Prediction of all-cause mortality for chronic kidney disease patients using four models of machine learning.

Journal: Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
PMID:

Abstract

BACKGROUND: The prediction tools developed from general population data to predict all-cause mortality are not adapted to chronic kidney disease (CKD) patients, because this population displays a higher mortality risk. This study aimed to create a clinical prediction tool with good predictive performance to predict the 2-year all-cause mortality of stage 4 or stage 5 CKD patients.

Authors

  • Nu Thuy Dung Tran
    UMR 5558 CNRS Lyon, Université Claude Bernard Lyon 1, Hospices Civils de Lyon, Hôpital Edouard Herriot, Service de Néphrologie, Lyon, France.
  • Margaux Balezeaux
    UMR 5558 CNRS Lyon, Université Claude Bernard Lyon 1, Hospices Civils de Lyon, Hôpital Edouard Herriot, Service de Néphrologie, Lyon, France.
  • Maelys Granal
    UMR 5558 CNRS Lyon, Université Claude Bernard Lyon 1, Hospices Civils de Lyon, Hôpital Edouard Herriot, Service de Néphrologie, Lyon, France.
  • Denis Fouque
    Department of Nephrology, Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, Pierre-Bénite, France.
  • Michel Ducher
    Pharmacie, Hospices Civils de Lyon, EMR3738 Ciblage thérapeutique en oncologie, Université Claude Bernard Lyon 1, Lyon, France.
  • Jean-Pierre Fauvel
    Hospices Civils de Lyon, Hôpital Edouard Herriot, Service de Néphrologie, Université Claude Bernard Lyon 1, Lyon, France.