Using body temperature and variables commonly available in the EHR to predict acute infection: a proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients

  1. Carl T. Berdahl
  2. An T. Nguyen
  3. Marcio A. Diniz
  4. Andrew J. Henreid
  5. Teryl K. Nuckols
  6. Christopher P. Libby
  7. Joshua M. Pevnick

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Abstract

Objectives

Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day.

Methods

For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients’ COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3.

Results

Data from 117 patients were included. The models’ AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI −0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI −0.021 to 0.097, p=0.10) between model 3 and 2.

Conclusions

By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.

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  1. Version published to 10.1515/dx-2021-0020
  2. ScreenIT

    SciScore for 10.1101/2021.01.21.21250261: (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

    Software and Algorithms
    SentencesResources
    Measures: Analyses:
    Measures
    suggested: None

    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:
    We recognize that this initial analysis has several limitations. First, we expect that these AUCs are optimistic because they are evaluated in a derivation sample at a single site,[20] though we attempted to mitigate this effect by reporting optimism-adjusted performance measures of calibration and discrimination. Second, our data used a subset of patients who were tested for COVID-19 during a time when testing supplies were limited, making selection bias a potential limitation. Third, members of the research team who generated pretest probability estimates were not blinded to the study hypothesis regarding which covariates should affect interpretation of temperature. This may have caused their estimates to differ from estimates that would otherwise by generated in practicing clinicians. Fourth, even when AUC is increased, it must be considered in a clinical context to ensure that yields clinically significant benefit. Fifth, we were unable to include two important classes of variables in our analysis because of gaps in the sample: patients’ baseline temperature values and variables related to women’s hormonal cycles. Accounting for patients’ baseline temperature has been endorsed by the Infectious Disease Society of America[21]. Moreover, use of hormonal contraceptives and estrogen replacement therapy have been demonstrated to impact temperature interpretation,[7] which suggests that time since a woman’s last menstrual period may also affect temperature elevation in the sett...

    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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  3. Version published to 10.1101/2021.01.21.21250261v1 on medRxiv