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

Abstract

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.