Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

Journal: Critical care (London, England)
PMID: 38730421

Abstract

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear.

Authors

  • Kullaya Takkavatakarn
    Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of Nephrology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
  • Wonsuk Oh
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Lili Chan
    Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Ira Hofer
  • Khaled Shawwa
    Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA.
  • Monica Kraft
    Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Neomi Shah
    Division of Pulmonary, Critical Care and Sleep Medicine, Department of 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.
  • Girish N Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), 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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