Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

Journal: Critical care (London, England)
Published Date: May 2, 2023

Abstract

BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed 'pharmacophenotypes') correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality).

Authors

  • Andrea Sikora
    Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA, United States.
  • Alireza Rafiei
    Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. Electronic address: alirezarafiei@ut.ac.ir.
  • Milad Ghiasi Rad
    Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • Kelli Keats
    Department of Pharmacy, Augusta University Medical Center, Augusta, GA, USA.
  • Susan E Smith
    Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, GA, United States.
  • John W Devlin
    Northeastern University School of Pharmacy, Boston, MA, USA.
  • David J Murphy
    Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA, USA.
  • Brian Murray
    University of Colorado Skaggs Schools of Pharmacy and Pharamceutical Sciences, Aurora, CO, United States.
  • Rishikesan Kamaleswaran
    Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.

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