Comparative analysis of clinical characteristics, imaging and laboratory findings of different age groups with COVID-19.
Identifieur interne : 000034 ( Main/Exploration ); précédent : 000033; suivant : 000035Comparative analysis of clinical characteristics, imaging and laboratory findings of different age groups with COVID-19.
Auteurs : Xuemei Liu [République populaire de Chine] ; Jie Lv [République populaire de Chine] ; Lin Gan [Australie] ; Ying Zhang [République populaire de Chine] ; Feng Sun [République populaire de Chine] ; Bo Meng [États-Unis] ; Andrew Jheon [États-Unis] ; Fang Yan [République populaire de Chine] ; Bin Li [République populaire de Chine] ; Zhou Xuan [République populaire de Chine] ; Xiumin Ma [République populaire de Chine] ; Muhuyati Wulasihana [République populaire de Chine]Source :
- Indian journal of medical microbiology [ 1998-3646 ]
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Betacoronavirus (croissance et développement), Chine (MeSH), Diagnostic précoce (MeSH), Dépistage de masse (méthodes), Facteurs âges (MeSH), Femelle (MeSH), Humains (MeSH), Hôpitaux universitaires (MeSH), Infections à coronavirus (anatomopathologie), Infections à coronavirus (diagnostic), Infections à coronavirus (imagerie diagnostique), Jeune adulte (MeSH), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (anatomopathologie), Pneumopathie virale (diagnostic), Pneumopathie virale (imagerie diagnostique), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Techniques de laboratoire clinique (méthodes), Tests diagnostiques courants (méthodes), Tomodensitométrie (méthodes).
- MESH :
- anatomopathologie : Infections à coronavirus, Pneumopathie virale.
- croissance et développement : Betacoronavirus.
- diagnostic : Infections à coronavirus, Pneumopathie virale.
- imagerie diagnostique : Infections à coronavirus, Pneumopathie virale.
- méthodes : Dépistage de masse, Techniques de laboratoire clinique, Tests diagnostiques courants, Tomodensitométrie.
- Adulte, Adulte d'âge moyen, Chine, Diagnostic précoce, Facteurs âges, Femelle, Humains, Hôpitaux universitaires, Jeune adulte, Mâle, Pandémies, Sujet âgé, Sujet âgé de 80 ans ou plus.
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- Adult (MeSH), Age Factors (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Betacoronavirus (growth & development), China (MeSH), Clinical Laboratory Techniques (methods), Coronavirus Infections (diagnosis), Coronavirus Infections (diagnostic imaging), Coronavirus Infections (pathology), Diagnostic Tests, Routine (methods), Early Diagnosis (MeSH), Female (MeSH), Hospitals, University (MeSH), Humans (MeSH), Male (MeSH), Mass Screening (methods), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnosis), Pneumonia, Viral (diagnostic imaging), Pneumonia, Viral (pathology), Tomography, X-Ray Computed (methods), Young Adult (MeSH).
- MESH :
- geographic : China.
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- diagnostic imaging : Coronavirus Infections, Pneumonia, Viral.
- growth & development : Betacoronavirus.
- methods : Clinical Laboratory Techniques, Diagnostic Tests, Routine, Mass Screening, Tomography, X-Ray Computed.
- pathology : Coronavirus Infections, Pneumonia, Viral.
- Adult, Age Factors, Aged, Aged, 80 and over, Early Diagnosis, Female, Hospitals, University, Humans, Male, Middle Aged, Pandemics, Young Adult.
Abstract
Objective
This study aims to provide scientific basis for rapid screening and early diagnosis of the coronavirus disease 2019 (COVID-19) through analysing the clinical characteristics and early imaging/laboratory findings of the inpatients.
Methods
Three hundred and three patients with laboratory-confirmed COVID-19 from the East Hospital of People's Hospital of Wuhan University (Wuhan, China) were selected and divided into four groups: youth (20-40 years, n = 64), middle-aged (41-60 years, n = 89), older (61-80 years, n = 118) and elderly (81-100 years, n = 32). The clinical characteristics and imaging/laboratory findings including chest computed tomography (CT), initial blood count, C-reactive protein [CRP]), procalcitonin (PCT) and serum total IgE were captured and analysed.
Results
(1) The first symptoms of all age groups were primarily fever (76%), followed by cough (12%) and dyspnoea (5%). Beside fever, the most common initial symptom of elderly patients was fatigue (13%). (2) Fever was the most common clinical manifestation (80%), with moderate fever being the most common (40%), followed by low fever in patients above 40 years old and high fever in those under 40 years (35%). Cough was the second most common clinical manifestation and was most common (80%) in the middle-aged. Diarrhoea was more common in the middle-aged (21%) and the older (19%). Muscle ache was more common in the middle-aged (15%). Chest pain was more common in the youth (13%), and 13% of the youth had no symptoms. (3) The proportion of patients with comorbidities increased with age. (4) Seventy-one per cent of the patients had positive reverse transcription-polymerase chain reaction results and 29% had positive chest CT scans before admission to the hospital. (5) Lesions in all lobes of the lung were observed as the main chest CT findings (76%). (6) Decrease in lymphocytes and increase in monocytes were common in the patients over 40 years old but rare in the youth. Eosinophils (50%), red blood cells (39%) and haemoglobin (40%) decreased in all age groups. (7) The proportion of patients with CRP and PCT elevation increased with age. (8) Thirty-nine per cent of the patients had elevated IgE, with the highest proportion in the old (49%).
Conclusion
The clinical characteristics and imaging/laboratory findings of COVID-19 patients vary in different age groups. Personalised criteria should be formulated according to different age groups in the early screening and diagnosis stage.
DOI: 10.4103/ijmm.IJMM_20_133
PubMed: 32719214
Affiliations:
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Xuemei" uniqKey="Liu X" first="Xuemei" last="Liu">Xuemei Liu</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author><author><name sortKey="Lv, Jie" sort="Lv, Jie" uniqKey="Lv J" first="Jie" last="Lv">Jie Lv</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author><author><name sortKey="Gan, Lin" sort="Gan, Lin" uniqKey="Gan L" first="Lin" last="Gan">Lin Gan</name><affiliation wicri:level="1"><nlm:affiliation>Faculty of Medicine and Health Sciences, Macquarie University, New South Wales, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Medicine and Health Sciences, Macquarie University, New South Wales</wicri:regionArea><wicri:noRegion>New South Wales</wicri:noRegion></affiliation></author><author><name sortKey="Zhang, Ying" sort="Zhang, Ying" uniqKey="Zhang Y" first="Ying" last="Zhang">Ying Zhang</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author><author><name sortKey="Sun, Feng" sort="Sun, Feng" uniqKey="Sun F" first="Feng" last="Sun">Feng Sun</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author><author><name sortKey="Meng, Bo" sort="Meng, Bo" uniqKey="Meng B" first="Bo" last="Meng">Bo Meng</name><affiliation wicri:level="3"><nlm:affiliation>School of Dental Medicine, University of California San Francisco, San Francisco, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Dental Medicine, University of California San Francisco, San Francisco</wicri:regionArea><placeName><settlement type="city">San Francisco</settlement><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Jheon, Andrew" sort="Jheon, Andrew" uniqKey="Jheon A" first="Andrew" last="Jheon">Andrew Jheon</name><affiliation wicri:level="3"><nlm:affiliation>School of Dental Medicine, University of California San Francisco, San Francisco, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Dental Medicine, University of California San Francisco, San Francisco</wicri:regionArea><placeName><settlement type="city">San Francisco</settlement><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Yan, Fang" sort="Yan, Fang" uniqKey="Yan F" first="Fang" last="Yan">Fang Yan</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author><author><name sortKey="Li, Bin" sort="Li, Bin" uniqKey="Li B" first="Bin" last="Li">Bin Li</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author><author><name sortKey="Xuan, Zhou" sort="Xuan, Zhou" uniqKey="Xuan Z" first="Zhou" last="Xuan">Zhou Xuan</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author><author><name sortKey="Ma, Xiumin" sort="Ma, Xiumin" uniqKey="Ma X" first="Xiumin" last="Ma">Xiumin Ma</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University; Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University; Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author><author><name sortKey="Wulasihana, Muhuyati" sort="Wulasihana, Muhuyati" uniqKey="Wulasihana M" first="Muhuyati" last="Wulasihana">Muhuyati Wulasihana</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi</wicri:regionArea><wicri:noRegion>Urumqi</wicri:noRegion></affiliation></author></analytic><series><title level="j">Indian journal of medical microbiology</title><idno type="eISSN">1998-3646</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Age Factors (MeSH)</term><term>Aged (MeSH)</term><term>Aged, 80 and over (MeSH)</term><term>Betacoronavirus (growth & development)</term><term>China (MeSH)</term><term>Clinical Laboratory Techniques (methods)</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (diagnostic imaging)</term><term>Coronavirus Infections (pathology)</term><term>Diagnostic Tests, Routine (methods)</term><term>Early Diagnosis (MeSH)</term><term>Female (MeSH)</term><term>Hospitals, University (MeSH)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Mass Screening (methods)</term><term>Middle Aged (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (diagnosis)</term><term>Pneumonia, Viral (diagnostic imaging)</term><term>Pneumonia, Viral (pathology)</term><term>Tomography, X-Ray Computed (methods)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Betacoronavirus (croissance et développement)</term><term>Chine (MeSH)</term><term>Diagnostic précoce (MeSH)</term><term>Dépistage de masse (méthodes)</term><term>Facteurs âges (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Hôpitaux universitaires (MeSH)</term><term>Infections à coronavirus (anatomopathologie)</term><term>Infections à coronavirus (diagnostic)</term><term>Infections à coronavirus (imagerie diagnostique)</term><term>Jeune adulte (MeSH)</term><term>Mâle (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (anatomopathologie)</term><term>Pneumopathie virale (diagnostic)</term><term>Pneumopathie virale (imagerie diagnostique)</term><term>Sujet âgé (MeSH)</term><term>Sujet âgé de 80 ans ou plus (MeSH)</term><term>Techniques de laboratoire clinique (méthodes)</term><term>Tests diagnostiques courants (méthodes)</term><term>Tomodensitométrie (méthodes)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="croissance et développement" xml:lang="fr"><term>Betacoronavirus</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="growth & development" xml:lang="en"><term>Betacoronavirus</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Clinical Laboratory Techniques</term><term>Diagnostic Tests, Routine</term><term>Mass Screening</term><term>Tomography, X-Ray Computed</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Dépistage de masse</term><term>Techniques de laboratoire clinique</term><term>Tests diagnostiques courants</term><term>Tomodensitométrie</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Age Factors</term><term>Aged</term><term>Aged, 80 and over</term><term>Early Diagnosis</term><term>Female</term><term>Hospitals, University</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Pandemics</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Chine</term><term>Diagnostic précoce</term><term>Facteurs âges</term><term>Femelle</term><term>Humains</term><term>Hôpitaux universitaires</term><term>Jeune adulte</term><term>Mâle</term><term>Pandémies</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</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>Objective</b></p><p>This study aims to provide scientific basis for rapid screening and early diagnosis of the coronavirus disease 2019 (COVID-19) through analysing the clinical characteristics and early imaging/laboratory findings of the inpatients.</p></div><div type="abstract" xml:lang="en"><p><b>Methods</b></p><p>Three hundred and three patients with laboratory-confirmed COVID-19 from the East Hospital of People's Hospital of Wuhan University (Wuhan, China) were selected and divided into four groups: youth (20-40 years, n = 64), middle-aged (41-60 years, n = 89), older (61-80 years, n = 118) and elderly (81-100 years, n = 32). The clinical characteristics and imaging/laboratory findings including chest computed tomography (CT), initial blood count, C-reactive protein [CRP]), procalcitonin (PCT) and serum total IgE were captured and analysed.</p></div><div type="abstract" xml:lang="en"><p><b>Results</b></p><p>(1) The first symptoms of all age groups were primarily fever (76%), followed by cough (12%) and dyspnoea (5%). Beside fever, the most common initial symptom of elderly patients was fatigue (13%). (2) Fever was the most common clinical manifestation (80%), with moderate fever being the most common (40%), followed by low fever in patients above 40 years old and high fever in those under 40 years (35%). Cough was the second most common clinical manifestation and was most common (80%) in the middle-aged. Diarrhoea was more common in the middle-aged (21%) and the older (19%). Muscle ache was more common in the middle-aged (15%). Chest pain was more common in the youth (13%), and 13% of the youth had no symptoms. (3) The proportion of patients with comorbidities increased with age. (4) Seventy-one per cent of the patients had positive reverse transcription-polymerase chain reaction results and 29% had positive chest CT scans before admission to the hospital. (5) Lesions in all lobes of the lung were observed as the main chest CT findings (76%). (6) Decrease in lymphocytes and increase in monocytes were common in the patients over 40 years old but rare in the youth. Eosinophils (50%), red blood cells (39%) and haemoglobin (40%) decreased in all age groups. (7) The proportion of patients with CRP and PCT elevation increased with age. (8) Thirty-nine per cent of the patients had elevated IgE, with the highest proportion in the old (49%).</p></div><div type="abstract" xml:lang="en"><p><b>Conclusion</b></p><p>The clinical characteristics and imaging/laboratory findings of COVID-19 patients vary in different age groups. Personalised criteria should be formulated according to different age groups in the early screening and diagnosis stage.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32719214</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>04</Day></DateCompleted><DateRevised><Year>2020</Year><Month>08</Month><Day>04</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1998-3646</ISSN><JournalIssue CitedMedium="Internet"><Volume>38</Volume><Issue>1</Issue><PubDate><MedlineDate>2020 Jan-Mar</MedlineDate></PubDate></JournalIssue><Title>Indian journal of medical microbiology</Title><ISOAbbreviation>Indian J Med Microbiol</ISOAbbreviation></Journal><ArticleTitle>Comparative analysis of clinical characteristics, imaging and laboratory findings of different age groups with COVID-19.</ArticleTitle><Pagination><MedlinePgn>87-93</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.4103/ijmm.IJMM_20_133</ELocationID><Abstract><AbstractText Label="Objective">This study aims to provide scientific basis for rapid screening and early diagnosis of the coronavirus disease 2019 (COVID-19) through analysing the clinical characteristics and early imaging/laboratory findings of the inpatients.</AbstractText><AbstractText Label="Methods">Three hundred and three patients with laboratory-confirmed COVID-19 from the East Hospital of People's Hospital of Wuhan University (Wuhan, China) were selected and divided into four groups: youth (20-40 years, n = 64), middle-aged (41-60 years, n = 89), older (61-80 years, n = 118) and elderly (81-100 years, n = 32). The clinical characteristics and imaging/laboratory findings including chest computed tomography (CT), initial blood count, C-reactive protein [CRP]), procalcitonin (PCT) and serum total IgE were captured and analysed.</AbstractText><AbstractText Label="Results">(1) The first symptoms of all age groups were primarily fever (76%), followed by cough (12%) and dyspnoea (5%). Beside fever, the most common initial symptom of elderly patients was fatigue (13%). (2) Fever was the most common clinical manifestation (80%), with moderate fever being the most common (40%), followed by low fever in patients above 40 years old and high fever in those under 40 years (35%). Cough was the second most common clinical manifestation and was most common (80%) in the middle-aged. Diarrhoea was more common in the middle-aged (21%) and the older (19%). Muscle ache was more common in the middle-aged (15%). Chest pain was more common in the youth (13%), and 13% of the youth had no symptoms. (3) The proportion of patients with comorbidities increased with age. (4) Seventy-one per cent of the patients had positive reverse transcription-polymerase chain reaction results and 29% had positive chest CT scans before admission to the hospital. (5) Lesions in all lobes of the lung were observed as the main chest CT findings (76%). (6) Decrease in lymphocytes and increase in monocytes were common in the patients over 40 years old but rare in the youth. Eosinophils (50%), red blood cells (39%) and haemoglobin (40%) decreased in all age groups. (7) The proportion of patients with CRP and PCT elevation increased with age. (8) Thirty-nine per cent of the patients had elevated IgE, with the highest proportion in the old (49%).</AbstractText><AbstractText Label="Conclusion">The clinical characteristics and imaging/laboratory findings of COVID-19 patients vary in different age groups. Personalised criteria should be formulated according to different age groups in the early screening and diagnosis stage.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Xuemei</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lv</LastName><ForeName>Jie</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gan</LastName><ForeName>Lin</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Faculty of Medicine and Health Sciences, Macquarie University, New South Wales, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Ying</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Feng</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Meng</LastName><ForeName>Bo</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>School of Dental Medicine, University of California San Francisco, San Francisco, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jheon</LastName><ForeName>Andrew</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>School of Dental Medicine, University of California San Francisco, San Francisco, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yan</LastName><ForeName>Fang</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Bin</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xuan</LastName><ForeName>Zhou</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ma</LastName><ForeName>Xiumin</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University; Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wulasihana</LastName><ForeName>Muhuyati</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Indian J Med Microbiol</MedlineTA><NlmUniqueID>8700903</NlmUniqueID><ISSNLinking>0255-0857</ISSNLinking></MedlineJournalInfo><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="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000367" MajorTopicYN="N">Age Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName><QualifierName UI="Q000254" MajorTopicYN="Y">growth & development</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019411" MajorTopicYN="N">Clinical Laboratory Techniques</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000000981" MajorTopicYN="N">diagnostic imaging</QualifierName><QualifierName UI="Q000473" MajorTopicYN="Y">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003955" MajorTopicYN="N">Diagnostic Tests, Routine</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D042241" MajorTopicYN="N">Early Diagnosis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006785" MajorTopicYN="N">Hospitals, University</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008403" MajorTopicYN="N">Mass Screening</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000000981" MajorTopicYN="N">diagnostic imaging</QualifierName><QualifierName UI="Q000473" MajorTopicYN="Y">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014057" MajorTopicYN="N">Tomography, X-Ray Computed</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Age</Keyword><Keyword MajorTopicYN="Y">clinical characteristics</Keyword><Keyword MajorTopicYN="Y">coronavirus disease 2019</Keyword><Keyword MajorTopicYN="Y">imaging</Keyword><Keyword MajorTopicYN="Y">laboratory investigations</Keyword></KeywordList><CoiStatement>None</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>7</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>7</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32719214</ArticleId><ArticleId IdType="pii">IndianJMedMicrobiol_2020_38_1_87_290685</ArticleId><ArticleId IdType="doi">10.4103/ijmm.IJMM_20_133</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Australie</li><li>République populaire de Chine</li><li>États-Unis</li></country><region><li>Californie</li></region><settlement><li>San Francisco</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Liu, Xuemei" sort="Liu, Xuemei" uniqKey="Liu X" first="Xuemei" last="Liu">Xuemei Liu</name></noRegion><name sortKey="Li, Bin" sort="Li, Bin" uniqKey="Li B" first="Bin" last="Li">Bin Li</name><name sortKey="Lv, Jie" sort="Lv, Jie" uniqKey="Lv J" first="Jie" last="Lv">Jie Lv</name><name sortKey="Ma, Xiumin" sort="Ma, Xiumin" uniqKey="Ma X" first="Xiumin" last="Ma">Xiumin Ma</name><name sortKey="Sun, Feng" sort="Sun, Feng" uniqKey="Sun F" first="Feng" last="Sun">Feng Sun</name><name sortKey="Wulasihana, Muhuyati" sort="Wulasihana, Muhuyati" uniqKey="Wulasihana M" first="Muhuyati" last="Wulasihana">Muhuyati Wulasihana</name><name sortKey="Xuan, Zhou" sort="Xuan, Zhou" uniqKey="Xuan Z" first="Zhou" last="Xuan">Zhou Xuan</name><name sortKey="Yan, Fang" sort="Yan, Fang" uniqKey="Yan F" first="Fang" last="Yan">Fang Yan</name><name sortKey="Zhang, Ying" sort="Zhang, Ying" uniqKey="Zhang Y" first="Ying" last="Zhang">Ying Zhang</name></country><country name="Australie"><noRegion><name sortKey="Gan, Lin" sort="Gan, Lin" uniqKey="Gan L" first="Lin" last="Gan">Lin Gan</name></noRegion></country><country name="États-Unis"><region name="Californie"><name sortKey="Meng, Bo" sort="Meng, Bo" uniqKey="Meng B" first="Bo" last="Meng">Bo Meng</name></region><name sortKey="Jheon, Andrew" sort="Jheon, Andrew" uniqKey="Jheon A" first="Andrew" last="Jheon">Andrew Jheon</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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