Assessing the utility of lymphocyte count to diagnose COVID-19

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Abstract

Background

COronaVirus 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).

Methods

We 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 (×10 9 /L) were calculated (i.e., sensitivity, specificity, area under the receiver operating curve [AUC]). All analyses were conducting using R.

Results

There 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 ×10 9 /L (interquartile range [IQR]:0.7,2.0) for COVID-19, 0.9 ×10 9 /L (IQR 0.6,1.0) for influenza, and 1.0 ×10 9 /L (IQR 0.6,2.0) for CAP. At a lymphocyte threshold of less than 2.0 ×10 9 /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%.

Interpretation

Lymphocyte 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 ×10 9 /L may be useful in ruling out COVID-19 (sensitivity = 87%).

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  1. SciScore for 10.1101/2021.03.17.21252922: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Another important limitation of our study is that we lacked data on chronic medical conditions pre-dating the hospitalization known to cause lymphopenia (e.g., hematologic malignancies, autoimmune disorders), however, we expect these conditions to be rare and not differential across the three respiratory illnesses. The lymphopenia we consistently observed across the three illnesses in our study may reflect a non-specific sign of illness severity, with limited diagnostic utility in identifying underlying etiologies.5

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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