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Distinct Laboratory Test Result Profiles between SARS-CoV-2 and Seasonable Influenza Infected Patients

  • Junyi Liu
  • , Lars F. Westblade
  • , Fei Wang
  • , Amy Chadburn
  • , Richard Fideli
  • , Arryn Craney
  • , Sophie Rand
  • , Melissa M. Cushing
  • , Jingjing Meng
  • , Zhen Zhao
  • , He S. Yang
  • SUNY Buffalo
  • Cornell University
  • New York Presbyterian Hospital

Research output: Contribution to journalArticlepeer-review

Abstract

Objective. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses are contagious respiratory pathogens with similar symptoms but require different treatment and management strategies. This study investigated the differences in laboratory test result profiles between SARS-CoV-2 and influenza infected patients upon presentation to emergency department (ED). Methods. Laboratory test results and demographic information from 723 influenza positive (2018/1/1 to 2020/3/15) and 1,281 SARS-CoV-2 positive (2020/3/11 to 2020/6/30) ED patients were retrospectively analyzed. The dataset was randomly divided into a training/validation set (2/3) and a test set (1/3) with the same SARS-CoV-2/influenza ratio. Four machine learning models in differentiating the laboratory profiles of RT-PCR confirmed SARS-CoV-2 and influenza positive patients were evaluated. The Shapley Additive Explanations technique was employed to visualize the impact of laboratory tests on the overall differentiation. Furthermore, the model performance was also evaluated in a new test dataset including 519 SARS-CoV-2 ED patients (2020/12/1 to 2021/2/28) and the previous influenza positive patients (2018/1/1 to 2020/3/15). Results. A laboratory test result profile consisting of 15 blood tests, together with patient age, gender, and race can discriminate the two types of viral infections using a random forest (RF) model. The RF model achieved an area under the receiver operating characteristic curve (AUC) of 0.90 in the test set. Among the profile of 15 laboratory tests, the serum total calcium level exhibited the greatest contribution to the overall differentiation. Furthermore, the model achieved an AUC of 0.81 in a new test set. Conclusion. We developed a laboratory tests-based RF model differentiating SARS-CoV-2 from influenza, which may be useful for the preparedness of overlapping COVID-19 resurgence and future seasonal influenza.

Original languageEnglish
Pages (from-to)871-879
Number of pages9
JournalAnnals of Clinical and Laboratory Science
Volume52
Issue number6
StatePublished - Dec 2022

Keywords

  • COVID-19
  • influenza
  • machine learning
  • routine laboratory tests
  • SARS-CoV-2

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