A critical population management challenge concerns chronic kidney disease (CKD), which impacts about half of the Medicare population and of diabetics. More than 50% of adults over 30 years of age are likely to develop CKD during their lifetime, and the prevalence of CKD is expected to climb over the next 15 years. Current CKD management is variable and suboptimal, as categorizing the very heterogeneous CKD patient population into risk cohorts for purposes of appropriate treatment is inaccurate. Without accurate risk classification, many patients are over-treated, leading to wasted expenses and adverse events, while others are not identified in time to receive interventions that change the course of the disease. A new algorithm has been created that predicts patients’ risk of renal failure based on a specific set of laboratory tests combined with patient age and gender. Validated by more than 720,000 patients spanning 30 countries, it can reliably predict a patient’s risk of experiencing renal failure requiring dialysis or transplant. Studies show that a lab-based analytics program that incorporates this algorithm with care protocols, dashboards, and educational patient reports can generate substantial savings and improved outcomes for ACOs and health systems.