External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.

Journal: Journal of nephrology

Published Date: Jul 4, 2024

Abstract

BACKGROUND: Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to externally validate the recently published 2-year all-cause mortality prediction tool developed using machine learning.

Authors

Dung N T Tran

Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS Lyon, Université Claude Bernard Lyon 1, 43 Boulevard du 11 Novembre 1918, 69100, Lyon, Villeurbanne, France.
Michel Ducher

Pharmacie, Hospices Civils de Lyon, EMR3738 Ciblage thérapeutique en oncologie, Université Claude Bernard Lyon 1, Lyon, France.
Denis Fouque

Department of Nephrology, Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, Pierre-Bénite, France.
Jean-Pierre Fauvel

Hospices Civils de Lyon, Hôpital Edouard Herriot, Service de Néphrologie, Université Claude Bernard Lyon 1, Lyon, France.

Keywords

Aged Area Under Curve Cause of Death Female Humans Machine Learning Male Middle Aged Predictive Value of Tests Prognosis Renal Insufficiency, Chronic Reproducibility of Results Risk Assessment Risk Factors ROC Curve Severity of Illness Index Time Factors

External Resources

View on PubMed Access via DOI PubMed (38965199)

External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.

Abstract

Authors

Keywords

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