Decreased prealbumin level is associated with increased risk for mortality in elderly hospitalized patients with COVID-19.
Identifieur interne : 000686 ( Main/Exploration ); précédent : 000685; suivant : 000687Decreased prealbumin level is associated with increased risk for mortality in elderly hospitalized patients with COVID-19.
Auteurs : Peiyuan Zuo [République populaire de Chine] ; Song Tong [République populaire de Chine] ; Qi Yan [République populaire de Chine] ; Ling Cheng [République populaire de Chine] ; Yuanyuan Li [République populaire de Chine] ; Kaixin Song [République populaire de Chine] ; Yuting Chen [République populaire de Chine] ; Yue Dai [République populaire de Chine] ; Hongyu Gao [République populaire de Chine] ; Cuntai Zhang [République populaire de Chine]Source :
- Nutrition (Burbank, Los Angeles County, Calif.) [ 1873-1244 ] ; 2020.
Descripteurs français
- KwdFr :
- Analyse multifactorielle (MeSH), Appréciation des risques (MeSH), Betacoronavirus (MeSH), Chine (épidémiologie), Facteurs de risque (MeSH), Femelle (MeSH), Humains (MeSH), Infections à coronavirus (mortalité), Infections à coronavirus (sang), Marqueurs biologiques (sang), Modèles logistiques (MeSH), Mortalité hospitalière (MeSH), Mâle (MeSH), Odds ratio (MeSH), Pandémies (MeSH), Pneumopathie virale (mortalité), Pneumopathie virale (sang), Préalbumine (analyse), Sujet âgé (MeSH), Unités de soins intensifs (statistiques et données numériques), Ventilation artificielle (statistiques et données numériques).
- MESH :
- analyse : Préalbumine.
- mortalité : Infections à coronavirus, Pneumopathie virale.
- sang : Infections à coronavirus, Marqueurs biologiques, Pneumopathie virale.
- statistiques et données numériques : Unités de soins intensifs, Ventilation artificielle.
- épidémiologie : Chine.
- Analyse multifactorielle, Appréciation des risques, Betacoronavirus, Facteurs de risque, Femelle, Humains, Modèles logistiques, Mortalité hospitalière, Mâle, Odds ratio, Pandémies, Sujet âgé.
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- Aged (MeSH), Betacoronavirus (MeSH), Biomarkers (blood), China (epidemiology), Coronavirus Infections (blood), Coronavirus Infections (mortality), Female (MeSH), Hospital Mortality (MeSH), Humans (MeSH), Intensive Care Units (statistics & numerical data), Logistic Models (MeSH), Male (MeSH), Multivariate Analysis (MeSH), Odds Ratio (MeSH), Pandemics (MeSH), Pneumonia, Viral (blood), Pneumonia, Viral (mortality), Prealbumin (analysis), Respiration, Artificial (statistics & numerical data), Risk Assessment (MeSH), Risk Factors (MeSH).
- MESH :
- chemical , analysis : Prealbumin.
- chemical , blood : Biomarkers.
- geographic , epidemiology : China.
- blood : Coronavirus Infections, Pneumonia, Viral.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- statistics & numerical data : Intensive Care Units, Respiration, Artificial.
- Aged, Betacoronavirus, Female, Hospital Mortality, Humans, Logistic Models, Male, Multivariate Analysis, Odds Ratio, Pandemics, Risk Assessment, Risk Factors.
Abstract
OBJECTIVES
High-risk patients ≥65 y of age with coronavirus disease 2019 (COVID-19) tended to have lower serum prealbumin concentrations. The aim of this study was to investigate the association of prealbumin at baseline on COVID-19-related mortality in elderly patients (≥65 y of age).
METHODS
We non-selectively and consecutively collected participants from Tongji Hospital in Wuhan from January 17 to February 17, 2020. Univariate and multivariate logistic regression models were employed to evaluate the correlation between prealbumin and in-hospital outcomes (in-hospital mortality, admission to the intensive care unit [ICU], and mechanical ventilation) in elderly patients with COVID-19. Linear trend was performed by entering the median value of each category of prealbumin tertile as a continuous variable and was visually confirmed by using generalized additive models. Interaction and stratified analyses were conducted as well.
RESULTS
We included 446 elderly patients with COVID-19 in the final analyses. In-hospital mortality was 14.79%. Of the 446 patients, 15.47% were admitted to the ICU and 21.3% required mechanical ventilation. Compared with patients in the highest tertile, the prealbumin of patients in the lowest tertile had a 19.09-fold higher risk for death [odds ratio (OR), 20.09; 95% confidence interval (CI), 3.62-111.64; P = 0.0006], 25.39-fold higher risk for ICU admission (OR, 26.39; 95% CI, 4.04-172.39; P = 0.0006), and 1.8-fold higher risk for mechanical ventilation (OR, 2.8; 95% CI, 1.15-6.78; P = 0.0227) after adjustment for potential confounders. There was a linear trend correlation between serum prealbumin concentration and risk for in-hospital mortality, ICU admission, and mechanical ventilation in elderly patients with COVID-19 infection.
CONCLUSION
Prealbumin is an independent risk factor of in-hospital mortality for elderly patients with COVID-19. Assessment of prealbumin may help identify high-risk individuals ≥65 y of age with COVID-19.
DOI: 10.1016/j.nut.2020.110930
PubMed: 32854020
PubMed Central: PMC7333599
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Medical College, Huazhong University of Science and Technology, Wuhan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Gao, Hongyu" sort="Gao, Hongyu" uniqKey="Gao H" first="Hongyu" last="Gao">Hongyu Gao</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: hygao@tjh.tjmu.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Zhang, Cuntai" sort="Zhang, Cuntai" uniqKey="Zhang C" first="Cuntai" last="Zhang">Cuntai Zhang</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 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last="Tong">Song Tong</name><affiliation wicri:level="3"><nlm:affiliation>Department of Thoracic Surgery, Union Hospital, Tongji Medical College Huazhong University of Science and Technology, Wuhan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Thoracic Surgery, Union Hospital, Tongji Medical College Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Yan, Qi" sort="Yan, Qi" uniqKey="Yan Q" first="Qi" last="Yan">Qi Yan</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Cheng, Ling" sort="Cheng, Ling" uniqKey="Cheng L" first="Ling" last="Cheng">Ling Cheng</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Li, Yuanyuan" sort="Li, Yuanyuan" uniqKey="Li Y" first="Yuanyuan" last="Li">Yuanyuan Li</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Song, Kaixin" sort="Song, Kaixin" uniqKey="Song K" first="Kaixin" last="Song">Kaixin Song</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Chen, Yuting" sort="Chen, Yuting" uniqKey="Chen Y" first="Yuting" last="Chen">Yuting Chen</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Dai, Yue" sort="Dai, Yue" uniqKey="Dai Y" first="Yue" last="Dai">Yue Dai</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Gao, Hongyu" sort="Gao, Hongyu" uniqKey="Gao H" first="Hongyu" last="Gao">Hongyu Gao</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: hygao@tjh.tjmu.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author><author><name sortKey="Zhang, Cuntai" sort="Zhang, Cuntai" uniqKey="Zhang C" first="Cuntai" last="Zhang">Cuntai Zhang</name><affiliation wicri:level="3"><nlm:affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: ctzhang0425@163.com.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan</wicri:regionArea><placeName><settlement type="city">Wuhan</settlement><region type="région">Hubei</region></placeName></affiliation></author></analytic><series><title level="j">Nutrition (Burbank, Los Angeles County, Calif.)</title><idno type="eISSN">1873-1244</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Biomarkers (blood)</term><term>China (epidemiology)</term><term>Coronavirus Infections (blood)</term><term>Coronavirus Infections (mortality)</term><term>Female (MeSH)</term><term>Hospital Mortality (MeSH)</term><term>Humans (MeSH)</term><term>Intensive Care Units (statistics & numerical data)</term><term>Logistic Models (MeSH)</term><term>Male (MeSH)</term><term>Multivariate Analysis (MeSH)</term><term>Odds Ratio (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (blood)</term><term>Pneumonia, Viral (mortality)</term><term>Prealbumin (analysis)</term><term>Respiration, Artificial (statistics & numerical data)</term><term>Risk Assessment (MeSH)</term><term>Risk Factors (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse multifactorielle (MeSH)</term><term>Appréciation des risques (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Chine (épidémiologie)</term><term>Facteurs de risque (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (mortalité)</term><term>Infections à coronavirus (sang)</term><term>Marqueurs biologiques (sang)</term><term>Modèles logistiques (MeSH)</term><term>Mortalité hospitalière (MeSH)</term><term>Mâle (MeSH)</term><term>Odds ratio (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (mortalité)</term><term>Pneumopathie virale (sang)</term><term>Préalbumine (analyse)</term><term>Sujet âgé (MeSH)</term><term>Unités de soins intensifs (statistiques et données numériques)</term><term>Ventilation artificielle (statistiques et données numériques)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Prealbumin</term></keywords><keywords scheme="MESH" type="chemical" qualifier="blood" xml:lang="en"><term>Biomarkers</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="analyse" xml:lang="fr"><term>Préalbumine</term></keywords><keywords scheme="MESH" qualifier="blood" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="sang" xml:lang="fr"><term>Infections à coronavirus</term><term>Marqueurs biologiques</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Intensive Care Units</term><term>Respiration, Artificial</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Unités de soins intensifs</term><term>Ventilation artificielle</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Chine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Betacoronavirus</term><term>Female</term><term>Hospital Mortality</term><term>Humans</term><term>Logistic Models</term><term>Male</term><term>Multivariate Analysis</term><term>Odds Ratio</term><term>Pandemics</term><term>Risk Assessment</term><term>Risk Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse multifactorielle</term><term>Appréciation des risques</term><term>Betacoronavirus</term><term>Facteurs de risque</term><term>Femelle</term><term>Humains</term><term>Modèles logistiques</term><term>Mortalité hospitalière</term><term>Mâle</term><term>Odds ratio</term><term>Pandémies</term><term>Sujet âgé</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>République populaire de Chine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVES</b></p><p>High-risk patients ≥65 y of age with coronavirus disease 2019 (COVID-19) tended to have lower serum prealbumin concentrations. The aim of this study was to investigate the association of prealbumin at baseline on COVID-19-related mortality in elderly patients (≥65 y of age).</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We non-selectively and consecutively collected participants from Tongji Hospital in Wuhan from January 17 to February 17, 2020. Univariate and multivariate logistic regression models were employed to evaluate the correlation between prealbumin and in-hospital outcomes (in-hospital mortality, admission to the intensive care unit [ICU], and mechanical ventilation) in elderly patients with COVID-19. Linear trend was performed by entering the median value of each category of prealbumin tertile as a continuous variable and was visually confirmed by using generalized additive models. Interaction and stratified analyses were conducted as well.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>We included 446 elderly patients with COVID-19 in the final analyses. In-hospital mortality was 14.79%. Of the 446 patients, 15.47% were admitted to the ICU and 21.3% required mechanical ventilation. Compared with patients in the highest tertile, the prealbumin of patients in the lowest tertile had a 19.09-fold higher risk for death [odds ratio (OR), 20.09; 95% confidence interval (CI), 3.62-111.64; P = 0.0006], 25.39-fold higher risk for ICU admission (OR, 26.39; 95% CI, 4.04-172.39; P = 0.0006), and 1.8-fold higher risk for mechanical ventilation (OR, 2.8; 95% CI, 1.15-6.78; P = 0.0227) after adjustment for potential confounders. There was a linear trend correlation between serum prealbumin concentration and risk for in-hospital mortality, ICU admission, and mechanical ventilation in elderly patients with COVID-19 infection.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>Prealbumin is an independent risk factor of in-hospital mortality for elderly patients with COVID-19. Assessment of prealbumin may help identify high-risk individuals ≥65 y of age with COVID-19.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32854020</PMID><DateCompleted><Year>2020</Year><Month>09</Month><Day>29</Day></DateCompleted><DateRevised><Year>2020</Year><Month>09</Month><Day>29</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-1244</ISSN><JournalIssue CitedMedium="Internet"><Volume>78</Volume><PubDate><Year>2020</Year><Month>10</Month></PubDate></JournalIssue><Title>Nutrition (Burbank, Los Angeles County, Calif.)</Title><ISOAbbreviation>Nutrition</ISOAbbreviation></Journal><ArticleTitle>Decreased prealbumin level is associated with increased risk for mortality in elderly hospitalized patients with COVID-19.</ArticleTitle><Pagination><MedlinePgn>110930</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0899-9007(20)30213-6</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.nut.2020.110930</ELocationID><Abstract><AbstractText Label="OBJECTIVES">High-risk patients ≥65 y of age with coronavirus disease 2019 (COVID-19) tended to have lower serum prealbumin concentrations. The aim of this study was to investigate the association of prealbumin at baseline on COVID-19-related mortality in elderly patients (≥65 y of age).</AbstractText><AbstractText Label="METHODS">We non-selectively and consecutively collected participants from Tongji Hospital in Wuhan from January 17 to February 17, 2020. Univariate and multivariate logistic regression models were employed to evaluate the correlation between prealbumin and in-hospital outcomes (in-hospital mortality, admission to the intensive care unit [ICU], and mechanical ventilation) in elderly patients with COVID-19. Linear trend was performed by entering the median value of each category of prealbumin tertile as a continuous variable and was visually confirmed by using generalized additive models. Interaction and stratified analyses were conducted as well.</AbstractText><AbstractText Label="RESULTS">We included 446 elderly patients with COVID-19 in the final analyses. In-hospital mortality was 14.79%. Of the 446 patients, 15.47% were admitted to the ICU and 21.3% required mechanical ventilation. Compared with patients in the highest tertile, the prealbumin of patients in the lowest tertile had a 19.09-fold higher risk for death [odds ratio (OR), 20.09; 95% confidence interval (CI), 3.62-111.64; P = 0.0006], 25.39-fold higher risk for ICU admission (OR, 26.39; 95% CI, 4.04-172.39; P = 0.0006), and 1.8-fold higher risk for mechanical ventilation (OR, 2.8; 95% CI, 1.15-6.78; P = 0.0227) after adjustment for potential confounders. There was a linear trend correlation between serum prealbumin concentration and risk for in-hospital mortality, ICU admission, and mechanical ventilation in elderly patients with COVID-19 infection.</AbstractText><AbstractText Label="CONCLUSION">Prealbumin is an independent risk factor of in-hospital mortality for elderly patients with COVID-19. Assessment of prealbumin may help identify high-risk individuals ≥65 y of age with COVID-19.</AbstractText><CopyrightInformation>Copyright © 2020 Elsevier Inc. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zuo</LastName><ForeName>Peiyuan</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tong</LastName><ForeName>Song</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Thoracic Surgery, Union Hospital, Tongji Medical College Huazhong University of Science and Technology, Wuhan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yan</LastName><ForeName>Qi</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cheng</LastName><ForeName>Ling</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Yuanyuan</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Song</LastName><ForeName>Kaixin</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Yuting</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dai</LastName><ForeName>Yue</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gao</LastName><ForeName>Hongyu</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: hygao@tjh.tjmu.edu.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Cuntai</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: ctzhang0425@163.com.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D023362">Evaluation Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Nutrition</MedlineTA><NlmUniqueID>8802712</NlmUniqueID><ISSNLinking>0899-9007</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D015415">Biomarkers</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011228">Prealbumin</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>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015415" MajorTopicYN="N">Biomarkers</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="Y">blood</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017052" MajorTopicYN="Y">Hospital Mortality</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007362" MajorTopicYN="N">Intensive Care Units</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016015" MajorTopicYN="N">Logistic Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015999" MajorTopicYN="N">Multivariate Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016017" MajorTopicYN="N">Odds Ratio</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="Y">blood</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011228" MajorTopicYN="N">Prealbumin</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012121" MajorTopicYN="N">Respiration, Artificial</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">COVID-19</Keyword><Keyword MajorTopicYN="Y">Prealbumin</Keyword><Keyword MajorTopicYN="Y">Prognosis</Keyword><Keyword MajorTopicYN="Y">Risk factors</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>06</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>06</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>9</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32854020</ArticleId><ArticleId IdType="pii">S0899-9007(20)30213-6</ArticleId><ArticleId IdType="doi">10.1016/j.nut.2020.110930</ArticleId><ArticleId IdType="pmc">PMC7333599</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Hubei</li></region><settlement><li>Wuhan</li></settlement></list><tree><country name="République populaire de Chine"><region name="Hubei"><name sortKey="Zuo, Peiyuan" sort="Zuo, Peiyuan" uniqKey="Zuo P" first="Peiyuan" last="Zuo">Peiyuan Zuo</name></region><name sortKey="Chen, Yuting" sort="Chen, Yuting" uniqKey="Chen Y" first="Yuting" last="Chen">Yuting Chen</name><name sortKey="Cheng, Ling" sort="Cheng, Ling" uniqKey="Cheng L" first="Ling" last="Cheng">Ling Cheng</name><name sortKey="Dai, Yue" sort="Dai, Yue" uniqKey="Dai Y" first="Yue" last="Dai">Yue Dai</name><name sortKey="Gao, Hongyu" sort="Gao, Hongyu" uniqKey="Gao H" first="Hongyu" last="Gao">Hongyu Gao</name><name sortKey="Li, Yuanyuan" sort="Li, Yuanyuan" uniqKey="Li Y" first="Yuanyuan" last="Li">Yuanyuan Li</name><name sortKey="Song, Kaixin" sort="Song, Kaixin" uniqKey="Song K" first="Kaixin" last="Song">Kaixin Song</name><name sortKey="Tong, Song" sort="Tong, Song" uniqKey="Tong S" first="Song" last="Tong">Song Tong</name><name sortKey="Yan, Qi" sort="Yan, Qi" uniqKey="Yan Q" first="Qi" last="Yan">Qi Yan</name><name sortKey="Zhang, Cuntai" sort="Zhang, Cuntai" uniqKey="Zhang C" first="Cuntai" last="Zhang">Cuntai Zhang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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