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Assessing the utility of lymphocyte count to diagnose COVID-19
Preprint
em En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-21252922
ABSTRACT
BackgroundCOronaVirus Disease 2019 (COVID-19) can be challenging to diagnose, because symptoms are non-specific, clinical presentations are heterogeneous, and false negative tests can occur. Our objective was to assess the utility of lymphocyte count to differentiate COVID-19 from influenza or community-acquired pneumonia (CAP). MethodsWe conducted a cohort study of adults hospitalized with COVID-19 or another respiratory infection (i.e., influenza, CAP) at seven hospitals in Ontario, Canada.The first available lymphocyte count during the hospitalization was used. Standard test characteristics for lymphocyte count (x109/L) were calculated (i.e., sensitivity, specificity, area under the receiver operating curve [AUC]). All analyses were conducting using R. ResultsThere were 869 hospitalizations for COVID-19, 669 for influenza, and 3009 for CAP. The mean age across the three groups was 67 and patients with pneumonia were older than those with influenza or COVID19, and approximately 46% were woman. The median lymphocyte count was nearly identical for the three groups of patients 1.0 x109/L (interquartile range [IQR]0.7,2.0) for COVID-19, 0.9 x109/L (IQR 0.6,1.0) for influenza, and 1.0 x109/L (IQR 0.6,2.0) for CAP. At a lymphocyte threshold of less than 2.0 x109/L, the sensitivity was 87% and the specificity was approximately 10%. As the lymphocyte threshold increased, the sensitivity of diagnosing COVID-19 increased while the specificity decreased. The AUC for lymphocyte count was approximately 50%. InterpretationLymphocyte count has poor diagnostic discrimination to differentiate between COVID-19 and other respiratory illnesses. The lymphopenia we consistently observed across the three illnesses in our study may reflect a non-specific sign of illness severity. However, lymphocyte count above 2.0 x109/L may be useful in ruling out COVID-19 (sensitivity = 87%).
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Texto completo:
1
Coleções:
09-preprints
Base de dados:
PREPRINT-MEDRXIV
Tipo de estudo:
Cohort_studies
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Diagnostic_studies
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Experimental_studies
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Observational_studies
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Prognostic_studies
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Rct
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Preprint