Validation of the Klinrisk CKD Progression Model in the Dapagliflozin and Prevention of Adverse Outcomes in CKD (DAPA-CKD) Population.

BACKGROUND: Early identification and treatment of high-risk CKD can lower the risk of kidney failure and cardiovascular events. A laboratory-based machine-learning model (Klinrisk) was developed to predict the risk of CKD progression in adults with, or at risk for, CKD. It has shown good accuracy in the general population and in clinical trial populations with diabetic kidney disease, but its performance in non-diabetic CKD is unclear. We evaluated Klinrisk in a clinical trial of the sodium glucose cotransporter 2 inhibitor dapagliflozin in patients with CKD, with and without type 2 diabetes. METHODS: We externally validated Klinrisk in the DAPA-CKD trial population for prediction of CKD progression (first occurrence of sustained ≥40% eGFR decline or kidney failure) up to 3 years. Performance was assessed with the area under the receiver operating characteristic curve (AUC), Brier scores and calibration plots, and compared with Kidney Disease Improving Global Outcomes (KDIGO) heat-map categories based on eGFR and urine albumin-to-creatinine ratio. Consistency of the dapagliflozin effect on CKD progression across Klinrisk risk categories was also examined. RESULTS: The analysis included 4,304 DAPA-CKD participants, of whom 578 experienced CKD progression events over a median follow-up of 2.4 years. Klinrisk achieved an AUC of 0.79 (95% CI 0.77-0.81) and a Brier score of 0.10 (95% CI 0.09-0.11). It outperformed KDIGO heat-map categories in subgroups of patients with and without type 2 diabetes, and in those with IgA nephropathy. Dapagliflozin reduced CKD progression compared with placebo, irrespective of baseline risk. CONCLUSIONS: In DAPA-CKD, Klinrisk accurately predicted CKD progression and outperformed KDIGO-based risk stratification in patients with and without type 2 diabetes. Eight of the 22 model inputs were unavailable and supplied from the development model rather than measured. Dapagliflozin reduced CKD progression across the full spectrum of predicted risk compared with placebo.

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PubMed
Published
2026-10-13
DOI
https://doi.org/10.34067/kid.0000001339
Primary Topic
Chronic Kidney Disease and Diabetes
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article
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article

Validation of the Klinrisk CKD Progression Model in the Dapagliflozin and Prevention of Adverse Outcomes in CKD (DAPA-CKD) Population.

Thomas W. Ferguson, Brendon Lange Neuen, Niels Jongs, Hiddo J.L. Heerspink et al.
PubMed
Chronic Kidney Disease and Diabetes
article

Validation of the Klinrisk CKD Progression Model in the Dapagliflozin and Prevention of Adverse Outcomes in CKD (DAPA-CKD) Population.

Thomas W. Ferguson, Brendon Lange Neuen, Niels Jongs, Hiddo J.L. Heerspink, Pam Gottschal, Ryan J. Bamforth, Navdeep Tangri
article en

Abstract

BACKGROUND: Early identification and treatment of high-risk CKD can lower the risk of kidney failure and cardiovascular events. A laboratory-based machine-learning model (Klinrisk) was developed to predict the risk of CKD progression in adults with, or at risk for, CKD. It has shown good accuracy in the general population and in clinical trial populations with diabetic kidney disease, but its performance in non-diabetic CKD is unclear. We evaluated Klinrisk in a clinical trial of the sodium glucose cotransporter 2 inhibitor dapagliflozin in patients with CKD, with and without type 2 diabetes. METHODS: We externally validated Klinrisk in the DAPA-CKD trial population for prediction of CKD progression (first occurrence of sustained ≥40% eGFR decline or kidney failure) up to 3 years. Performance was assessed with the area under the receiver operating characteristic curve (AUC), Brier scores and calibration plots, and compared with Kidney Disease Improving Global Outcomes (KDIGO) heat-map categories based on eGFR and urine albumin-to-creatinine ratio. Consistency of the dapagliflozin effect on CKD progression across Klinrisk risk categories was also examined. RESULTS: The analysis included 4,304 DAPA-CKD participants, of whom 578 experienced CKD progression events over a median follow-up of 2.4 years. Klinrisk achieved an AUC of 0.79 (95% CI 0.77-0.81) and a Brier score of 0.10 (95% CI 0.09-0.11). It outperformed KDIGO heat-map categories in subgroups of patients with and without type 2 diabetes, and in those with IgA nephropathy. Dapagliflozin reduced CKD progression compared with placebo, irrespective of baseline risk. CONCLUSIONS: In DAPA-CKD, Klinrisk accurately predicted CKD progression and outperformed KDIGO-based risk stratification in patients with and without type 2 diabetes. Eight of the 22 model inputs were unavailable and supplied from the development model rather than measured. Dapagliflozin reduced CKD progression across the full spectrum of predicted risk compared with placebo.

PubMed
University Medical Center Groningen (NL), Royal North Shore Hospital (AU), UNSW Sydney (AU), The George Institute for Global Health (AU), Orthopaedic Innovation Centre (CA), University of Manitoba (CA)
Openalex Percentile: Top 12%
Chronic Kidney Disease and Diabetes
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