A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease.

Journal: Cardiovascular diabetology
PMID: 40375260

Abstract

BACKGROUND: Individuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. We sought to develop an accurate means by which to stratify risk in DKD.

Authors

  • James L Jr Januzzi
    Cardiology Division, Baim Institute for Clinical Research, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 0211, USA. jjanuzzi@mgb.org.
  • Naveed Sattar
  • Muthiah Vaduganathan
    Brigham and Women's Hospital Heart and Vascular Center, Department of Medicine, Harvard Medical School, Boston, MA.
  • Craig A Magaret
    Prevencio, Inc., Kirkland, Washington.
  • Rhonda F Rhyne
    Prevencio, Inc., Kirkland, Washington.
  • Yuxi Liu
    Beijing Key Laboratory for Green Catalysis and Separation, Key Laboratory of Beijing on Regional Air Pollution Control, Key Laboratory of Advanced Functional Materials, Education Ministry of China, Laboratory of Catalysis Chemistry and Nanoscience, Department of Environmental Chemical Engineering, School of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
  • Serge Masson
    Roche Diagnostics Inc, Rotkreuz, CH, Switzerland.
  • Javed Butler
    Department of Medicine, University of Mississippi Medical Center, Jackson, MS.
  • Michael K Hansen
    Janssen Research & Development, LLC, Spring House, PA, USA.

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