ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.

Journal: Critical care (London, England)
Published Date: May 26, 2025

Abstract

BACKGROUND: Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney injury (AKI). Existing prediction models for MAKE30 are static and overlook dynamic changes in clinical status. We introduce ORAKLE, a novel deep-learning model that utilizes evolving time-series data to predict MAKE30, enabling personalized, patient-centered approaches to AKI management and outcome improvement.

Authors

  • Wonsuk Oh
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Marinela Veshtaj
    Touro College of Osteopathic Medicine, New York, NY, USA.
  • Ashwin Sawant
    Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.S.).
  • Pulkit Agrawal
    Department of Electrical Engineering and Computer Science, University of California, Berkeley (J.Z., P.A., L.A.H., R.B.).
  • Hernando Gomez
    Department of Critical Care Medicine, Center for Critical Care Nephrology, University of Pittsburgh, Pittsburgh, PA, USA.
  • Mayte Suarez-Farinas
    Icahn School of Medicine at Mount Sinai Medical Center, Department of Dermatology, New York, New Yor, United States.
  • John Oropello
    Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Roopa Kohli-Seth
    Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Kianoush Kashani
    Division of Nephrology and Hypertension.
  • John A Kellum
    Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Girish Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Ankit Sakhuja
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. ankit.sakhuja@mssm.edu.

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