Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children.

Journal: Critical care explorations
PMID: 39688905

Abstract

BACKGROUND: Continuous renal replacement therapy (CRRT) is the favored renal replacement therapy in critically ill patients. Predicting clinical outcomes for CRRT patients is difficult due to population heterogeneity, varying clinical practices, and limited sample sizes.

Authors

  • Sameer Thadani
    Department of Pediatric, Division of Critical Care Medicine and Nephrology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX.
  • Tzu-Chun Wu
    Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.
  • Danny T Y Wu
    Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH.
  • Aadil Kakajiwala
    Department of Pediatrics, Division of Critical Care Medicine, Children's National Hospital, Washington, DC.
  • Danielle E Soranno
    Departments of Pediatrics, Bioengineering and Medicine, University of Colorado, Aurora, Colorado.
  • Gerard Cortina
    Department of Pediatrics, Medical University of Innsbruck, Innsbruck, Austria.
  • Rachana Srivastava
    Division of Nephrology, Department of Pediatrics, University of California Los Angeles, Los Angeles, CA.
  • Katja M Gist
    Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
  • Shina Menon
    University of Washington and Seattle Children's Hospital, Seattle, Washington.

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