Mortality and antibiotic timing in deep learning-derived surviving sepsis campaign risk groups: a multicenter study.

Journal: Critical care (London, England)
Published Date:

Abstract

BACKGROUND: The current Surviving Sepsis Campaign (SSC) guidelines provide recommendations on timing of administering antibiotics in sepsis patients based on probability of sepsis and presence of shock. However, there have been minimal efforts to stratify patients objectively into these groups and describe patient outcomes as a function of antibiotic timing recommendations based on risk stratification using this approach.

Authors

  • Ben J Gross
    Division of Biomedical Informatics, University of California , La Jolla, San Diego, USA.
  • Allison Donahue
    Department of Emergency Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
  • James S Ford
    Department of Emergency Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
  • Xiaolei Lu
  • Aaron Boussina
    Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA.
  • Atul Malhotra
    Division of Pulmonary, Critical Care, and Sleep Medicine, University of California, San Diego, La Jolla, CA. Electronic address: amalhotra@health.ucsd.edu.
  • Kai Zheng
    University of California, Irvine, Irvine, CA, USA.
  • Shamim Nemati
    Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA.
  • Gabriel Wardi
    Emergency Medicine, University of California San Diego, La Jolla, California, USA.