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

Journal: Clinical chemistry
PMID: 32821907

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.

Authors

  • He S Yang
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Yu Hou
    Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA.
  • Ljiljana V Vasovic
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Peter A D Steel
    New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY.
  • Amy Chadburn
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Sabrina E Racine-Brzostek
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Priya Velu
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Melissa M Cushing
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Massimo Loda
    Dana-Farber Cancer Institute, Boston, MA, USA.
  • Rainu Kaushal
    Weill Cornell Medicine and New York-Presbyterian Hospital, New York, New York (R.K., D.K.).
  • Zhen Zhao
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.

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