Can we use temperature measurements to identify pre‐symptomatic SARS‐CoV‐2 infection in nursing home residents?

  1. Salaheldin Elhamamsy
  2. Frank DeVone
  3. Thomas Bayer
  4. Chris Halladay
  5. Marilyne Cadieux
  6. Kevin McConeghy
  7. Ashna Rajan
  8. Moniyka Sachar
  9. Nadia Mujahid
  10. Mriganka Singh
  11. Aman Nanda
  12. Lynn McNicoll
  13. James L. Rudolph
  14. Stefan Gravenstein

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Abstract

Background

COVID‐19 has had a severe impact on morbidity and mortality among nursing home (NH) residents. Earlier detection of SARS‐CoV‐2 may position us to better mitigate the risk of spread. Both asymptomatic and pre‐symptomatic transmission are common in outbreaks, and threshold temperatures, such as 38C, for screening for infection could miss timely detection in the majority of residents. We hypothesized that in long‐term care residents, temperature trends with SARS‐CoV‐2 infection could identify infection in pre‐symptomatic individuals earlier than standard screening.

Methods

We conducted a retrospective cohort study using electronic health records in 6176 residents of the VA NHs who underwent SARS‐CoV‐2 testing triggered by symptoms. We collected information about age and other demographics, baseline temperature, and specific comorbidities. We created standardized definitions, and a hypothetical model to test measures of temperature variation and compare outcomes to the VA standard of care.

Results

We showed that a change from baseline of 0.4C identified 47% of NH residents who became SARS‐CoV‐2 positive, earlier than standard testing by an average of 42.2 h. Temperature variability of 0.5C over 3 days when paired with a 37.2C temperature cutoff identified 55% of NH residents who became SARS‐CoV‐2 positive earlier than the standard of care testing by an average of 44.4 h. A change from baseline temperature of 0.4C when combined with temperature variability of 0.7C over 3 days identified 52% of NH residents who became SARS‐CoV‐2 positive, earlier than standard testing by an average of 40 h, and by more than 3 days in 22% of the residents. This earlier detection comes at the expense of triggering 57,793 tests, as compared to the number of trigger tests ordered in the VA system of 40,691.

Conclusions

Our model suggests that early temperature trends with SARS‐CoV‐2 infection may identify infection in pre‐symptomatic long‐term care residents.

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  1. Version published to 10.1111/jgs.17972
  2. ScreenIT

    SciScore for 10.1101/2021.07.23.21260676: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was approved by the Providence Veterans Administration Medical Center’s Institutional Review Board.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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:
    Study limitations; our scenario is a hypothetical model, we only have point estimates for the temperatures, ideally we’d have continuous temperature monitoring to better understand the effect of SARS-CoV-2 on temperature over time. Our cohort is also older and mainly male and white (see table 1), we know these demographic variations can affect temperature. Our creation of a hypothetical model prevents us from performing more traditional statistical tests on our results, we created the measures discussed as our best substitute for more traditional sensitivity and specificity analysis. Some of these limitations could be overcome by using continuous temperature monitoring devices. Also, NHs typically do not have a documented baseline temperature defined for their residents. To do so, the EMR will need to be programmed to use existing temperature data that establish and track person-level baseline temperatures; then, it can also be set to alert the clinician to relevant changes from baseline. Resident records did not distinguish whether clinical symptoms triggered the SARS-CoV-2 test. We therefore relied on a conservative definition for “trigger test” that limited our sample size by more than half. Had this information been available, we likely would have had a much larger portion of our sample available for the analysis. A prospective study to test the prediction of the model is needed.

    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

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  3. Version published to 10.1101/2021.07.23.21260676v1 on medRxiv