</creator><creator>Hu, Wenjia</creator><creator>Guo, Dong</creator><creator>Ullah, Hafiz</creator><creator>Yang, Yang</creator><creator>Zhang, Qiuhan</creator><creator>Wang, Xin</creator><creator>Sajid, Muhanmmad</creator><creator>Huang, Zhixiang</creator><creator>Deng, Liping</creator><creator>Chen, Tielong</creator><creator>Liu, Fang</creator><creator>Xu, Ke</creator><creator>Liu, Yuan</creator><creator>Zhang, Qi</creator><creator>Liu, Yingle</creator><creator>Xiong, Yong</creator><creator>Chen, Guozhong</creator><creator>Lan, Ke</creator><creator>Chen, Yu</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><general>Taylor & Francis Group</general><scope>0YH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-2868-1816</orcidid><orcidid>https://orcid.org/0000-0003-0678-4064</orcidid><orcidid>https://orcid.org/0000-0003-1300-4652</orcidid></search><sort><creationdate>20200101</creationdate><title>ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens</title><author>Suo, Tao ; Liu, Xinjin ; Feng, Jiangpeng ; Guo, Ming ; Hu, Wenjia ; Guo, Dong ; Ullah, Hafiz ; Yang, Yang ; Zhang, Qiuhan ; Wang, Xin ; Sajid, Muhanmmad ; Huang, Zhixiang ; Deng, Liping ; Chen, Tielong ; Liu, Fang ; Xu, Ke ; Liu, Yuan ; Zhang, Qi ; Liu, Yingle ; Xiong, Yong ; Chen, Guozhong ; Lan, Ke ; Chen, Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c628t-3b669e8683ea2032373babfb912ae9458a29c0d61049a89c13f0c78f9fcbe1c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Betacoronavirus - genetics</topic><topic>clinical detection</topic><topic>Coronavirus Infections - diagnosis</topic><topic>COVID-19</topic><topic>droplet digital PCR</topic><topic>false negative</topic><topic>False Negative Reactions</topic><topic>Humans</topic><topic>Limit of Detection</topic><topic>Pandemics</topic><topic>Pneumonia, Viral - diagnosis</topic><topic>Real-Time Polymerase Chain Reaction - methods</topic><topic>RNA, Viral - genetics</topic><topic>RT-PCR</topic><topic>SARS-CoV-2</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Viral Load - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Suo, Tao</creatorcontrib><creatorcontrib>Liu, Xinjin</creatorcontrib><creatorcontrib>Feng, Jiangpeng</creatorcontrib><creatorcontrib>Guo, Ming</creatorcontrib><creatorcontrib>Hu, Wenjia</creatorcontrib><creatorcontrib>Guo, Dong</creatorcontrib><creatorcontrib>Ullah, Hafiz</creatorcontrib><creatorcontrib>Yang, Yang</creatorcontrib><creatorcontrib>Zhang, Qiuhan</creatorcontrib><creatorcontrib>Wang, Xin</creatorcontrib><creatorcontrib>Sajid, Muhanmmad</creatorcontrib><creatorcontrib>Huang, Zhixiang</creatorcontrib><creatorcontrib>Deng, Liping</creatorcontrib><creatorcontrib>Chen, Tielong</creatorcontrib><creatorcontrib>Liu, Fang</creatorcontrib><creatorcontrib>Xu, Ke</creatorcontrib><creatorcontrib>Liu, Yuan</creatorcontrib><creatorcontrib>Zhang, Qi</creatorcontrib><creatorcontrib>Liu, Yingle</creatorcontrib><creatorcontrib>Xiong, Yong</creatorcontrib><creatorcontrib>Chen, Guozhong</creatorcontrib><creatorcontrib>Lan, Ke</creatorcontrib><creatorcontrib>Chen, Yu</creatorcontrib><collection>Access via Taylor & Francis (Open Access Collection)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Emerging microbes & infections</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Suo, Tao</au><au>Liu, Xinjin</au><au>Feng, Jiangpeng</au><au>Guo, Ming</au><au>Hu, Wenjia</au><au>Guo, Dong</au><au>Ullah, Hafiz</au><au>Yang, Yang</au><au>Zhang, Qiuhan</au><au>Wang, Xin</au><au>Sajid, Muhanmmad</au><au>Huang, Zhixiang</au><au>Deng, Liping</au><au>Chen, Tielong</au><au>Liu, Fang</au><au>Xu, Ke</au><au>Liu, Yuan</au><au>Zhang, Qi</au><au>Liu, Yingle</au><au>Xiong, Yong</au><au>Chen, Guozhong</au><au>Lan, Ke</au><au>Chen, Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens</atitle><jtitle>Emerging microbes & infections</jtitle><addtitle>Emerg Microbes Infect</addtitle><date>2020-01-01</date><risdate>2020</risdate><volume>9</volume><issue>1</issue><spage>1259</spage><epage>1268</epage><pages>1259-1268</pages><issn>2222-1751</issn><eissn>2222-1751</eissn><abstract>Quantitative real time PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 RNA from 77 patients, and compared with RT-PCR in terms of the diagnostic accuracy based on the results of follow-up survey. 26 patients of COVID-19 with negative RT-PCR reports were reported as positive by ddPCR. The sensitivity, specificity, PPV, NPV, negative likelihood ratio (NLR) and accuracy were improved from 40% (95% CI: 27-55%), 100% (95% CI: 54-100%), 100%, 16% (95% CI: 13-19%), 0.6 (95% CI: 0.48-0.75) and 47% (95% CI: 33-60%) for RT-PCR to 94% (95% CI: 83-99%), 100% (95% CI: 48-100%), 100%, 63% (95% CI: 36-83%), 0.06 (95% CI: 0.02-0.18), and 95% (95% CI: 84-99%) for ddPCR, respectively. Moreover, 6/14 (42.9%) convalescents were detected as positive by ddPCR at 5-12 days post discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the RT-PCR.</abstract><cop>United States</cop><pub>Taylor & Francis</pub><pmid>32438868</pmid><doi>10.1080/22221751.2020.1772678</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-2868-1816</orcidid><orcidid>https://orcid.org/0000-0003-0678-4064</orcidid><orcidid>https://orcid.org/0000-0003-1300-4652</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Betacoronavirus - genetics clinical detection Coronavirus Infections - diagnosis COVID-19 droplet digital PCR false negative False Negative Reactions Humans Limit of Detection Pandemics Pneumonia, Viral - diagnosis Real-Time Polymerase Chain Reaction - methods RNA, Viral - genetics RT-PCR SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Viral Load - methods |
| title | ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens |
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