Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE trial.

Autor: Tangri N; Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Canada.; Department of Medicine, University of Manitoba, Winnipeg, Canada., Ferguson TW; Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Canada.; Department of Medicine, University of Manitoba, Winnipeg, Canada., Bamforth RJ; Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Canada., Leon SJ; Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Canada., Arnott C; The George Institute for Global Health, University of New South Wales, Sydney, Australia.; Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia., Mahaffey KW; Department of Medicine, Stanford University, Stanford, California, USA., Kotwal S; The George Institute for Global Health, University of New South Wales, Sydney, Australia.; Department of Nephrology, Prince of Wales Hospital, Sydney, Australia., Heerspink HJL; Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, The Netherlands., Perkovic V; The George Institute for Global Health, University of New South Wales, Sydney, Australia., Fletcher RA; The George Institute for Global Health, University of New South Wales, Sydney, Australia., Neuen BL; The George Institute for Global Health, University of New South Wales, Sydney, Australia.; Department of Renal Medicine, Royal North Shore Hospital, Sydney, Australia.
Jazyk: angličtina
Zdroj: Diabetes, obesity & metabolism [Diabetes Obes Metab] 2024 Aug; Vol. 26 (8), pp. 3371-3380. Date of Electronic Publication: 2024 May 28.
DOI: 10.1111/dom.15678
Abstrakt: Aim: 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.
Materials and Methods: 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.
Results: 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).
Conclusions: 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.
(© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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