Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids.

Journal: Clinical therapeutics
PMID: 33865643

Abstract

PURPOSE: Coronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.

Authors

  • Carson Lam
    Department of Biomedical Data Science, Stanford University, Stanford, California, United States.
  • Anna Siefkas
    Dascena, Inc., San Francisco, CA, USA. Electronic address: anna@dascena.com.
  • Nicole S Zelin
    Dascena Inc, Houston, Texas.
  • Gina Barnes
    Dascena, Inc., San Francisco, CA, USA.
  • R Phillip Dellinger
    Division of Critical Care Medicine, Cooper University Hospital/Cooper Medical School of Rowan University, Camden, NJ, USA.
  • Jean-Louis Vincent
    Department of Intensive Care, Erasme Hospital, Université libre de Bruxelles, Route de Lennik 808, 1070, Brussels, Belgium. jlvincent@intensive.org.
  • Gregory Braden
    Kidney Care and Transplant Associates of New England, Springfield, MA, USA.
  • Hoyt Burdick
    Cabell Huntington Hospital, Huntington, WV, USA; Marshall University School of Medicine, Huntington, WV, USA.
  • Jana Hoffman
    Dascena Inc., San Francisco, CA, United States.
  • Jacob Calvert
    Dascena Inc., Hayward, California, USA.
  • Qingqing Mao
    Dascena Inc., Hayward, California, USA.
  • Ritankar Das
    Dascena, Inc, Hayward, California, USA.

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