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About:
Preliminary study to identify severe from moderate cases of COVID-19 using NLR&RDW-SD combination parameter
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research paper
schema:ScholarlyArticle
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Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
has title
Preliminary study to identify severe from moderate cases of COVID-19 using NLR&RDW-SD combination parameter
Creator
Li, Tao
Zhang, Xiaomei
Ding, Xiao
Deng, Rongrong
Fu, Weiyang
Fu, Zhongxiao
Gou, Liyao
Li, Chengbin
Shao, Feng
Wang, Changzheng
Wang, Guanzhen
Xiao, Jianping
Xiao, Xiulin
Source
MedRxiv
abstract
Objectives: Investigate the characteristics and rules of hematology changes in patients with COVID-19, and explore the possibility to identify moderate and severe patients using conventional hematology parameters or combined parameters. Methods: The clinical data of 45 moderate and severe type patients with SARS-CoV-2 infections in Jingzhou Central Hospital from January 23 to February 13, 2020 were collected. The epidemiological indexes, clinical symptoms and laboratory test results of the patients were retrospectively analyzed. Those parameters with significant differences between the two groups were analyzed, and the combination parameters with best diagnostic performance were selected using the LDA method. Results: Of the 45 patients with COVID-19 (35 moderate and 10 severe cases), 23 were male and 22 female, aged 16-62 years. The most common clinical symptoms were fever (89%) and dry cough (60%). As the disease progressed, WBC, Neu#, NLR, PLR, RDW-CV and RDW-SD parameters in the severe group were significantly higher than that in the moderate group (P<0.05); meanwhile, Lym#, Eos#, HFC%, RBC, HGB and HCT parameters in the severe group were significantly lower than that in the moderate group (P<0.05). For NLR, the AUC, the best cut-off value, the sensitivity and the specificity were 0.890, 13.39, 83.3% and 82.4% respectively, and for PLR , the AUC, the best cut-off, the sensitivity and the specificity were 0.842, 267.03, 83.3% and 74.0% respectively. The combined parameter NLR&RDW-SD had the best diagnostic efficiency (AUC was 0.938) and when the cut-off value was 1.046, the sensitivity and the specificity were 90.0% and 84.7% respectively, followed by the fitting parameter NLR&RDW-CV (AUC = 0.923). When the cut-off value was 0.62, the sensitivity and the specificity for distinguishing severe type from moderate cases of COVID-19 were 90.0% and 82.4% respectively. Conclusions: The combined parameter NLR&RDW-SD is the best hematology index and can help clinicians to predict the severity of COVID-19 patients, and it can be used as a useful indicator to help prevent and control the epidemic.
has issue date
2020-04-14
(
xsd:dateTime
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bibo:doi
10.1101/2020.04.09.20058594
has license
medrxiv
sha1sum (hex)
327358abbb515d09feba4f66052429bbdab1a7ef
schema:url
https://doi.org/10.1101/2020.04.09.20058594
resource representing a document's title
Preliminary study to identify severe from moderate cases of COVID-19 using NLR&RDW-SD combination parameter
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covid:327358abbb515d09feba4f66052429bbdab1a7ef#body_text
is
schema:about
of
named entity 'hematology'
named entity 'PARAMETER'
named entity 'MODERATE'
named entity 'COVID-19'
named entity 'explore'
named entity 'moderate'
named entity 'COVID-19'
named entity 'medRxiv'
named entity 'People's Republic of China'
named entity 'underlying diseases'
named entity 'platelets'
named entity 'hematology'
named entity 'mortality rate'
named entity 'Pregnant women'
named entity 'receiver operating characteristic'
named entity 'COVID'
named entity 'WBC'
named entity 'fever'
named entity 'Harvard University'
named entity 'WHO'
named entity 'LDA'
named entity 'peripheral blood'
named entity 'dry cough'
named entity 'FiO2'
named entity 'Wuhan'
named entity 'COVID'
named entity 'LDA'
named entity 'dry cough'
named entity 'staining'
named entity 'SARS-CoV'
named entity 'Biotechnology'
named entity 'early diagnosis'
named entity 'laboratory test'
named entity 'Wuhan'
named entity 'Beijing'
named entity 'SARS-CoV-2 virus'
named entity 'fluorescent'
named entity 'multivariate analysis'
named entity 'thrombocytosis'
named entity 'mortality rate'
named entity 'Fever'
named entity 'RDW'
named entity 'underlying diseases'
named entity 'ROC'
named entity 'lymphocyte count'
named entity 'CBC'
named entity 'palliative treatment'
named entity 'vector space'
named entity 'lymphocytes'
named entity 'lymphopenia'
named entity 'respiratory symptoms'
named entity 'MERS-CoV'
named entity 'Wuhan'
named entity 'dry cough'
named entity 'nucleic acid detection'
named entity 'red blood cell'
named entity 'medRxiv'
named entity 'diagnosis of a disease'
named entity 'COVID'
named entity 'SARS'
named entity 'LDA'
named entity 'COVID'
named entity 'LDH'
named entity 'COVID-19'
named entity 'Yangtze University'
named entity 'COVID'
named entity 'lymphoid organs'
named entity 'prognosis'
named entity 'blood cells'
named entity 'immature red blood cells'
named entity 'prognosis'
named entity 'laboratory test'
named entity 'biochemical'
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