Abstract The National Kidney Foundation's Kidney Disease Outcomes Quality Initiative
(KDOQI) has provided evidence-based guidelines for hemodialysis vascular access since …

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting
the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease …

Background Prediction tools that identify chronic kidney disease (CKD) patients at a high
risk of developing kidney failure have the potential for great clinical value, but limited uptake …

Objective To provide a review of prediction models that have been used to measure clinical
or pathological progression of chronic kidney disease (CKD). Design Scoping review. Data …

Background Various prediction models have been developed to predict the risk of kidney
failure in patients with CKD. However, guideline-recommended models have yet to be …

Background Given the high prevalence of chronic kidney disease (CKD), primary care
physicians (PCPs) frequently manage early stage CKD. Nonetheless, there are challenges …

Rationale & Objective Stratification of chronic kidney disease (CKD) patients at risk for
progressing to kidney failure requiring kidney replacement therapy (KFRT) is important for …

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of
patients who will progress to late-stage CKD is of paramount importance for patient care. To …

Rationale & Objective The Kidney Failure Risk Equation (KFRE) predicts the 2-year risk of
kidney failure for patients with chronic kidney disease (CKD). Translating KFRE-predicted …

Early identification of chronic kidney disease (CKD) provides an opportunity to implement
therapies to improve kidney function and slow progression. This chapter will first review the …