As patients with chronic kidney disease are followed up longitudinally, can accumulating information that becomes available over time be used to improve prediction of the risk for end-stage kidney disease? In this issue of AJKD, Dr Tangri and coauthors1 address this question by comparing the performance of static risk prediction based on demographic, clinical, and laboratory covariates available at a single baseline time point with the performance that could in principle be achieved by a time-updated approach if it were possible to incorporate future information for changes in the covariates after baseline.