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[Construction and evaluation of a novel diagnosis process for 2019-Corona Virus Disease].

Identifieur interne : 000070 ( an2020/Analysis ); précédent : 000069; suivant : 000071

[Construction and evaluation of a novel diagnosis process for 2019-Corona Virus Disease].

Auteurs : Z. Xiong [République populaire de Chine] ; L. Fu [République populaire de Chine] ; H. Zhou [République populaire de Chine] ; J K Liu [République populaire de Chine] ; A M Wang [République populaire de Chine] ; Y. Huang [République populaire de Chine] ; X. Huang [République populaire de Chine] ; B. Yi [République populaire de Chine] ; J. Wu [République populaire de Chine] ; C H Li [République populaire de Chine] ; J. Quan [République populaire de Chine] ; M. Li [République populaire de Chine] ; Y S Leng [République populaire de Chine] ; W J Luo [République populaire de Chine] ; C P Hu [République populaire de Chine] ; W H Liao [République populaire de Chine]

Source :

RBID : pubmed:32157849

Abstract

Objective: To construct and evaluate a diagnosis process for 2019-Corona Virus Disease (COVID-19). Methods: A continuous cohort of adults and adolescent (≥12 years) who screened COVID-19 was included in Xiangya Hospital of Central South University from January 23 to February 3, 2020 in which cases were divided the test library and the verification library. Their gender, age, onset time were recorded. Take epidemiological history, fever, and the blood lymphocytes decline as clinical indicators, used CT to evaluate the possibility of COVID-19 and range of lung involvement. According to the current national standards, throat swabs of suspected cases were collected and the nucleic acid of COVID-19 was detected by reverse transcription-polymerase chain reaction (RT-PCR). The Xiangya process was constructed according to multi-index, compared with clinical indicators, CT results and national standards, the efficiency of detecting confirmed cases were verified in the test and verification library. Results: A continuous cohort of 382 adults who screened COVID-19 was included in which 261 cases were in the test library and 121 cases were in the verification library. In the 382 cases, 192 were males (50.3%) and 190 were females (49.7%), with a median age of 35 years (range: 15-92 years). There were 183 cases (47.9%) with epidemiological history, 275 cases (72.0%) with fever, 212 cases (55.5%) with decreased hemolytic lymphocytes, CT positive 114 cases (29.8%), 43 cases (11.3%) with positive CT-COVID-19, and 30 cases (7.9%) with positive throat swab nucleic acid. Compared with clinical indicators, the sensitivity and specificity of CT were 0.950 and 0.704, respectively. The accuracy of CT to make a definite diagnosis was higher than that of epidemiological history, fever, and blood lymph count decline (0.809 vs 0.660, 0.532, 0.596, P=0.001, 0.002, 0.003, respectively). The sensitivity of this process and the program recommended by the Health Commission all were high (all were 1.000) , and the specificity and accuracy of the process were higher than the program recommended by the Health Commission (0.872 vs 0.765, 0.778 vs 0.592, both P<0.001). The CT-COVID-19 would have reduced the missed diagnosis rate caused by false negative of nucleic acid test (31 vs 64, difference rate 51.6%), the positive rate of nucleic acid test was 64.5% (20/31). In validation library, the specificity and accuracy of the Xiangya process was 0.967, the positive rate of nucleic acid test was 76.9% (10/13). Conclusions: The Xiangya process can predict the nucleic acid test results of COVID-19 well, and can be applied as a reliable basis for confirmed cases detection in adults and adolescent (≥12 years) in areas other than Hubei during the epidemic period of COVID-19. The cohort size needs to be increased for further validation.

DOI: 10.3760/cma.j.cn112137-20200228-00499
PubMed: 32157849


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pubmed:32157849

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<name sortKey="Zhou, H" sort="Zhou, H" uniqKey="Zhou H" first="H" last="Zhou">H. Zhou</name>
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<nlm:affiliation>Department of Radiology, Xiangya Hospital of Central South University, Changsha 410008, China.</nlm:affiliation>
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<name sortKey="Liu, J K" sort="Liu, J K" uniqKey="Liu J" first="J K" last="Liu">J K Liu</name>
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<nlm:affiliation>Department of Radiology, Xiangya Hospital of Central South University, Changsha 410008, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
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<name sortKey="Wang, A M" sort="Wang, A M" uniqKey="Wang A" first="A M" last="Wang">A M Wang</name>
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<nlm:affiliation>Department of Emergency, Xiangya Hospital of Central South University, Changsha 410008, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Emergency, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
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<name sortKey="Huang, Y" sort="Huang, Y" uniqKey="Huang Y" first="Y" last="Huang">Y. Huang</name>
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<name sortKey="Huang, X" sort="Huang, X" uniqKey="Huang X" first="X" last="Huang">X. Huang</name>
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<nlm:affiliation>Department of Infection Control Center, Xiangya Hospital of Central South University, Changsha 410008, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Infection Control Center, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
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<name sortKey="Yi, B" sort="Yi, B" uniqKey="Yi B" first="B" last="Yi">B. Yi</name>
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<wicri:regionArea>Department of Clinical Laboratory, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
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<name sortKey="Wu, J" sort="Wu, J" uniqKey="Wu J" first="J" last="Wu">J. Wu</name>
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<name sortKey="Li, C H" sort="Li, C H" uniqKey="Li C" first="C H" last="Li">C H Li</name>
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<name sortKey="Quan, J" sort="Quan, J" uniqKey="Quan J" first="J" last="Quan">J. Quan</name>
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<nlm:affiliation>Department of Infectious Diseases, Xiangya Hospital of Central South University, Changsha 410008, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Infectious Diseases, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
<wicri:noRegion>Changsha 410008</wicri:noRegion>
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<name sortKey="Li, M" sort="Li, M" uniqKey="Li M" first="M" last="Li">M. Li</name>
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<wicri:regionArea>Department of Respiratory and Critical Care Medicine, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
<wicri:noRegion>Changsha 410008</wicri:noRegion>
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<name sortKey="Leng, Y S" sort="Leng, Y S" uniqKey="Leng Y" first="Y S" last="Leng">Y S Leng</name>
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<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Radiology, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
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<name sortKey="Luo, W J" sort="Luo, W J" uniqKey="Luo W" first="W J" last="Luo">W J Luo</name>
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<nlm:affiliation>Department of Radiology, Xiangya Hospital of Central South University, Changsha 410008, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Radiology, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
<wicri:noRegion>Changsha 410008</wicri:noRegion>
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<name sortKey="Hu, C P" sort="Hu, C P" uniqKey="Hu C" first="C P" last="Hu">C P Hu</name>
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<nlm:affiliation>Department of Respiratory and Critical Care Medicine, Xiangya Hospital of Central South University, Changsha 410008, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Respiratory and Critical Care Medicine, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
<wicri:noRegion>Changsha 410008</wicri:noRegion>
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<name sortKey="Liao, W H" sort="Liao, W H" uniqKey="Liao W" first="W H" last="Liao">W H Liao</name>
<affiliation wicri:level="1">
<nlm:affiliation>Department of Radiology, Xiangya Hospital of Central South University, Changsha 410008, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Radiology, Xiangya Hospital of Central South University, Changsha 410008</wicri:regionArea>
<wicri:noRegion>Changsha 410008</wicri:noRegion>
</affiliation>
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<series>
<title level="j">Zhonghua yi xue za zhi</title>
<idno type="ISSN">0376-2491</idno>
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<date when="2020" type="published">2020</date>
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<front>
<div type="abstract" xml:lang="en">
<b>Objective:</b>
To construct and evaluate a diagnosis process for 2019-Corona Virus Disease (COVID-19).
<b>Methods:</b>
A continuous cohort of adults and adolescent (≥12 years) who screened COVID-19 was included in Xiangya Hospital of Central South University from January 23 to February 3, 2020 in which cases were divided the test library and the verification library. Their gender, age, onset time were recorded. Take epidemiological history, fever, and the blood lymphocytes decline as clinical indicators, used CT to evaluate the possibility of COVID-19 and range of lung involvement. According to the current national standards, throat swabs of suspected cases were collected and the nucleic acid of COVID-19 was detected by reverse transcription-polymerase chain reaction (RT-PCR). The Xiangya process was constructed according to multi-index, compared with clinical indicators, CT results and national standards, the efficiency of detecting confirmed cases were verified in the test and verification library.
<b>Results:</b>
A continuous cohort of 382 adults who screened COVID-19 was included in which 261 cases were in the test library and 121 cases were in the verification library. In the 382 cases, 192 were males (50.3%) and 190 were females (49.7%), with a median age of 35 years (range: 15-92 years). There were 183 cases (47.9%) with epidemiological history, 275 cases (72.0%) with fever, 212 cases (55.5%) with decreased hemolytic lymphocytes, CT positive 114 cases (29.8%), 43 cases (11.3%) with positive CT-COVID-19, and 30 cases (7.9%) with positive throat swab nucleic acid. Compared with clinical indicators, the sensitivity and specificity of CT were 0.950 and 0.704, respectively. The accuracy of CT to make a definite diagnosis was higher than that of epidemiological history, fever, and blood lymph count decline (0.809 vs 0.660, 0.532, 0.596,
<i>P</i>
=0.001, 0.002, 0.003, respectively). The sensitivity of this process and the program recommended by the Health Commission all were high (all were 1.000) , and the specificity and accuracy of the process were higher than the program recommended by the Health Commission (0.872 vs 0.765, 0.778 vs 0.592, both
<i>P</i>
<0.001). The CT-COVID-19 would have reduced the missed diagnosis rate caused by false negative of nucleic acid test (31 vs 64, difference rate 51.6%), the positive rate of nucleic acid test was 64.5% (20/31). In validation library, the specificity and accuracy of the Xiangya process was 0.967, the positive rate of nucleic acid test was 76.9% (10/13).
<b>Conclusions:</b>
The Xiangya process can predict the nucleic acid test results of COVID-19 well, and can be applied as a reliable basis for confirmed cases detection in adults and adolescent (≥12 years) in areas other than Hubei during the epidemic period of COVID-19. The cohort size needs to be increased for further validation.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>République populaire de Chine</li>
</country>
</list>
<tree>
<country name="République populaire de Chine">
<noRegion>
<name sortKey="Xiong, Z" sort="Xiong, Z" uniqKey="Xiong Z" first="Z" last="Xiong">Z. Xiong</name>
</noRegion>
<name sortKey="Fu, L" sort="Fu, L" uniqKey="Fu L" first="L" last="Fu">L. Fu</name>
<name sortKey="Hu, C P" sort="Hu, C P" uniqKey="Hu C" first="C P" last="Hu">C P Hu</name>
<name sortKey="Huang, X" sort="Huang, X" uniqKey="Huang X" first="X" last="Huang">X. Huang</name>
<name sortKey="Huang, Y" sort="Huang, Y" uniqKey="Huang Y" first="Y" last="Huang">Y. Huang</name>
<name sortKey="Leng, Y S" sort="Leng, Y S" uniqKey="Leng Y" first="Y S" last="Leng">Y S Leng</name>
<name sortKey="Li, C H" sort="Li, C H" uniqKey="Li C" first="C H" last="Li">C H Li</name>
<name sortKey="Li, M" sort="Li, M" uniqKey="Li M" first="M" last="Li">M. Li</name>
<name sortKey="Liao, W H" sort="Liao, W H" uniqKey="Liao W" first="W H" last="Liao">W H Liao</name>
<name sortKey="Liu, J K" sort="Liu, J K" uniqKey="Liu J" first="J K" last="Liu">J K Liu</name>
<name sortKey="Luo, W J" sort="Luo, W J" uniqKey="Luo W" first="W J" last="Luo">W J Luo</name>
<name sortKey="Quan, J" sort="Quan, J" uniqKey="Quan J" first="J" last="Quan">J. Quan</name>
<name sortKey="Wang, A M" sort="Wang, A M" uniqKey="Wang A" first="A M" last="Wang">A M Wang</name>
<name sortKey="Wu, J" sort="Wu, J" uniqKey="Wu J" first="J" last="Wu">J. Wu</name>
<name sortKey="Yi, B" sort="Yi, B" uniqKey="Yi B" first="B" last="Yi">B. Yi</name>
<name sortKey="Zhou, H" sort="Zhou, H" uniqKey="Zhou H" first="H" last="Zhou">H. Zhou</name>
</country>
</tree>
</affiliations>
</record>

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