Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data.

Journal: Annals of emergency medicine
PMID: 32713624

Abstract

STUDY OBJECTIVE: Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidney disease, and death. Early acute kidney injury treatment can improve outcomes. However, current decision support is not able to detect patients at the highest risk of developing acute kidney injury. We analyzed routinely collected emergency department (ED) data and developed prediction models with capacity for early identification of ED patients at high risk for acute kidney injury.

Authors

  • Diego A Martinez
    School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
  • Scott R Levin
    Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD; Division of Health Sciences Informatics, Johns Hopkins University, Baltimore, MD.
  • Eili Y Klein
    Center for Disease Dynamics, Economics & Policy, Silver Spring, Maryland, United States.
  • Chirag R Parikh
    Department of Medicine, Division of Nephrology, Johns Hopkins University, Baltimore, MD.
  • Steven Menez
    Department of Medicine, Division of Nephrology, Johns Hopkins University, Baltimore, MD.
  • Richard A Taylor
    Emergency Medicine Department, Yale School of Medicine, 464 Congress Avenue, Suite #260, New Haven, CT, 06450, USA. Richard.taylor@yale.edu.
  • Jeremiah S Hinson
    Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, United States.

Keywords

External Resources

Popular Topics
Recent Journals