Risk Prediction Models in CKD

Abstract

Chronic kidney disease (CKD) currently affects 20 million Americans and is associated with increased morbidity and mortality. Resource-efficient and appropriate treatment of CKD benefits the patient and provides improved resource allocation for the health care system. Prediction models can be useful in efficiently allocating resources, and currently are being used at the bedside for several important clinical decisions. There is a paucity of prediction models in use in nephrology, but one such model, the Kidney Failure Risk Equation, uses routinely collected laboratory values and can inform clinical decisions related to the following: (1) triage of nephrology referrals, (2) evaluating the need for more intensive interdisciplinary clinic care, (3) determining the timing of modality education, and (4) dialysis access planning. The development of new models that predict survival and quality of life on dialysis, success on home modalities, failure of arteriovenous fistulas, and risk of cardiovascular disease in patients with CKD is needed.

Keywords: Risk prediction models; chronic kidney disease; dialysis.