Integrating risk prediction models into chronic kidney disease care

Purpose of review Although the concept of risk prediction in chronic kidney disease (CKD) is not new, how to integrate risk prediction models into CKD care remains largely unknown, particularly in the prevention and early management of CKD. The present review presents a timely overview of recent CKD risk prediction models and conceptualizes how these may be integrated into the care of patients with CKD. Recent findings In recent literature, prediction of time-to-ESKD has been thoroughly validated in multiple international cohorts, new models focused on CKD incidence, morbidity, and mortality have been developed, and ongoing work will determine the impact of integrating risk prediction models into CKD care on patients, nephrologists, and health systems. With the availability of new models focused on CKD incidence, the United States Preventive Task Force should reconsider its determination of insufficient evidence for primary screening of CKD, which was due in part to the absence of validated risk models to guide CKD screening. Models predicting CKD morbidity and mortality present a new opportunity to standardize the intensity and frequency of care across nephrology practices.