A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients

  1. Naveena Yanamala
  2. Nanda H. Krishna
  3. Quincy A. Hathaway
  4. Aditya Radhakrishnan
  5. Srinidhi Sunkara
  6. Heenaben Patel
  7. Peter Farjo
  8. Brijesh Patel
  9. Partho P. Sengupta

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Abstract

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

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  1. Version published to 10.1038/s41746-021-00467-8
  2. Version published to 10.1101/2021.01.13.21249540v2 on medRxiv
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    SciScore for 10.1101/2021.01.13.21249540: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analysis was performed using Medcalc for Windows, version 19.5.3 (MedCalc Software, Ostend, Belgium), and python for windows, version 3.8.3.
    Medcalc
    suggested: (MedCalc, RRID:SCR_015044)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Another limitation is the need to continually adjust these models to fit new trends in SARS-CoV spread and infectivity. Our models benefit from the inclusion of data spanning from February to October, which can better simulate the current COVID-19 epidemic and influenza season but will still require future iterations and validations to accurately predict SARS-CoV 2 infections. Further, an understanding of COVID-19 and viral co-infections is needed to appropriately model the risks of patients presenting with both illnesses 28-30.

    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.

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  4. Version published to 10.1101/2021.01.13.21249540v1 on medRxiv