Impaired immune and coagulation systems may be early risk factors for COVID-19 patients: A retrospective study of 118 inpatients from Wuhan, China.
Identifieur interne : 000402 ( Main/Corpus ); précédent : 000401; suivant : 000403Impaired immune and coagulation systems may be early risk factors for COVID-19 patients: A retrospective study of 118 inpatients from Wuhan, China.
Auteurs : Zhi-Jun Qin ; Lei Liu ; Qun Sun ; Xia Li ; Jian-Fei Luo ; Jia-Sheng Liu ; Dan LiuSource :
- Medicine [ 1536-5964 ] ; 2020.
English descriptors
- KwdEn :
- Adult (MeSH), Aged (MeSH), Betacoronavirus (MeSH), China (epidemiology), Coronavirus Infections (blood), Coronavirus Infections (immunology), Coronavirus Infections (mortality), Female (MeSH), Fibrin Fibrinogen Degradation Products (analysis), Hospital Mortality (MeSH), Hospitalization (statistics & numerical data), Humans (MeSH), Leukocyte Count (methods), Leukocyte Count (statistics & numerical data), Male (MeSH), Pandemics (MeSH), Pneumonia, Viral (blood), Pneumonia, Viral (immunology), Pneumonia, Viral (mortality), Predictive Value of Tests (MeSH), Procalcitonin (analysis), Prognosis (MeSH), Prothrombin Time (methods), Prothrombin Time (statistics & numerical data), Retrospective Studies (MeSH), Risk Assessment (methods), Risk Factors (MeSH).
- MESH :
- chemical , analysis : Fibrin Fibrinogen Degradation Products, Procalcitonin.
- geographic , epidemiology : China.
- blood : Coronavirus Infections, Pneumonia, Viral.
- immunology : Coronavirus Infections, Pneumonia, Viral.
- methods : Leukocyte Count, Prothrombin Time, Risk Assessment.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- statistics & numerical data : Hospitalization, Leukocyte Count, Prothrombin Time.
- Adult, Aged, Betacoronavirus, Female, Hospital Mortality, Humans, Male, Pandemics, Predictive Value of Tests, Prognosis, Retrospective Studies, Risk Factors.
Abstract
The coronavirus disease 2019 (COVID-19) outbreak has become a global health threat and will likely be one of the greatest global challenges in the near future. The battle between clinicians and the COVID-19 outbreak may be a "protracted war."The objective of this study was to investigate the risk factors for in-hospital mortality in patients with COVID-19, so as to provide a reference for the early diagnosis and treatment.This study retrospectively enrolled 118 patients diagnosed with COVID-19, who were admitted to Eastern District of Renmin Hospital of Wuhan University from February 04, 2020 to March 04, 2020. The demographics and laboratory data were collected and compared between survivors and nonsurvivors. The risk factors of in-hospital mortality were explored by univariable and multivariable logistic regression to construct a clinical prediction model, the prediction efficiency of which was verified by receiver-operating characteristic (ROC) curve.A total of 118 patients (49 males and 69 females) were included in this study; the results revealed that the following factors associated with in-hospital mortality: older age (odds ratio [OR] 1.175, 95% confidence interval [CI] 1.073-1.287, P = .001), neutrophil count greater than 6.3 × 10 cells/L (OR 7.174, (95% CI 2.295-22.432, P = .001), lymphocytopenia (OR 0.069, 95% CI 0.007-0.722, P = .026), prothrombin time >13 seconds (OR 11.869, 95% CI 1.433-98.278, P = .022), D-dimer >1 mg/L (OR 22.811, 95% CI 2.224-233.910, P = .008) and procalcitonin (PCT) >0.1 ng/mL (OR 23.022, 95% CI 3.108-170.532, P = .002). The area under the ROC curve (AUC) of the above indicators for predicting in-hospital mortality were 0.808 (95% CI 0.715-0.901), 0.809 (95% CI 0.710-0.907), 0.811 (95% CI 0.724-0.898), 0.745 (95% CI 0.643-0.847), 0.872 (95% CI 0.804-0.940), 0.881 (95% CI 0.809-0.953), respectively. The AUC of combined diagnosis of these aforementioned factors were 0.992 (95% CI 0.981-1.000).In conclusion, older age, increased neutrophil count, prothrombin time, D-dimer, PCT, and decreased lymphocyte count at admission were risk factors associated with in-hospital mortality of COVID-19. The prediction model combined of these factors could improve the early identification of mortality risk in COVID-19 patients.
DOI: 10.1097/MD.0000000000021700
PubMed: 32871887
PubMed Central: PMC7458161
Links to Exploration step
pubmed:32871887Le document en format XML
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last="Li">Xia Li</name><affiliation><nlm:affiliation>Department of Intensive Care Unit.</nlm:affiliation></affiliation></author><author><name sortKey="Luo, Jian Fei" sort="Luo, Jian Fei" uniqKey="Luo J" first="Jian-Fei" last="Luo">Jian-Fei Luo</name><affiliation><nlm:affiliation>Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei.</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Jia Sheng" sort="Liu, Jia Sheng" uniqKey="Liu J" first="Jia-Sheng" last="Liu">Jia-Sheng Liu</name><affiliation><nlm:affiliation>Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei.</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Dan" sort="Liu, Dan" uniqKey="Liu D" first="Dan" last="Liu">Dan Liu</name><affiliation><nlm:affiliation>Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, 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Liu</name><affiliation><nlm:affiliation>Department of Infection Management, Sichuan Provincial Orthopedic Hospital, Chengdu, Sichuan.</nlm:affiliation></affiliation></author><author><name sortKey="Sun, Qun" sort="Sun, Qun" uniqKey="Sun Q" first="Qun" last="Sun">Qun Sun</name><affiliation><nlm:affiliation>Department of Intensive Care Unit.</nlm:affiliation></affiliation></author><author><name sortKey="Li, Xia" sort="Li, Xia" uniqKey="Li X" first="Xia" last="Li">Xia Li</name><affiliation><nlm:affiliation>Department of Intensive Care Unit.</nlm:affiliation></affiliation></author><author><name sortKey="Luo, Jian Fei" sort="Luo, Jian Fei" uniqKey="Luo J" first="Jian-Fei" last="Luo">Jian-Fei Luo</name><affiliation><nlm:affiliation>Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei.</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Jia Sheng" sort="Liu, Jia Sheng" uniqKey="Liu J" first="Jia-Sheng" last="Liu">Jia-Sheng Liu</name><affiliation><nlm:affiliation>Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei.</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Dan" sort="Liu, Dan" uniqKey="Liu D" first="Dan" last="Liu">Dan Liu</name><affiliation><nlm:affiliation>Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Medicine</title><idno type="eISSN">1536-5964</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>China (epidemiology)</term><term>Coronavirus Infections (blood)</term><term>Coronavirus Infections (immunology)</term><term>Coronavirus Infections (mortality)</term><term>Female (MeSH)</term><term>Fibrin Fibrinogen Degradation Products (analysis)</term><term>Hospital Mortality (MeSH)</term><term>Hospitalization (statistics & numerical data)</term><term>Humans (MeSH)</term><term>Leukocyte Count (methods)</term><term>Leukocyte Count (statistics & numerical data)</term><term>Male (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (blood)</term><term>Pneumonia, Viral (immunology)</term><term>Pneumonia, Viral (mortality)</term><term>Predictive Value of Tests (MeSH)</term><term>Procalcitonin (analysis)</term><term>Prognosis (MeSH)</term><term>Prothrombin Time (methods)</term><term>Prothrombin Time (statistics & numerical data)</term><term>Retrospective Studies (MeSH)</term><term>Risk Assessment (methods)</term><term>Risk Factors (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Fibrin Fibrinogen Degradation Products</term><term>Procalcitonin</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="blood" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Leukocyte Count</term><term>Prothrombin Time</term><term>Risk Assessment</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Hospitalization</term><term>Leukocyte Count</term><term>Prothrombin Time</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Aged</term><term>Betacoronavirus</term><term>Female</term><term>Hospital Mortality</term><term>Humans</term><term>Male</term><term>Pandemics</term><term>Predictive Value of Tests</term><term>Prognosis</term><term>Retrospective Studies</term><term>Risk Factors</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The coronavirus disease 2019 (COVID-19) outbreak has become a global health threat and will likely be one of the greatest global challenges in the near future. The battle between clinicians and the COVID-19 outbreak may be a "protracted war."The objective of this study was to investigate the risk factors for in-hospital mortality in patients with COVID-19, so as to provide a reference for the early diagnosis and treatment.This study retrospectively enrolled 118 patients diagnosed with COVID-19, who were admitted to Eastern District of Renmin Hospital of Wuhan University from February 04, 2020 to March 04, 2020. The demographics and laboratory data were collected and compared between survivors and nonsurvivors. The risk factors of in-hospital mortality were explored by univariable and multivariable logistic regression to construct a clinical prediction model, the prediction efficiency of which was verified by receiver-operating characteristic (ROC) curve.A total of 118 patients (49 males and 69 females) were included in this study; the results revealed that the following factors associated with in-hospital mortality: older age (odds ratio [OR] 1.175, 95% confidence interval [CI] 1.073-1.287, P = .001), neutrophil count greater than 6.3 × 10 cells/L (OR 7.174, (95% CI 2.295-22.432, P = .001), lymphocytopenia (OR 0.069, 95% CI 0.007-0.722, P = .026), prothrombin time >13 seconds (OR 11.869, 95% CI 1.433-98.278, P = .022), D-dimer >1 mg/L (OR 22.811, 95% CI 2.224-233.910, P = .008) and procalcitonin (PCT) >0.1 ng/mL (OR 23.022, 95% CI 3.108-170.532, P = .002). The area under the ROC curve (AUC) of the above indicators for predicting in-hospital mortality were 0.808 (95% CI 0.715-0.901), 0.809 (95% CI 0.710-0.907), 0.811 (95% CI 0.724-0.898), 0.745 (95% CI 0.643-0.847), 0.872 (95% CI 0.804-0.940), 0.881 (95% CI 0.809-0.953), respectively. The AUC of combined diagnosis of these aforementioned factors were 0.992 (95% CI 0.981-1.000).In conclusion, older age, increased neutrophil count, prothrombin time, D-dimer, PCT, and decreased lymphocyte count at admission were risk factors associated with in-hospital mortality of COVID-19. The prediction model combined of these factors could improve the early identification of mortality risk in COVID-19 patients.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32871887</PMID><DateCompleted><Year>2020</Year><Month>09</Month><Day>08</Day></DateCompleted><DateRevised><Year>2020</Year><Month>09</Month><Day>13</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1536-5964</ISSN><JournalIssue CitedMedium="Internet"><Volume>99</Volume><Issue>35</Issue><PubDate><Year>2020</Year><Month>Aug</Month><Day>28</Day></PubDate></JournalIssue><Title>Medicine</Title><ISOAbbreviation>Medicine (Baltimore)</ISOAbbreviation></Journal><ArticleTitle>Impaired immune and coagulation systems may be early risk factors for COVID-19 patients: A retrospective study of 118 inpatients from Wuhan, China.</ArticleTitle><Pagination><MedlinePgn>e21700</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1097/MD.0000000000021700</ELocationID><Abstract><AbstractText>The coronavirus disease 2019 (COVID-19) outbreak has become a global health threat and will likely be one of the greatest global challenges in the near future. The battle between clinicians and the COVID-19 outbreak may be a "protracted war."The objective of this study was to investigate the risk factors for in-hospital mortality in patients with COVID-19, so as to provide a reference for the early diagnosis and treatment.This study retrospectively enrolled 118 patients diagnosed with COVID-19, who were admitted to Eastern District of Renmin Hospital of Wuhan University from February 04, 2020 to March 04, 2020. The demographics and laboratory data were collected and compared between survivors and nonsurvivors. The risk factors of in-hospital mortality were explored by univariable and multivariable logistic regression to construct a clinical prediction model, the prediction efficiency of which was verified by receiver-operating characteristic (ROC) curve.A total of 118 patients (49 males and 69 females) were included in this study; the results revealed that the following factors associated with in-hospital mortality: older age (odds ratio [OR] 1.175, 95% confidence interval [CI] 1.073-1.287, P = .001), neutrophil count greater than 6.3 × 10 cells/L (OR 7.174, (95% CI 2.295-22.432, P = .001), lymphocytopenia (OR 0.069, 95% CI 0.007-0.722, P = .026), prothrombin time >13 seconds (OR 11.869, 95% CI 1.433-98.278, P = .022), D-dimer >1 mg/L (OR 22.811, 95% CI 2.224-233.910, P = .008) and procalcitonin (PCT) >0.1 ng/mL (OR 23.022, 95% CI 3.108-170.532, P = .002). The area under the ROC curve (AUC) of the above indicators for predicting in-hospital mortality were 0.808 (95% CI 0.715-0.901), 0.809 (95% CI 0.710-0.907), 0.811 (95% CI 0.724-0.898), 0.745 (95% CI 0.643-0.847), 0.872 (95% CI 0.804-0.940), 0.881 (95% CI 0.809-0.953), respectively. The AUC of combined diagnosis of these aforementioned factors were 0.992 (95% CI 0.981-1.000).In conclusion, older age, increased neutrophil count, prothrombin time, D-dimer, PCT, and decreased lymphocyte count at admission were risk factors associated with in-hospital mortality of COVID-19. The prediction model combined of these factors could improve the early identification of mortality risk in COVID-19 patients.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Qin</LastName><ForeName>Zhi-Jun</ForeName><Initials>ZJ</Initials><AffiliationInfo><Affiliation>Department of Intensive Care Unit.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Lei</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Infection Management, Sichuan Provincial Orthopedic Hospital, Chengdu, Sichuan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Qun</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>Department of Intensive Care Unit.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Xia</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Department of Intensive Care Unit.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Luo</LastName><ForeName>Jian-Fei</ForeName><Initials>JF</Initials><AffiliationInfo><Affiliation>Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Jia-Sheng</ForeName><Initials>JS</Initials><AffiliationInfo><Affiliation>Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Dan</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Medicine (Baltimore)</MedlineTA><NlmUniqueID>2985248R</NlmUniqueID><ISSNLinking>0025-7974</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D005338">Fibrin Fibrinogen Degradation Products</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000077740">Procalcitonin</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="C036309">fibrin fragment D</NameOfSubstance></Chemical></ChemicalList><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>AIM</CitationSubset><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="Y">Coronavirus Infections</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName><QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName><QualifierName UI="Q000401" MajorTopicYN="N">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005338" MajorTopicYN="N">Fibrin Fibrinogen Degradation Products</DescriptorName><QualifierName UI="Q000032" 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Viral</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName><QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName><QualifierName UI="Q000401" MajorTopicYN="N">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011237" MajorTopicYN="N">Predictive Value of Tests</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000077740" MajorTopicYN="N">Procalcitonin</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011379" MajorTopicYN="N">Prognosis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011517" MajorTopicYN="Y">Prothrombin Time</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective 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