Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE trial.
CKD
CKD progression
SGLT2 inhibitors
machine learning
random forest
Journal
Diabetes, obesity & metabolism
ISSN: 1463-1326
Titre abrégé: Diabetes Obes Metab
Pays: England
ID NLM: 100883645
Informations de publication
Date de publication:
28 May 2024
28 May 2024
Historique:
revised:
26
04
2024
received:
05
04
2024
accepted:
04
05
2024
medline:
29
5
2024
pubmed:
29
5
2024
entrez:
29
5
2024
Statut:
aheadofprint
Résumé
To validate the Klinrisk machine learning model for prediction of chronic kidney disease (CKD) progression in patients with type 2 diabetes in the pooled CANVAS/CREDENCE trials. We externally validated the Klinrisk model for prediction of CKD progression, defined as 40% or higher decline in estimated glomerular filtration rate (eGFR) or kidney failure. Model performance was assessed for prediction up to 3 years with the area under the receiver operating characteristic curve (AUC), Brier scores and calibration plots of observed and predicted risks. We compared performance of the model with standard of care using eGFR (G1-G4) and urine albumin-creatinine ratio (A1-A3) Kidney Disease Improving Global Outcomes (KDIGO) heatmap categories. The Klinrisk model achieved an AUC of 0.81 (95% confidence interval [CI] 0.78-0.83) at 1 year, and 0.88 (95% CI 0.86-0.89) at 3 years. The Brier scores were 0.020 (0.018-0.022) and 0.056 (0.052-0.059) at 1 and 3 years, respectively. Compared with the KDIGO heatmap, the Klinrisk model had improved performance at every interval (P < .01). The Klinrisk machine learning model, using routinely collected laboratory data, was highly accurate in its prediction of CKD progression in the CANVAS/CREDENCE trials. Integration of the model in electronic medical records or laboratory information systems can facilitate risk-based care.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.
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