About: Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China
Creator	Chen, Li Li, Jianming Liu, Yingxia Peng, Ling Wang, Fuxiang Yang, Liuqing Jiang, Xiao Wu, Weibo Cao, Mengli Chen, Chuming Feng, Shiyan Liang, Zhichao Li, Yinfeng Liu, Quanying Meng, Lingxiang Wang, Haihui Wu, Huishan Zhang, Peiyan Zhao, Fang
Source	Elsevier; PMC
abstract	Among 417 COVID-19 patients in Shenzhen, demographic characteristics, clinical manifestations and baseline laboratory tests showed significant differences between mild-moderate cohort and severe-critical cohort.Based on these differences, a convenient mathematical model was established to predict the illness severity of COVID-19. The model includes four parameters: age, BMI, CD4(+) lymphocytes and IL-6 levels. The AUC of the model is 0.911.The high risk factors for developing to severe COVID-19 are: age ≥ 55 years, BMI > 27 kg / m(2), IL-6 ≥ 20 pg / ml, CD4(+) T cell ≤ 400 count / μ L.Among 249 discharged COVID-19 patients, those who recovered after 20 days had a lower count of platelet, a higher level of estimated glomerular filtration rate, and higher level of interleukin-6 and myoglobin than those who recovered within 20 days.
has issue date	2020-05-21(xsd:dateTime)
bibo:doi	10.1016/j.xinn.2020.04.007
has license	els-covid
sha1sum (hex)	18aa00ecfa05a716590998599ff9ffa9a404d622
schema:url	https://doi.org/10.1016/j.xinn.2020.04.007
resource representing a document's title	Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China
has PubMed Central identifier	PMC7237911
schema:publication	The Innovation
resource representing a document's body	covid:18aa00ecfa05a716590998599ff9ffa9a404d622#body_text
is schema:about of	named entity 'patients' named entity 'count' named entity 'Cohort' named entity 'China' named entity 'Outcomes' named entity 'ILLNESS SEVERITY' named entity 'MODEL' covid:arg/18aa00ecfa05a716590998599ff9ffa9a404d622 named entity 'T cell' named entity 'demographic' named entity 'lymphocytes' named entity 'interleukin' named entity 'Innovation' named entity 'myoglobin' named entity 'lymphocytes' named entity 'COVID-19' named entity 'platelet' named entity 'mathematical model' named entity 'COVID' named entity 'laboratory tests' named entity 'COVID' named entity 'CD8 +' named entity 'Chi-square test' named entity 'potassium' named entity 'efficacies' named entity 'complement' named entity 'headache' named entity 'liver' named entity 'sodium' named entity 'underlying diseases' named entity 'lymphocytes' named entity 'TnI' named entity 'probability' named entity 'probability' named entity 'IL-6' named entity 'probability' named entity 'IL-6' named entity 'COVID-19' named entity 'zymogram' named entity 'IL-6' named entity 'lymphocytes' named entity 'moderate group' named entity 'lymphocyte' named entity 'epidemiological' named entity 'creatine kinase' named entity 'macrophage inflammatory protein' named entity 'sensitivity and specificity' named entity 'immune response' named entity 'ROC curves' named entity 'infection' named entity 'BMI' named entity 'BMI' named entity 'HDL' named entity 'cohort study' named entity 'lymphocytes' named entity 'COVID-19' named entity 'biomarkers' named entity 'current research' named entity 'CD4' named entity 'Hubei province' named entity 'lymphocytes' named entity 'COVID-19' named entity 'BMI' named entity 'sex ratio' named entity 'early diagnosis'

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