npj Digital Medicine (Jun 2021)

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

  • Naveena Yanamala,
  • Nanda H. Krishna,
  • Quincy A. Hathaway,
  • Aditya Radhakrishnan,
  • Srinidhi Sunkara,
  • Heenaben Patel,
  • Peter Farjo,
  • Brijesh Patel,
  • Partho P. Sengupta

DOI
https://doi.org/10.1038/s41746-021-00467-8
Journal volume & issue
Vol. 4, no. 1
pp. 1 – 10

Abstract

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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.