Practical Utilization of Prediction Equations in Chronic Kidney Disease

Chronic kidney disease (CKD) is common and can lead to kidney failure, cardiovascular complications, and early mortality. While nephrologists can provide valuable insights for patients at all stages of CKD, these scarce resources should be targeted at patients with the highest risk of progression and adverse outcomes. Prediction models are tools that can help providers risk stratify patients if they are effectively implemented into the clinical workflow. We believe these equations should demonstrate (1) clinical utility: where they can provide useful information to the physician and patients; and (2) clinical usability: where they are able to be easily integrated into clinical workflow and do not result in unnecessary costs or visits. CKD often remains unrecognized until later stages when a large window of opportunity to delay progression has already passed. Models to determine progression of CKD using thresholds such as a 40% decline in eGFR can provide clinical utility in risk stratifying patients at all stages of CKD, an endpoint that has been recommended by the FDA for the evaluation of drug approvals for disease-modifying therapies. For patients at more advanced stages of CKD with a greater risk of kidney failure, tools such as the kidney failure risk equation can be implemented to help guide most costly decisions, such as referral to multidisciplinary care, commencing dialysis modality education, or planning for vascular access placement surgery. In addition, models focused on determining outcomes following dialysis initiation can help inform shared decision-making between patient and provider to better inform decisions around conservative care. To ensure widespread adoption of these tools, it is important to ensure that they are broadly generalizable to many health settings and easily implemented into existing clinic workflows with minimum disruption.