Optimizing predictive strategies for acute kidney injury after major vascular surgery.

Journal: Surgery
Published Date: Feb 27, 2021

Abstract

BACKGROUND: Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery.

Authors

  • Amanda C Filiberto
    Department of Surgery, University of Florida Health, Gainesville, FL, USA.
  • Tezcan Ozrazgat-Baslanti
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Tyler J Loftus
    Department of Surgery, University of Florida Health, Gainesville, FL. Electronic address: tyler.loftus@surgery.ufl.edu.
  • Ying-Chih Peng
    Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville FL.
  • Shounak Datta
    Electronics and Communication Sciences Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata-700 108, India. Electronic address: shounak.jaduniv@gmail.com.
  • Philip Efron
    Department of Surgery, University of Florida, Gainesville, FL; Department of Anesthesia, University of Florida, Gainesville, FL.
  • Gilbert R Upchurch
    TCV Division, Department of Surgery, University of Virginia Medical Center, Charlottesville, Virginia.
  • Azra Bihorac
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Michol A Cooper
    Department of Surgery, University of Florida, Gainesville, FL. Electronic address: Michol.cooper@surgery.ufl.edu.

Keywords

External Resources

Popular Topics
Recent Journals