Predictive models in chronic kidney disease: essential tools in clinical practice

Purpose of reviewThe integration of risk prediction in managing chronic kidney disease (CKD) is universally considered a key point of routine clinical practice to guide time-sensitive choices, such as dialysis access planning or counseling on kidney transplant options. Several prognostic models have been developed and validated to provide individualized evaluation of kidney failure risk in CKD patients. This review aims to analyze the current evidence on existing predictive models and evaluate the different advantages and disadvantages of these tools.Recent findingsSince Tangri et al. introduced the Kidney Failure Risk Equation in 2011, the nephrological scientific community focused its interest in enhancing available algorithms and finding new prognostic equations. Although current models can predict kidney failure with high discrimination, different questions remain unsolved. Thus, this field is open to new possibilities and discoveries.SummaryAccurately informing patients of their prognoses can result in tailored therapy with important clinical and psychological implications. Over the last 5 years, the number of disease-modifying therapeutic options has considerably increased, providing possibilities to not only prevent the kidney failure onset in patients with advanced CKD but also delay progression from early stages in at-risk individuals.