Validation of the Klinrisk Machine Learning Model for CKD Progression in a Large Representative US Population.

Journal: Journal of the American Society of Nephrology : JASN
Published Date: Aug 6, 2025

Abstract

BACKGROUND: Early identification of high-risk chronic kidney disease (CKD) can facilitate optimal medical management and improve outcomes. We aimed to validate the Klinrisk machine learning model for prediction of CKD progression in large US commercial, Medicare, and Medicaid populations.

Authors

  • Navdeep Tangri
    Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada.
  • Thomas W Ferguson
    Department of Internal Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada.
  • Chia-Chen Teng
  • Ryan J Bamforth
    Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Canada.
  • Joseph L Smith
    Carelon Research, Wilmington, DE, United States.
  • Maria Guzman
    Carelon Research, Wilmington, DE, United States.
  • Ashley Goss
    Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States.

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