Diagnostic Yield of Screening for SARS-CoV-2 among Patients Admitted for Alternate Diagnoses

  1. Phil Davis
  2. Rhonda J. Rosychuk
  3. Jeffrey P Hau
  4. Ivy Cheng
  5. Andrew D. McRae
  6. Raoul Daoust
  7. Eddy Lang
  8. Joel Turner
  9. Jaspreet Khangura
  10. Patrick T. Fok
  11. Maja Stachura
  12. Baljeet Brar
  13. Corinne Hohl

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Abstract

Objectives

To determine the diagnostic yield of screening patients for SARS-CoV-2 who were admitted with a diagnosis unrelated to COVID-19, and identify risk factors for positive tests.

Design

Cohort from the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry

Setting

30 acute care hospitals across Canada

Participants

Patients hospitalized for non-COVID-19 related diagnoses who were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between March 1, and December 29, 2020

Main outcome

Positive nucleic acid amplification test (NAAT) for SARS-CoV-2

Outcome measure

Diagnostic yield

Results

We enrolled 15,690 consecutive eligible adults who were admitted to hospital without clinically suspected COVID-19. Among these patients, 122 tested positive for COVID-19, resulting in a diagnostic yield of 0.8% (95% CI 0.64% – 0.92%). Factors associated with a positive test included presence of a fever, being a healthcare worker, having a positive household contact or institutional exposure, and living in an area with higher 7-day average incident COVID-19 cases.

Conclusions

Universal screening of hospitalized patients for COVID-19 across two pandemic waves had a low diagnostic yield and should be informed by individual-level risk assessment in addition to regional COVID-19 prevalence.

Trial registration

NCT04702945

SUMMARY BOXES

Section 1: Universal screening of admitted patients for SARS-CoV-2 was implemented in many hospitals at the beginning of the pandemic. The Infections Diseases Society of America (IDSA) recommended avoiding universal screening of asymptomatic hospitalized patients in areas and times of low-COVID prevalence (defined as <2% prevalence) with very low certainty of evidence, based on studies of COVID-19 prevalence among asymptomatic individuals in the community.

Section 2: This study supports IDSA recommendations to avoid universal screening for COVID-19 in times and areas of low COVID prevalence and identifies patient-level risk factors strongly associated with positive testing that should be considered for screening.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.09.23.21264036: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: The research ethics boards of all participating institutions approved this study with a waiver of informed consent for data collection and linkage.
    IRB: The research ethics boards of all participating institutions approved this study with a waiver of informed consent for data collection and linkage.
    Consent: The research ethics boards of all participating institutions approved this study with a waiver of informed consent for data collection and linkage.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data Collection: Trained research assistants collected data retrospectively from electronic and/or paper-based medical records into a central, web-based REDCap database (Vanderbilt University; Nashville, TN, USA).
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    Statistical analysis was preformed using Stata (Version 16.1, StataCorp, College Station, Texas).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    A limitation of our study is that we only considered NAATs and did not consider the diagnostic yield of antigen-based COVID-19 tests, as they were not widespread in Canada in 2020 (16). We were unable to examine the sensitivity and specificity of the SARS-CoV-2 NAATs as we were unable to define false positive tests, so it is possible that some of the positive test results we encountered are false positives, leading to an overestimation of diagnostic yield. While our study is based on a Canadian population without international sites, we believe our findings are generalizable given their wide geographic spread, and the cultural and racial diversity of our patient population. Finally, as data becomes available on the fourth wave of the pandemic, a future study should examine the impact of widespread vaccination on the yield of screening. As a larger proportion of the population is protected from severe disease and death through vaccination, decision makers should carefully consider the low diagnostic yield of a universal testing strategy going forward.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04702945RecruitingCanadian COVID-19 Emergency Department Registry

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  2. Version published to 10.1101/2021.09.23.21264036v1 on medRxiv