Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection
Background There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease. Method...
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| Veröffentlicht in: | Journal of clinical immunology 2020-10, Vol.40 (7), p.960-969 |
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| container_title | Journal of clinical immunology |
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| creator | Luo, Ying Mao, Liyan Yuan, Xu Xue, Ying Lin, Qun Tang, Guoxing Song, Huijuan Wang, Feng Sun, Ziyong |
| description | Background
There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease.
Methods
A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal (
n
= 51) and survived (
n
= 688) groups according to the patient’s outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously.
Results
The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4
+
T cells, CD8
+
T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-α on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4
+
T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death.
Conclusions
Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19. |
| doi_str_mv | 10.1007/s10875-020-00821-7 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7357264</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2423800350</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-1a22a7f894e7ebd5611777b65b1c61519f3c790c2184b098882914a4269297ed3</originalsourceid><addsrcrecordid>eNp9kU1v1DAQhi1ERZeFP8ABWeLCxdSexHZyQSoRH5W2omKBq-Ukk92UxN7aCdL-e9zdtnwcOFnWPPPOvPMS8kLwN4JzfRYFL7RkHDjjvADB9COyEFJnDGQJj8mCgxasFDmckqcxXnPOMwXyCTnNQCmhS70g8Spg2zdT7x299C0O9J2N2NL0nbZIKz_WvbOHsu9otZ_8j95hpNa1dLUfd1vf7Cek67mOOEXa-UCvgt84H_t427E-_7Jmlf_OgF64Dg-DnpGTzg4Rn9-9S_Ltw_uv1Se2-vzxojpfsSbX-cSEBbC6K8ocNdatVEJorWsla9EoIUXZZY0ueQOiyGteFkUByavNQZVQamyzJXl71N3N9Yhtg24KdjC70I827I23vfm74vqt2fifRmdSg8qTwOs7geBvZoyTGfvY4DBYh36OBnLIinRUyRP66h_02s_BJXsGpMqkKjioRMGRaoKPMWD3sIzg5jZTc8zUpEzNIdO0y5K8_NPGQ8t9iAnIjkBMJbfB8Hv2f2R_ATdNq9Q</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2563568026</pqid></control><display><type>article</type><title>Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Luo, Ying ; Mao, Liyan ; Yuan, Xu ; Xue, Ying ; Lin, Qun ; Tang, Guoxing ; Song, Huijuan ; Wang, Feng ; Sun, Ziyong</creator><creatorcontrib>Luo, Ying ; Mao, Liyan ; Yuan, Xu ; Xue, Ying ; Lin, Qun ; Tang, Guoxing ; Song, Huijuan ; Wang, Feng ; Sun, Ziyong</creatorcontrib><description>Background
There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease.
Methods
A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal (
n
= 51) and survived (
n
= 688) groups according to the patient’s outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously.
Results
The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4
+
T cells, CD8
+
T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-α on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4
+
T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death.
Conclusions
Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19.</description><identifier>ISSN: 0271-9142</identifier><identifier>EISSN: 1573-2592</identifier><identifier>DOI: 10.1007/s10875-020-00821-7</identifier><identifier>PMID: 32661797</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Aged ; Aged, 80 and over ; Betacoronavirus - genetics ; Betacoronavirus - immunology ; Betacoronavirus - isolation & purification ; Biomarkers - blood ; Biomedical and Life Sciences ; Biomedicine ; CD4 antigen ; CD8 antigen ; China - epidemiology ; Clinical Laboratory Techniques - methods ; Coronavirus Infections - blood ; Coronavirus Infections - diagnosis ; Coronavirus Infections - epidemiology ; Coronavirus Infections - immunology ; Coronavirus Infections - mortality ; Coronaviruses ; COVID-19 ; COVID-19 Testing ; Cytokines ; Cytokines - blood ; Cytokines - immunology ; Death ; Female ; Humans ; Immunology ; Infectious Diseases ; Interleukin 2 ; Interleukin 6 ; Interleukin 8 ; Internal Medicine ; Length of Stay ; Lymphocyte Count ; Lymphocyte Subsets - immunology ; Lymphocytes ; Lymphocytes B ; Lymphocytes T ; Male ; Medical Microbiology ; Middle Aged ; Models, Biological ; Original ; Original Article ; Pandemics ; Patients ; Pneumonia, Viral - blood ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - immunology ; Pneumonia, Viral - mortality ; Prediction models ; Prognosis ; Reverse Transcriptase Polymerase Chain Reaction ; Risk Assessment - methods ; RNA, Viral - isolation & purification ; SARS-CoV-2 ; Severe acute respiratory syndrome coronavirus 2 ; Tumor necrosis factor-α</subject><ispartof>Journal of clinical immunology, 2020-10, Vol.40 (7), p.960-969</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. This work is published under 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-1a22a7f894e7ebd5611777b65b1c61519f3c790c2184b098882914a4269297ed3</citedby><cites>FETCH-LOGICAL-c474t-1a22a7f894e7ebd5611777b65b1c61519f3c790c2184b098882914a4269297ed3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10875-020-00821-7$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10875-020-00821-7$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27915,27916,41479,42548,51310</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32661797$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Luo, Ying</creatorcontrib><creatorcontrib>Mao, Liyan</creatorcontrib><creatorcontrib>Yuan, Xu</creatorcontrib><creatorcontrib>Xue, Ying</creatorcontrib><creatorcontrib>Lin, Qun</creatorcontrib><creatorcontrib>Tang, Guoxing</creatorcontrib><creatorcontrib>Song, Huijuan</creatorcontrib><creatorcontrib>Wang, Feng</creatorcontrib><creatorcontrib>Sun, Ziyong</creatorcontrib><title>Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection</title><title>Journal of clinical immunology</title><addtitle>J Clin Immunol</addtitle><addtitle>J Clin Immunol</addtitle><description>Background
There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease.
Methods
A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal (
n
= 51) and survived (
n
= 688) groups according to the patient’s outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously.
Results
The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4
+
T cells, CD8
+
T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-α on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4
+
T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death.
Conclusions
Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Betacoronavirus - genetics</subject><subject>Betacoronavirus - immunology</subject><subject>Betacoronavirus - isolation & purification</subject><subject>Biomarkers - blood</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>CD4 antigen</subject><subject>CD8 antigen</subject><subject>China - epidemiology</subject><subject>Clinical Laboratory Techniques - methods</subject><subject>Coronavirus Infections - blood</subject><subject>Coronavirus Infections - diagnosis</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - immunology</subject><subject>Coronavirus Infections - mortality</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 Testing</subject><subject>Cytokines</subject><subject>Cytokines - blood</subject><subject>Cytokines - immunology</subject><subject>Death</subject><subject>Female</subject><subject>Humans</subject><subject>Immunology</subject><subject>Infectious Diseases</subject><subject>Interleukin 2</subject><subject>Interleukin 6</subject><subject>Interleukin 8</subject><subject>Internal Medicine</subject><subject>Length of Stay</subject><subject>Lymphocyte Count</subject><subject>Lymphocyte Subsets - immunology</subject><subject>Lymphocytes</subject><subject>Lymphocytes B</subject><subject>Lymphocytes T</subject><subject>Male</subject><subject>Medical Microbiology</subject><subject>Middle Aged</subject><subject>Models, Biological</subject><subject>Original</subject><subject>Original Article</subject><subject>Pandemics</subject><subject>Patients</subject><subject>Pneumonia, Viral - blood</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - immunology</subject><subject>Pneumonia, Viral - mortality</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Reverse Transcriptase Polymerase Chain Reaction</subject><subject>Risk Assessment - methods</subject><subject>RNA, Viral - isolation & purification</subject><subject>SARS-CoV-2</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Tumor necrosis factor-α</subject><issn>0271-9142</issn><issn>1573-2592</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU1v1DAQhi1ERZeFP8ABWeLCxdSexHZyQSoRH5W2omKBq-Ukk92UxN7aCdL-e9zdtnwcOFnWPPPOvPMS8kLwN4JzfRYFL7RkHDjjvADB9COyEFJnDGQJj8mCgxasFDmckqcxXnPOMwXyCTnNQCmhS70g8Spg2zdT7x299C0O9J2N2NL0nbZIKz_WvbOHsu9otZ_8j95hpNa1dLUfd1vf7Cek67mOOEXa-UCvgt84H_t427E-_7Jmlf_OgF64Dg-DnpGTzg4Rn9-9S_Ltw_uv1Se2-vzxojpfsSbX-cSEBbC6K8ocNdatVEJorWsla9EoIUXZZY0ueQOiyGteFkUByavNQZVQamyzJXl71N3N9Yhtg24KdjC70I827I23vfm74vqt2fifRmdSg8qTwOs7geBvZoyTGfvY4DBYh36OBnLIinRUyRP66h_02s_BJXsGpMqkKjioRMGRaoKPMWD3sIzg5jZTc8zUpEzNIdO0y5K8_NPGQ8t9iAnIjkBMJbfB8Hv2f2R_ATdNq9Q</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Luo, Ying</creator><creator>Mao, Liyan</creator><creator>Yuan, Xu</creator><creator>Xue, Ying</creator><creator>Lin, Qun</creator><creator>Tang, Guoxing</creator><creator>Song, Huijuan</creator><creator>Wang, Feng</creator><creator>Sun, Ziyong</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</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>7T5</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20201001</creationdate><title>Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection</title><author>Luo, Ying ; Mao, Liyan ; Yuan, Xu ; Xue, Ying ; Lin, Qun ; Tang, Guoxing ; Song, Huijuan ; Wang, Feng ; Sun, Ziyong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-1a22a7f894e7ebd5611777b65b1c61519f3c790c2184b098882914a4269297ed3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Betacoronavirus - genetics</topic><topic>Betacoronavirus - immunology</topic><topic>Betacoronavirus - isolation & purification</topic><topic>Biomarkers - blood</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>CD4 antigen</topic><topic>CD8 antigen</topic><topic>China - epidemiology</topic><topic>Clinical Laboratory Techniques - methods</topic><topic>Coronavirus Infections - blood</topic><topic>Coronavirus Infections - diagnosis</topic><topic>Coronavirus Infections - epidemiology</topic><topic>Coronavirus Infections - immunology</topic><topic>Coronavirus Infections - mortality</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 Testing</topic><topic>Cytokines</topic><topic>Cytokines - blood</topic><topic>Cytokines - immunology</topic><topic>Death</topic><topic>Female</topic><topic>Humans</topic><topic>Immunology</topic><topic>Infectious Diseases</topic><topic>Interleukin 2</topic><topic>Interleukin 6</topic><topic>Interleukin 8</topic><topic>Internal Medicine</topic><topic>Length of Stay</topic><topic>Lymphocyte Count</topic><topic>Lymphocyte Subsets - immunology</topic><topic>Lymphocytes</topic><topic>Lymphocytes B</topic><topic>Lymphocytes T</topic><topic>Male</topic><topic>Medical Microbiology</topic><topic>Middle Aged</topic><topic>Models, Biological</topic><topic>Original</topic><topic>Original Article</topic><topic>Pandemics</topic><topic>Patients</topic><topic>Pneumonia, Viral - blood</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>Pneumonia, Viral - immunology</topic><topic>Pneumonia, Viral - mortality</topic><topic>Prediction models</topic><topic>Prognosis</topic><topic>Reverse Transcriptase Polymerase Chain Reaction</topic><topic>Risk Assessment - methods</topic><topic>RNA, Viral - isolation & purification</topic><topic>SARS-CoV-2</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Tumor necrosis factor-α</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luo, Ying</creatorcontrib><creatorcontrib>Mao, Liyan</creatorcontrib><creatorcontrib>Yuan, Xu</creatorcontrib><creatorcontrib>Xue, Ying</creatorcontrib><creatorcontrib>Lin, Qun</creatorcontrib><creatorcontrib>Tang, Guoxing</creatorcontrib><creatorcontrib>Song, Huijuan</creatorcontrib><creatorcontrib>Wang, Feng</creatorcontrib><creatorcontrib>Sun, Ziyong</creatorcontrib><collection>Springer Nature OA Free Journals</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>Immunology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</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>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</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 Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of clinical immunology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luo, Ying</au><au>Mao, Liyan</au><au>Yuan, Xu</au><au>Xue, Ying</au><au>Lin, Qun</au><au>Tang, Guoxing</au><au>Song, Huijuan</au><au>Wang, Feng</au><au>Sun, Ziyong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection</atitle><jtitle>Journal of clinical immunology</jtitle><stitle>J Clin Immunol</stitle><addtitle>J Clin Immunol</addtitle><date>2020-10-01</date><risdate>2020</risdate><volume>40</volume><issue>7</issue><spage>960</spage><epage>969</epage><pages>960-969</pages><issn>0271-9142</issn><eissn>1573-2592</eissn><abstract>Background
There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease.
Methods
A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal (
n
= 51) and survived (
n
= 688) groups according to the patient’s outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously.
Results
The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4
+
T cells, CD8
+
T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-α on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4
+
T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death.
Conclusions
Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>32661797</pmid><doi>10.1007/s10875-020-00821-7</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
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| ispartof | Journal of clinical immunology, 2020-10, Vol.40 (7), p.960-969 |
| issn | 0271-9142 1573-2592 |
| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Aged Aged, 80 and over Betacoronavirus - genetics Betacoronavirus - immunology Betacoronavirus - isolation & purification Biomarkers - blood Biomedical and Life Sciences Biomedicine CD4 antigen CD8 antigen China - epidemiology Clinical Laboratory Techniques - methods Coronavirus Infections - blood Coronavirus Infections - diagnosis Coronavirus Infections - epidemiology Coronavirus Infections - immunology Coronavirus Infections - mortality Coronaviruses COVID-19 COVID-19 Testing Cytokines Cytokines - blood Cytokines - immunology Death Female Humans Immunology Infectious Diseases Interleukin 2 Interleukin 6 Interleukin 8 Internal Medicine Length of Stay Lymphocyte Count Lymphocyte Subsets - immunology Lymphocytes Lymphocytes B Lymphocytes T Male Medical Microbiology Middle Aged Models, Biological Original Original Article Pandemics Patients Pneumonia, Viral - blood Pneumonia, Viral - epidemiology Pneumonia, Viral - immunology Pneumonia, Viral - mortality Prediction models Prognosis Reverse Transcriptase Polymerase Chain Reaction Risk Assessment - methods RNA, Viral - isolation & purification SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Tumor necrosis factor-α |
| title | Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection |
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