Clinical assessment of the criticality index - dynamic, a machine learning prediction model of future care needs in pediatric inpatients.

Journal: PloS one
PMID: 40305490

Abstract

OBJECTIVE: To assess patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations (ICU vs. non-ICU) by the Criticality Index-Dynamic (CI-D), with the goal of enhancing the CI-D.

Authors

  • Anita K Patel
    Department of Pediatrics, Division of Critical Care Medicine, Children's National Hospital and George Washington University School of Medicine and Health Sciences, Washington, DC.
  • Taylor Olson
    Department of Pediatrics, Division of Critical Care Medicine, Children's National Health System, Washington, District of Columbia, United States of America.
  • Christopher Ray
    Department of Pediatrics, Division of Critical Care Medicine, Children's Hospital of Richmond at the Virginia Commonwealth University, Richmond, Virginia, United States of America.
  • Eduardo A Trujillo-Rivera
    Department of Pediatrics, Division of Critical Care Medicine, Children's National Health System, Washington, District of Columbia, United States of America.
  • Hiroki Morizono
    Children's National Research Institute, Associate Research Professor of Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC.
  • Murray M Pollack
    Department of Pediatrics, Division of Critical Care Medicine, Children's National Hospital and George Washington University School of Medicine and Health Sciences, Washington, DC.

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