title: Predicting Progression in CKD: Perspectives and Precautions.
abstract: Predicting outcomes to guide clinical care, decision making, and resource allocation is a challenging undertaking in chronic kidney disease (CKD). Many prediction models have been developed, but few have been appropriately externally validated and even fewer have been assessed to be usable in the clinical setting. This contributes to the currently infrequent use of existing prediction models. Patients with CKD are a particularly heterogeneous group with significant biological variability, making the development of useful prediction models even more challenging. This article explores the different challenges in the development, validation, and application of prediction models in CKD. We explore the notion that newer biomarkers offer potential for enhancing existing and future prediction models and that modern technology is an opportunity to make prediction models more accessible and less cumbersome to use in clinical practice. Despite the challenges associated with their development and implementation, clinical prediction models have the potential to be a powerful tool for clinicians, researchers, and policy makers alike.
authors.affiliation: Division of Nephrology, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Matthew James
authors.initials: MJ
authors.lastname: Kadatz
authors.affiliation: Division of Nephrology, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Elizabeth Sunmin
authors.initials: ES
authors.lastname: Lee
authors.affiliation: Division of Nephrology, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: alevin@providencehealth.bc.ca.
authors.forename: Adeera
authors.initials: A
authors.lastname: Levin
mesh_terms: D000419:Albuminuria; D015415:Biomarkers / Q000378:metabolism*; D000066491:Clinical Decision-Making; D003404:Creatinine / Q000097:blood / Q000652:urine; D003661:Decision Support Techniques; D018450:Disease Progression; D005919:Glomerular Filtration Rate; D006801:Humans; D007676:Kidney Failure, Chronic / Q000378:metabolism* / Q000503:physiopathology; D011379:Prognosis; D051436:Renal Insufficiency, Chronic / Q000378:metabolism* / Q000503:physiopathology; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors
chemical_list: D015415:Biomarkers; D003404:Creatinine
keywords: Prediction models; biomarkers; chronic kidney disease (CKD); decision making; disease progression; outcomes; prognosis; review; risk calculator; risk equation; risk prediction tool; usefulness; validity