Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the <scp>CANVAS</scp> Program and <scp>CREDENCE</scp> trial
Navdeep Tangri, Thomas W. Ferguson, Ryan J. Bamforth, Silvia J. Leon, Clare Arnott, Kenneth W. Mahaffey, Sradha Kotwal, Hiddo J.L. Heerspink, Vlado Perkovic, Robert A. Fletcher, Brendon L. Neuen
Abstract
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.