Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing.

Journal: Archives of pathology & laboratory medicine
PMID: 33635951

Abstract

CONTEXT.—: Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI.

Authors

  • Hooman H Rashidi
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Electronic address: rashidihh@upmc.edu.
  • Amy Makley
    The Department of Surgery, University of Cincinnati, Cincinnati, Ohio (Makley).
  • Tina L Palmieri
    Division of Burn Surgery, Dept. of Surgery, United States.
  • Samer Albahra
    Pathology & Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio.
  • Julia Loegering
    From the Department of Pathology and Laboratory Medicine (Rashidi, Albahra, Loegering, Tran), University of California, Davis, Sacramento.
  • Lei Fang
    Nanomix, Inc, Emeryville, California (Fang, Yamaguchi).
  • Kensuke Yamaguchi
    Nanomix, Inc, Emeryville, California (Fang, Yamaguchi).
  • Travis Gerlach
    The Department of Surgery, David Grant Medical Center, Travis Air Force Base, Fairfield, California (Gerlach).
  • Dario Rodriquez
    The Department of Surgery, 711th Human Performance Wing, Wright-Patterson Air Force Base, Cincinnati, Ohio (Rodriquez Jr).
  • Nam K Tran
    Dept. of Pathology and Laboratory Medicine, United States. Electronic address: nktran@ucdavis.edu.

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