Clinical Ordering Practices of the SARS-CoV-2 Antibody Test at a Large Academic Medical Center

  1. Joesph R Wiencek
  2. Carter L Head
  3. Costi D Sifri
  4. Andrew S Parsons

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Abstract

Background

The novel severe acute respiratory coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) originated in December 2019 and has now infected almost 5 million people in the United States. In the spring of 2020, private laboratories and some hospitals began antibody testing despite limited evidence-based guidance.

Methods

We conducted a retrospective chart review of patients who received SARS-CoV-2 antibody testing from May 14, 2020, to June 15, 2020, at a large academic medical center, 1 of the first in the United States to provide antibody testing capability to individual clinicians in order to identify clinician-described indications for antibody testing compared with current expert-based guidance from the Infectious Diseases Society of America (IDSA) and the Centers for Disease Control and Prevention (CDC).

Results

Of 444 individual antibody test results, the 2 most commonly described testing indications, apart from public health epidemiology studies (n = 223), were for patients with a now resolved COVID-19-compatible illness (n = 105) with no previous molecular testing and for asymptomatic patients believed to have had a past exposure to a person with COVID-19-compatible illness (n = 60). The rate of positive SARS-CoV-2 antibody testing among those indications consistent with current IDSA and CDC guidance was 17% compared with 5% (P < .0001) among those indications inconsistent with such guidance. Testing inconsistent with current expert-based guidance accounted for almost half of testing costs.

Conclusions

Our findings demonstrate a dissociation between clinician-described indications for testing and expert-based guidance and a significantly different rate of positive testing between these 2 groups. Clinical curiosity and patient preference appear to have played a significant role in testing decisions and substantially contributed to testing costs.

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  1. Version published to 10.1093/ofid/ofaa406
  2. ScreenIT

    SciScore for 10.1101/2020.07.12.20152165: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Chart review was performed under a protocol that was approved by the University of Virginia Institutional Review Board (IRB-HSR #13310).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Before any patients were tested, the SARS-CoV-2 IgG antibody test was validated similarly as previously described.
    SARS-CoV-2 IgG
    suggested: None
    Due to the retrospective focus of the study, we did not include RT-PCR tests in our data that occurred on dates after the SARS-CoV-2 antibody test.
    SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    Antibody testing was performed in our central clinical laboratory on the Abbott Architect i2000 analyzer utilizing the EUA SARS-CoV-2 IgG antibody immunoassay.
    Abbott Architect
    suggested: (Abbott ARCHITECT i1000sr System, RRID:SCR_019328)
    Statistical Analysis: All data was de-identified and collected into Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Cost Analysis: An estimated cost analysis of the SARS-CoV-2 antibody test was performed utilizing all reviewed tests between May 14, 2020 to June 15, 2020.
    Cost
    suggested: (COST, RRID:SCR_014098)

    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:
    Fortunately, stewardship of laboratory testing is not a new concept.(18,29) Well-designed and carefully thought-out testing strategies have been shown to help increase the awareness of limitations (i.e., false positivity and negativity rate) and appropriateness of a specific test. A possible evidence-based approach to control unnecessary SARS-CoV-2 antibody testing may be in the development and implementation of clinical decision support (CDS) tools that leverage the EMR.(30) This would restrict clinicians to ordering the test under options of CDC and IDSA guidance only until more evidence becomes available. This study has some limitations. First, retrospective review of clinical documentation is inherently subjective. To counter this limitation, we maintained a conservative threshold for including any single testing indication within a given category. In this study, 11% of testing indications were categorized as “no indication provided,” a frequency consistent with other published literature reliant on retrospective chart review.(31) Second, the duration of antibody response following infection is unclear and asymptomatic individuals may not develop a positive antibody response.(14) Third, IDSA and CDC guidance on testing may change based on evolving literature. Finally, there are concerns about increased false-positive antibody tests due to the low prevalence of COVID-19 in our patient population. However, the use of the EUA Abbott Architect SARS-CoV-2 IgG antibody test in ...

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.07.12.20152165v1 on medRxiv