Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study

  1. Jeffrey G Klann
  2. Zachary H Strasser
  3. Meghan R Hutch
  4. Chris J Kennedy
  5. Jayson S Marwaha
  6. Michele Morris
  7. Malarkodi Jebathilagam Samayamuthu
  8. Ashley C Pfaff
  9. Hossein Estiri
  10. Andrew M South
  11. Griffin M Weber
  12. William Yuan
  13. Paul Avillach
  14. Kavishwar B Wagholikar
  15. Yuan Luo
  16. The Consortium for Clinical Characterization of COVID-19 by EHR (4CE)
  17. Gilbert S Omenn
  18. Shyam Visweswaran
  19. John H Holmes
  20. Zongqi Xia
  21. Gabriel A Brat
  22. Shawn N Murphy

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Abstract

Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)–based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification.

Objective

The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification.

Methods

From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as “admitted with COVID-19” (incidental) versus specifically admitted for COVID-19 (“for COVID-19”). EHR-based phenotyping was used to find feature sets to filter out incidental admissions.

Results

EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity.

Conclusions

A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

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  1. Version published to 10.2196/37931
  2. Version published to 10.1101/2022.02.10.22270728v3 on medRxiv
  3. Version published to 10.2196/preprints.37931
  4. Version published to 10.1101/2022.02.10.22270728v2 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2022.02.10.22270728: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was approved by the Institutional Review Board at each site.
    Sex as a biological variablenot detected.
    RandomizationEligible patients for this study were those included in the 4CE COVID-19 cohort: all hospitalized patients (pediatric and adult) with their first positive SARS-CoV-2 PCR test seven days before to 14 days after hospitalization.2 Chart Review: Each development site randomly sampled an equal number of admissions in each quarter (BIDMC, MGB) or month (NWU, UPITT) from their cohort of SARS-CoV-2 PCR positive patients over the period March 2020 until at least March 2021.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    A trendline was fitted with loess regression using ggplot2 and was weighted by the number of chart reviews performed that month.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    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.

  6. Version published to 10.1101/2022.02.10.22270728v1 on medRxiv