A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation.

Journal: Kidney360
Published Date: Sep 29, 2022

Abstract

BACKGROUND: The first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning-based tool for predicting mortality could inform patient-clinician shared decision making on whether to initiate dialysis or pursue medical management. We used the eXtreme Gradient Boosting (XGBoost) algorithm to predict mortality in the first 90 days after dialysis initiation in a nationally representative population from the United States Renal Data System.

Authors

  • Summer Rankin
    Booz Allen Hamilton, McLean, Virginia.
  • Lucy Han
    Booz Allen Hamilton, McLean, Virginia.
  • Rebecca Scherzer
    Kidney Health Research Collaborative (KHRC), University of California San Francisco (UCSF), San Francisco, California.
  • Susan Tenney
    Booz Allen Hamilton, McLean, Virginia.
  • Matthew Keating
    Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
  • Kimberly Genberg
    Booz Allen Hamilton, McLean, Virginia.
  • Matthew Rahn
    Office of the National Coordinator for Health Information Technology (ONC), Washington, DC.
  • Kenneth Wilkins
    National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Bethesda, Maryland.
  • Michael Shlipak
    Kidney Health Research Collaborative (KHRC), University of California San Francisco (UCSF), San Francisco, California.
  • Michelle Estrella
    Kidney Health Research Collaborative (KHRC), University of California San Francisco (UCSF), San Francisco, California.

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