Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning

He S. Yang, Yu Hou, Ljiljana V. Vasovic, Peter A.D. Steel, Amy Chadburn, Sabrina E. Racine-Brzostek, Priya Velu, Melissa M. Cushing, Massimo Loda, Rainu Kaushal, Zhen Zhao, Fei Wang

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

Abstract

Background: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. Method: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. Results: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. Conclusion: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Original languageEnglish (US)
Pages (from-to)1396-1404
Number of pages9
JournalClinical chemistry
Volume66
Issue number11
DOIs
StatePublished - Nov 1 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 American Association for Clinical Chemistry 2020. All rights reserved. For permissions, please email: [email protected].

Keywords

  • COVID-19
  • SARS-CoV-2
  • gradient boosted decision tree
  • machine learning
  • routine laboratory tests

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